Joouurrnnaall aooff tMMaannaggeemmeennt … › kcfinder › upload › files ›...
Transcript of Joouurrnnaall aooff tMMaannaggeemmeennt … › kcfinder › upload › files ›...
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
11
The Journal of Management and Business Research Editor-In-Chief
Dr. Abdul Sraiheen
Editor-In-Chief:
Kutztown University, USA
(610) 683-4593
Associate Editor Dr. Okan Akcay
Kutztown University, USA
(610) 683-4590
Associate Editor Dr. Roger Hibbs
Kutztown University, USA
(610) 683-4580
The Journal of Management and Business Research (JMBR) is a refereed, quarterly journal that
serves the need for the Management field and Business Research while bridging theoretical and
applied Business systems research that benefits both academics and Management professionals.
The intention of the journal is to help the local and global business communities to efficiently
exploit Business research towards efficient Management and the creation of business value. The
journal welcomes all types of theoretical and applied research studies in Management and
Business that add value to enterprise owners, customers, developers, and evaluators. That is,
efficient business management, applied studies in Management science, enterprise resource
planning, business process reengineering, and business decision support are particularly sought.
All manuscripts in relevant areas of Management, Technology, and Business are also considered
if they bear implications for the creation of business value through efficient Management and
decision support. The audience of the Journal is members of the local and global business
communities, researchers, students, and industrial practitioners in relation to information science.
The journal invites original papers and technical reports that are not published or not being
considered for publication anywhere else. The journal is published by the Berks Group of
Management and Business Research. JMBR appears quarterly. The printing is assured by the
American Institute of Technology and Business Research.
Indexed at Ulrich's, EBSCO, Sciseek, Index Copernicus, Scirus, Pascal, PKP, Google Scholar &
Pending Others
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
22
FROM THE EDITOR-IN-CHIEF
The Journal of Management and Business Research is a refereed journal which aims to publish
articles of high quality dealing with two major areas: Management and Business.
Even though most online business technologies attracted only large businesses and banks in the
past, due to the high costs involved, the rapid development of the Internet made it feasible for
public agencies, individual consumers and small businesses to participate. Today, every
organization in the global market place is certainly affected by global computing and the new
trends in Management and Business activities that have come with it.
Owners, however, are faced with the real challenge of creating business value in all their
management and business activities and in redefining the new requirements and directions for
survival and success in this global computing world. Owners do not hide the fact that intelligent
computing, information technology, and business intelligence have become not only necessary
for success, but a fundamental requisite for survival. Today, Management and Business research
take a very important piece of every company’s budget.
With its exceptional preeminence, Management and Business activities rely on technology which
embraces almost the whole fields of business, education, and science and touches at some point
or other, on almost every social issue of our time.
Let us have this forum where we all learn how to generate great business value though scientific
Management and Business research and efficient technology management.
Abdul Sraiheen, Ph.D.
Editor In-Chief
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
33
Table of Contents
4 Eco-Friendly IT: Greener Approach to IT Rushabh Shah
25 Possibilistic Group Support System For Pricing And Inventory Problems Emna Boumediene, Lotfi Boumediene, Bel G Raggad
37 Saudi Arabia’s Economic Diversification: A Case Study in Entrepreneurship Kimanthi Ali Thompson, Dalal Thair Al-Aujan, Roaa AL-Nazha, Sara Al
Lwaimy, and Sumayah Al-Shehab
41 How to Effectively Manage IT Project Risks
Bradley Sean Susser, Pace University, NY
68 Efficiency and Productivity Analysis of Tunisian Banks
During a Recent Deregulation Period
Raéf Bahrini, Institute of High Commercial Studies of Sousse, Tunisia
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
44
Eco-Friendly IT: Greener Approach to IT Rushabh Shah, Pace U, New York
Abstract
Information Technology is widely considered as a key tool that can help address the frightening energy and environmental challenges facing the world today. Environmental issues are receiving unprecedented attention from businesses and governments around the world. Eco-friendly Information Technology, also known as Green Computing, in particular, is geared towards utilizing Information Technology in creating a more environmentally friendly and cost-effective use of power and production in technology. Eco-friendly Information Technology starts with manufacturers producing environmentally friendly products and encouraging various departments to consider more friendly options like virtualization, power management and proper recycling habits.
Feeling pressure from customers and other stakeholders, organizations have begun to make serious improvements in their environmental performance, recognizing that if they fail to deliver on this, it frequently translates into a negative impact on profit. Many governments are introducing aggressive environmental policy, encompassing everything from greenhouse gas reduction and natural resource protection to clean power initiatives and incentives for energy efficiency.
The main purpose of this research paper is to discover the various issues relating to the harsh environmental impact caused by high energy resource consumption of data centers as well as discuss various eco-friendly solutions to address the issues. Advantages of implementing identified eco-friendly solutions to resolve the highlighted issues are also discussed in this research paper. Moreover, related case studies are presented to support how influential Information Technology companies resolved various issues pertaining to high energy consumption in data centers – companies that were able to utilized eco-friendly technology in resolving the issues facing the modern industry today. This research paper aims to establish and highlight the important link between the environment and Information Technology. This further emphasize that Information Technology can be a vital instrument in saving the environment through various eco-friendly solutions available. Keywords: Green IT, virtualization, eco-friendly, energy-efficient, environment.
1. Introduction
In recent years, we have seen a great increase in the number of companies joining the green movement bandwagon. As more and more organizations are becoming aware of their responsibilities to the environment, numerous efforts towards saving the environment are being implemented. Some companies see the move as a necessity as regulators consider limits on greenhouse gas emissions and consumers demand environmentally friendly products.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
55
The compounding effect of high gas emission, toxic waste materials, and high energy consumption has put a toll on the environment. Increasingly, more organizations are becoming aware of their responsibility to the environment as numerous efforts towards saving the environment are implemented through utilization of eco-friendly IT. As the name implies, eco-friendly IT refers to environmentally sustainable computing or Information Technology. The main goal of eco-friendly IT is to reduce the use of hazardous materials, maximize energy efficiency during the product’s lifetime, and promote the recyclability or biodegradability of obsolete products and factory waste.
In the Information Technology industry, energy consumption is considered to be a critical issue today. As data centers grow, their carbon footprints increases. One would think that a computer does not consumed much energy; however, if you think of it on a bigger scale, such as in the case of data centers, where you have thousands of computers with many processors and numerous memory cards - energy consumption becomes probabilistic for company owners as well as for the environment. The IT industry is not the only one experiencing such issues relating to high energy consumption, various highly developed industries as well has the same dilemma of coping up with the effects of modernization.
The effect of modernization has harsh impact on the environment, as the world becomes more modernized – various products are developed, manufactured, and used to keep abreast with the constant changes brought by the modern world. All aspects that go along with manufacturing a certain product produce unwanted toxic elements and pollutants that can have an adverse effect on the environment and the public health. Issues relating to the effect of production waste disposal, packaging materials discarding, and recycling obsolete products must be addressed by every organization to minimize pollution.
Various companies in the Information Technology industry adopted various eco-friendly IT solutions in support of creating a sustainable environment. Sustainability is an issue that affects organizations of all sizes. With the awareness of “green” issues at an all-time high, it is important that every company make every effort to be as environmentally conscious as possible.
Many businesses have discovered that eco-friendly IT initiatives offer costs savings benefits while reforming the organization, meeting stakeholder demands and complying with laws and regulations. In this study, IBM and Info-Tech Research Group find that businesses who complete eco-friendly IT initiatives realize significant cost savings alongside superior environmental performance.
2. Eco-friendly Adoption Needs
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
66
Energy consumption is a critical issue for Information Technologies organizations today, whether the goal is to reduce cost, save the environment or keep data centers running efficiently and cost effectively. Data centers consume so much electricity that United States’ data centers alone consume 4.5 kWh annually which is 1.5% of the country’s total energy consumption. Industry analysts estimates that over the next 5 years, most enterprise data centers will spend as much on energy (power and cooling) as they do on hardware infrastructure. This number would likely double in the next few years as the demand for data centers increases due to the central computing need to support businesses and lifestyles. Servers basically are driving energy consumption and costs.
Rising energy costs has already had an impact on all businesses, and all businesses have increasingly been judged according to their environmental credentials, by legislators, customers and shareholders. This won’t just affect the obvious, traditionally power-hungry ‘smoke-belching’ manufacturing and heavy engineering industries, and the power generators. The Information Technology industry is more vulnerable than most – it has sometimes been a reckless and profligate consumer of energy. Development and improvements in technology have largely been achieved without regard to energy consumption.
The total amount of electricity used to operate data center servers and related infrastructure equipment in the United States was $2.7 billion in 2005 in comparison to $1.3 billion in 2000. Worldwide the total bill was $7.2 billion in 2005, compared with $3.2 billion in 2000. Looking at it in a different way, U.S. data center power consumption in 2005 was equivalent to about five 1,000- megawatt power plants or five typical nuclear or coal power according to analysts. In the United States, in 2005 Data center servers consumed 0.6 percent of all electricity. When counting with the infrastructure equipment such as network and cooling gear that figure goes up to 1.2 percent, about the same percentage consumed for televisions.
Today's data center design decisions all pivot around maximizing efficiency, while giving companies a path for future growth, says Steve Sams, VP of global site and facilities services for IBM. "We see our customers make very different design decisions than they used to," Sams says. "And the end result is that they are saving 30 percent in operational costs over the lifetime of the data center."
In many companies, there has been a shift away from dedicated data centers, as part of an attempt to provide all IT requirements by using smaller boxes within the office environment. Many have found this solution too expensive, experiencing a higher net spend on staff as well as with higher support costs. Energy consumption of distributed IT environments is difficult to audit, but some have also noted a progressive increase in power consumption with the move from centralized to decentralized, then to distributed architecture, and finally to mobility-based computing. Even where distributed computing remains dominant, the problems of escalating energy prices and environmental concernsnare present, albeit at a lower order of magnitude than in the data center environment, and even though the problems are rather more diffuse and more difficult to solve.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
77
Increase in server demand can be accounted to the huge market demand for Web content, video on demand, music downloads, and Internet telephony. Factors that contribute to excessive energy consumption in data centers are as follows:
Underutilized server hardware -Studies proved that a server consumes 80% of the total IT load and 40% of total data center consumption in 2006. Site infrastructure accounts to the 50% of total data center consumption -Servers typically house only single application where processors sit idle 85-95% of the time and while sitting idle, these servers uses nearly as much power as they do. -The inefficiency caused by running single application on x86 is not only wasteful but expensive due to electricity costs and increase in continuous computing demand.
Inefficient and aging data centers
-Many organizations have older application (legacy) running on older hardware. These applications and the hardware that they run on are expensive to manage and maintain because power consumption and hardware maintenance for older hardware is generally higher. -Companies running out of power and/or capacity to support the increase energy demands on inefficient and aging data centers caused by the following;
Utility incapable of providing adequate power High power consuming and dense equipment
Inability of IT staff to respond rapidly to changing business needs and computing requirements
-Work load in IT varies depending on the day or month and increases over time as the company grow or the demand for application increases. Due to the static nature of IT physical infrastructure, hardware and servers are over-provisioned to work for peak load. This is mainly due to the fact that applications are very difficult to reconfigure to different hardware once it is installed. Consequently, the inability to provision the physical infrastructure dynamically to accommodate these fluctuations leads to wasteful practices in the data centers which results to high energy consumptions.
Businesses in various industries are looking for different ways to relief themselves with the burden of increasing energy demands and costs. Moreover, businesses are seeking to free themselves from the constraints of inflexible and underutilized hardware. Many of these dilemmas are now being resolved through eco-friendly IT solutions that are catered towards creating a sustainable environment. Virtualization is on the top list of solutions which is the fundamental element of the green data centers.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
88
3. Eco-friendly Adoption Methods and Solutions
Modernization has brought increase need to have high-performance servers to meet the increasing demands for new applications. Consequently, energy usage in data centers rises in order to keep up with the trends which are becoming a major dilemma for many IT managers and corporate executives. Thus, the amount of power cooling systems needed for these servers increases as well which attributes to high electricity cost. Industries have noted that companies with data centers attribute 40% of their operating cost to power and cooling-related expenses alone. Furthermore, data centers are accounted for 23% of carbon emissions from global information and communications technology and claim about 1.5 percent of total electricity usage in the U.S. Much of this consumption comes from cooling the space used to house data servers. The data centers high operating cost has drove big companies like Microsoft, Google, and Yahoo to establish data centers in locations where hydro-electric power and wind energy is abundant. This move has compelling advantages for such companies to address the high operating cost of maintaining its data centers as well as supports its movement towards a greener environment. However, building massive data centers to a well situated location requires huge investments which not all companies could afford. Google and Microsoft alone have spent an estimated $1.15 billion to create their data centers. These companies feel that such effort towards driving down the data center operating cost is much needed to keep abreast with the continuous evolution of the Internet while reducing their corporate carbon footprint to save the environment. Companies want to reduce power usage these days, both to save cash on energy bills and to reduce their environmental impact. Saving energy is more than saving trees. Not only the environment clearly benefits from power-saving measures, but also the companies benefits from saving energy. That’s because solutions to improve energy efficiency is often cost effective. Various companies have acquired eco-friendly IT solutions to address the pressing environmental problems caused by inefficient usage of high energy consuming servers, aging servers, and the tremendous demand for cooling data centers. Various solutions to address power consumption are server virtualization, cutting data center energy consumption and changing data centers design and architecture. The following further details the eco-friendly IT solutions to address the aforementioned issues discussed in this research paper:
Virtualization and Server Consolidation
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
99
Virtualization solutions has successfully reduced corporate carbon footprint and positively impacting the environment all over the world. Virtualization is the creation of a virtual version of hardware platform, operating system, and storage device or network resources. Virtualization provides tremendous energy benefits and lifeline to datacenters that are running low on capacity and high on power and cooling costs. Through virtualization, businesses can create virtualized, dynamic IT environments that are cost and energy efficient as well as support the eco-friendly movement that various companies are aiming to implement in their daily business operation. The ever changing demands on IT infrastructure are challenging the way we implement data storage. Mounting pressures due to capacity, skill shortage and reduction in IT related costs is forcing businesses to optimize available storage assets. This can be done by consolidating the use of geographically dispersed and underutilized servers and storage. Datafence provides expertise in designing and implementing server and storage consolidation and virtualization solutions, server consolidation & server virtualization. Initially we can perform a needs assessment based on current requirements. Thereafter we can design and deploy the most effective way to consolidate and centrally manage your data.
Source: http://www-03.ibm.com/press/attachments/GreenIT-final-Mar.4.pdf
Figure 1: Virtualisation Projets The following are advantages of virtualization:
Ability to contain an consolidate the number of servers in a data center
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1100
-Allows businesses to run multiple application and Operating System workload on the same server. The typical setup is a 10 server workload running on a single physical server; however, there are companies that consolidate 30 to 40 server workload on one server.
-Dramatic reduction in server count results on lower IT energy consumption. Reducing the number of physical servers through virtualization cuts power and cooling cost and provides more computing power in less space. Virtualization can decrease energy consumption by 80 percent.
Ability to respond rapidly to changing business needs and computing requirements -Various companies providing virtualization services have diverse virtualization technology that allows administrators to move running virtual machines from one server to another with no disruption to the application or end users. They have the technology to monitor the utilization of pool of servers. Moreover, they have the technology to dynamically rebalance virtual machines across an entire resource pool of physical servers on an ongoing basis. Other technologies includes reduction of power consumption by turning off servers when there is unneeded capacity and servers are powered back on when the capacity is required,
Virtualization technology helps the environment -Every server that is virtualized saves 7,000 kWh of electricity and 4 tons of carbon dioxide emission per year. With more than a million workloads running on virtualization technology, the cumulative power savings are about 8 billion kWh.
Increases existing server and storage utilization and efficiency. Helps devise a centrally managed server storage plan. Centralizes and Efficiently Managed backup and recovery operations. Helps devise a simple disaster recovery and business continuity plan. Virtualization is used to consolidate the workloads of several under-utilized servers
to fewer machines, perhaps a single machine (server consolidation), bringing your savings on hardware, environmental costs, management, and administration of the server infrastructure.
Your legacy applications might simply not run on newer hardware and/or operating systems. Even if it does, if may under-utilize the server, so as above, it makes sense to consolidate several applications. Virtualization helps you here, as such applications are usually not written to co-exist within a single execution environment.
Virtualization can provide the illusion of hardware, or hardware configuration that you do not have (such as SCSI devices, multiple processors etc.). This can also be used to simulate networks of independent computers.
Virtualization allows for powerful debugging and performance monitoring. You can put such tools in the virtual machine monitor, for example. Operating systems can be debugged without losing productivity, or setting up more complicated debugging scenarios.
Virtualization makes software easier to migrate, thus aiding application and system mobility.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1111
25% of organizations expect server spending to grow by 5 percent to 10 percent, and 6 percent expect it to grow by 10 percent or more. And to reduce operating and capital costs, companies should consider server virtualization. Masking of server resources, including the number and identity of individual physical servers, processors, and operating systems, from server users is called as Server virtualization. Software application is used to divide one physical server into multiple isolated virtual environments and these virtual environments are sometimes called virtual private servers, but they are also known as guests, instances, containers or emulations. There are various approaches to server virtualization such as the virtual machine model, the paravirtual machine model, and virtualization at the operating system (OS) layer. Reasons for server virtualization are (1) Virtualization reduces the overall energy consumption of the server footprint, and hence it allows the same workload to run on fewer physical servers; (2) Virtualization alleviates out-of-space, power, and cooling constraints; and lastly, (3) Virtualization reduces the overall server footprint and cuts energy-related carbon dioxide emissions also electronic waste is reduced as less server equipment are required.
Even if server virtualization is used, there is still room to improve energy savings. The three process improvements that can help organizations to cut server energy costs are maximize virtual machines, cooling and design, and energy efficient servers.
I. Maximize virtual machines: Virtualization is not enough in addition to increasing
the overall server virtualization footprint, the main aim is additional energy savings by virtualizing more efficiently. Server virtualization ratios are not keeping pace with modern hardware and virtualization platform capabilities such as three virtual servers need one host server. Virtualizing more efficiently can help in avoiding new server purchases, not to mention the additional power, cooling, and space expenses from this new equipment. According to Doug Washburn, Forrester (Jan11, 2011) a key ratio that administrators use to determine the acceptable number of VMs per physical host is server CPU utilization. The direct relationship between CPU utilization is VMs per physical host, and energy savings. A standalone non virtualized server might run at an average of 10 percent to 15 percent utilization, whereas virtualized servers could theoretically approach 100 percent. If the numbers of VMs are increased per physical host, the total numbers of physical servers are decreased and energy consumption is also reduced. As server teams become more comfortable with higher server virtualization utilization ratios, they can safely add more VMs per physical server without diminishing service levels.
II. Cooling and design: Packing all this technology into such a small space generates a large amount of heat, and it is the power used by cooling and air conditioning systems that often makes up the majority of the utility bill in the datacenter.
Some manufacturers have been experimenting with different ways of cooling densely packed server, storage and network components, including server cabinet door designs that feature a variety of liquid cooled tubes to distribute cold air across racks, and direct spray technology that douses CPUs themselves with chemically treated water.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1122
Gartner estimates that improved row- and rack-based cooling techniques can reduce energy consumption by 15 per cent, for example, while redesigning datacenter floor plans and racks to bring colder air in and disperse heat (often called hot aisle, cold aisle design) more effectively can also take the weight off over-worked air conditioning systems.
Energy Efficient Servers and Architecture Management:
Datacenter management software: One of the biggest problems facing datacenter managers under pressure to reduce electricity consumption and utility bills is how to get accurate usage information.
Some manufacturers, such as IBM, have added power metering and monitoring utilities to their servers and racks, and linked management software to the power distribution units that monitor individual and multiple racks of servers, network switches and storage appliances to find out exactly how much power the equipment on each unit is using. Elsewhere, IntelliData Systems provides cabinet and rack-mounted power strips with built-in metering, environmental monitoring and remote shutdown capabilities for any attached equipment, as well as inline devices for individual mainframe computers.
A number of software vendors offer reporting tools that can detail trends and patterns in power usage, total power input, carbon emissions and costs, some for billing and charge-back purposes. Also available is modeling software that predicts how equipment can be re-arranged for optimum temperature control, making it easier for organizations to identify ways to reduce datacenter energy consumption.
Scottish and Southern Energy (SSE) has been using datacenter performance management suite since 2009, for example. The software has helped the utility company to map existing rack, server and network hardware and the relationships between them, and to migrate two datacenters from one provider to another when the existing facilities began to run out of capacity. SSE also uses it to predict and prevent failures, using modeling tools to identify potential problems with the electricity supply. Steve Wallage, managing director of Broad Group Consulting, a company specializing in giving advice on datacenters, managed services, outsourcing and virtualization, says more organizations are taking a closer interest not just in datacenter hardware, but also the applications and services that run on top of it to identify where potential efficiency improvements could be made. “There is a lot more effort now to understand datacenters and what goes on inside, not just the power units and chillers, but also the data and applications,” he says. “The banks have detailed analysis of every application in use, for example, and use information on different classes of datacenter infrastructure and location to decide whether they could move them into the cloud, and we will see a lot more corporate effort in that direction.” Datacenter pods: In some cases, both enterprises and service providers may not have to spend millions on building or leasing customized datacenter facilities: scaled down,
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1133
“containerized” datacenters that fit into the back of a truck can meet permanent or temporary demand for infrastructure resources so long as there is somewhere close to the network point of presence to park it.
Running out of Processing Power:
This feature is completely because of the reason of the huge amount of storage involved and the business reports say the way the technology has been progressing very soon the storage capacity would be exhausted. The traditional methods were adding of additional hard disks and servers that needed to be installed, these days the servers are being installed virtually on the cloud that consume less power than these normal additional servers and hard drives.
Desktop Virtualization and Thin Clients Moving Desktops to a virtual server than keeping them on the actual server helps us a lot. They consume less power and also the storage problem can be solved to a great extent. Thin clients are generally without a CPU, RAM and are directly connected to the cloud server. The shared resources model inherent in desktop virtualization offers advantages over the traditional model, in which every computer operates as a completely self-contained unit with its own operating system, peripherals, and application programs. Overall hardware expenses may diminish as users can share resources allocated to them on an as-needed basis. Virtualization potentially improves the data integrity of user information because all data can be maintained and backed-up in the data center. Some of the advantages of Desktop Virtualization can be listed as follows:
Simpler provisioning of new desktops. Reduced downtime in the event of server or client hardware-failures. Lower cost of deploying new applications. Desktop image-management capabilities Increased data security. Longer refresh cycle for client desktop infrastructure. Secure remote access to an enterprise desktop environment.
Server Room Upgrades & New Server Room Builds Most of the Mid-size businesses face a preponderance of issues related to a server. There are many reasons that we need to upgrade to a new server.
Decrease cost and increase the effectiveness of the server as the server is not generally prepared for full capacity conditions.
Increase the server and the computing capacity of the server. The server rooms need to be increased as they are either too small or not compatible to the virtual
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1144
servers they are connected to. The reliability of the old servers is questionable as they need to be upgraded after a
definite period of time. The mounting and maintenance of these old servers are questionable as it is often
very expensive to maintain these servers and handle the effective increase in the storage.
The infrastructure also needs to be sufficient enough to keep up to server expansion and the other aspects related to the new technologies that keep coming up.
Some of the advantages of these server room upgrades would be that the company would be in the competition for being one of the most innovative companies. The market keeps changing with the ever change in the technology. Thus it becomes very important for us the company to come up with new and better ideas that would keep it in competition in the market. These room upgrades and new servers have become a necessity the company needs to take a further step towards eco-friendly IT and have virtual servers that tend to consume less power and facilitate in smooth running of the company and enables it not to violate with the environment. This is has in turn enabled companies to develop successful projects.
Information Technology Energy Measurement A recent Info-Tech study found that 28% of mid-sized enterprises are piloting or implementing IT energy measurement, and another 25% plan to implement in the next 12 months. Adoption is driven by rising electricity costs, a need for data and guidance in planning future initiatives involving energy efficiency, and greater awareness of the impact of carbon emissions on energy consumption.
Source: http://www-03.ibm.com/press/attachments/GreenIT-final-Mar.4.pdf
Figure 2: Server Room upgrades
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1155
This note demonstrates how to move through a gradual but effective energy measurement implementation, including:
Adoption drivers for energy measurement solutions. Simple, cost-effective metering solutions to estimate Information Technology's total
cost of energy. Using the total energy estimate to educate stakeholders about the cost and impact of
energy. Building a solid business case for a formal measurement solution. Success factors for moving through each stage of this energy measurement
implementation approach. A disguised case study of a real company, ABC Foods.
We need to understand how organizations can quantify the total cost of energy for IT, drive interest and attention for this operational cost, and ultimately build a business case for formal tools that allow full reporting, better infrastructure planning, and new quantifiable energy efficiency opportunities. IT Energy measurement can also be dealt by preventing the unnecessary wastage of the energy within a company like unwanted usage of the computers and use of printers.
Printer Consolidation Most of the companies across the United States have the best printers in the market and print over 300,000 pages in a fiscal year. As per a survey conducted it was found that more than 60% of the paper used goes in trash and more three forth of the paper that is wasted cannot be recycled. So we can imagine the amount the paper that has been wasted over the last few years with the increase in technology. Along with the printers, the maintenance of the printers, toners, cartridges etc. proves to be very expensive for the company. So one of the most important aspects within a firm would be to cut down on the use of printers within the firm and avoid the wastage of so much of paper that would help us preserve the energy and not harm the environment. Thus a very unique measure was undertaken by the companies where they only provide printouts where necessary and the rest of the important would be stored on the servers or share across virtually.
Remote Conferencing and Telecommunication Strategies
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1166
The fuel prices have reached the skies and on the other hand emitting out so much of waste in the air tends to pollute the air. It tends to pollute the environment and creates an imbalance in the nature. Greenhouse effect comes into picture with the emission of such harmful gases in the environment. Human beings, plants and animals and every living creature are affected by such ways and means of emitting fuel. Thus we need to conserve the fuel for the right time and also save our planet. Thus in this initiative in the paper we would study the ways and means of remote conferencing and telecommunication strategies. Remote Conferencing and Collaboration involves two major aspects video conferencing and implementing them between two different offices or client sites. It also involves online collaboration environments. This feature helps us to convey our message in a much efficient and better way and also enables us to protect the environment as well. Telecommunication strategies and capabilities also have proven quite worthwhile and also enable us to protect the environment from the different hazards caused. Virtual private networks are the next big thing in these days. It has enabled users to start working from home and thus offices are getting less crowded, people need not travel by cars or other vehicles and consume fuel that would in turn pollute the environment. People prefer working from home as they can multi-task their work. They can take care of their family, chat with friends, watch television and do their work side by side. They need not wear proper office wear and be in their casuals doing their work. It has created employment for those who can work from home. It has enable the physically challenged to work from their own space and lead a life of dignity. Not surprisingly, businesses adopting travel reduction initiatives seek to decrease the travel and fuel consumption costs associated with driving or flying between office locations and to client sites. Some of these initiatives not only reduce costs of fuel, flights, hotels and related expenses, but also result in higher employee satisfaction.
Another major factor pushing companies to implement these initiatives, particularly telecommuting strategies, is to satisfy employees. This rang true for one CIO of a North American public company who notes that, “Our employees, faced with high gas prices, are coming back to us and saying, ‘I really like working here but I’m driving 30 miles one way, I may have to look at something else. People don’t want to move, especially for the salaries that we can pay. Telework is going to open up some avenues for us to get employees that are, frankly, out of our reach right now.” Organizations are also gaining access to remote talent that they otherwise would not be able to tap. In two-thirds of all travel reduction projects, organizations report their employees are very satisfied with the increased flexibility they are now offered.
Information Technology Equipment Recycling:
The IT industry has taken its share of plaudits for embracing the green agenda over the past few years. This is certainly well-deserved considering the substantial investment in virtual and related technologies that have helped reduce overall energy consumption.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1177
However, many of the measures have rightly been described as "low-hanging fruit" in that they were fairly easy to accomplish and produced relatively quick, quantifiable returns on investment. That may not be the case in the next phase of the green data center movement, however, in which the industry will increasingly be asked to do what's right for the environment even if it does not produce significant benefit, and may in fact be detrimental, to the bottom line.
Out of all initiatives in this study, the success of IT equipment recycling relies not on a business case with cost savings, but on a combination of environmental responsibility and regulatory pressures. The single most important factor in adopting recycling initiatives is to decrease waste sent to landfills. A close secondary consideration is ensuring equipment is responsibly discarded at end of life. Additionally, there appears to be greatly increased customer demand for responsible recycling practices. Space, too, plays an issue: Many IT departments are simply running out of closets and crannies to store old equipment.
A key example is recycling. Enterprises have traditionally left disposal of old equipment to suppliers or distributors, essentially washing their hands of it once depreciation had eroded its value. That approach isn't likely to hold up much longer considering the impact that refuse enterprise hardware is having on both the environment and municipal budgets that have to accommodate the e-waste.
Obsolete computers or other electronics are a valuable source for secondary raw materials, if treated properly; if not treated properly, they are a source of toxins and carcinogens. Rapid technology change, low initial cost, and planned obsolescence have resulted in a fast-growing surplus of computers or other electronic components around the globe. Technical solutions are available, but in most cases a legal framework, a collection system, logistics, and other services need to be implemented before applying a technical solution. The U.S. Environmental Protection Agency, estimates 30 to 40 million surplus PCs, classified as "hazardous household waste" would be ready for end-of-life management in the next few years. The U.S. National Safety Council estimates that 75% of all personal computers ever sold are now surplus electronics. Computer components contain many toxic substances, like dioxins, polychlorinated biphenyls (PCBs), cadmium, chromium, radioactive isotopes, and mercury. A typical computer monitor may contain more than 6% lead by weight, much of which is in the lead glass of the cathode ray tube (CRT). A typical 15-inch computer monitor may contain 1.5 pounds (1 kg) of lead, but other monitors have been estimated to have up to 8 pounds (4 kg) of lead. Circuit boards contain considerable quantities of lead-tin solders that are more likely to leach into groundwater or create air pollution due to incineration. The processing (e.g. incineration and acid treatments) required to reclaim these precious substances may release, generate, or synthesize toxic byproducts.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1188
Export of waste to countries with lower environmental standards is a major computer or electronic recycling concern. The Basel Convention includes hazardous wastes from computer CRT screens as an item that may not be exported Trans continentally without prior consent of both the country exporting the waste and that receiving the waste. Companies may find it cost-effective in the short term to sell outdated computers to less developed countries with lax regulations. It is commonly believed that a majority of surplus laptops are routed to developing nations as "dumping grounds for e-waste". The high value of working and reusable laptops, computers, and components (e.g. RAM) can help pay the cost of transportation for many worthless "commodities".
We have several recycling methods available and some of them can be listed as follows:
Consumer recycling involves taking the products directly back to the manufacturer or a refurbish firm.
Corporate recycling involves several businesses seeking a cost-effective way to recycle large amounts of computer equipment responsibly face a more complicated process. Businesses also have the options of sale or contacting the Original Equipment Manufacturers (OEMs) and arranging recycling options. Some companies pick up unwanted equipment from businesses, wipe the data clean from the systems, and provide an estimate of the product’s remaining value. For unwanted items that still have value, these firms buy the excess IT hardware and sell refurbished products to those seeking more affordable options than buying new.
Sale involves online auction of products and they get a good price in turn for the products that are need to be scrapped.
Donation involves the process of changing the parts that are required within the computer and then the entire computer would be given to a person in need of it.
Take back involves researching computer companies before a computer purchase, consumers can find out if they offer recycling services. Most major computer manufacturers offer some form of recycling. At the user's request they may mail in their old computers, or arrange for pickup from the manufacturer.
Exchange involves offering a free replacement service when purchasing a new PC. Dell Computers and Apple Inc. take back old products when one buys a new one. Both refurbish and resell their own computers with a one-year warranty.
Many companies purchase and recycle all brands of working and broken laptops and notebook computers, from individuals and corporations. Building a market for recycling of desktop computers has proven more difficult than exchange programs for laptops, smartphones, and other smaller electronics.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
1199
Scrapping/Recycling has become very essential due the rising price of precious metals — coupled with the high rate of unemployment during the Great Recession — has led to a larger number of amateur "for profit" electronics recyclers. Computer parts, for example, are stripped of their most valuable components and sold for scrap. Metals like copper, aluminum, lead, gold, and palladium are recovered from computers, televisions and more.
PC Power Management: Many look to managing end-user device power consumption as an easy, effective way to reduce energy costs. These power management initiatives include the following:
-Using software that centrally manages energy settings of PCs and monitors. -Enforcing standardized power settings on all PCs before distributing to end users. -Procuring energy-efficient equipment, such as Energy Star certified devices.
Older computers can use up to 300 watts during peak load, but less than eight watts during sleep modes. By maximizing the number of PCs and monitors controlled for hibernate, sleep or shut-down times, companies reduce the amount of energy consumed during lengthy idle times, particularly overnight. Procuring Energy Star-compliant devices or more energy-efficient equipment can also reduce power consumption during equipment use. This includes replacing old desktops with laptops, or refreshing CRT monitors with LCD flat-screens. Altogether, these power management strategies result in significant energy and maintenance cost savings; such benefits are realized by 65% of companies that complete such initiatives.
4. Key Success Factors in Eco-friendly IT Projects
The likelihood that companies will successfully implement Eco-friendly initiatives depends on the following factors:
1. Stakeholder Support: Any project in a firm has some stakeholder. It is indeed critical to have their support
for the success of that particular project, especially as far as eco-friendly use of
technology is concerned. Major stakeholders include C-level executives, IT directors, IT
staff, employees, and in some cases, property or facilities management. Although
gaining buy-in from all levels is important, the likelihood of success is higher when
implementations have support of C-level executives – specifically, the CEO. The most
successful projects are strongly supported by the CEO in more than three-quarters of
implementations. As an IT manager at a finance company said, “One of the reasons
we’ve been able to move forward with this is because of sponsorship and support from
the CEO and his executive team. Without that, we wouldn’t have the funding to do it. It
wouldn’t be pushed.”
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2200
2. Lack of Implementation:
Source: http://www-03.ibm.com/press/attachments/GreenIT-final-Mar.4.pdf
Figure 3 : Facing extreme implementation barriers
Companies adopting eco-friendly information technology initiatives may face barriers
that inhibit the successful approval and implementation of these projects. A lack of
choice due to missed refresh cycles, inadequate funding, misalignment with physical
facilities, and a lack of resources, such as IT staff, can all be barriers. However, it is
found that less than one-third of respondents cite these as major barriers to
implementation; only 7% say they face extreme barriers. The most common barrier
for this latter group is a lack of flexibility due to missed refresh cycles.
3. Economic Trade-offs:
In a recent survey a few respondents were asked to anticipate the impact of the
downturn on their revenues, IT budget, prioritization of projects, and funding for eco-
friendly information technology projects for the next 12 months. Approximately 61%
of respondents did not believe that they will be affected by in such areas. These also
include more than 50% of respondents who do not think that tat funding for eco-
friendly information technology projects will drastically decrease. This is a positive
signal for eco-friendly information technology, showing cost-cutting benefits. This is
believed by 38% of the companies felt that cost saving would prove a success to their
projects.
5. Company Case Analysis Hewlett Packard HP’s Performance Optimized Datacenter (POD) is one example, with others available from
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2211
Sun Microsystems (now Oracle), IBM and APC. The POD, which comes in 20ft and 40ft versions, provides up to 20 standard 19in 50U racks and 600kW of power (34kW per rack), and uses chilled water to keep the servers cool, alongside blower fans and heat exchangers, backed up by dual active power distribution paths for redundancy purposes. Whichever, if any, of these technologies or datacenter design methodologies individual organizations choose to deploy will depend very much on what they have in place already and the extent of the upgrade budget available to them. But the potential of innovative datacenter design to deliver reduced capital and operational costs means few IT departments can afford to ignore them.
DELL Dan Traynor, IT infrastructure director, Southern Company, United States Dell’s Challenge was its rapid business growth which created server sprawl, threatening to outstrip the available space in Southern Company’s data centers and driving up costs by consuming more energy each year.
Virtualizing and consolidating on Dell PowerEdge servers enable the Southern Company IT team to save data center space reduce costs and increase energy efficiency. Some of the benefits which Dell achieved were the virtual infrastructure that helped speed up new server deployment time by a week. Dell virtual infrastructure enabled IT to accommodate future growth, while slowing down the pace of energy consumption; Dell PowerEdge servers enabled up to 26:1 server consolidation to save data center space; Southern Company avoids over 2 million kilowatt hours of energy use with virtualized Dell servers; consolidating on Dell servers enabled IT to avoid an estimated U.S. $1.3 million in capital expenditures.
Dell’s approach is called the Efficient Data Center, and it can help you free up some 50 percent of your IT budget while also lowering your carbon footprint. Built on virtualization, automation and consolidation, this strategy yields open, robust and cost-effective solutions that help optimize the current center virtualize in a time frame that makes sense for the business and leverage cloud technologies where appropriate. In addition, the Efficient Data Center improves business continuity. Downtime costs money, drains resources and can harm a company's reputation. With an infrastructure that's virtual-ready, you can recover from server failure rapidly and without having to rebuild from scratch. Within minutes the functions performed by the failed server — whether it's virtual or physical — can be retargeted to an available spare server so that the applications are back up.
SAMSUNG: Kim Seungh-ho. October 4th, 2010."Samsung Electronics unveils ‘Smart & Green plus’
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2222
Strategy" Data Center Electricity Consumption Doubles: A big increased in the number of server accounts for 90% of the extra power consumption, based on a study conducted by Stanford’s Jonathan Koomey. The energy consumed by data center servers, cooling equipment, and related infrastructure more than doubled in the United States and worldwide between 2000 and 2005, according to a new study.
An increased in the number of servers accounts for 90% of the additional power consumption, according to a study by author, Jonathan Koomey, a consulting professor at the Stanford University and a staff scientist at Lawrence Berkeley National Laboratory. The study was conducted by Advanced Micro Devices, which is touting its energy-efficient processors. Only 5% to 8% of the increase in data center electricity consumption is attributed to power use per unit. What is driving the server proliferation is the insatiable appetite for Web content, video on demand, music downloads, and Internet telephony. The total amount of electricity used to operate data center servers and related infrastructure equipment in the United States was $2.7 billion in 2005 in comparison to $1.3 billion in 2000. Worldwide the total bill was $7.2 billion in 2005, compared with $3.2 billion in 2000. Looking at it in a different way U.S. data center power consumption in 2005 was equivalent to about five 1,000- megawatt power plants or five typical nuclear or coal power plants says Koomey. In the United States in 2005 Data center servers consumed 0.6 percent of all electricity. When counting with the infrastructure equipment such as network and cooling gear that figure goes up to 1.2 percent, about the same percentage consumed for televisions. To overcome this big consumption of electricity by data center servers companies such as Samsung have lunched strategies to a more “smart and Green” approach. Samsung Electronics revealed the "smart & green plus" strategy at the 2010 Samsung mobile solution forum held in Taiwan on Sept 7. "The strategy reflects Samsung's strong will to lead the world's mobile semiconductor industry with high-function, low electric power and environment-friendly semiconductors," said at the forum Kwon Oh-hyun the president of the semiconductor business of Samsung Electronics. "At the same time, we will effectively cope with changes in the new mobile market environment by strengthening the win-win partnership between semiconductor manufacturers and set makers," Kwon Oh-hyun. "Samsung also plans to expand the "green memory campaign" to three fields - server, PC and mobile. Through updating their green memory campaign website, the company expects to introduce four top green memory products - DDR3, SSD, LPDDR2 and GDDR5," said Kwon. At the forum, Samsung introduced new mobile semiconductor products in keeping with the smart & green plus strategy, including 1GHz dual core application processor designed on low-power process technology, a high-performance 16gigabyte moviNANDTM chip with an eMMC4.41 interface, and an engineering sample of the world's first application processor utilizing 32 nanometer (nm) low-power process technology.
6. Conclusion
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2233
The soaring demand for powerful servers and cooling equipment to support the data centers has brought vast effect in the energy consumption requirements of the modern industry today. This has become a problem of majority of the highly industrialized companies – problems with coping up with the demand for higher level technology while keeping the operation cost low has been quite a challenge for many businesses. Moreover, as industries are becoming more aware of the ill effects of globalization to our planet, everyone is doing their part and taking its steps towards contributing to a sustainable environment. Businesses around the world have discovered that going green isn’t just good for the planet; it is good for their bottom lines. The paper highlights how mid-size companies are realizing significant cost savings when they adopt eco-friendly information technology initiatives. Issues relating to high energy consumption of data centers are mostly attributed on how companies manage their system requirements. Most of the companies purchase a new server whenever there is a need for a new system. The accumulation of servers running in a single system brings so much impact on the high cost of electricity bill to run the machine as well as it has adverse effect to the environment. Servers emit a great amount of heat which can cause damage to the machine. Cooling equipment is needed to control the heat being emitted from the machines which also consume so much energy resources. Consolidating these systems into one server alone does not serve as a solution for this problem. Virtualization is the most popular eco-friendly solution to address the high energy consumption of data center. This is usually the first step that the IT department takes to consolidate their servers to significantly bring down the cost of maintaining data centers and high energy cost that is associated to it. One significant finding that we learned from this research paper is that virtualization alone is not the entire solution to address the pressing issues – it needs processes, procedures and management to benefit from the advantages that virtualization can bring in solving the aforementioned issues presented in this research paper.
References Dell 1, Practical solutions for environmental issues(2010). Retrieved from
http://content.dell.com/us/en/corp/dell-earth.aspx
Dell 2, Dell working on the various solutions for Green IT; Retrieved from www.dell.com/environment. Frrester, Ways to cut data center energy costs. Retrieved from http://features.techworld.com/data-centre/3245222/forrester-three-ways-to-cut-data-centre-energy-costs/?pn=1
IBM, Green IT: Why mid-size companies are investing now, http://www-
03.ibm.com/press/attachments/GreenIT-final-Mar.4.pdf
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2244
McGee, M.K. InformationWeek February 17, 2007 "Data Center Electricity Bills Double” http://www.informationweek.com/news/197006830 Seungh-ho, K.. October 4th, 2010."Samsung Electronics unveils ‘Smart & Green plus’ Strategy"
http://www.informationweek.com/news/197006830
Techworld 1, Various Solutions for Green IT; Retrieved from http://features.techworld.com/latest/?cid=27##
Techworld 2, Data center design. Retrieved from http://features.techworld.com/data-centre/3229944/trends-shaping-data-centre-design/
Techworld 3, Data center management. Retrieved from http://features.techworld.com/data-centre/3208465/the-new-shape-of-data-centres/
Techtarget, What is Server Virtualization? Retrieved from http://searchservervirtualization.techtarget.com/definition/server-virtualization
Traynor, D., IT infrastructure director, Southern Company. Case Study of Dell (Virtualizing and consolidating); Retrieved from http://content.dell.com/us/en/enterprise/d/corporate~case-studies~en/Documents~2009-southern-company-10007421.pdf.aspx
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2255
Possibilistic Group Support System For Pricing And Inventory Problems
Emna Boumediene, ISCAE, University of Manouba, Tunisia
Lotfi Boumediene, ISG, University of Tunis, Tunisia
Bel G Raggad, Pace U, New York
Abstract
The paper proposes a Possibilistic Group Support System (PGSS) for the retailer pricing and
inventory problem when possibilistic fluctuations of product parameters are controlled by a set
of possibilistic optimality conditions. Experts in various functional areas convey their subjective
judgment to the PGSS in the form of analytical models (for product parameters estimation),
fuzzy concepts (facts), and possibilistic propositions (for validation and choice procedures).
Basic probability assignments are used to elicit experts' opinions. They are then transformed into
compatibility functions for fuzzy concepts using the falling shadow technique. Evidence is
processed in the form of fuzzy concepts then is rewritten back to basic probability assignments
using the principle of least ignorance on randomness.
The PGSS allows the user (inventory control) to examine a trade-off between the belief value of
a greater profit and a lower amount of randomness associated with it. Managerial pricing and
inventory strategy is controlled using three fuzzy concepts expressing whether management is
acting softly, moderately, or aggressively. Management can soften their strategy and reinvoke
the PGSS until a final system recommendation becomes satisfactory.
Keywords: Possibilistic theory, Expert system, Group support system, Fuzzy set theory
1. Introduction
The determination of subjective probability needed to process subjective judgment relies
considerably on the perception of the human expert. The acquisition process of distributions for
subjective probabilities is usually characterized with inconsistency, which can expand when
multiple experts are involved in the estimation process.
Subjective judgment is often used in decision making under uncertainty. It is not uncommon that
experts produce different probability distributions for the same subject. When this happens,
combining their views requires a very difficult and costly inference process. However,
despite the arbitrariness, inconsistency, and cost of processing subjective managerial judgment,
experts' estimation of uncertainty associated with the decision domain remains a consequential and
valuable conceptual resource for current decision-making processes. Nevertheless, experts can
only provide incomplete and rough estimation for domain uncertainty. Estimates for domain
parameters are usually presented in a linguistic form.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2266
The retailer pricing and inventory problem treated in this article depends on subjective judgment
from various players, namely the purchase manager, the sales manager, the inventory manager,
suppliers, marketing and finance management. Obviously the decision maker alone cannot
possess expertise concerning all functional areas affecting the retailer pricing and inventory
policy. An efficient approach for managing the randomness intricating such a decision making process should
take into account the diversity of the group of experts and device a sound inference process.
The article proposes a Possibilistic Group Support System (PGSS) for the retailer pricing and
inventory problem when possibilistic fluctuations of product parameters are controlled by a set
of possibilistic optimality conditions. Experts in various functional areas convey their subjective
judgement to the PGSS in the form of analytical models (for product parameters estimation),
fuzzy concepts (facts), and possibilistic propositions (for validation and choice procedures).
Two known techniques are usually employed in possibilistic reasoning: basic probability
assignments and compatibility functions for fuzzy concepts. Even though they are very effective
in eliciting experts' opinions, basic probability assignments are very complex and costly for
combining evidence. In contrast, while compatibility functions are very easy to process, they are
characterized by their arbitrariness in representing experts' opinions as fuzzy concepts. In order
to avoid the disadvantages of bath techniques, the PGSS diversifies their usage by employing basic
probability assignments in the elicitation process and compatibility functions in the combining of
evidence.
The PGSS also allows the user (inventory control) to examine a trade-off between the belief
value of a greater profit and a lower amount of randomness associated with it. Managerial pricing
and inventory strategy is controlled using three fuzzy concepts expressing whether management is acting
softly, moderately, or aggressively. Management can soften their strategy and reinvoke the PGSS until a final system
recommendation becomes satisfactory.
2. The retailer pricing and inventory problem
While demand is constant in the EOQ problem, Lee (1993) explicitly allowed for the interdependency between demand
and price [7]. Lee attempted to determine the optimal selling price and order quantity of a retailer when the demand is a
nonlinear factor of the price and with quantity-discount cost. In general, the task is to maximize a retailer profit π (p,q)
where p and q are the price and order quantity, respectively. The profit is computed as follows:
π(p,q)=r-c-h-b
r: revenue
c: ordering cost
h: inventory holding cost
b: purchase cost.
The optimal solution is obtained by the maximization of π (p,q). This is an unconstrained signomial problem with one
degree of difficulty. Lee (1993) transformed the problem into a posynomial one as in Duffin, Peterson and Zener [2]. In
an intermediary stage, Lee proposed the computation of four important decision parameters: δ1, δ2, δ3 and δ4 which
respectively represent the proportions (or weights) of profit, ordering cost, inventory holding, and purchase cost to the
total revenue. These proportions play a very important prediction role of the deviation of various output variables from
their optimal values.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2277
Real data concerning the proportions of ordering cost and inventory cost to the total revenue may be easily obtained [4].
The two remaining decision parameters, proportions of profit and purchase costs to total revenue, may be approximated
as in [4]:
δ1 + δ3= cons1
δ3 - δ2= cons2
δ3 + δ4= cons3
where cons1, cons2, and cons3 are constants defined in [4].
Even though the optimal pricing and inventory policy changes when input parameters change, Lee proposed a set of
optimality conditions to control fluctuations in the input variables. This set of optimality conditions will be useful to
identify those output variables (price, quantity, revenue, profit), which are not realistic. The analytical model is not easy
to solve when demand is a nonlinear function of price with a constant elasticity. This problem can become even more
difficult if multiple products are involved in the study, or if the nonlinear function gets more complex.
The article considers the pricing and inventory problem for the same class of products assuming possibilistic fluctuations
of input parameters. If optimality conditions are fuzzified then the randomness on the profit function will be worthwhile
to study.
3. PGSS design
The PGSS is a computer-based information system designed to support a group of experts in the process of their
perception and cognizance of fuzzy concepts regarding a specific decision domain towards the definition of a common
possibilistic outcome. This article proposes and demonstrates a prototype of the PGSS for the pricing and inventory
policy problem.
The PGSS, as depicted in Figure 1, consists of a user-system dialog subsystem (USDS or just user subsystem) and the
expert system dialog subsystem (ESDS or just expert subsystem). The user subsystem is a computer interactive program
that assists the user in submitting a realistic input vector to the PGSS. The expert subsystem is an interactive computer
program that assists various experts, from different functional areas affecting pricing and inventory decisions, in
organizing, processing, and making their subjective judgment available to inventory control users.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2288
Figure 1: PGSS Design
4. Randomness management and possibilistic inference
Randomness is traditionally represented using additive probability distributions, in which the probability figures are
distributed to all singletons of the universe of discourse. On the other hand when experts do not hold sufficient
knowledge about the domain, their probability estimates can only go to same subsets of the universe. Ignorance of a
given concept can put the expert in a position where he/she can neither support nor reject the concept; the sum of
probabilities of the concept or its negation is therefore less than 1. This nonadditive property of the probability measure
is incorporated in Shafer's theory of basic probability assignment [1; 5; 6; 9].
Plausibility and belief measures are associated with a function called the basic probability assignment m defined as
follows:
m: 2u ---> [0, 1]
m(φ)=0 and ΣA≤U m(A)=1.
The value m(A) represents the degree of belief that a specific element of U belongs to the set A, but not to any special
subset of A.
The belief measure Bel is defined in terms of the basic assignment m as follows:
Bel: 2U ---> [0, 1]
Bel(A)= ΣB≤U m(B).
Group of Experts
(Functional Areas)
Users: Inventory Control
Exact Reasoning
Model Base User Judgement
Validation Validation Fuzzy
Concept Base
Basic Prob. Assignement
Base
Possibilistic
Inference
Process
Enhanced Pricing and Inventory Policy
Choice
Fuzzy Concept Base
Falling Shadow
Base
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
2299
The plausibility measure Pl and the randomness Ran are defined in terms of the basic probability assignment as follows:
Pl, Ran: 2U ---> [0, 1]
Bel(A)= ΣB‡Φ m(B).
Ran(A)= Pl(A)-Bel(A)
Figure 2:
Basic
Probability
Assignment
The shaded areas, in Figure 2, are called focal elements of the basic probability assignment. In a prudent manner, the
belief function takes a minimal amount of probability, since only the intersections Ac and Ad are added to Bel(A). In
a more optimistic manner, the plausibility value, Pl(A), takes into account probability amounts associated with all subsets
intersecting A. The amount of randomness on the basic probability assignment is measured as the difference between the
plausibility and the belief functions.
Two problems are encountered in processing possibilistic evidence. While the process of basic probability assignments is
very complicate and complex, basic probability assignments better represent experts' perception and cognizance of fuzzy
concepts. In contrast, while compatibility functions are not easy to elicit from human experts, their process is known to
be quiet easy. The PGSS will employ basic probability assignment as a method for the representation of experts'
estimates and will use the compatibility functions of experts to use in the possibilistic inference process. In this manner,
those unwanted features of both methods are avoided.
A compatibility function can express the perception and the cognizance of the fuzzy concept by the individual expert.
Different experts may show different perception and cognizance of the same fuzzy concept. Experts may produce
different compatibility functions for the same fuzzy concept. When this occurs, it is necessary that the system
combines the individual fuzzy concepts to produce a common compatibility function of the fuzzy concept. This
article does not propose a direct and mathematically sound technique to combine the experts' compatibility functions of
the fuzzy concepts. We instead use a set-valued statistical method called the falling shadow of random subsets to
transform expert judgment into compatibility functions [8].
c
a
b
d
e
or : PL(A)=a+b+c+d : Bel(A)=a+b.
A
U
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3300
The falling shadow method represents an expert compatibility function of a fuzzy concept as an area coverage that
corresponds to each random subset in the basic probability assignment [8]. That is, the perception and cognizance of the
fuzzy concept by an individual expert is expressed using a basic probability assignment. After consulting various experts,
a collection of basic probability assignments is obtained. The falling shadows of various basic probability assignments
are then constructed. Because a probability distribution defined on the collection of basic probability assignments is not
usually available, the arithmetic average of the falling shadows is used as an approximation to the mean [8]. In fact for a
large group of experts, the arithmetic average of the following shadows approaches the mean [4; 5]. The overall
compatibility function of the fuzzy concept is then obtained directly from the arithmetic average of the falling shadows.
For more detailed information on the falling shadow technique, one may refer to [8].
5. Assessment and validation of the input vector
The user first submits a tentative input vector which is next validated using validation propositions expressed as
fuzzy concepts and stored in the validation fuzzy concept base. If it is not realistic, the input vector will be then
transmitted back to the user for refinement and resubmission.
The user expresses his/her judgment using an input vector (a, b0, k, , , δ1, δ2, δ3, δ4) defined as follows:
Input vector:
a: inventory carrying rate per unit
b0: no-discount cost unit
k: scaling constant
: price elasticity
: quantity discount coefficient
δ1: profit proportion to total revenue
δ2: ordering cost proportion to total revenue
δ3: inventory holding cost proportion to total revenue
δ4: purchase cost proportion to total revenue
Let v be the variable name of one of the vector components estimated by management. The validation fuzzy concept
base includes three fuzzy concepts defined by their fuzzy subsets UNDER (underestimated), REAL (realistic), and
OVER (overestimated) and their respective compatibility functions UNDER, REAL and OVER. The compatibility
values UNDER(v), REAL(v) and OVER(v) of managerial estimates of the variable v with the three fuzzy concepts
UNDER, REAL, and OVER are then computed and examined. The fuzzy concept that corresponds to Max
{UNDER(v), REAL(v), OVER(v)} is most compatible with managerial judgment. In this manner, the validation
subsystem produces the validation status (underestimated, realistic, or overestimated) of managerial judgment
concerning all components of the input vector.
A sample of the possibilistic propositions stored in the validation concept base is provided in Figure 5. A sample of
the possibilistic choice propositions is provided in Figure 6.
6. Possibilistic system recommendation
If managerial judgment concerning all components of the input vector is compatible with the concept represented by
the fuzzy subset REAL, then the control is transferred immediately to the possibilistic inference process following
which a pricing and inventory policy is recommended. The user invokes the system for the purpose of determining
the pricing and inventory policy that satisfies a predefined goal. The user's goal is expressed in terms of the profit
concept 'π≥τ', τ >0 (τ may be understood as a tolerated minimum profit) in one of the following forms:
Goal:
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3311
SOFT (low τ)
MODERATE (moderate τ)
AGGRESSIVE (high τ)
The system will provide an extended output vector for the price and inventory policy as follows:
Pricing and inventory policy vector:
p: price
q: order quantity
r: revenue
π: profit
Bel(π≥τ)
Ran(π≥τ)
Compatibility functions are obtained from basic probability assignments using their falling shadows. To avoid the
complex (and some times impossible) process of basic probability assignments, the inference process combines the
compatibility functions of the fuzzy concepts instead. The inference process returns the set of compatibility
functions for the fuzzy concepts {'π≥τ', τ>0}.
Multiple basic probability assignments may have the same falling shadow of random subsets. Liang and Song [8]
showed that the principle of least ignorance on randomness produces a unique basic probability assignment. In order
to compute randomness on the final system recommendation, the principle of least ignorance on randomness is used
to induce a basic probability assignment from the fuzzy concepts {'π≥τ', τ>0}.
For various values of Goal parameter
Figure 3: Compatibility function of the profit concept
The compatibility function of the fuzzy concept 'π≥τ' has no left tail, as shown in Figure 3 for various values of τ.
The support set is the interval [π- π
+]. The smallest interval on which µ=1 is the interval [π
- π
0] where π
0 is such that
π0=Min{x:µ(x)<1}. We therefore use Shafer's consonant belief structure. A consonant belief is characterized by its
nested focal intervals I1≤I2≤ … ≤In. Because the plausibility of the union of two intervals Ii and Ij equals the
maximum of subsets plausibilities, the measure of plausibility is therefore a possibility measure. Also, the belief
measure is a necessity measure since Bel(Ii Ij) equals the minimum of {Bel(Ii), Bel(Ij)}.
As in [9] the fuzzy subset of the payoff concept may be associated with the consonant belief structure I1≤I2≤ … ≤In
and hence:
μ(u)=Σi:xIi=m(Ii)=Pl(u)
The consonant belief and plausibility functions can be reconstructed from the compatibility function of profit fuzzy
concept, treated as a contour function [6] since:
Pl(Ii)=MaxxIiPl(x) and Bel(Ii)=MinxIi1-Pl(x).
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3322
The interval [π0 π
+] is divided into N intervals of equal length δ. Horizontal rectangles of width
δ =[π+-π
0]/N and length (δ) are constructed as in Figure 4. That is, the focal elements {Ii, 1≤i≤N} are such that
m(Ii)=1/N for any i, 1≤i≤N. Also, for any subset Ax=[π- x],
I1
I2
I3
I4
I5
π- π
0 π
0+ … π
+
Figure 4: Induced basic probability assignment
The following concepts taken from the choice fuzzy concept base are defined as follows:
'SECURE(π, τ)' "Make sure that π≥τ;"
'τ=REAL': "Make sure that τ is realistic;"
'ALLOW(π, τ)': "The value of τ can yield the profit π."
The following two propositions are also reproduced from the choice fuzzy concept base, then applied on the above
fuzzy concepts, and combined together to yield the compatibility function of the profit fuzzy concept:
If τ is realistic and τ allows π
then π is realistic.
If π is realistic and π≥τ
then the concept 'π≥τ' is secured.
The fuzzy profit concept depends on the goal concept expressed by the fuzzy subsets: SOFT (low τ), MODERATE
(moderate τ), and AGGRESSIVE (high τ). The compositional operator will be used to process the fuzzy profit
concept.
'SECURE(π, τ)' {'τ =REAL' 'ALLOW(π, τ)'} 'π≥τ'
µSECURE(π, τ)= Maxz
{Mint
{[Maxx
{Miny
{µτ=REALµALLOW (π, τ)(x,y)}}]}
{µ(π≥τ) (z,t)}}
The PGSS computes {'τ =REAL' 'ALLOW(π, τ)' } 'π≥τ' as explained in the following steps:
Let πi=π
-+i(π
+-π
-)/N
1. Set up the compatibility values of 'τ =REAL' 'ALLOW(π, τ)' in the form of a matrix multiplication as follows:
µ'ALLOW(π, τ)'[π- … π
i … π
+]
π- 1.0 . 0.0
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3333
. . . .
µ'τ=REAL'=[π- … π
i … π
+] π
i 1.0 1.0 .
. . . .
π+ 1.0 . 1.0
2. After taking the row vector µ'τ=REAL'=[ π- … π
i … π
+] and matching pairwise with the first column of the
matrix. Select the minimum of each pair of this match for the row and the first column. Then select the maximum of
all elements in the resulting vector.
3. Do the same for the rest of the columns of the matrix. This will result in the desired vector:
µ'τ=REAL' 'ALLOW(π, τ) '(π- … π
i … π
+).
Let µ'τ=REAL' 'ALLOW(π, τ) '([π- … π
i … π
+])=(µ
1 … µ
i … µ
N)
4. Apply the steps 1 to 3 using the row vector µ'τ=REAL' 'ALLOW(π, τ) '(π- … π
i … π
+) on the matrix given below.
µπ≥τ[π- … π
i … π
+]
π- 1.0 . 0.0
. . . .
(µ1 … µ
i … µ
N) π
i 1.0 1.0 .
. . . .
π+ 1.0 . 1.0
The resulting vector is in fact µSECURE(π, τ).
If the profit level predicted is realistic and the amount of randomness on the profit concept 'π≥τ' is low, then the
pricing and inventory policy recommended by the system is adopted. If however, either the profit is not realistic, or
the amount of randomness is high, then the user needs to communicate with some of the experts to discuss a possible
trade-off between the level of profit and the amount of randomness on the profit concept. At this stage, managerial
goal concepts may be revisited to see if it is possible to adjust the value of τ so that a softer strategy (SOFT (low τ),
MODERATE (moderate τ), and AGGRESSIVE (high τ)) could be considered. The system will be reinvoked in the
same manner, until a final enhanced pricing and inventory policy is accepted.
Validation Propositions:
if δ2 In overestimated
then the price will be overestimated.
if δ2 in overestimated
then the lot size will be underestimated.
if δ3 in overestimated then
then price will be overestimated.
if δ3 is overestimated
then the lot size will be underestimated.
if δ2 in underestimated
then the price will be underestimated.
if δ2 is underestimated
then the lot size will be overestimated.
if δ3 in underestimated
then the price will be underestimated.
if δ3 in underestimated
then the lot size will be overestimated.
Figure 5: Correction rules
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3344
Choice Propositions:
if i and r increase then
the optimal price increases.
if i and r increase
then the optimal lot size decreases.
if k increase
then the optimal price increases.
if k increase
then the optimal lot size decreases.
if i and r decrease
then the optimal price decreases.
if i and r decrease
then the optimal lot size increases.
when =0 then if the ordering cost
then increases the optimal price goes up.
if τ is low and π≥τ
then π=π is realistic.
if τ is moderate and π≥τ
then π=( π+ π)/2 is realistic.
if τ is low and π≥τ
then π=π is realistic.
Figure 6: Sample of Choice propositions
Consider as an example [4], the demand D=105P
-3. The quantity discount function is c=5Q
.01. The optimal figures
[4] are p=7.3, q=13702, and π=3113.12.
Lee's proposed procedure only works for the problem of maximizing a signomial profit function with one
posynomial term. The procedure cannot be of use if multiple products are treated, since the objective function will
have more than one posynomial term. That is, approximation techniques become necessary when the degree of
difficulty increases. Those techniques are usually costly, lengthy, and often not so robust.
An alternative will be to apply possibilistic theory where evidence is processed in a logically sound manner. The
possibilistic propositions developed above are induced from the optimality conditions and solution bounds obtained
through exact reasoning in [4].
In this example, the profit bounds are computed in [4] as π-=3102.84 and π
+=3121.24. Suppose that the weights δ1,
δ2, δ3, and δ4 are estimated to be .5, .02, .08, and .4 respectively. Suppose that the weight estimates are examined by
the validation procedure and are found unrealistic. In this situation, those estimates are corrected and resubmitted.
This process terminates when the input vector becomes valid given the validation propositions stored in the
validation fuzzy concept base.
Let us vary the values of τ to examine the possible trade-off between a higher profit and a lower belief value. We
considered the three values of τ=3102 (low), τ=3117 (Moderate), and τ =3121 (high). The values of compatibility
with the fuzzy profit concept 'π≥τ', of possible profit values x, and the belief values associated with the intervals [π,
x] are provided in Table 1 (x [π- π
+]).
The higher the value of τ gets, the more difficult is the realization of a profit π greater than τ. The belief values for
the same subset of profit values decrease when τ goes up. As illustrated in Table 1, the softer is the pricing and
inventory policy, the higher will be the belief value for any subset in [π- x]. For example, for a fixed belief value of
.5, the maximum profit values associated with this belief when τ is low, when τ is moderate, and when τ is high, are
3111, 3116, and 3118.5 respectively. That is, the aggressive policy yields a higher profit given a fixed belief value.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3355
Let us consider now a higher belief value, say of 0.7, the maximum profit values associated with this belief when τ
is low, when τ is moderate, and when τ is high, are 3115, 3118, and 3119.5 respectively. With a higher belief value,
say 0.7, the maximum profit values for low, moderate, and high τ, all increase, with the aggressive policy yielding a
better profit.
Furthermore, let us fix the maximum profit x to 3117; Table 1 shows that the belief values for the subset [π- x] for
the soft, moderate, or aggressive policies are .80, .60, or .20 respectively. If we increase the profit x to 3119, then the
belief values for the subset [π- x] for the soft, moderate, or aggressive policies are .90, .80, or .20 respectively. In
both cases, the softer the policy, the lower is the belief value for a given profit.
It is very important therefore that the pricing and the inventory manager thinks of a trade-off between a higher belief
value (for π≥τ) and lower profit, according to the relationship structure explained above.
Table 1: Trade-off between higher beliefs and lower profits
PROFIT
π
LOW τ MODERATE τ HIGH τ
Membership Bel Membership Bel Membership Bel
3102 1.000000 0.05 1.000000 0.00 1.00 0.00
3103 0.947368 0.10 1.000000 0.00 1.00 0.00
3104 0.894736 0.15 1.000000 0.00 1.00 0.00
3105 0.842105 0.20 1.000000 0.00 1.00 0.00
3106 0.789473 0.25 1.000000 0.00 1.00 0.00
3107 0.736842 0.30 1.000000 0.00 1.00 0.00
3108 0.684210 0.35 1.000000 0.00 1.00 0.00
3109 0.631578 0.40 1.000000 0.00 1.00 0.00
3110 0.578947 0.45 1.000000 0.00 1.00 0.00
3111 0.526315 0.50 1.000000 0.00 1.00 0.00
3112 0.473684 0.55 1.000000 0.10 1.00 0.00
3113 0.421052 0.60 0.888888 0.20 1.00 0.00
3114 0.368421 0.65 0.777777 0.30 1.00 0.00
3115 0.315789 0.70 0.666666 0.40 1.00 0.00
3116 0.263157 0.75 0.555555 0.50 1.00 0.00
3117 0.210526 0.80 0.444444 0.60 1.00 0.20
3118 0.157894 0.85 0.333333 0.70 0.75 0.40
3119 0.105263 0.90 0.222222 0.80 0.50 0.60
3120 0.052631 0.95 0.111111 0.90 0.25 0.80
3121 0.000000 1.00 0.000000 1.00 0.00 1.00
6. Conclusion
The article considered the retailer pricing and inventory problem when possibilistic fluctuations of product
parameters are controlled by a set of possibilistic optimality conditions. Experts in various functional areas (for
example, the purchase manager, the sales manager, the inventory manager, suppliers, and marketing and finance
managers) convey their subjective judgment to the Possibilistic Group Support System (PGSS) in the form of
analytical models (for product parameters estimation), fuzzy concepts (facts), and possibilistic propositions (for
validation and choice procedures).
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3366
In order to avoid the complexity of combining basic probability assignments, and in order to reduce the arbitrariness
of compatibility functions as a method for the representation of Experts' opinions as fuzzy concepts, the PGSS
reverses the roles of basic probability assignments and compatibility functions: Experts' opinions are represented
using basic probability assignments which are then transformed into falling shadows, then to compatibility functions
of fuzzy concepts. Possibilistic evidence is therefore processed as compatibility functions (not as basic probability
assignments). At the end of the process, the possibilistic recommendation (the fuzzy profit concept) is rewritten as a
basic probability assignment using the principle of least ignorance on randomness.
The PGSS also allows the user (inventory control) to examine a trade-off between the belief function of a greater
profit and a lower amount of randomness associated with it. Managerial pricing and inventory strategy is controlled
using three fuzzy concepts expressing whether management is playing softly ('π≥τ', low τ), moderately ('π≥τ',
moderate τ), or aggressively ('π≥τ', high τ). Management can soften their strategy and reinvoke the PGSS until a
final system recommendation becomes satisfactory.
References
1. Dubois, D., Fuzzy Set Connections as Combinations of Relief Structures, Information Sciences, 66, 245-275,
1992.
2. Duffin, R.J., Peterson, E.L. and C. Zener, Geometric Programming: Theory and Applications, Wiley, New York,
1967.
3. Goodman, I.R., Fuzzy Sets as Equivalent Classes of Random Sets and Possibility Theory, Pergamon Press, 1982.
4. Goodman, I.R. and H.T. Nguyen, Uncertainty Models for Knowledge-based Systems, North-Holland, New York,
1985.
5. Gonzalez, A. and Vila, M.A., Dominance Relations on Fuzzy Numbers, 64, 1-16, 1992.
6. Klir, G.J., Where Do We Stand on Measures of Uncertainty, Ambiguity, Fuzziness, and the Like?, Fuzzy Sets and
Systems, 24, 141-160, 1987.
7. Lee, W.J., Determining order Quantity and Selling Price by Geometric Programming: Optimal Solution, Rounds,
and Sensitivity, Decision Sciences, 24, 1, 76-88, 1993.
8. Liang, P. and F. Song, Computer-Aided Risk Evaluation System for Capital Investment, 22, 4, 391-400, 1994.
9. Shafer, G.A., A Mathematical Theory of Evidence, Princeton University Press, N.J. (1979).
10. Zadeh, L.A., Fuzzy Sets as a Basis for a Theory of Possibility, Fuzzy Sets and Systems, 1, 3-28, 1978.
11. Zadeh, L.A., A Theory of Approximate Reasoning, Machine Intelligence, 9, 149-194, 1979.
12. Zahedi, F., Intelligent Systems for Business, Wadsworth Inc., 1993.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3377
Saudi Arabia’s Economic Diversification: A Case Study in
Entrepreneurship
Kimanthi Ali Thompson, Prince Mohammad Bin Fahd University
Dalal Thair Al-Aujan, Prince Mohammad Bin Fahd University
Roaa AL-Nazha, Prince Mohammad Bin Fahd University
Sara Al Lwaimy, Prince Mohammad Bin Fahd University
Sumayah Al-Shehab, Prince Mohammad Bin Fahd University
Abstract
The Saudi Arabian economy is primarily dependent on a natural resource that is expected to
be depleted within the next 20-years. To date, 75% of all Saudi Arabia’s revenues are
generated from oil & gas exports, so in order for the Saudi economy to reach its goal of
sustainability - diversification will play a critical role for success. Forty years ago, Saudi
Arabia’s leaders developed what have become a series of 5-year economic development plans
aimed at achieving diversification by creating new business within major industry sectors that
include; communications, economic, health, housing, human resources management,
municipal and transportation. The KSA Ninth Development Plan (2010-2014) is a spending
initiative worth SR1, 444bn (US$385.2bn) and if successfully implemented the plan will
realize an annual GDP growth rate of 5.2% over the current plan’s five year life span.
Private sector growth will be the main driver for the economic diversification of Saudi
Arabia’s economy. To date, the majority of new business start-ups in Saudi have come in the
form of franchises deriving mainly from existing U.S. business models. Although
franchising provides a quick one-stop solution for establishing a business, the issue is that its
practice does not provide an adequate foundation for the economic sustainability of a country.
In order for Saudi Arabia to achieve economic diversification true Entrepreneurship must
begin within the Kingdom where new businesses are created based on innovation, technology
and the use of Saudi’s valuable resources.
Keywords: Diversification, Economic, Entrepreneurship, Franchising, GDP Growth,
Health Services, Human Resources, Innovation, KSA Ninth Development Plan, Middle East,
Oil, Petroleum, Saudi Arabia, Sustainability, Technology, U.S.
1. Introduction
Oil was discovered in Saudi Arabia during the 1930’s and since Saudi has grown into the
world’s largest producer and exporter of petroleum with the second largest proven reserves
(OPEC, 2010). As a result of abundant oil Saudi’s economy has been on a continuous path of
transformation and development that makes the country one of the fastest growing economies
in the world (Economy of Saudi Arabia, 2011). However, this transformation, mostly
predicated on a limited resource, hasn’t naturally progressed like most developing nations.
The Kingdom’s quick rise and huge wealth has been built largely without a sustainable
foundation, and unless Saudi Arabia’s leadership can find ways to decrease its dependency
off oil based products and services the country will eventually lose 75% of their petroleum
based export revenues. Therefore, developing sustainable strategies to diversify the Saudi
economy is a crucial and immediate mission of the government (Affairs, 2011).
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3388
In a recent interview, Abdel Salam Al-Suhaimi, a public affairs official for the Saudi
Electricity Company, stated that his countries oil supply could be depleted by 2030 (Al-
Suhaimi, 2011). Furthermore, oil prices continuously fluctuate in response to global
economic and political changes. For example, in 2009 global economic growth declined and
as a result demand for energy decreased which lead to a sharp reduction in oil prices. Oil
exporting countries like Saudi Arabia were considerably impacted by this reduction and as a
result of a reduction in oil payment revenues the economy slowed (Ninth Development Plan,
2011). Ongoing economic development plans originally established to protect the Saudi
economy during turbulent times have historically had little effect because to date the sale of
oil still accounts for 80% of Saudi Arabia’s national income (Al-Suhaimi, 2011).
2. KSA Ninth Development Plan (2010 – 2014)
The latest development plan, the 2nd
KSA Ninth Development Plan (2010-2014) is a spending
initiative worth SR1, 444bn (US$385.2bn) that aims at realizing average annual GDP growth
of 5.2% (Ninth Development Plan, 2011). The growth in GDP is expected to result in
increased GDP per capita income from SR46, 200 (US$12bn) in 2009 to around SR53, 200
(US$14bn) in 2014 (Ninth Development Plan, 2011). The primary contributor to this growth
will be the nonoil private sector, which the government expects to grow 6.6% per year, on
average, during the 5-years taking its share of GDP to 61% from 48% ("Saudi Arabia GDP
growth", 2010). The government has allocated SR137.6bn (US$36.7bn) to be spent on
Human Resources Development and SR9bn (US$2.4bn) to be spent on Educational
Development. These spending plans include building community colleges and more career
training institutes, as well as additional public schools and technological facilities (Global
Education, 2010). These types of spending initiatives will ensure the availability of a highly
skilled and motivated Saudi work force in the future; however, more focused
entrepreneurship is still needed.
Other plan sectors might provide the best catalyst for future entrepreneurial development
within the Kingdom of Saudi Arabia. These sectors include; social & health services,
economic resources, transportation & communication and municipal & housing related
services. One example of a new business concept within Saudi stems from its cultural
heritage of large family units who live in the same household and improvements within the
healthcare industry. As the generation who initially benefited from the discovery of oil
begins to age new business opportunities will arise within the Kingdom in the form of health
care services. Innovated entrepreneurs can capitalize on this opportunity by creating
businesses within the health services sector that will care for and provide medical attention to
their specific needs. These types of healthcare businesses include; home health care services,
retirement communities & assisted living services and the medical supply and equipment
companies that will be needed to service them.
3. Entrepreneurship
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
3399
Using positive examples of Western capitalism, the Saudi government has made steps to
decentralize the economy and as a result the entrepreneurial spirit has begun to take shape
within the Kingdom. Evidence of this transformation can be seen by the number of
recognizable brands already located within Saudi Arabia. Over the past five years,
franchising has tremendously grown and many brand names are already well entrenched in
the market. Industry sources state that fast food franchises already account for more than
60% of the total Saudi franchise market ("Saudi Arabia Franchise Statistics", 2010).
American firms have the lion share with more than 70% of all franchised operations in Saudi
Arabia from fast food, clothing outlets, hotels, car leasing, laundry services and printing
("Saudi Arabia Franchise Statistics", 2010).
Although private business ownership is not new to the Kingdom, until now it has primarily
been focused on franchising. This has resulted in a largely undiversified economy that is not
contributing to achieving the 9th
Development Plan’s objectives - which is sustainability
through economic diversification. As such, true innovation in the form of entrepreneurship
must be the key driver to economic diversification. One example of innovation is using
Saudi’s naturally hot climate to power a desalination plant. King Abdulaziz City for Science
and Technology (KACST), is currently building what will be the world’s largest solar-
powered desalination plant in the city of AL-Khafji, Saudi Arabia. Once complete the plant
will use a new kind of concentrated solar photovoltaic (PV) technology and new water-
filtration technology, which KACST developed jointly with IBM. Once completed, the plant
will produce 30,000 cubic meters of desalinated water per day which will meet the needs of
100,000 people (Patel, 2012). This example shows how true entrepreneurship will eventually
decrease the need to import while producing a product/service through the use of new
technology that can one day be exported to other countries.
4. Simple Conclusion
For over a half a century, American manufacturing has dominated the globe though new
technology creation and leading innovation. Today, the decline in U.S. based manufacturing,
the primary benefactor of America’s innovation dominance, has lead to a decline in the U.S.
economy and the displacement of disposable income. It’s this displacement that has left a
void within the international marketplace where products/ services are sold/ bought and
technology and innovation is shared. The Middle East, with its vast oil reserves, is mostly
sheltered from the economic disparity that is inflicting many nations today. These oil
reserves have created the catalyst for a new industrial revolution taking place within many
Middle Eastern countries, like Saudi Arabia.
The majority of research concerning the U.S., Saudi Arabia and the Middle Eastern is based
on Oil & Gas. The next wave of innovation in the form of entrepreneurship must come from
countries how the economic ability and marketplace to sustain future development have.
Doing business 2011 data for Saudi Arabia shows that out of 183 economies Saudi ranks 1st
in registering a property, 6th
in paying taxes, 13th
in starting a business, 14th
in dealing with
construction permits, 16th
in protecting investors and 18th
in trading across borders (World
Bank Group, n.d.). These statistics show an economy that is becoming less dependent on oil
revenues and more focused on non-oil economic dependence and diversification.
References
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4400
1. Affairs, B.O. (2011, May 6). Saudi Arabia. Retrieved October 15, 2011, from U.S.
Department of State: http://www.state.gov/r/pa/ei/bgn/3584.htm
2. Al-Suhaimi, A.S. (2011, December 24). (Al-Aujan, D.T. Interviewer)
3. Economy of Saudi Arabia. (2011, October 7). Retrieved October 9, 2011, from
Wikipedia: http://en.wikipedia.org/wiki/Economy_of_Saudi_Arabia
4. Education in Saudi Arabia. (2011, December). Retrieved December 2011, from
Wikipedia: http://en.wikipedia.org/wiki/Education_in_Saudi_Arabia#cite_note-20
5. Ninth Development Plan. (2011). Retrieved December 10, 2011, from Ministry of
Economy and Planning:
http://www.mep.gov.sa/index.jsp%3bjsessionid%3d809DB039138CE6C654F00EB6
CE95FAEB.beta?event=ArticleView&Article.ObjectID=79
6. OPEC Share of World Crude Oil Reserves. (2010). Retrieved December 2011, from
OPEC: http://www.opec.org/opec_web/en/data_graphs/330.htm
7. Patel, P. (2012). Solar-Powered Desalination. Technology Review/MIT. Retrieved
from http://www.technologyreview.com/energy/25010/
8. Saudi Arabia Franchise Statistics. (2010, October 27). Retrieved from
http://www.franchiseek.com/saudi_arabia/franchise_saudi_arabia_statistics.htm
9. World Bank Group. (n.d.). Retrieved from
http://www.doingbusiness.org/data/exploreeconomies/saudi-arabia/
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4411
How to Effectively Manage IT Project Risks
Bradley Sean Susser, Pace University, NY
Abstract
Although project management in its more contemporary form has continued to evolve over
the last 50 years it has continued to be plagued with Information Technology (IT) risks and
pitfalls. The evolution of Project Management has indeed been helpful to sovereigns and
companies over the years in organizing work around projects that span multiple industries by
providing better standards, policies, procedures, tools and techniques that have allowed many
to acquire knowledge in the areas of project scope management, project time management,
project costs management, project quality management, human resource management, project
communications management, project risk management and project procurement
management.
However despite all these improvements IT projects continue to be renowned for their high
rates of failure which is clearly evident in empirical backed research such as the one that was
provided by the Standish Group's 2009 CHAOS study that demonstrated a decrease in project
success rates, with 32% of all projects succeeding which are delivered on time, on budget,
with required features and functions. In contrast 44% of projects were challenged by being
late, over budget, and/or with less than the required features and functions and 24% failed
which were cancelled prior to completion or delivered and never used[The Standish Group
(Oct. 2009)]. It must be noted that in CHAOS Manifesto 2011, The Standish Group's showed
a marked increase in project success rates from 2008 to 2010 [The Standish Group (Oct.
2011)] but in 2011 PM Solutions Research also came out with a report called Strategies for
Project Recovery where they followed 163 companies split between small, medium, and large
organizations [PM Solutions (2011)]. On average, respondents managed $200 million in
projects each year of which approximately 37 percent were at risk. The average company in
the study therefore faced $74 million of at risk projects each year. The last two reports are
affirmations that organizational projects risk profiles are still quite high and remain a key
challenge in today’s environment.
Therefore in this paper we will provide a brief history on the evolution of Project
Management, the most common reasons projects fail, a detailed case study of a well-known
project failure, solutions and how to effectively manage and mitigate risks in IT projects,
incorporate an opinion from a highly recognized Project Management consulting firm on an
evolving risk management approach and then conclude by offering an added opinion which
will comprise of how to attain desirable outcomes.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4422
1. Introduction
The innovation and speed in IT has created considerable advancements in the last several
decades however due to the increasing number of governments and organizations around the
globe moving away from centralized system models to more distributed mediums, means the
complexity of businesses has been growing therefore the need to optimize the implementation
of Project Management approaches has overwhelmingly become of even greater significance.
Although IT projects have been the primary focus of project management its roots go back to
the late nineteenth century focusing at that time primarily on government initiatives. We
discuss project management in its historical context because several of the core principals,
methodologies, tools and techniques that comprise of project management not only are
essential in improving the success of a project but if applied properly also help mitigate any
risks while maximizing the potential for project success. It was in this period of the late
nineteenth century that the initial ground work in the area of Project Management was said to
be first formulated. In the United States for example the first extensive government project
was the transcontinental railroad, which began construction in the 1860s during the Industrial
age whereby industry luminaries were confronted with the intimidating task of organizing the
manual labor of thousands of workers and the processing and assembly of exceptional
quantities of raw material [Microsoft Corporation (No Date)].
Near the turn of this century, Frederick Winslow Taylor (1856–1915) an American industrial
engineer was one of the first to begin detailed studies of work by devising a system he coined
Scientific Management to determine the optimum means for carrying out a task in the
smallest amount of time by focusing on shifting knowledge of production from the workers to
the managers. He applied scientific reasoning to work in showing that labor can be analyzed
and improved by breaking up industrial production into very small and highly regulated steps
which required workers to obey the instructions of managers concerning the proper way to
perform very specific actions. Taylor’s theory was primarily applied in steel mills, such as
shoveling, lifting and moving parts. Prior to this time the only way to augment productivity
was to require individuals to work laboriously by putting in long hours. Taylor changed all
that by introducing the concept of working more efficiently rather than working harder and
longer therefore his theories had determined to be the very best way to perform these specific
isolated tasks. In 1887 Henry Gantt (1861-1919) a mechanical engineer, partnered up with
Frederick W. Taylor to leverage the theory of scientific management at Midvale Steel and
Bethlehem Steel, where they worked together until 1893 [Roebuck. K (May 2011)].
Gantt studied in great detail the order of operations in work. His studies of management
focused on navy ship construction during World War I and his Gantt Charts which were first
conceptualized in 1917 are one of the most frequently used project scheduling and progress
assessment tools to date. This was in fact the first quantitative technique of project
management in the area of schedule risk analysis. Although it has been refined over the years
in simple terms the chart can be described as a horizontal bar chart that illustrates project
tasks against a calendar whereby each bar represents a project task which are listed vertically
in the left hand column and the horizontal axis represents a calendar timeline. Tasks can
overlap one another by being carried out at the same time and can be shaded to indicate
project progress and percentage completion to depict which tasks are ahead of or behind
schedule providing further guidance for mitigating the potential for Scope Creep. Microsoft
Office Project over the years has improved upon Gantts original work but he is the one that
truly provided the initial foundation that is now incorporated into Microsoft’s widely used
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4433
project software. Both Taylor and Gantt’s initial works were clearly revolutionary at the time
as they helped to establish a prerequisite for good Project Management that required a well-
defined development process making project management an unequivocal business
application. In the years leading up to World War II, marketing approaches, industrial
psychology, and human relations began to take hold as integral parts of project management.
During World War II, complicated government and military projects and a diminishing war-
time labor supply, accessed the need for new organizational structures. This lead to further
the evolution of project management when the U.S. navy in the 1950’s first developed the
Project Evaluation and Review technique whose acronym is PERT, while working on the
Polaris missile project during the Cold War era [Johnson, S. B. (March 2002)]. Sometimes
referred to as network diagrams the PERT chart lists the specific activities that make up a
project and the activities that must be completed before a specific activity can start. In more
detail the chart consists of a number of nodes that represent project tasks whereby each node
which can be depicted as either circles or rectangles are numbered showing the task, its
duration, the starting date and the completion date.
The directions of arrows on the lines that are incorporated in the chart indicate the order of
tasks and shows which activities must be completed before another activity may begin. One
of the primary functions of PERT charts was to address issues related to costs. In the early
1960’s organizations around the world began to seek out new management strategies and
applied the previous approaches and techniques described above to assist in allowing
businesses to better cope with the rapid expansion and changing business environment that
spanned across all industries worldwide. It is in this time period of the early 1960’s that
project management was viewed as an essential approach that all organizations and
sovereigns needed to make use of and began to form the contemporary foundation that is
embedded in today’s society for all businesses to continue to exist and flourish. Inclusive is
that many of these techniques can and should be applied to minimize any organizational
project risks while increasing profits in order to gain a competitive edge in today’s overall
market place.
2. Related of Literature
Significant analysis collected over time comprising of project success and failure rates have
been well documented so to begin with here is a brief summary on some of studies deemed
appropriate in this area. In a report issued back in a 2008 white paper written by Kathy Ellis
of IAG Consulting (www.iag.biz) titled “Business Analysis benchmark” The Impact of
Business Requirements on the Success of Technology Projects included surveys of over 100
companies with the average project size of $3 million which was certainly a wake-up call
[Ellis, K. (2008)]. The survey measured the current ability of organizations in performing
business requirements and an evaluation of the underlying causes of poor quality
requirements. Firm’s with inadequate business analysis capability were said to have 3 times
as many project failures as successes and 68 percent of the companies are less likely to
succeed based on the way they approach business analysis. In fact additional findings found
50 percent of group projects were “ runaways” taking over 180% of target time to deliver,
consuming in excess of 160 percent of the estimated budget , delivering under 70 percent of
the target required functionality, paying a premium of as much as 60% on time and budget
when they use poor requirement practices on their projects and over 41% of the IT
development budget for software, staff and external professional services was said to be
consumed by poor requirements at the average company using average analysts versus the
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4444
optimal organization. IAG found using best requirements practices will estimate a project at
$3 million and better than half the time will spend $3 million on that project including all
failures, Scope Creep, and mistakes across the entire portfolio of projects.
This group will spend on average, $3.63 million per project while firms using poor
requirement practices will pay on average $5.87 million per project due to excessively high
time and budget overruns. In 2009 as we described in the above abstract the CHAOS report
issued by Standish Group International found 68 percent of projects either failed or 44
percent have been challenged[The Standish Group (Oct. 2009)]. Standish also stated 38
percent of projects between $750,000 and $3 million have a chance at success but when the
cost of a project exceeded $10 million there was only a 2 percent chance of success. In an
article titled “Making Change Work” a survey of 1,500 change management executives
issued by IBM in October of 2008 discovered 44 percent of all projects failed to meet time,
quality and budget goals while 15 percent were either halted or did not meet all the objectives
[Jorgensen H., Owen L., Neus A. (Oct.2008)].
Finally in a review of federally funded technology projects by the U.S. Government
Accountability office in July of 2008 they ascertained 49 percent of federal IT projects where
inadequately planned, inadequately performing or both [Powner, D. (July 2008)]. The
question than arises if failure in projects still persist than how can we increase the chances for
success? In essence that is what the rest of this paper aims to accomplish which is, we must
provide you with the reasons why projects fail and in contrast you will then be able to
determine through this assessment what not to do. We also intend to closely evaluate a major
project case study describing many of the variables that adversely impacted that particular
project and finally we offer a clear-cut outline through various methodologies, approaches
and techniques to properly mitigate and manage risk inclusive is the what is already
recognized throughout Project Management as the six major processes involved in risk
management and the evolving Committee of Sponsoring Organizations of the Treadway
Commission’s (COSO) Enterprise Resource Management (ERM) framework so that the
chances for a project being successful are increased substantially.
3. The Most Common Reasons Project Fail
In the figures provided by some of the research described above we ascertained some of the
projects pitfalls but going further we have comprised of a more detailed summary to discern
why projects fail. One of the first reasons is that Project sponsors are often times not devoted
to the projects objective by not actively being involved in the project strategy and they have
an insufficient comprehension of the overall project [Progress, Project (2008)]. It is also
unfortunate but a multitude of projects do not meet the strategic vision of the company
therefore if business requirements are not precisely defined, it can cause a project to not add
value to the bottom/top line or improve business processes. Remember IT projects must align
with overall business objectives. Another issue is projects commence for all the inappropriate
reasons as some begin solely to implement new technology without any concern for whether
the technology is accommodating of organizational business requirements. The opposite of
this is a project that does not support existing technology developing extensive Scope Creep
resulting in additional capital expenditures. In delving into the work breakdown structure
which is also a part of the project scope management knowledge area, it may be used
inefficiently such as not
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4455
administering enough dedicated staff allocated to projects and team members may have
limited experience with a lack of required qualifications. Insufficient experience can also
cause project teams often times to take shortcuts to catch up to schedule and make up costs
by skipping steps.
Lack of communication and collaboration is also sometimes inefficient among all
stakeholders as this can certainly cause a project to fail. Indicative of another potential risk is
that executives and supervisors believe that they will be able to succeed in leading a project
but are rarely available therefore focusing in this regard is not on project delivery but on the
contentment of the project manager and his own time management. Another project pitfall is
an incomplete project scope definition which does not provide a project's advantages and the
deliverables that will produce them. One may assume that before a project is initiated a plan
and the processes that it is comprised of would be implemented however this is unfortunately
not always the case. A project plan that is non-existent, out of date, incomplete or
inadequately constructed and where just not enough time and effort is spent on project
leading can significantly have an adverse impact on any project.
This also can mean that value is not put into use to calculate baseline costs agreed during
baseline transfer against actual costs spent at any given time therefore costs do not form an
integral part of the project during execution. Moving on, insufficient funding and incorrect
budgeting is still a major reason for projects not delivering their goals and objectives within
the quality framework that was required because projects always need to deliver yesterday
within a specific budget. In addition premature commitment to a fixed budget and schedule
are usually inconsistent. When we discussed in its historical context how project management
came to fruition it is intriguing that many firms who are aware of how project management
came into being still continue to have no established project leading methodologies and best
practices aligned with the company's specific needs to assist in project performance.
Surprisingly, companies do not want to invest in best of breed methodologies that will benefit
the bottom line over a specified period, with projects delivered within budget. Remember
methodologies are the foundation of project management so you would have to wonder why
any organization would be incapable of understanding the various methodologies that are
rooted in Project Management. Inclusive companies do not recognize the value of using a
methodology to support and enable them to record their own best practice project results for
future reference and to build a knowledge base within the company. Also not all projects go
through a methodical signing off process using a proper post project approach to determine
lessons learned and to construct one’s own reference model for future use. Even more
astonishing is that many projects do not consist of good end to end testing procedures even if
a project has a signoff process as project managers sometimes do not manage to engage all
the necessary test resources for the final testing ahead of time. Finally, a certificate signed off
between sponsors and other third-parties will demonstrate project success but even that is
quite rare.
The following additional data below shows some of the most dominant risk factors identified
by Wiegers (1998), who categories these factors by sector:
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4466
Project Sector Risk Factor % of Projects at Risk
MIS Creeping User Requirements 80
Excessive Schedule Pressure 65
Low Quality 60
Cost Overruns 55
Inadequate Configuration Control 50
Commercial Inadequate User Documentation 70
Low User Satisfaction 55
Excessive time to market 50
Harmful competitive actions 45
Litigation expense 30
Table 1: Most common risk factors for various project types (Wiegers 1998)
Governance can be a separate category all its own, comprised and correlated with
many of the reasons projects fail. The Office of Commerce of the UK Government together
with the National Audit Office lists eight common causes of project failure but we will just
focus on the six that deal primarily with governance related issues [AON Risk Solutions,
(2011)]. The first is a lack of a clear link between the project and the organizations key
strategic priorities including agreed measures of success; the second is lack of clear
ownership and leadership for the project from the organizations governing body; third is lack
of skills and proven approach to project management and risk management; evaluation of
proposals driven by initial price rather than long term value for money especially securing
delivery of business benefits; lack of understanding of and contact with project
contractors/service vendors at senior levels in the organization; and finally lack of project
team integration between clients, the supplier team and the supply chain. We cannot
emphasize enough how Governance is an essential factor to mitigating risks therefore we will
further discuss why it remains crucial to incorporate the proper governance framework in the
chapters to follow.
4. Detailed Case Study on Project Management Failure
Case studies are important in depicting real world events so we would be remiss in not
providing you with at least one notable case study that offers great insight of a large scale
project failure. We are referring to the construction of one of the most advanced reservation
systems in U.S. history, recognized by many as the CONFIRM Project. The project was
formulated back in 1988 by a consortium consisting of Hilton Hotels, Marriott (NYSE:
MAR), Budget Rent-A-Car (NASDAQ: CAR) and American Airlines Information Services
(AMRIS), a subsidiary of American Airlines (AAMRQ.PK), almost all publicly traded except
for Hilton which was on the New York Stock Exchange until it was acquired by Blackstone
Group for $20 billion back in July of 2007 [Cauley, L. (July 2007]. AMRIS was
subcontracted as the managing partner and Intrico was a newly established organization
whose responsibility was to exclusively run the new system. These organizations at the time
teamed up to cultivate and market what was expected to be the most state of the art
reservation system to be used for travel, car rental, and lodging services. Five years later after
numerous lawsuits and millions of dollars in cost overruns, the CONFIRM Project was
finally cancelled over grievous accusations from many of the leading executives that had
involvement in the project [Oz, E., 1994]. Although the objectives were articulated as
achievable in the original requirements document provided by AMRIS, they proved to be
quite ambiguous causing many of the initial requirements to change.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4477
In other words, there was a general agreement among the organizations regarding the need
for a new system however there was no clarity of what the new system’s goals and objectives
should be in order to satisfy the specific information requirements of the consortium therefore
continuous change in requirements resulted in an exorbitant amount of wasteful capital
expenditures. This of course is a prime example of Scope Creep which is where the
requirements and expectations of a project increase, often without regard to the impact on
budget and schedule. All stakeholders require specific responsibilities to be made clear and in
particular due to the disparate background of the players involved in the CONFIRM Project it
was believed that lack of communication and disorganization further fueled confusion about
requirements and design decisions among all project members.
Claims were also made that Intrico heads only met once a month when they should have had
meetings much more frequently. Also projects that are enormous in scope tend to have
elevated risks and levels of complexity that can discourage even the most competent of
teams. For example, the then president of AMRIS is reported to have indicated that “the task
of tying together CONFIRM’s Transaction Processing Facility-based central reservation
system with its decision support system proved to be overwhelming. . . We found they were
not integrable” [Halper, M., August 3, 1992]. Since the complexity of the project was so
evident it is even of greater significance that effective coordination must be implemented in
order to ensure the successful completion of a project.
Furthermore, the complexity issue should also have been analyzed more meticulously in the
early phases of the project life cycle because costs are significantly higher when major
changes to a project are made in the latter phases as opposed to the initial phases. In addition,
the failure of the database to recover in the event of a crash was, in the words of the VP of
Operations, due to the fact that “in the development of the DB2-based decision support
system, the company mistakenly implemented a version of Texas Instruments’ Information
Engineering Facility (IEF) computer-aided software engineering tool in which IEF generates
its own database structure.”
Also, the VP is reported to have suggested that for CONFIRM‘s size, they “should have
implemented a version of IEF in which the structure is dictated because the system was so big
that what IEF generated would have been impossible to maintain” [Halper, M., Aug. 10,
1992]. The VP of operations above quote is a primary example of not only a lack of
coordination due to mistakenly implementing Texas Instruments’ Information Engineering
Facility (IEF) computer-aided software engineering tool but also emphasizes once again the
importance of performing efficient analysis in the early stages of a projects lifecycle that
would have enabled the consortium to have selected a version of IEF in which the structure is
dictated to avoid unwieldy spending. Deficiencies in structure and organizational objectives
in the team’s efforts was additionally exacerbated by no clear leadership and active
interaction among parties in the CONFIRM Project causing complications in later phases of
the system development lifecycle. This is evident when AMRIS made allegations in its
lawsuit that the other three companies they worked with made poor staffing assignments that
crippled the project [Halper, M., October 12, 1992]. By not incorporating an appropriate
structure and project phased lifecycle approach the CONFIRM Project adversely impacted
the project team by not enabling them to recognize what the deliverables for each stage were
and to know if they had been satisfied. Clearly there was a lack of project feasibility phases
as well as project acquisition phases.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4488
In fact, the CEO of AMR is quoted as stating in a letter to the other three companies, “The
individuals to whom we gave responsibility for managing CONFIRM have proven to be
inept. Additionally, they have apparently deliberately concealed a number of important
technical and performance problems” [Zellner, W., 1994]. This letter implied that project
management failure created an environment where activities were not properly monitored and
concealed. But even if the allegations by AMR’s CEO were true, the IBM review
commission spoke “to the need of more critical review and immediate corrective action by
AMRIS management. Not doing so would almost assuredly result in failure” [Zellner, W.,
1994]. After all AMRIS was made ‘Managing Partner of Development’ for CONFIRM and
took on the responsibility for all aspects of the design and development of the system. In fact,
AMRIS executives initially stated to the consortium that the system would not be expensive
to run and would be completed in time to outpace competition in the hotel and car rental
industries however this statement proved to be false.
As with all failures the problems can be viewed from a number of levels. In its simplest form,
the CONFIRM project failed because those making key decisions underestimated the
complexity involved. Other contributing factors for CONFIRM projects debacle include a
lack of planning resulting in subsequent changes in strategy; making firm commitments in the
face of massive risks and uncertainty; lack of management oversight; poor stakeholder
management; communication breakdowns; failure to perform risk management and the list
goes on and on. The initial cost of the project was originally estimated at $55.7 million in
April 1988 with a completion date of June 1992. It was revised to $72.6 million in September
1989. This trend in escalating project cost continued till the project was canceled in July
1992, after 3 1/2 years and $125 million in costs [Oz, E., Oct. 1994]. Perhaps CONFIRM’s
project failure was a prelude to American Airlines more recent woes as the airlines current
status is that in late 2011 it filed for chapter eleven bankruptcy as its shares currently trade on
the Pink Sheet Exchange under the symbol “AAMRQ” at around .49 cents a share [Milford,
Phil, Schlangenstein, Mary and McLaughlin, David (Nov. 2011)]. The “Q” at the end of a
symbol denotes that a publicly traded company is in the process of bankruptcy.
5. Solutions to Effectively Manage and Mitigate Risks in IT Projects
Mapping the primary activities of each Project Management process group for each
knowledge area is an integral part of project management which can be found in PMI’s
PMBOK guide, a standard that describes best practices for what should be done to manage a
project effectively. Our focus however is take a closer look at one of the project management
knowledge areas that should be adapted over the whole project life cycle, that being Risk
Management. The discipline of Risk Management has evolved considerably over the years
including a number of standards and methodologies used to identify risks, measure them,
monitor them and ultimately mitigate the overall project risk profile. In fact in this section we
will meticulously examine and look at the process of how to best select and implement
countermeasures to address an organizations risk requirements.
Risk Management is often times overlooked but it can have a significant effect on the choice
of projects from deciding on the scope of projects and cultivating pragmatic schedules and
cost estimates as well as assisting project stakeholders to comprehend the description of the
project involving teams in defining strengths, weaknesses, opportunities and threats via
SWOT analysis and further helping to integrate the other project knowledge areas. This can
all lead to improving a projects success.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
4499
Before proceeding in describing in detail how to effectively manage project IT Risks it must
be noted that unfortunately this among all knowledge areas was shown to be of less
importance than all of the other areas and furthermore was among the least mature. This can
be seen in a survey performed by William Ibbs professor and group leader of the
Construction Management program at the University of California at Berkeley and Young-
Hoon Kwak, Ph.D., currently an assistant professor for the Project Management Program at
The George Washington University (GWU) [Ibbs, William and Young Kwak, Hoon (March
2000)]. The two surveyed over a period of two years four different industries and application
areas to collect project management practices information.A total of 38 large international
companies, including private and public sector organizations, participated in this study. The
four industries were: engineering and construction (EC); information management and
movement (IMM), also known as telecommunications; information systems (IS), also known
as software development; and hi-tech manufacturing (HTM). The Project Management
Maturity Assessment covered the project management knowledge areas of scope, time, cost,
quality, human resources, communications, risk and procurement weighted on a relative scale
of 1 (lowest) to 5 (highest). What they discovered was in their Project Management Maturity
Assessment methodology, that all companies averaged 3.26 on a relative scale of 1 (lowest)
to 5 (highest) which suggests that all areas could use improvement but the anomaly was in the
area of risk. Risk Management’s project management maturity level was the lowest among all
eight knowledge areas. Risk Management was the only knowledge area where overall project
management maturity rating was less than 3.
Inefficiencies in project risk can also be seen particular in the wake of the stock markets 2008
credit crisis. This was caused primarily by a lack of Governance and Risk Management
initiatives. When the US Senate Banking Committee asked US Federal Reserve Chairman
Ben S. Bernanke what lessons were learned from the current economic crisis, he replied,
“The importance of being very aggressive and not being willing to allow banks, you know,
too much leeway, particular when they’re inadequate in areas such as Risk Management
[Wyatt, E. (Feb. 2011) The irony of the downturn is that financial institutions bundled up
mortgages and sold many institutions on the idea that the housing market had increasingly
gone up throughout the years and the risk of any downturn was minimal to say the least.
These mortgages were then insured by many organizations to reduce any financial
institutional losses. In particular many hedge funds and insurance companies were to provide
a hedge to these financial institutions by insuring many of the mortgages provided by certain
institutions in case they went into default. Unfortunately many of the insurers did not have
the capital to cover the losses on these defaulted mortgages. This in turn led to insurers
having to sell off assets across the entire market spectrum causing a precipitous drop in all
global markets. If the proper countermeasures were in place due to proper risk management
this may have never occurred. One being, that mortgages should have had more stringent
criteria in place so as to not sell to those who evidently could not afford these homes. The no
money down policies and lack of resources should have been a clear indication that many
people could not afford such real estate. Regulators could have easily prevented this from
happening if the proper risk management was in place so Bernanke’s response to the U.S.
Senate Banking Committee is ironic to say the least as they also had a fiduciary responsibility
to the public at large. Governmental agencies should also have required those that insured
these bundled mortgages to have enough capital to cover any losses however this was never
implemented. The Project Management Maturity Assessment survey above and the brief
stock market synopsis are proof of the vital importance risk management plays throughout all
industries, organizations and governments.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5500
Before moving ahead with how to best mitigate risk or use the knowledge of accessing
projects that are at risk to our advantage we must define what Risk Management is and then
ask the three fundamental questions that should be addressed for proper risk analysis. Risk
Management is the identification, assessment, and prioritization of risks followed by
coordinated and economical application of resources to minimize, monitor, and control the
probability and/or impact of unfortunate events or to maximize the realization of
opportunities [Hubbard, Douglas (April 2009)]. The word Risk itself comes from the ancient
Italian word “Risicare” which means to dare [Zweig, J. (April, 2012)]. As described in the
definition of Risk Management above, risks can be either negative similar to insurance which
is a party undertaking to indemnify or guarantee another against loss by a specified
contingency or danger.
In referencing a project insurance is an activity taken to minimize the impact of a possible
threat to a project. In contrast positive Risk Management can be investing in opportunities
taking advantage of the risk as opposed to protecting against it. The phrase “the greater the
risk the greater the chance for reward” is clearly indicative of why companies take on risk
depending on their appetite for risk which is the level of risk an organization views as
acceptable. This depends on the type of organization and how it conducts business for
example financial institutions are typically risk averse but conservative meaning they want a
low residual risk and are willing to use whatever capital necessary to achieve this while in
contrast a retail company with a new clothing line may have a much greater tolerance for risk
as their primary objective is to obtain a competitive edge therefore with limited resources
they wish to spend less on risk controls. When first beginning the Risk Management process,
it is a good idea to identify the organizations boundaries of risk assessment but also we must
ask the following questions. How long will this project eventually take? (schedule risk), How
much will it finally cost? (cost risk), and Will its product perform according to
specifications? (performance risk) [GALWAY, L (Feb. 2004)]. After accessing the
organizations risk appetite it is now time to implement a Risk Management plan. Risk
Management planning is the process of deciding how to approach and administer risk
activities for the project. Planning is crucial in initiating the significance of Risk
Management, allocating proper resources and time to Risk Management and establishing the
foundation for analyzing risk. The goal of the Risk Management Plan is to determine the
strategy to manage project related risks such that there is acceptable minimal impact on cost
and schedule, as well as operational performance.
The next element is to identify the risks which are an initial and cyclical effort to identify
measure and document risks as they are identified. This process is analogous to a detailed risk
analysis approach that is a standard in the ISO 13335 series whereby the initial assessment it
identification of assets. A foundation of risks sets should be constructed and entered into
what is known as a project Risk Register or Risk Log, a document helping you track issues
and address problems as they arise [Staff, CIO (Sept. 2011)]. The Register will document the
various risks with their classification, mitigation and handling strategies, impact on cost and
schedule, and action items. As stated above this is a cyclical process, therefore baseline risks
should be identified through the normal course of the project planning process and
identification of any other risks should be performed throughout the entire project lifecycle.
Several risk identification tools and techniques include Brainstorming a relaxed, informal
approach to problem-solving with lateral thinking where there should be no criticism of ideas
[Schwalbe, K (2011)]. Ideas should only be evaluated at the end of the brainstorming session
which is then the time to explore solutions further using conventional approaches.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5511
Next is the Delphi technique based on the Hegelian Principle of achieving oneness of mind
through a three step process of thesis, antithesis and synthesis. In thesis and antithesis, all
present their opinion or views on a given subject, establishing views and opposing views. In
synthesis, opposites are brought together to form the new thesis. All participants are then to
accept ownership of the new thesis and support it, changing their own views to align with the
new thesis. Through a continual process of evolution, oneness of mind will supposedly
occur. Interviewing is a fact finding technique for collecting information in face to face,
phone, email or instant messaging discussions. Finally there is SWOT analysis an acronym
for strengths, weaknesses, opportunities and threats. Risk Management demands that it is
necessary to avoid, eliminate, or at the very least, minimize identified weaknesses and
threats. Weaknesses should be closely scrutinized in order to determine whether or not it is
possible to convert them into assets. Similarly, threats should be closely examined for the
opportunity of building strength in areas where they stood, once they have been eliminated.
Strengths and opportunities should be closely studied as well in order to maximize their
effectiveness.
Project Management would be well advised to take advantage of this simple, cost effective
management tool and to make it a fundamental step in the planning process. Additional
identification methods may be via check lists, assumption analysis and diagramming
techniques such as making use of flow charts and Cause-and-Effect diagrams. All of the
techniques used during the risk identification process increase collaboration to locate risks
before they become problems, set program priorities to arrive at a joint understanding of what
is important and identifying new risks and changes. Risk statements should be written for
each identified risk in a clear concise manner while containing only one risk condition and
one or more consequences of that condition.
The project manager than ensures that all project stakeholders are responsible for identifying
and capturing new risks which than should be added to the Risk Register right before the
initial project risk kick-off meeting. A Risk Register should record active risks along with the
date identified, date updated, target date and closure date. Also include a unique risk
identification number so that you know if that risk develops during the project and what the
status of the risk is at any given time, a description of the risk, type and severity of risk, its
impact, possible response action and the current status of risk [Staff, CIO (Sept. 2011)]. A
Risk Register framework usually consists of three ratings for impact; High, Medium and Low
according to Northrop Grumman Corporation (NOC) [Northrop Grumman Corporation,
(Nov. 2007)]. Northrop Grumman founded in Virginia in 1939, provides technologically
advanced, innovative products, services, and integrated solutions in aerospace, electronics,
information and services. Northrop’s impact categories are accessed by determining the cost
of an impact, the scope, schedule and quality all which are incorporated in its Risk Register
and is a good example of how companies should make use of the register.
NOC describes cost as an impact typically calculated as a dollar amount that has a direct
impact on the project. However, cost is sometimes estimated and reported as just added
resources, equipment, etc. This is true whenever these additional resources will not result in a
direct financial impact to the project due to the fact the resources are loaned or volunteered,
the equipment is currently idle and there is no cost of use, or there are other types of
donations that won’t impact the project budget. Regardless of whether there is a direct cost,
the additional resources should be documented in the risk statement as part of the mitigation
cost. Whenever there is the potential that the final product will not be completed as originally
intended there is a scope impact.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5522
Scope impact for representation purposes could be measured as a reduction of the number of
tower sites, elimination of trunking for a site, or not providing a back-up power source. It is
very important to estimate the schedule impact of a risk event as this often results is the basis
for elevating the other impact categories. Schedule delays frequently result in cost increases
and may result in a reduction of scope or quality. Schedule delays may or may not impact the
critical path of the project and an associated push out of the final end date. As an example, a
road wash-out for a tower site might delay completion of that site for 3 weeks but if another
site is scheduled to complete after delayed site the 3 week delay won’t impact the final end
date. Finally quality is frequently overlooked as an impact category and too often a reduction
in quality is the preferred choice for mitigation of a risk. Short cuts and low cost
replacements are ways of reducing cost impacts. If not documented appropriately and
approved by the project sponsor, mitigation strategies that rely upon a reduction in quality
can result in significant disappointment by the stakeholders [Northrop Grumman
Corporation, (Nov. 2007)].
The next step in the process is to perform risk analysis which is examining identified risks to
decide on the probability of occurrence, impact, and timeframe. The analysis step can be
performed by using either a quantitative or qualitative approach. Some of this was described
in the Northrop Risk Register model however we will extrapolate on these approaches in this
paragraph. While most organizations appear to use a qualitative approach especially for
accessing risks it is important to recognize the difference between the two as quantitative
analysis does follow qualitative analysis more often than not. However before we precede
further it must be noted that P.L. Bannerman in his studies discovered that none of the
seventeen IT projects he investigated used quantitative risk analysis [Bannerman, P.L., (Dec.
2008)].
Qualitative analysis is a methodology that uses a probability/impact risk level matrix analysis
to prioritize the identified project risks using a pre-defined rating scale. Risks are scored
based on their probability or likelihood of occurring and the impact on project objectives
should they occur. Probability/likelihood is commonly ranked on a high, medium to low
rating or a zero to one scale for example, .3 equating to a 30% probability of the risk event
occurring. The impact scale additionally is organizationally depicted for example as a high,
medium to low scale, with a high rating having the largest impact on project objectives such
as budget, schedule, or quality. Likelihood is used to provide an order of magnitude this is
than updated in the Risk Register as in the NOC case. Below I have acquired what I believe
to be an excellent descriptive example of the use of implementing Qualitative analysis charts
developed by Hulett & Associates, LLC, Project Management Consultants [LLC, Hulett &
Associates (2005)].
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5533
Figure 1: The Separation of Risks into High, Medium to Low Rating. Hulett & Associates,
LLC
Figure 2: The Likelihood and Impact of a Risk Event measured between 0.0 (no likelihood)
and 1.0 (certainty) Hulett & Associates, LLC
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5544
Figure 3: Impact of a Risk Should it Occur on Performance Objectives. Questions and
Associated Ratings Are Constructed. Hulett & Associates, LLC
Figure 4: Impact on Schedule Objective Hulett & Associates, LLC
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5555
Figure 5: Probability-Impact Matrix Ranking Risks into Classes with Red, Yellow and Green
Designations of High, Moderate and Low risks Hulett & Associates, LLC
Any risk can be classified as high, moderate or low depending on its position in the P-I
matrix. Remember however that in order to better effectively make use of Qualitative
analysis it may be best to create charts for both positive and negative risks [Schwalbe, K
(2011)].
Quantitative analysis is additional analysis of the highest priority risks amid which a
arithmetical or a quantitative rating is appointed in order to establish a probabilistic analysis
of the project. This analysis measures the potential consequences for the project and evaluates
the probability of accomplishing distinct project goals, makes judgments when there is
ambiguity and constructs reasonable and attainable cost, schedule or scope targets. In order to
carry on a Quantitative risk analysis you require high-quality data, a well-constructed project
model and prioritized lists of project risks typically from carrying out a Qualitative risk
analysis. Remember this should only be done if it’s worth spending the time and effort
analyzing the risk or else it’s better to move from qualitative risk analysis to risk response
planning which is the next step in risk management. Again usually this type of analysis is
done for highest risks on the project to further investigate them. So the updated list in the
updated risk to the Risk Register is the input for
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5566
Quantitative risk analysis. The numerical Quantitative risk data is typically collected by
analyzing past project data or by expert judgment. Sometimes numerical data are also used
for simulation and one of the simulation techniques is Monte Carlo analysis. For instance,
using Monte Carlo analysis you can check if the project is executed one hundred times. What
is the probability of completing a project on a specific date? Similar analysis can be done for
risk as well. Software packages such as Oracle’s “Crystal Ball” offer a suite for predictive
modeling, forecasting, Monte Carlo simulation and optimization to improve the strategic
decision-making process. Numerical data also helps in using the Decision Tree concept to
objectively analyze project risk and impact. However let me again emphasize this type of
analysis should only be done when it is worth doing it which is usually the case when you are
working with a complex multi-year project. The output of this process is the quantified list of
prioritized risks. Along with this sometimes the amount of contingency reserve in terms of
time and cost is also calculated as part of this process [Schwalbe, K (2011)]. Plenty of
empirical data shows that such techniques as
Monte Carlo analysis and Decision trees are quite effective but to avoid bogging you down
with details of all of these approaches we will just provide a comprehensive description of
just one, that being the Decision Tree. For example, your project requires you to place a
substantial equipment order but you believe there is a 20% risk that your principal hardware
supplier may be unable to provide all the equipment you need for a large order in a timely
manner [Mochal, T (July. 2008)]. This could be risk A. Two way your options you
correspond with a second vendor to see if they can execute the equipment order immediately
but as luck may have it this vendor who normally has the equipment in stock may have the
possibility of a strike which can cause a plant disruption so you now access this to be a 25%
possibility which is risk B. You need to then do is calculate the total risk for both of these
scenarios. The total risk is calculated by multiplying the individual risks. Since there is a 20%
chance of risk A, and a 25% chance of risk B, the probability that both risks will occur is 5%
(.20 x .25). You can use risk trees to come up with financial implications so let’s closely
examine figure A.
Figure 6: Generated By Tom Mochal
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5577
This decision tree shows risks A and B. Risk A has two outcomes; outcome 1 is 20% likely to
occur, and outcome 2 is 80% likely to occur. The monetary value of risk A is $10,000. If
outcome 1 occurs, a second risk B is introduced, and there are three likely outcomes, 1.1, 1.2,
and 1.3. The monetary value of risk B is $30,000. Using the decision tree, you see that the
financial risks of the various outcomes are as follows:
Outcome 1.1 has a financial risk of $9,500 ($10,000 x .20) + ($30,000 x .25).
Outcome 1.2 has a financial risk of $23,000 ($10,000 x .20) + ($30,000 x .70).
Outcome 1.3 has a financial risk of $3,500 ($10,000 x .20) + ($30,000 x .05).
Outcome 2 has a financial risk of $8,000 ($10,000 x .80).
So the optimal choice is outcome 1.3 because it has the smallest financial risk impact. As you
can see a decision tree can mitigate your risk by enabling you to determine the probability
and impact of each risk combination so that you can make a more informed decision
[Mochal, T (July. 2008)].
Analogous to Project Management Quantitative and Qualitative analysis can be represented
in the following example to help to further understand the difference in the two approaches.
Risk analysis overlaps many areas of industries and organizations alike so we have
incorporated an example of both methodologies to further explain differences in both
Qualitative and Quantitative approaches by making use of a hybrid framework in the area of
IT security. Take for example a small banking institution that has 1,000 records. Assuming
these records were compromised you could then come up with the cost involved with the
compromise. Costs could involve getting in contact with the customers, creating new debit
card numbers for the files, and constructing and reissuing new debit cards. You would now
know the cost, which under meticulous examination you come up with a figure of $40 per
record. Again 1,000 records were exploited you can multiply the number of records times the
$40 deciphered for each record that had been compromised giving you a monetary cost of
$40,000. Assume the number of records grew to 500,000; you can then access the cost of a
breach to be $20 million. This is a prime example of quantitative analysis in terms that can
easily be understood. Pretty simplistic, except this is only one dimensional.
As the records increased so did the issue of complexity which is why now you must
incorporate a qualitative approach. Within the above example, in addition, you now have an
auditor walk through the door who says that you have 90 days to fix the vulnerability of the
system, which had no encryption mechanism between the database and the web server or on
the database server itself therefore the auditor points out that the bank is not in compliance
with specific financial standards. Now we will take a look at additional vulnerabilities such as
a code review, in which we discover that our assets are prone to an SQL injection attack (an
appended message to exploit the system and the data within it).
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5588
Hence, there has to be controls in place to filter out such an attack. Currently, we have the
cost associated with the vulnerabilities in the system, and now the likelihood of
discoverability must be assessed. Using quantitative analysis, worst-case scenario would be
the compromise of 500,000 records, coming to a cost of $20 million as cited above. Going
by quantitative analysis, this is again a 1 dimensional evaluation therefore we must have a
way to assign risk level to vulnerability that takes other factors into consideration such as
making use of a high-medium-low rating scale. The information that we’ve gathered thus far
is the number of records could be from 1,000 to 500,000, records are valued at $40 each, the
data is not encrypted in transit or at rest, multiple business units could access and modify the
data, systems are maintained by the operations group and lastly, we have an audit
requirement to document encryption and apply mitigation controls. Let’s incorporate one
additional piece to our assessment which is reputation. Reputation encompasses impact on
earnings, consumer confidence, and publicity. We can easily assign a Qualitative risk level
of high as an SQL injection attack is not often detected by system logs and intrusion detection
services.
Reputation is at risk from going public with a loss of 500,000 consumer records and that once
this vulnerability is known there will be an increase in this type of attack on banking systems.
We now have the Qualitative cost and the Quantitative cost, both of which have a high risk
factor. Now here is where management plays an important role in why we incorporate the
single loss expectancy (SLE) formula. In using this example, we take the value of the asset
($40 in this case), and the exposure level (500,000) and multiply the asset value by the
exposure level to come up with an SLE of $20 million. We now calculate the annual loss
expectancy (ALE), which determines how many times per year this will occur. To do this,
you will take the SLE and multiply it by the annual rate of occurrence (ARO). In this
scenario let’s say the database is very new, so we can’t use historical examples.
Going back to a Qualitative approach, we can come up with an appropriate cost-benefit
analysis. So, we would come up with a way to mitigate this risk by customizing intrusion
detection signatures for traffic analysis that poses a threat to the database and host intrusion
detection software installed on both the web server and database server. Due to these
initiatives, we now feel comfortable reducing the risk rating from high to medium.
Furthermore, we could reduce the threat level to low via additional code testing. Inclusive is
HIPS (Hosted Intrusion Prevention Software) and IDS (Intrusion Detection Software) tools
being properly configured. The above example although exclusive to IT security risk
assessment is a good overview that can be used to describe Quantitative and Qualitative
analysis. After all Project Management encompasses a multitude of industries therefore the
area of IT security can also be inclusive. It must also be stated that IT security is a relevant
illustration in that it also documents the results of its risk analysis process in a Risk Register
to provide organizations information to make appropriate decisions as to how to best manage
identified risks.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
5599
The next step in the process of Risk Management is planning risk responses. This is the
identification of taking action or inaction chosen for the aim of efficiently controlling a given
risk. Specified action or inaction procedures should be chosen after the probable impact on
the project has been accessed. In simplistic terms this is responding to threats or
opportunities. There are many variations of standards in reference to response strategies, for
instance some organizations incorporate less strategies and some more. Again to keep things
easy we will provide you with five basic response strategies for treating negative risks and
four for treating positive risks. The idea here is to just provide you with a brief overview on
how to handle threats and opportunities.
The five strategies for treating negative risk are accept the risk, avoid the risk, reduce
likelihood of the risk occurring, impact mitigation and transfer the risk [Australian Agency
for International Development, (Nov. 2005)]. Accepting a risk is deciding to accept the
repercussions and likelihood of a specific risk. Sometime this is done because the
organization accesses it as being too low of a rating to have any effect on the project or they
may lack the resources to take care of the threat. If the latter is the case than monitoring
should always be inclusive. Avoiding the risk all together is the second category which
means not implementing any controls to counteract the risk because the rating once again
may be excessively low or you may not even perform a particular activity making the risk
nonexistent.
The Australian agency does state “that inappropriate risk avoidance could result in significant
cost penalties, diminished efficiency and impair the achievement of outcomes.” Reducing the
likelihood of a risk occurring is initializing countermeasures and controls that could include,
for example regular audits and checks, preventative maintenance, and education and training.
Impact mitigation is usually used when the likelihood of a threat is low but the impact if
propagated is high. Performing mitigation reduces the consequences of risk through efforts to
alleviating and dealing with the impacts such as making use of contingency planning. Finally
there is the transfer of risk which is allocating risk responsibilities from one party to another.
This is usually done by subcontracting to a third party but if this option is chosen the
Australian agency recommends collaboration and communication must occur on a regular
basis. The risk of choosing this strategy is the potential for increased capital expenditures or
the issue of accountability which is exactly what occurred in the CONFIRM project headed
by American Airlines subsidiary AMRIS. Now we get to what are the four basic response
strategies for positive risks those being exploiting, sharing, enhancing and acceptance
[Sharma, R. (Sept. 2009)]. Exploiting a positive risk is doing everything possible to increase
the probability that the risk will occur. An exploit example would be some members of your
team have devised a new technique to construct a product which would eventually lead to the
duration of the project to be diminished by 20 percent therefore you can exploit this by
ensuring that all team members use this new technique. The next positive risk would be
sharing which is collaborating or communicating with another individual, organization or
department to exploit a positive risk. For example, after conducting a SWOT Analysis you
decide to pursue a business deal which requires you to make use of Agile development
practices which is a systems development strategy wherein the system developers are given
the flexibility to select from a variety of tools and techniques to best accomplish a given task.
In your company, there is no knowledge of Agile development therefore you partner with
another organization that specialized in Agile development. In this scenario both parties
benefit.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6600
A third positive risk category is enhancing which includes identifying the root cause of a
positive risk so that you can influence the root cause to increase the likelihood of the positive
risk. For example, in order for you to get a business deal, your workforce needs to have
substantial JAVA skills so in order for your company to close the deal you can enhance the
positive risk by training your workforce on JAVA or hiring JAVA software specialists.
Finally the last category that encompasses positive risk is acceptance which means that you
select not to take any action towards a risk as sometimes opportunities simply fall on your lap
and you choose to accept them.
The final stage in Risk Management is the monitoring and control stage where information on
risk and metrics that was assessed during planning should be collected, tracked and analyzed
for patterns. Risk assessment, risk audits, variance and trend analysis, technical performance
measurements, reserve analysis and status meetings or periodic reviews are all tools and
techniques for performing monitoring and control. Outputs for this particular process are
updating the Risk Register, organizational process assets updates such as lessons learned
information, change requests and updates to the Project Management plan and other
documents [Schwalbe, K (2011)].
Before we proceed to the next chapter, we must stress that having the proper governance
framework in place is a significant element in mitigating risk which is probably why widely
applied approaches such as IT Governance Institute’s Control Objectives for Information and
related Technology (COBIT) approach, and ISO 9000 standards are being increasingly
utilized to manage IT risk as well as offering guidance on building IT risk into governance
processes. The proper governance hierarchal organizational structure is essential in making
sure that IT projects align with the overall business objectives as we have discussed several
times and as you ascend or descend through each group that is within the hierarchy it helps to
place added checks and balances or controls to ensure all projects succeed. This also could be
considered a top down approach some refer to as Enterprise Risk Management whose
acronym is ERM and which we will get into further detail about in the next section of this
paper [Warrier, S.R. & Chandrashekhar, P, (2006)].
Effective governance means that an organization is better able to access what the risks are
and have a plan in place to treat the difficult risks that would remain [AON Risk Solutions,
(2011)]. An efficient governance hierarchy from top to bottom comprises of the board
(ensuring accountability, monitoring & supervising, auditing, making strategic decisions and
making policies inclusive is successive planning) followed by the senior executive team
(making management decisions, formulating and executing strategy and managing assets).
The executive team is usually there to provide business governance. Just as great of
significance or even more so is what we call our key stakeholders whose primary function is
to provide project governance. This includes the project steering committee, sponsor and
chief risk officer (ensuring accountability, making project decisions, monitoring &
supervising projects and setting project policies) and will obviously be formed during the
initiation of a project. It must be further noted that the steering committee is integral and has
the primary responsibility to ensure project outcomes can be integrated into the business
processes.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6611
These project policies and especially project risk management are than passed down to the
project management team where a project manager is assigned to manage the projects. An
added department such as a Projects Management Office (PMO) can also be created to
maintain standards for project management within a company and be there for guidance,
documentation, and metrics related to the practices involved in managing and implementing
projects within the organization [TechTarget (Jan. 2008). Smaller organizations may not be
able to formulate an organizational hierarchal infrastructure like the one illustrated in the
former sentences because of insufficient resources therefore a small firm can certainly make
great use by adding a PMO to its organization. Having said that it must be emphasized that in
the wake of the financial calamity of 2008 it is of even greater significance to create an
organizational structure that includes the proper governance to set policies, procedures and
standards in order to mitigate the risk of any possible legal ramifications and the risks
associated with projects to minimize any financial losses or costs while maximizing profits.
In spite of wide spread and complete body of research on IT risk, there is extensive evidence
that the research findings and recommendations are not being applied in practice [Pfleeger,
(Sept. 2000)]. Governance initiatives and organizational commitment can certainly provide
the proper leadership and influence over all stakeholders to recognize the importance of
applying these research findings and recommendations to increase the chances for project
success. This prescribed infrastructure is an essential addendum to risk management.
6. Evolving Enterprise Risk Management (ERM) Assessment
Infosys Limited a Bangalore, Karnataka, India based organization with around 145,000 full
time employees, is a well renowned leader in the area of project management consulting
[Limited, Infosys (Jan. 2012)]. The company has been around since 1981 and has annual
revenues of $6.82 billion, gross profit of $2.54 billion and $3.72 billion in cash on hand
compared to zero debt. Therefore any recommendations and services they offer should be
taken seriously and this especially holds true for IT project Risk Management. After all the
company provides an enormous amount of products and services that encompass a multitude
of project management offerings through all market segments on a global scale. This is why
they have been focusing on and incorporating what many believe to be an evolutionary
discipline coined the Enterprise Risk Management (ERM) framework to optimally manage
their own risks as well as their clients. However Infosys does emphasize that the models
components must be customized to suit the needs of whatever organization integrates this
process [Warrier, S.R. & Chandrashekhar, P, (2006)]. In fact Microsoft Corporation also
acknowledges the value of ERM as it bought ERM vendor Prodiance back in the summer of
2011.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6622
It was around 1994 that the Committee of Sponsoring Organizations of the Treadway
Commission (COSO) issued Internal Control Integrated Framework to help businesses and
other entities assess and enhance their internal control systems [Committee of Sponsoring
Organizations of the Treadway Commission. Sept. 2004)]. In the wake of excessive financial
losses and business scandals COSO decided to team up with PricewaterhouseCoopers in 2001
to make enhancements to its initial approach by augmenting corporate governance and risk
management, with high level goals aligned with and supporting an organization’s mission,
making efficient use of and safeguarding resources, improving upon the reliability of
reporting and complying with applicable laws and regulations to formulate what is now
known as the Enterprise Risk Management framework. COSO defines ERM verbatim as “a
process, affected by an entity’s board of directors, management and other personnel, applied
in strategy setting and across the enterprise, designed to identify potential events that may
affect the entity, and manage risk to be within its risk appetite, to provide reasonable
assurance regarding the achievement of entity objectives.” Notice how the word Governance
continues to come up time and time again! ERM is believed to be the potential trend of the
future that takes a more holistic approach towards Risk Management. In fact many people in
the field of project management state there is a clear and concise correlation between ERM
processes and their advantages, primarily influenced by a multitude of factors including the
competency of management, the appetite for risk and risk culture to further demonstrate the
true value of ERM.
As talked about above governance is a critical element in accessing risk. Therefore the need
for corporate governance, internal control and risk management has become of vital concern
to organizations as many have suggested for the unification of all three with a single
management method known as the integrated governance, risk and compliance [Dittmar, L.
(no date)]. This led to what is now recognized as ERM because it highlights all three aspects
within its application process. In fact a the series of high profile business scandals and
failures which was caused by a lack of Risk Management provided additional support for the
renewed interest and popularity of ERM and a modification to a more coordinated, holistic
Risk Management approach that acknowledges the interdependencies of risks [Jablonowski,
M. (Sept. 2009)].
There are eight interrelated components that ERM is comprised of. These components are
very similar to many of the Risk Management planning categories we described in the
chapter above with some notable differences. They include:
• Internal Environment – The internal environment is the risk appetite of the organization
based on the individuals that make up the firm inclusive is their risk management philosophy,
integrity and ethical values, and the environment in which they do business.
• Objective Setting – This assures that objects are set and that they align with both the
mission of the organization and its appetite for risk
• Event Identification – Identifying internal and external events by comparing risks and
opportunities so that and organization can accomplish its objectives which is than redirected
back to the objective setting.
• Risk Assessment – Risks are rated on the likelihood and impact of the occurrence of an
event on a cyclical basis.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6633
• Risk Response – Management decides whether to avoid, accept, reduce or share the risk.
Once this is performed management constructs a specified set of actions to align risks with
the overall organizations risk appetite.
• Control Activities – Policies and procedures are constructed and incorporated to ensure the
appropriate risk responses are carried out.
• Information and Communication – The appropriate information is identified, collected and
communicated through some medium and timeframe that would allow stakeholders to
perform their responsibilities. This communication needs to span effectively across the entire
organization.
• Monitoring – The entire organization is monitored through ongoing management activities,
separate evaluations, or both. Inclusive are any modifications [Committee of Sponsoring
Organizations of the Treadway Commission. Sept. 2004)].
Infosys emphasizes as with any project that even with ERM all stakeholders must be involved
in order to get them to buy in to the overall risk management plan [Warrier, S.R. &
Chandrashekhar, P, (2006)]. Again although ERM has a specified framework the integration
of the approach may fluctuate somewhat as each organization has varying objectives, cultures
and is unique so an ERM discipline must be tweaked in order to align with an organization’s
overall goals. Infosys also suggests that prior to implementation of any ERM approach pilot
models should be constructed to ensure that the organization makes certain that the model is
effective, efficient and suites the needs of all stakeholders in order for an organization to
acquire robust results from the ERM. Some elements and standards are exclusive across all
industries and organizations however components such as different modes of communication,
technology enablers (dashboards, data, calculations such as Monte Carlo and reporting),
governance models, resourcing plans, risk appetite and so on must again be meticulously
analyzed before one begins to institutionalize an ERM approach deemed to be the most
appropriate fit.
Infosys recognizes that if ERM can be utilized appropriately it can provide organizations with
significant benefits which is why they have been integrating its framework in many of their
products and service offerings as well as making use of the approach internally. An example
of Infosys making use of ERM can be described in its ERM web application user interface
whereby a leading assessment agency in admission tests based in the UK used the application
to automate the process of moderation to increase the efficiency of the moderation process,
reduce lead time, minimize paper work, reduce costs and ensure process standardization
[Limited, Infosys Case Study, (2012)]. The results where that through the implementation of
the ERM application the UK based company was able to abled the assessment agency to
reduce manual intervention, accelerate the moderation process and ensure cost savings
inclusive was reducing paperwork, diminishing the turn-around time of the moderation
process, consistency and augmenting moderation, Multi-platform accessibility and finally
enhancing the customers' impression about the client.
7. Conclusion
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6644
The complexity of projects throughout organizations around the globe has caused an
increasing number of risks therefore they must be addressed continuously in order to mitigate
any adverse organizational impacts while improving factors for success. This includes
constant risk analysis, participation and incorporating the necessary policies, procedures and
standards that must align IT technology with business objectives to effectively maximize an
entity’s return on investments (ROI) and return on objectives (ROO). Also keeping up with
growing number of new laws and regulations is an integral part in the overall process of risk
management to help reduce any financial loss attributed to lack of compliance. Furthermore
advancements in technology, although they have provided many benefits have also been
moving at such a rapid pace over the last decade that it is often difficult at times to properly
align IT with organizational goals. This is why Risk Management must be able to keep up
with these rapid advancements and the wider threat environment by regularly improving on
effective risk practices to augment project outcomes.
In summary the purpose of this paper was to provide a detailed understanding through
documentation and research such as that from the Standish Group to increase awareness of
the inherent risks associated with project failures as well as explaining how each organization
has different objectives therefore the Risk Management approaches we described above must
be customized and tailored to meet the objectives that are unique to each entity. This again is
analogous not only to IT Project Management but other areas of discipline such as IT
security. For example in IT Security Risk assessment four approaches have been established
through formal standards such as ISO 13335 in order to provide a range of alternative
approaches to access risk as each organization’s needs differ. There is a baseline approach
(applying the most basic level of security controls against the most common threats
recommended for small companies), the informal approach (recommended for small and mid-
size companies that apply a less structured process by just using the expertise and knowledge
of individuals performing analysis), the detailed risk analysis approach (a formal structured
and more complex approach that includes numerous stages of risk assessment usually suited
for large organizations) and a combined approach (making use of the baseline, informal and
detailed risk analysis approaches).
It is our hope that the information embedded in this paper will enlighten organizations and
sovereigns to recognize the importance of the empirical data so they can take the appropriate
measures to actively apply certain methodologies while accessing and managing projects
throughout their lifecycles and optimally utilizing the various approaches in practice that
have been discussed in great detail to achieve project success.
References
Bannerman, P.L. (Dec. 2008). Risk and Risk Management in Software Projects: a
reassessment. The Journal of Systems and Software Vol. 81, Issue 12 2118–2133.
Cauley, Leslie (Oct. 2007). Blackstone, Hilton Deal is Marriage of Titans. Retrieved from
USA TODAY: http://www.usatoday.com/money/industries/travel/2007-07-03-blackstone-
hilton_N.htm
Commission, Committee of Sponsoring Organizations of the Treadway (Sept. 2004).
Enterprise Risk Management - Integrated Framework. Retrieved from COSO.org:
http://www.coso.org/Publications/ERM/COSO_ERM_ExecutiveSummary.pdf
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6655
Corporation, Microsoft (no date). A Quick History of Project Management. Retrieved from
Microsoft Corporation: http://office.microsoft.com/en-us/project-help/a-quick-history-of-
project-management-HA010351563.aspx
Corporation, Northrop Grumman (Nov. 2007). IM Risk Management Plan. Retrieved from
Interoperability Montana
http://interop.mt.gov/content/docs/IM_Risk_Management_Plan_v4_0.pdf
Development, Australian Agency for International (Nov. 2005). 6.3 Guidelines "Managing
Risk. Retrieved 13 April 2012 from Commonwealth of Australia:
http://www.ausaid.gov.au/ausguide/pdf/ausguideline6.3.pdf
Dittmar, Lee (no date). What are the Primary Challenges and Trends in Governance, Risk and
Compliance?. Retrieved from Deloitte Consulting LLP:
http://compliance.mashnetworks.com/player.aspx?channelGUID=74fe7a5d-7fce-427e-863d-
c0b597c427fb&clipGUID=5615f32e-e0fc-4cd6-9edc-f259766a6abd
Ellis, Kathy (2008). Business Analysis Benchmark. Retrieved from IAG Consulting:
http://www.iag.biz
GALWAY, LIONEL (Feb. 2005). Quantitative Risk Analysis for Project Management.
Retrieved 13 April 2012 from Rand Corporation:
http://www.rand.org/pubs/working_papers/2004/RAND_WR112.pdf
Group, The Standish (Oct. 2009). CHAOS Manifesto. Retrieved from The Standish Group:
http://standishgroup.com
Group, The Standish (Oct. 2009). CHAOS Manifesto. Retrieved from The Standish Group:
http://standishgroup.com
Group, The Standish (March. 2011). CHAOS Manifesto. Retrieved from The Standish
Group: http://standishgroup.com.
Halper, Mark (Aug. 1992) Outsourcer Confirms Demise of Reservation Coalition Plan.
Computerworld Vol. 26.
Halper, Mark (Aug. 2009). IS cover-up charged in system kill. Computerworld Vol. 26.
Halper, Mark. (Oct. 1992)."Too Many Pilots." Computerworld.
Hubbard, Douglas (April. 2009). The Failure of Risk Management: Why It's Broken and
How to Fix It. John Wiley & Sons. 1E, p. 46.
Ibbs, William and Young Kwak, Hoon (March 2000)“Assessing Project Maturity” Project
Management Journal 31
Jablonowski, Mark (Sept. 2009). The Bigger Picture: Recognizing Risk Management's Social
Responsibility. Retrieved from Deloitte Consulting LLP:
http://findarticles.com/p/articles/mi_qa5332/is_7_56/ai_n35637633/
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6666
Johnson, Stephen B. (March. 2002). Bernard Schriever and The Scientific Vision. Retrieved
from Air Force Historical Foundation:
http://www.thefreelibrary.com/Bernard+Schriever+and+the+scientific+vision.-a083791580
Jorgensen, Hans Henrik, Owen, Lawrence and Neus, Andreas (Oct. 2008). Making Change
Work. Retrieved from IBM: http://www-935.ibm.com/services/us/gbs/bus/pdf/gbe03100-
usen-03-making-change-work.pdf
Limited, Infosys (2012). Infosys Limited Case Study. Retrieved from Infosys Limited: .
http://www.infosys.com/industries/education/case-studies/Pages/erm.aspx
Limited, Infosys (2012). Form 6k UNITED STATES SECURITIES AND EXCHANGE
COMMISSION Filing. Retrieved from Infosys Limited:
http://sec.gov/Archives/edgar/data/1067491/000106749112000007/index.htm
LLC, Hulett & Associates (2005). Qualitative Risk Assessment. Retrieved from
Interoperability Montana: http://www.projectrisk.com/qual_assess.html
Milford, Phil, Schlangenstein, Mary and McLaughlin, David (Nov. 2011). American Airlines
Parent AMR Files for Bankruptcy as Horton Is Named CEO. Retrieved from Bloomberg
News: http://www.bloomberg.com/news/2011-11-29/amr-files-for-bankruptcy-protection-in-
new-york-as-talks-with-pilots-end.html
Mochal, Tom (July 2005). See Effect of Dependent Risk by Using a Decision Tree. Retrieved
from CBS Interactive: http://www.techrepublic.com/blog/tech-manager/see-effect-of-
dependent-risk-by-using-a-decision-tree/569
Oz , Effy. (Oct. 1994). When Professional Standards are Lax: The CONFIRM Failure and its
Lessons: Communications of the ACM 37, 10, 29-36.
Pfleeger, S.L. (Sept. 2000). Risky Business: What we have yet to learn about risk
management, Journal of Systems and Software Vol. 53 Issue 3: 265–273
Powner, David A. (2008). OMB and Agencies Need to Improve Planning, Management, and
Oversight of Projects Totaling Billions of Dollars. Retrieved from U.S. Government
Accountability Office: http://www.gao.gov/assets/130/120968.pdf
Progress, Project (2008). The Bigger Picture: Recognizing Risk Management's Social
Responsibility. Retrieved from Project Progress providers of PRINCE2:
http://www.projectprogress.com/index.htm
Roebuck, Kevin (May. 2011). Project Portfolio Management - Optimizing for Payoff.
Retrieved from Tebbo: (163-166)
Schwalbe, Kathy (2011). Information technology Project Management. Retrieved from
Cengage Learning. 6E, (421-452)
Sharma, Rupen (Sept. 2009). How to Respond to Positive Risks. Retrieved from
brighthub.com: http://www.brighthub.com/office/project-management/articles/48400.aspx
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6677
Solutions, AON Risk (2011). Governance of Project Risk. Retrieved from AON:
http://www.aon.com/hongkong/about-aon/attachments/project-governance-risk-guide.pdf
Solutions, PM (2011). . Strategies for Project Recovery- A PM SOLUTIONS RESEARCH
REPORT. Retrieved from Project Managament Solutions Inc.:
http://www.pmsolutions.com/collateral/research/Strategies%20for%20Project%20Recovery
%202011.pdf
Staff, CIO (Sept. 2011). How to Create a Risk Register. Retrieved from IDG
Communications: http://www.cio.com.au/article/401244/how_create_risk_register/
TechTarget (Jan. 2008). Project Management Office (PMO) Definition. Retrieved from
TechTarget: http://searchcio.techtarget.com/definition/Project-Management-Office
Warrier, S.R. & Chandrashekhar, P, (2006) “Enterprise Risk Management” from the
boardroom floor Infosys White Paper http://www.infosys.com/industries/insurance/white-
papers/Documents/enterprise-risk-management-paper.pdf
Wiegers, K. E. (Oct. 1998). Know Your Enemy: Software Risk Management Vol. 6(10), 38-
42
Wyatt, Edward, (Feb. 2011) “Fed Chief Says US Bolstered Its Ability to Handle Failure of a
Big Bank,” Retrieved from The New York Times:
http://www.nytimes.com/2011/02/18/business/economy/18regulate.html
Zellner, Wendy, (Jan. 1994) "Portrait of a Project As a Total Disaster," Business Week
Zweig, Jason (2012). Why Investors Can't Escape 'Risk. Retrieved from Wall Street Journal:
http://blogs.wsj.com/totalreturn/2012/04/06/why-investors-cant-escape-risk/
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6688
Efficiency and Productivity Analysis of Tunisian Banks
During a Recent Deregulation Period
Raéf Bahrini, Institute of High Commercial Studies of Sousse, Tunisia
Abstract
The main object of this paper is to assess and to analyze efficiency and productivity dynamics
of Tunisian banks following new environmental changes such as deregulation, financial
innovations and progress in Information and Communication Technologies (ICT).
In order to provide an in-depth analysis, we applied the non-parametrical frontier efficiency
method called DEA (Data Envelopment Analysis) and the Malmquist Index able to measure
and to decompose technical efficiency and productivity changes. These methods were mostly
used in empirical studies in assessing the efficiency and productivity dynamics of banks
(Wheelock and Wilson, 1999; Alam, 2001; Berger and Mester, 2003; Isik and Hasan, 2003;
Amel et al., 2004; Staikouras et al., 2008; Huang and Tan Fu, 2009)
We find that Tunisian commercial banks have recorded an increase in their overall technical
efficiency during the period of 1999 to 2008. This increase was mainly due to pure technical
inefficiency and not to scale inefficiency. Thus, Tunisian commercial banks should focus on
improving their managerial methods in order to better control their techniques of production
and to offer the maximum services with the minimum resources available.
Our study shows also that Tunisian banks have increased their Total Factor Productivity over
the period 1999-2008. Through the Malmquist Index approach we showed that the
improvement of Tunisian banking productivity is mainly due to technological progress. In
fact, Tunisian banks were responsive to new technological changes by incorporating the
advanced technologies in their production process.
Keywords: Overall technical efficiency, Scale efficiency, Pure technical inefficiency, Total
Factor Productivity, Production technologies, Environmental changes, Data Envelopment
Analysis, Malmquist Index.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
6699
1. Introduction
Deregulation in banking sectors, structural changes in financial systems and progress in
Information and Communication Technologies are the principal changes that influenced
banking environment since the early 80s.
In order to evaluate the impact of the environmental changes on the intermediary role of
banks, many studies found that following these changes, banks have known a great decrease
in their market shares which resulted in profitability decline (Boyd & Gertler, 1994;
Hackethal, 2001; Allen & Santomero, 2001; Samolyk, 2004; Mester, 2007, etc.). These
evolutions were explained by the increase of competitive pressures brought about by financial
market and non-banking intermediaries. These studies have also highlighted a keen tendency
of economic agents to use new financial markets instruments in their transactions like stocks,
bonds, options, swaps, futures, etc.
Given the importance of the banking sector in any economy and the dramatic impact of the
environmental changes, many studies have focused on the impact of these changes on bank
efficiency and productivity levels (Alam, 2001; Berger & Mester, 2003; Isik & Hasan, 2003;
Amel et al., 2004; Staikouras et al., 2008; Huang and Tan Fu, 2009; etc.). These studies are
based on the hypothesis that increased competition caused by changes in the banking
environment will push banks to improve their allocation of resources and to incorporate
technological progress in order to become more efficient and productive.
Many empirical methods are used by these studies to construct an efficient production
frontier which is a linear combination of efficient banks and to calculate for each bank in
each time period its efficiency and productivity level relatively to this frontier.
Furthermore, these methods, known as Frontier efficiency approaches, allow us, on one hand,
to assess overall technical efficiency change and to define the contribution of its components
(pure technical efficiency and scale efficiency) in this change. On the other hand, these
methods are also useful for measuring total factor productivity change and decomposing it in
technological change and efficiency change.
In Tunisia, financial liberalization was an essential part of the structural adjustment program
of 1987. This process has encouraged the relaxation of banking regulations, have increased
the dynamism of financial market and the modernization of financial institutions in order to
create a new competitive and an innovative financial system.
In an attempt to assess Tunisian banks responses to regulatory and technological changes, we
measure and analyze changes in bank efficiency and productivity during the recent
deregulation period (1999-2008). Our purpose is then to answer the following question: How
is the efficiency and productivity of Tunisian banks following the changes in their
environment and how can we explain the changes recorded?
The remainder of the paper is organized as follows. Section 2 includes a brief review of the
literature related to bank production, bank efficiency and productivity. Section 3 presents
empirical methodology followed to assess and analyze the evolutions of Tunisian banks
levels of technical efficiency and total factor productivity. We present empirical results in
Section 4 and section 5 and we conclude in section 6.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7700
2. Literature Review
2.1 Bank outputs and inputs:
Taking into account the changes in the banking environment, a new trend of research has
emerged to focus on the measurement of bank efficiency and productivity changes and to
determine the effects of these changes on their productive performance.
To attend this goal, these studies have developed the industrial approach of banks. Following
this approach, the bank is assimilated as a simple firm looking for maximizing its profits by
finding the ideal combination of its inputs and outputs. However, most studies have faced a
conceptual problem summarized by the following question: what are the outputs and the
inputs of a bank? Several authors attribute the conceptual problem of measuring and
identifying bank’s outputs and inputs to the interdependence of its products and services
(Berger & Humphrey, 1992).
Given the lack of consensus in the literature regarding the precise definition of bank output
and input, the previous empirical researches were mainly based on two approaches: the
intermediation approach and the production approach.
The Production approach considers the bank as a firm utilizing capital and human resources
to produce different types of deposits and loans accounts. Outputs are measured by the
number of accounts or by the number of transactions for each type of product (Parsons et al.,
1990; Colwell & Davis, 1992; Schaffnit et al., 1997). Following the production approach,
bank efficiency and productivity are measured by comparing the quantities of services
produced with the quantities of resources used (Mlima & Hjalmarsson, 2002).
The Intermediation Approach considers the bank as a financial intermediary supposed
to perform two major roles: mobilizing financial resources and distributing efficiently theses
funds to boost economic development. It measures bank inputs and outputs by their monetary
value and not by their quantity as in the case of the production approach. They also show that
labor and capital are inputs and deposits can be considered both as input and as output
(Colwell & Davis, 1994).
Moreover, there are several other approaches in the literature to measure bank outputs and
inputs like: the Value-Added Approach, the Asset Approach, the User-cost approach and the
Risk Management Approach (Mlima & Hjalmarsson, 2002).
2.2 Bank technical efficiency
Referring to Farrel (1957), Aly et al. (1990), Berger and Humphrey (1992), Berg et al.
(1992), Miller and Noulas (1996), Siems and Barr (1998), etc., two components form the
overall technical efficiency, namely: pure technical efficiency and scale efficiency.
Pure technical efficiency measures the ability of a firm to maximize its outputs given an
amount of input available or to use less input to produce the same amount of output. It
reflects the organizational performance of the bank in the sense that better organization can
permit a better management of the technical aspects of production.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7711
Scale efficiency measures the contribution of a change in size to the reduction of banking
costs. In fact, a bank can benefit from economies of scale when it is not yet at the optimal
size which permits to minimize average costs.
2.3 Bank Productivity
Sharpe (2002) defines productivity as the relationship between outputs produced (goods and
services) and inputs used in the production process (human and non human resources). This
relationship is often expressed as a ratio and outputs and inputs are measured in
quantities and they are not affected by the change in prices.
Improving productivity means producing more output with the same amount of input
or using less input to produce the same amount of outputs. It is therefore crucial to
any Decision Making Unit to measure and analyze its productivity level.
According to the literature, bank productivity can be measured either by partial productivity
or by total factor productivity measures.
Early productivity studies were based on partial productivity measures introduced by
Solow (1957). Under this method, productivity is measured by the ratio of aggregate
output divided by the observed quantity of a single input; it was generally labor.
Berger and Mester (2003) the Bureau of Labor Statistics (BLS) has developed a measure
of labor productivity for commercial banks which is an index having as
a numerator bank outputs measured by the number of transactions relating to demand and
time deposits, loans, transactions made through ATMs and as a denominator the number of
employee hours worked.
Many studies show that total factor productivity measure is better than partial productivity
measure. Indeed, the first one uses a ratio that relates many outputs to many inputs while
the second implicitly assume that the output produced is the result of a single input without
taking into account the contribution of the other inputs involved in the production process
(Colwell & Davis, 1992; Lipsey & Carlaw, 2000, etc.).
Total factor productivity measure is determined by the difference between the growth rates of
outputs and inputs combined. It measures the contribution of all factors of production (other
than capital and labor) to output growth. It reflects the technical efficiency and measure the
rate of change of production technology (Lipsey & Carlaw, 2000).
More explicitly, bank total factor productivity depends at least on three major factors:
1) The characteristics of the technology used;
2) The choice of the scale of the production and the possibility of introducing the
advanced technology;
3) The efficiency of bank in organizing its production process.
A bank can achieve a productivity growth by simply applying the advanced technology.
Similarly, the productivity can be determined by the scale of production. In this case, a bank
can be productive because of its larger size even if it makes less productivity efforts than the
other smaller banks. Finally, bank productivity depends on the efficiency of its financial
transformation process.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7722
In fact, if we compare two banks of the same sizes, utilizing identical techniques of
production and operating in the same market, one bank can be more productive and more
performing than the other, simply because it is technically and economically more efficient.
3. Methodology
3.1 Data Envelopment Analysis (DEA)
Data Envelopment Analysis is a non-parametric approach which is able to determine the
technological frontiers or the possible production frontiers of each firm during each year,
utilizing linear programming techniques based on inputs and outputs data without the use of a
functional application to determine the given production process. The analysis consists on
assessing the efficiency scores which measure the distance separating the units outside the
frontier (inefficient units) from the efficient frontier which is made of the efficient banks in
the given sample, which means the banks that are able to produce a maximal quantity of
outputs given a limited quantity of inputs.
DEA method was pioneered by Farrel (1957) reformulated by Charnes et al. (1978) as
follows: Given N banks, each producing m different outputs utilizing n different inputs. The
technical efficiency of the bank is measured as follows:
Where hs is the bank efficiency score; yis is the amount of the ith
output produced by sth
the
bank ; xjs is the amount of the jth
input used by the sth
bank ; ui is the output weight and vj is
the input weight. We can now resolve the linear programming as follows:
et
The above linear programming is used to maximize the efficiency score of the bank s under
the two following constraints: The first constraint is that the efficient score is less or equal
than one. The second constraint requires that the output and input weighs must be positive.
To resolve this linear equation, we must determine the values of ui and vj, in order to
maximize the efficiency score for each bank.
The above linear programming was converted by Charnes et al. (1978) as follows:
S
et
Utilizing the dual programming, the problem becomes as follows:
S
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7733
The variable is the measure of the overall technical efficiency and must be between zero
and one. The above Dual Programming estimates the efficient frontier under the hypothesis
of Constant Return of Scale (CRS). If we consider the example of a bank producing one
output using one input, the solution to the CRS problem is determined by the frontier OC of
the following figure:
Figure 1:
Source: Miller and Noulas (1996)
Each bank that is on the frontier is efficient. For this reason, the bank s which is found below
the frontier at the point S is inefficient. In this case, the overall technical efficiency ( ) is
determined by the ratio < 1. In fact, (1- ) measures the amount by which the inputs
must be reduced in order for the bank s to be able to produce the same output like the
efficient bank at the point F.
In addition, the overall technical efficiency estimated by the DEA method, can be
decomposed into “pure” technical efficiency and scale efficiency. To do so, we need to
resolve the linear program (4) to which we add another constraint to allow the estimate of the
efficiency frontier under the hypothesis of Variable Return of Scale (VRS). The added
constraint is as follows:
If we refer to the previous chart, the estimated efficient frontier (VRS) is represented by the
curve ABDV and the Pure Technical Efficiency of the bank s at the point S is given by the
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7744
ratio The overall technical efficiency is the combination of the pure technical
efficiency and the scale efficiency which means that Thus, scale efficiency is
given by the formula It is represented by the ratio
3.2 Malmquist Productivity Index Method
This method allows us to measure the productivity change and to decompose it in two
components: technological change and efficiency change. We adopt the methodology of
Alam (2001) who considers the scenario of a production based on one output and one input.
Figure 2:
Source: Alam (2001)
[(O, Tt) and (O, Tt+1)] represent the frontiers of the technology of production at time t and t
+1 under the hypothesis of the Constant Return of Scale (CRS). Tt+1 is above the Tt, this
means that there was an evolution of technology or a technical progress occurred between t
and t+1. Therefore, to determine the technical change, we must define the technological
frontiers at time t and at t+1.
Let’s consider the case of a firm n during a given time t represented in the figure above by the
point (Xnt, Ynt). This firm is inefficient view that is localized inside the efficient frontier [O,
Tt] and its efficiency level is measured by the ratio [oa/ob < 1]. At t+1, this same firm is
represented by the point (Xn,t+1, Yn,t+1) which is also inside the frontier [O, Tt+1]. It is again
inefficient and its efficiency is measured by the ratio [oe/of <1]
Since we have defined the technological frontiers and have determined the different
efficiency scores using the DEA technique, it is only left to determine the total change of
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7755
productivity and the contribution of each of its tow components in this change, which are in
fact measured by the Malmquist Productivity Index.
Grifell-Tatjé, and Lovell (1995) have defined the Malmquist Productivity Index for the
producer i between the time t and t+1 as follows:
Where :
If we return to the case of the firm “n” of the figure above, the decomposition can be
illustrated as follows:
= =
= Et+1 * At+1
Where Et+1 and At+1 represents respectively the technical efficiency change and the
technological change of the bank “n” between t and t + 1.
4. Assessing And Decomposing Technical Efficiency In The Case Of Tunisian Banks: A
DEA Approach
4.1 Variables and related data
As we work within the intermediation framework approach, we consider two outputs: Loans
(all forms of loans to customers) and Other Earning Assets (Portfolio securities or stocks and
loans to other banks and financial institution). For its production, the bank must need three
inputs: Fixed Assets, Interest bearing liabilities (Savings deposits, other deposits, interbank
deposits and special financial resources) and Labor (Number of full-time equivalent
employees).
Our aim is to measure and to analyze the Tunisian Banks efficiency change during the period
1999-2008 which was characterized by dramatic changes such as deregulation, financial
innovations and technological progress in informatics and communication.
We use Data Envelopment Analysis (DEA) to measure overall technical efficiency change as
well as its tow components: pure technical efficiency change and scale efficiency change.
The data of our study consist of the accounting values of inputs and outputs of the banks
which are from the Annual Reports published by the Tunisian Professional Banking
Associations (TPBA). Our sample consist of 10 Tunisian Commercial Banks which are active
and viable during all this period of our study (1999- 2008)
4.2- Results
The Following Table presents our results:
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7766
Table 1: Tunisian Bank’s Technical Efficiency and its components: Pure Technical and of
Scale efficiency during the period 1999 to 2008:
Year Overall technical efficiency Pure technical efficiency Scale efficiency
1999 0,906 0,963 0,942
2000 0,927 0,964 0,962
2001 0,916 0,956 0,959
2002 0,925 0,962 0,963
2003 0,927 0,957 0,962
2004 0,937 0,965 0,972
2005 0,949 0,968 0,971
2006 0,918 0,944 0,973
2007 0,935 0,962 0,981
2008 0,939 0,951 0,967
Mean 0,928 0,959 0,965
Source: Output file generated by DEAP VERSION 2.1 software
We can determine from the above results the technical inefficiency levels such as:
Average Overall Technical Inefficiency = 1 - 0,928 = 7,2%.
Average Pure Technical Inefficiency = 1- 0,959 = 4,1%.
Average Scale Inefficiency = 1-0,965 = 3,5%.
Estimated Pure Technical Inefficiency of 4,1% in average, means that inefficient banks can
reduce the quantity of used inputs by 4,1% as compared to efficient banks or banks having
the best practices. In addition, average level of inefficiency of scale of 3,5% means that the
Tunisian banks can reduce their production costs by an average of 3,5% if they increase their
sizes. We find that overall technical inefficiency of Tunisian banks is more explained by pure
technical inefficiency than by scale inefficiency. Most empirical studies showed that the
technical efficiency levels recorded are due to the scale efficiency and not to the pure
technical efficiency.
Thus, Tunisian banks have responded to the environmental changes by increasing their sizes
through mergers and acquisitions and/or by increasing the scale of their production. Hence,
they realized economies of scale and then they have increased their efficiencies of scale.
Therefore, pure technical inefficiency, which is due to bad allocation of resources and to
mediocre management methods of production, represents the essential source of the recorded
overall technical inefficiency in the case of Tunisian banks.
We present the following figure:
Figure 3: Overall Technical Efficiency change and its Components:
Pure Technical Change and Scale Efficiency Change
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7777
Source: Output file generated by DEAP VERSION 2.1 software
The figure above indicates that overall technical efficiency rapidly increased between 1999
and 2005 from 90,6% to 94,9%. Then it decreased between 2005 and 2008 to reach the level
of 93,9% by the end of the period. It shows also that pure technical efficiency have known
similar changes: it increased between 1999 and 2005 from 96,3% to 96,8%. Then it decreased
to reach the level of 95,1% by the end of 2008. Following this figure, scale efficiency have
increased during all the period going from 94,2% to 96,7%.
We conclude that overall technical inefficiency changes are mainly determined by pure
technical inefficiency changes suggesting that during the period 1999-2008 Tunisian banks
have not been able to improve their managerial performance. Indeed, they have to make
additional efforts to better control the technical aspects of their production and to improve the
quality of their organization.
We find also that during the study period Tunisian banks have reduced their scale
inefficiency which became at the level of 3,3 % by 2008. This means that most banks are
close to reach the optimal size which maximizes their scale efficiency. It is clear that they
preferred increasing their overall technical efficiency by increasing their sizes and not by
raising their pure technical efficiency levels.
Our results are consistent whit those of Chaffai and Dietsch (1998) which analyzed the
technical efficiency of the Tunisian banks during the period of 1986-1997 and found that
these banks have enhanced their scale efficiency from 82% to 97% at the expense of their
technical efficiency which have decreased from 82% to 68%.
5. Assessing And Decomposing Productivity Change In The Case Of Tunisian Banks:
Application Of The Malmquist Index
5.1 Data
The determination of the Malmquist Productivity Index’s values needs the estimation of the
efficiency frontiers using Data Evolution Analysis (DEA). Based on this fact, we will utilize
the same data and variables used in the precedent estimation.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7788
5.2 Results and Interpretations:
The estimated results of Tunisian commercial bank productivity using the Malmquist Index
within the DEA approach are represented in the table below:
Table 2: Total Factor productivity change and its components:
Technical and efficiency change between 1999 and 2008:
Year Efficiency
Change
Technical
Change
Pure
Efficiency
Change
Scale
Efficiency
Change
Total Factor
Productivity
Change
EFFCH TECHCH PECH SECH TFPCH
1999-2000 0,993 1,086 1,000 0,993 1,079
2000-2001 0,999 1,005 1,000 0,998 1,004
2001-2002 0,987 1,001 0,999 0,988 0,988
2002-2003 1,004 1,014 1,001 1,003 1,018
2003-2004 0,985 1,056 0,999 0,986 1,041
2004-2005 1,002 1,042 1,001 1,001 1,044
2005-2006 1,001 0,990 0,996 1,005 0,990
2006-2007 0,980 1,076 0,999 0,981 1,054
2007-2008 1,010 1,000 0,992 1,018 1,010
Mean 0,996 1,030 0,999 0,997 1,025
Source: Output file generated by DEAP VERSION 2.1 software
The Annual average of the total factor productivity has increased by 2,5 % (1.025-1 = 0.025)
between 1999 and 2008. This increase is explained by the enhancement of the technology by
3% (1.03-1 = 0.03) and also by a slight decrease of the technical efficiency of 0.2% or
(1.002-1 = 0.002).
Let us consider the figure below:
Figure 4: Total Factor productivity change in the case of Tunisian banks
between 1999 and 2008
Source: Output file generated by DEAP VERSION 2.1 software
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
7799
The figure shown above, shows that the index TFPCH is greater than 1 during most years in
the period 1999-2008 with the exception of two periods: 2001-2002 and 2005-2006. These
results suggest that Tunisian banks have responded to financial and technological changes
that have characterized their environment by improving their level of total factor productivity
during most years of the study period (1999-2008).
Let us consider another figure:
Figure 5:
Source: Output file generated by DEAP VERSION 2.1 software
The figure above shows that TFPCH index evolutions over the study period can be explained
by TECHCH index changes and not by EFFCH index changes. Thus, the increase of total
factor productivity achieved by Tunisian banks during the period1999-2008 is mainly due
to technological advances introduced rather than their level of technical efficiency.
These results are consistent with those of Wheelock and Wilson (1999), Robelo and Mendes
(2000), Alam (2001), Sufian (2009). Their results showed that banks have reacted to the
dramatic changes in their environment by introducing information and communication
technologies in order to modernize their services and to become more competitive and to
increase their productivity as well.
Furthermore, the Malmquist Index approach showed us that Tunisian banks can be more
productive by enhancing their pure technical efficiency levels. This means that these banks
are able to enhance their organizational qualities in order to better manage the technical
aspects of their production.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
8800
6. Conclusion
We have tried throughout this study to measure and to analyze Tunisian commercial banks
technical efficiency and productivity changes during a period characterized by several
reforms and technological changes which have dramatically affected their environment.
Firstly, we have estimated the evolution of technical efficiency based on the non-parametrical
frontier efficiency method, known as DEA (Data Envelopment Analysis). The results have
shown that Tunisian commercial banks have recorded an increase in their technical efficiency
during the period of 1999 to 2008.
Through the decomposition of the overall efficiency change into pure technical efficiency and
scale efficiency change, we concluded that the evolutions of overall technical inefficiency
recorded by Tunisian commercial banks was mainly due to pure technical inefficiency and
not to scale inefficiency.
In light of these results, we can conclude that Tunisian commercial banks should focus on
improving their managerial methods in order to better control their techniques of production
and to offer the maximum services with the minimum resources available. And they should
concentrate less on increasing their sizes, since the expected gains from the scale change have
diminished by the end of the period.
Secondly, we have followed the methodology of Alam (2001) based on the Malmquist
Productivity Index to asses Tunisian commercial banks productivity change and to
decompose the whole change into technical efficiency change and technological change.
Our results showed that Tunisian commercial banks have increased their productivity over
the period 1999-2008. Through the Malmquist Index approach we showed that the
improvement of Tunisian banking productivity is mainly due to technological progress. These
results are consistent with those of the European and American banks, which have found that
the increase in their total factor productivity is mainly determined by technological change.
Given that bank productivity depends on the technology used in its production process and on
its technical efficiency, we can conclude that Tunisian commercial banks have focused in the
enhancement of their productivity on incorporating new banking technologies rather than on
increasing their levels of technical efficiency.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
8811
References
Alam, I.M.S. (2001). A non-parametric approach for assessing productivity dynamics of large
banks. Journal of Money, Credit, and Banking, 33, 121–139.
Allen, F., & Santomero, A.M. (2001). What do Financial Intermediaries do? Journal of
Banking and Finance, 25(2), 271-294.
Aly, H.Y., Garbowski, R., Pasurka C., & Rangan, N. (1990). Technical, scale and allocative
efficiencies in US banking: an empirical investigation. The Review of Economics and
Statistics, 72(2), 211-218.
Amel, D., Barnes, C., Panetta, F., & Salleo, C. (2004). Consolidation and efficiency in the
financial sector: A review of the international evidence. Journal of Banking and
Finance, 28, 2493-2519.
Bauer, P.W., Berger, A.N., & Humphrey, D.B. (1993). Efficiency and productivity growth in
US banking. In H.O. Fried, K.A.L. Lovell & S.S. Schmidt, The Measurement of
Productive Efficiency: Techniques and Applications (pp. 386–413). Oxford University
Press, Oxford,
Berg, S.A., Forsund, F.R., & Jansen, E.S. (1992). Malmquist indices of productivity growth
during the deregulation of Norwegian banking, 1980–89. Scandinavian Journal of
Economics. 94 (Supplement), 211–228.
Berger, A.N., & Humphrey, D.B., (1992). Measurement and Efficiency Issues in Commercial
Banking. In Z. Grillitches (Eds.), Output Measurement in the Service Sectors (pp.
245-279). University of Chicago Press, Chicago.
Berger, A.N., & Mester L.J. (2003). Explaining the dramatic changes in performance of US
banks: technological change, deregulation, and dynamic changes in competition.
Journal of Financial Intermediation, 12, 57–95.
Boyd, J.H., & Gertler, M. (1994). Are Banks Dead? Or Are the Reports Greatly Exaggerated?
Federal Reserve Bank of Minneapolis Quarterly Review, 18(3), 2-23.
Chaffai, M.E., & Dietsch, M. (1998). Comment Accroître les Performances des Banques
Commerciales Tunisiennes : Une Question d’Organisation ou de Taille ? Finances &
Développement Au Maghreb, 24, 79-87.
Charnes A., Cooper W., & Rhodes, E. (1978). Measuring the efficiency of Decision-Making-
Units. European Journal of Operational Research, 2 (6), 429-444.
Colwell, R.J., & Davis, E.P. (1992). Output, Productivity and Externalities – the case of
Banking. Bank of England Working Papers, N°3, London.
Farrell, M.J. (1957). The measurement of productive efficiency. Journal of the Royal
Statistical Society, 120, 253-281.
Grifell-Tatjé, E., & Lovell, C.A.K. (1997). The sources of productivity change in Spanish
banking. European Journal of European Research, 98, 364-380.
Hackethal, A. (2001). How Unique Are US Bank?: The Role of Bank in Five Major Financial
Systems. Journal of Economics and Statistics, 221(5-6), 592-619.
Huang C., & Tan-Fu, T. (2009). Uncertainty and total factor productivity in the Taiwanese
banking industry. Applied Financial Economics, 19 (9), 753-766.
Isik, I., & Hassan, M.K. (2003). Efficiency, ownership and market Structure, corporate
control and governance in the Turkish banking industry. Journal of Business Finance
and Accounting, 30 (9-10), 1363-1421.
Johnson, G., & Scholes, K. (1997). Exploring Corporate Strategy: Text and Cases (4th ed.).
New York: Prentice-Hall.
Lipsey, R.G., & Carlaw, K. (2000). What Does Total Factor Productivity Measure?
International Productivity Monitor, 1, 31-40.
Mester, L.J. (2007). Some Thoughts on the Evolution of the Banking System and the Process
of Financial Intermediation. Economic Review, First and Second Quarters, 67-75.
JJoouurrnnaall ooff MMaannaaggeemmeenntt aanndd BBuussiinneessss RReesseeaarrcchh,, IISSSSNN 22116622--88995555,, VVooll.. 22,, NNoo.. 22,, AApprriill 22001122
8822
Miller, S.M., & Noulas, A.G. (1996). The Technical Efficiency of Large Bank Production.
Journal of Banking and Finance, 20, 495-509.
Milma, J.P., & Hjalmarsson, L. (2002). Measurement of Inputs and Outputs in The Banking
Industry. Tanzanet Journal, 3(1), 12-22.
Parsons, D.J., Gotlieb, C.C., & Denny, M. (1990). Productivity and computers in Canadian
banking. Journal of Productivity Analysis, 4, (1-2), 95-113.
Rebelo, J., & Mendez V. (2000). Malmquist indices of productivity change in Portuguese
banking: The deregulation period. International Advances in Economic Research, 6
(3), 531-543.
Samolyk, K. (2004). The Future of Banking in America: The evolving role of commercial
banks in U.S. credit markets. FDIC Banking Review, 16(2), 29-65.
Schaffinit, C., Rosen, D., & Paradis, J.C. (1997). Best Practice Analysis of Bank Branches:
An Application of DEA in Large Canadian Bank. European of Operational Research,
98, (2), 269-289.
Sharpe, A. (2002). Productivity Concepts, Trends and Prospects: An Overview. The Review
of Economic Performance and Social Progress, 2, 29-56.
Siems, T.F., & Barr, R.S. (1998). Benchmarking the productive efficiency of US banks.
Financial Industry Studies, 11-24.
Solow, R.M. (1957). Technical Change and the Aggregate Production Function. Review of
Economic and Statistics, 39, 312-320.
Staikouras C., Mamatzakis E., & Koutsomanoli-Filippaki A. (2008). Cost efficiency of the
banking industry in the South Eastern European region. Journal of International
Financial Markets, Institutions and Money, 18, 483-497.
Sufian, F. (2009). The impact of off-balance sheet items on banks’ total factor productivity:
empirical evidence from the Chinese banking sector. American Journal of Finance
and Accounting, 1(3), 213-238.
Wheelock, D.C., & Wilson, P.W. (1999). Technical progress, inefficiency and productivity
change in US banking, 1984–1993. Journal of Money, Credit, and Banking, 31, 213–
234.