1

Abstract Code: 011-0533

Designing a Simulation Laboratory Environment for

Service-Oriented Supply Chain Information Management

Stephen C. Shih1

Chikong Huang2

1

Associate Professor & Associate Director,

School of Information Systems and Applied Technologies

Southern Illinois University

Carbondale, Illinois 62901-6614, USA

E-mail: shihcs@siu.edu, Phone: (618) 453-7266

2 Professor, Department of Industrial Management,

Institute of Industrial Engineering and Management,

National Yunlin University of Science & Technology,

123 University Road, Section 3, Touliu, Yunlin, Taiwan 64002, R.O.C.

E-mail: huangck@yuntech.edu.tw, Phone: 886-5-5342601 ext. 5336

POM 20th

Annual Conference

Orlando, Florida U.S.A.

May 1 to May 4, 2009

2

Designing a Simulation Laboratory Environment for Service-Oriented Supply Chain

Information Management

Abstract

The primary objective of this research is to propose a simulation laboratory environment to

examine the interactions and information exchanges between various levels of the service-

oriented supply chain network. The research involves the exploration of three areas: (1) the

essential characteristics and associated requirements underlying various service-driven supply

chain transactions, (2) business assumptions underlying conventional service-driven supply chain

models, and (3) modeling theories adopted to redefine the new models. Furthermore, the

laboratory will be used as a viable base to develop fundamental theories for improved network

communications and increased operational efficiency. An important thesis of this research is to

explore the synergy between service supply chain logistics and information exchange.

Ultimately, this research has the potential to extend simulation techniques and information

technology to an important economic sectorthe the service sectorthat has long been

overlooked in the past.

Key Words: service-driven supply chain management and logistics, service supply chain

information management, and simulation.

3

1. Introduction

The primary objective of this research is to propose a framework and information technology

architecture for the design of a simulation laboratory environment to examine the interactions

and information exchanges between various levels of the service-oriented supply chain network.

The proposed simulation laboratory, Integrated Service Enterprise Systems Laboratory (ISES

Lab), will provide an environment for researchers and students in various disciplines, including

production and operations management, operations research, industrial engineering, and

information systems, to conduct research in a number of areas related to service supply chains

(e.g., exploration of the essential characteristics underlying service-driven supply chain

transactions). The ISES Lab will be further used as the viable bedrock for development of

theories on service supply chain network communications and information management.

The research conducted in the laboratory involves the exploration of three areas: (1) the

essential characteristics and associated requirements underlying various service-driven supply

chain transactions, (2) business assumptions underlying conventional service-driven supply chain

models, and (3) modeling theories adopted to redefine the new models. The lab research projects

will help explore the synergy between service supply chain logistics and information exchange.

This synergy has presented an unprecedented territory of innovation and challenge. Ultimately,

the research work and resulted nourished from the lab has the potential to extend simulation

techniques and information technology to an important economic sector, the service sector, that

has long been overlooked in the past.

2. Service Enterprise Systems and Service Supply Chains

Due to global expansion and the need for close collaboration with business partners, many

4

companies have grown in the direction of enterprise systems extension by including service

operations. Most of the manufacturing enterprises have historically focused mostly on the

supply chain management of designing and manufacturing physical products while paying little

attention to the so-called forgotten supply chain of their services and post-sales businesses.

Post-sales businesses represent all the maintenance, repair, and overhaul (MRO) services that are

either not included in the original equipment sales or not delivered by the original equipment

manufacturers (OEMs). For the post-sales service intensive enterprises (e.g., Boeing and Otis),

the profit margin of their MRO businesses is usually much greater than that of original goods or

equipment sales. With combined higher net margins and decreased capital requirements of the

post-sales service operations, greater financial value can be significantly created.

Complexity is inherent in most real-world service enterprise systems which are usually

characterized as a super-hybrid system. Service enterprise system modeling itself is a complex

task not only from the effort involved in mapping out all the information paths and business

processes in the service supply chain and the simulation models but also due to the complex

taxonomy of object types involved in the enterprise system. A generic learning tool for designing

and modeling an enterprise-level super-hybrid environment requires substantial innovative

learning mechanisms.

Service quality is typically a significant competitive factor for those service-intensive

industries that provide planning, monitoring, maintenance, and repairing services for their after-

sale equipment (e.g., elevators). The ability to plan, schedule, and manage the service activities is

crucial to the service-based companies. Nevertheless, in modern service industries, there are no

consistent methods for service-related tasks, such as workforce planning, scheduling, and

resource allocation. For global operations, most service-intensive organizations are still reliant

5

on a great number of different business processes and sub-optimized approaches to support each

individual regional operation. Even though numerous analytical techniques have been developed

for the scheduling of various manufacturing systems, only relatively few studies were targeted at

the service sector. Among them, Ahituv and Berman (1988) have developed techniques that can

be used in the management of various service networks including ambulance, police, fire, and

courier services; Kolesar and Blum (1973), Smith (1979), Hambleton (1982), Agnihothri and

Karmarkar (1992), Hill, March, Nachtsheim, and Shanker (1992) have studied the field service

territory planning problems that involve balancing the number of technicians and the size of the

geographic region they will service in order to achieve some performance objectives; Dzubow

(1972), Panson (1983), Agnihothri and Karmarkar (1992), Hill (1992) have considered various

dispatching rules for the Traveling Technician Problem (TTP); Haugen and Hill (1999) have

proposed three scheduling procedures to maximize field service quality in the TTP and the

comparison of these scheduling procedures against three dispatching rules in a simulated TTP

scenario has shown domination of all scheduling procedures in all four service quality criteria.

Most of these studies were too restrictive in their assumptions such as all service calls were of

the same service class and had the same repair time distribution (i.e., ignoring the factors of

various technician skill levels, parts/tools availability, planned regular maintenance vs.

emergency repair, etc), constant travel speed (i.e., ignoring the factor of various traffic

congestion levels at different times and locations), and do not account for the complexity of the

real-life field service scheduling problem. Also, there are quite a few field service management

software packages available in the market (Albright, 2002); however, their planning and

scheduling algorithms are proprietary and undisclosed.

6

3. Service Enterprise Systems Education

The trend of service enterprise systems evolution has urged many multinationals to view and

manage the entire operations from a whole new perspective. Specifically, this trend has

prompted companies to start focusing on dynamic, team-oriented organizational structure. As

more companies strive to coordinate activities seamlessly, it is imperative that their employees

be equipped with cross-functional knowledge and skills. Given that students often acquire

knowledge and skills in different functional areas, many attribute deficiencies mentioned

previously to the lack of systematic, cross-functional integration in the curricula. In other words,

academic disciplines including information systems, POM, and other curricula still follow the

stovepipe approach to educating their students (Albrecth and Sacks, 2000; Corbitt and

Mensching, 2000; Gorgone et al., 2002).

The leveling of organizational structure and the resultant changes in the workplace call

for an interdisciplinary curriculum. A review of the current service enterprise systems education

programs shows a lack of collaboration across the boundaries of academic disciplines. In

addition, research has shown that the cross-functional approach to enterprise systems design is

rarely the foci in the curricula (Trauth, 1993; Denis et al., 1995; Bandow, 2005). Such

educational settings are not conducive to developing interdisciplinary knowledge and skills in a

horizontal working environment. Consequently, many recent graduates were criticized for not

being productive team players in a cross-functional team setting due to their lacking of extended

enterprise perspectives and interdisciplinary problem-solving skills.

4. The Framework of ISES Lab

The proposed Integrated Service Enterprise Systems Laboratory (ISES Lab) pinpoints several

7

important trends that could profoundly shape and revolutionize the modern service enterprise

systems education, including enterprise systems evolution, lean manufacturing, and horizontal

organizations. Associated with each trend, major themes and focuses for advanced service

enterprise systems education are identified and highlighted in this paper. The research projects

and instructional lab assignments are unique in that it combines several service enterprise

systems topics with proven pedagogical methods for conducting research projects and

instructional assignments in service supply chain management. One of the foremost themes of

the ISES Lab is to underline the importance of an extended enterprise system (Davis and

Spekman, 2003) to go beyond its organizational boundary by including its service supply chain

partners in collaborative planning and execution of its aftermarket business operations. With the

notion of extended enterprise, the proposed laboratory environment emphasizes the importance

of seamless integration and multi-directional service supply chain coordination. Second, the lab

assignments fosters critical knowledge and practical applications of lean thinking (Womack

and Jones, 2003) in enterprise systems design to create a lean enterprise (Henderson et al., 1999;

Kennedy, 2003). Based on a holistic, value-stream approach, a lean enterprise is the application

of lean principles and techniques to an extended service enterprise and supply chains. Moreover,

another key tenet of conducting lab research projects and assignments in the laboratory is

interdisciplinary that underscores the significance of intra-organizational collaboration and

integration among business units to ensure a successful extended service enterprise system

implementation. The laboratory environment, along with the practical set of lab assignments,

provides a robust interdisciplinary framework for integrating service enterprise systems

education for students from the disciplines of information systems (IS) and production and

operations management (POM). Finally, the proposed laboratory incorporates several

8

experiential learning and instructional methods, such as web-enabled collaborative learning,

scenario-based simulation and discovery learning, and student-driven evaluation.

Several courses and project assignments are designed to offer the students practical

application opportunities on lean service enterprise systems improvement through establishing

baselines and target metrics for key business processes. In the wake of changing organizational

structure, the lab environment is created to help students develop cross-discipline knowledge and

skills needed in horizontal organizational structures and develop an integrated service enterprise

perspective by immersing them in a learning environment that closely simulates real-world

business scenarios.

4.1 Web-enabled, simulation-based, collaborative learning laboratory environment

To support the implementation of the ISES Lab, a web-enabled, simulation-based, collaborative

computing environment is set up as the foundation for both the instructional modules and the

laboratory projects. In a snapshot, this technology-enabled learning laboratory serves multiple

purposes:

The laboratory forms the basis of an ideal collaborative learning environment where students

can learn from hands-on experience via various assignments in real-life business contexts.

The ISES Lab provides a variety of software tools in the areas of computer simulation and

modeling (e.g., ProModel and ILOG), business applications development tools (e.g.,

Microsoft Visio Studio), database management systems (e.g., Microsoft SQL Server), and

knowledge discovery and data mining (e.g., Oracle Express and SAS Enterprise Miner) to

support the entire service enterprise systems development process. Specifically, ProModel is

recommended for developing the simulation models, while ILOG CPLEX should be installed

9

to develop C and C++ programs for linear programming, mixed integer programming, other

mathematical programming problems. In addition, ILOG Solver can be used to solve

problems in production planning, resource allocation, optimization, and management.

To simulate information sharing and transaction exchanges with business partners in a supply

chain, an ERP system (e.g., Microsoft Dynamics) is provided to support such learning tasks

as systems analysis, process mapping, system configuration, and testing.

Next, as an important tool in the simulated environment of intra and inter-organizational

collaboration and communication, an service enterprise portal is incorporated into the

laboratory to demonstrate how a web portal can facilitate more effective group decision-

making, information sharing, and collaborative planning and design among different team

members.

The ISES Lab includes a collaboration mechanism, shared reference space, for collaborative

learning. This multimedia-enriched cognitive support tool allows students to visually

conceptualize, analyze and communicate their analyses of complex service enterprise

systems problems, and apply lean principles in different settings of information exchange.

The shared reference space support both synchronous and asynchronous communications.

When students are present at the same time over the service network, the collaborative

learning support should assisting students in brainstorming ideas and exchanging information

to co-develop enterprise system solutions. This collaborative learning continues when they

are not working in the same workspace or time, since all the ideas, comments, findings, and

changes to the solution are tracked and recorded in the shared reference space. Collaborative

learning offers a viable, effective approach to deal with the dynamic, demand-responsive

nature of service enterprises.

10

The ISES lab also offers handicapped accessible laboratory facilities and other necessary

computing equipment (e.g., electronic smart board) for web-enabled collaborative teaching and

learning. A small-scale enterprise database is used as the underlying data source for the project.

Using a web-based information sharing mechanism and an integrated enterprise database,

students can perform the tasks mentioned above by extracting and aggregating information and

knowledge from different functional areas in an enterprises value chain, such as engineering

design, manufacturing, logistics, and services.

4.2 Laboratory projects and assignments

Through completing laboratory projects and assignments, students are imbued with important

concepts in analyzing and improving a service enterprise and its supply chain, such as business

workflow and production processes, balancing demand and supply (capacity), global supply

chain optimization and integration, infrastructure of information systems and computer networks,

interaction and information sharing among internal and external environment (i.e., upstream

suppliers, downstream dealers, retailers, and customers, as well as the third-party inbound and

outbound logistics providers), and implementation and deployment strategies.

The lab also provides a viable environment for developing fundamental theories to

improve service supply chain network communications and collaborative efficiency.

Specifically, the lab projects and assignments were designed to test several hypotheses of

complex behaviors in service-oriented supply chain operations. For instance, three tested

hypothesis are shown as follows:

Hypothesis # 1 The size and complexity of a service-oriented supply chain can evolve

dramatically with characteristic time-constants, such weeks and months. The dynamic,

11

demand-responsive nature of many services makes the modeling and decision making

under uncertainty significantly more critical than in manufacturing settings.

Hypothesis #2 Transactions and interactions in a supply chain network may track up to

thousands of supply chain units and each unit may further contain hundreds of

maintenance actions. Each maintenance action can represent a unique supply chain unit

including spare parts, repair processes, technical skills, logistics and distribution, etc.

Hypothesis #3 The object types in a supply chain may constitute a complex taxonomy

ranging from material properties, inventories, technical skills, to transportation dynamics.

It has been realized that substantial service improvements in quality as a result of

implementing visually rich simulation systems. The proposed laboratory environment applies

this idea to testing the hypotheses mentioned above via laboratory simulation and

experimentation. Laboratory simulation and experimentation involves the creation of an artificial

environment for isolating and better controlling of potentially confounding variables (Hersen &

Barlow 1976, Jarvenpaa et al. 1984, 1985, Jarvenpaa 1988, Benbasat 1990a, 1990b, DeSanctis

1990). A simulation-driven decision support laboratory will be developed to examine the

explosion in distributed supply chain network and information exchange activities. A set of

simulation models are developed and tested on a hypothetic or real-world demonstration problem

against an established baseline and the three hypotheses mentioned above. In addition, scenario-

building is adopted to gather new insights into relationships among transactional variables in a

service supply chain network.

12

4.3 Implementation principles and learning methods

The following paragraphs depict how the lab project is carried out through the application of

three fundamental implementation principles: (1) collaborative and just-in-time learning via

multidisciplinary product team formation and role-playing and (2) scenario-based simulations

and discovery learning.

Collaborative learning has long been promoted by numerous educators and researchers

(Johnson and Johnson, 1989; Kuhn, 1993). The ISES Lab supports collaborative learning by

creating a simulated, shared environment in which students can collectively investigate real-

world business problems in case studies of model companies and recommend viable enterprise

systems solutions. Moreover, to promote just-in-time learning, both the IS and OM teams are

encouraged to continually develop their competencies by learning new knowledge and skills

from their own disciplines as well as from other disciplines in an iterative cycle of learning and

application.

To assist students in successfully carrying out the laboratory projects, the blended

learning approach is adopted by combining instructor-led lectures with scenario-based

simulation learning and discovery learning methods (Bruner, 1961, 1966; Leutner, 1993;

Hammer, 1997; Johnson et al., 2003). The scenario-based simulation learning method is

recommended because computer simulation research has shown the effectiveness of computer

simulation as a strategic planning tool for organizations to implement lean principles (Fripp,

1993; Towne, 1995; Aldrich, 2003). With scenario-based simulation, students learn to analyze

and simulate different production scenarios, think hypothetically, and evaluate different potential

solutions before making the final decisions.

Systematic validation and statistical analysis of simulation results is an essential step of a

13

successful simulation modeling and development. To ensure the validity of the developed

simulation model, a validating process will be performed to examine if the model correctly

represents the essential aspects of the system within the real-life context. To begin with, a

number of validation criteria will be defined to facilitate fully understanding of a successful

simulation. The modeling assumptions and sensitivity will be examined by testing and changing

various parameters identified in the model. Statistical tests (e.g., goodness-of-fit tests) will be

performed on all inputs and internal processes. The model will be assessed to verify whether the

simulation model is built with respect to requirements and performance criteria established

during the requirements definition stage and further examine if it is a viable representation of the

real-life service system. Furthermore, an empiricists approach (Law, 1991) will be adopted for

supply chain model validity checking on the initial set of modules and a rationalists approach

for the newly defined model components. With the empiricists approach, the performance

results generated by the scenario simulation model are then compared with historical data in the

real-life environment. Hypothesis tests are used to determine if the disparity between the various

performance results is statistically significant between the real model structure. For the new

model development, historical data is unattainable. The newly developed model is then closely

examined and the assumptions are properly updated and justified.

To fully seize the essence of the behavior of the system, experimentations will be

indispensable. Formulating pertinent experimental conditions and simulation scenarios under

which the model behavior is examined. Given that statistical analysis of the results of each

experiment is appropriately conducted and the results are properly evaluated and compared, the

experimentation process will not only lead to a greater comprehension of the behavior of existing

system but also figure out the ways to enhance the system with desirable performances.

14

As shown in Figure 1, students are required to role-play as employees in two

organizational units at any typical organization as they were in the real world setting:

Department of Information Systems (IS) and Department of Production and Operations

Management (POM). In addition, students take turns playing the role of the end customers

who work with the product teams to provide insight from the customers perspectives. By

playing different roles and working with students from different disciplines, both the IS and

POM teams can acquire cross-functional knowledge and skills. Essentially, the laboratory project

requires the students to carry out multidisciplinary, collaborative tasks in the following areas:

Information processing support activities: The IS teams carry out information processing and

related support activities. They are responsible for developing right-sized information

systems to provide just-in-time ERP data and information

Primary production activities: The POM teams focus on the primary activities directly

related to services and after-sale operations. In addition, they are in charge of organizing all

the value-creating activities in the best sequence for a specific service along a value stream.

Continuous process improvement: While each team has its primary focus, both the POM and

IS teams will collectively define and continually refine the business and manufacturing

processes throughout the entire project.

5. A Generic Example of Service Enterprise Systems Simulation

To assist in conceiving both the theoretical and practical significance of the lab projects, a

generic examplefield service planning and scheduling of after-sale equipmentis

illustrated in this section. From a broader perspective, the primary sources of field service

work include planned maintenance tasks, callbacks, repair work and open-order requests.

15

Among them, planned maintenance and scheduled repairs are performed at regular intervals

on the serviced units through service contracts. Callbacks or emergency service calls are

unplanned service requests that are due to equipment failure and that arise randomly over

time. These work types have very different scheduling requirements. One of the integral

simulation models is to combine all the work types in one optimized scheduling solution to

address some highly dynamic and challenging problems. In the simulation process, various

real-life scenarios are to be set up and constraints on service work are to be defined,

including different work types, spatially separated service geography, skills, parts, and other

business rules.

Suppliers

Primary Production Activities

(role-played by the POM Team)

Information Processing Support Activities

(role-played by the IS Team)

Information Flows

Material Flows

Databases

Design

Programs

Design

Interfaces

Design

Networks

Design

The

CustomerGroup

Inbound

LogisticsOperations

Outbound

Logistics

Product

Team X

Product

Team Y

Product

Team Z

Figure 1. Role-Playing and Interdisciplinary Learning for Collaborative Planning and Design

16

To address the realism of the defined service demand assumptions and requirements, a

field service planning and scheduling process for automated and real-time resource allocation

and scheduling is to be simulated. This simulation task will be conducted based on a multi-

dimension assignment of service jobs, required resources, and time slots. For initial schedule

planning, an external Enterprise Resource Planning (ERP) system is connected for the provision

of basic data, such as equipment history and usage data, types of service work, parts availability,

etc. The planning module of the system is capable of developing an optimal plan by grouping the

service jobs based on both the distance-proximity clustering and time-proximity clustering

methods. For essential mapping information, an external Geographic Information System (GIS)

will be integrated to provide a map database containing layout of roads, geocode information,

direction and speed limits of roads, etc. As the heart of the simulation model, an automated and

integrated scheduling engine will be built to continuously run for maintaining a real-time

optimal or near-optimal solution across a rolling horizon as uncertain conditions, such as

service times, travel times and arrival of callbacks, are realized. The scheduling engine is

capable of integrating all service work types and the solution will faithfully abide by all the

objectives (soft business rules) and constraints (hard business rules). Figure 2 depicts a

conceptual architecture of the simulation environment mentioned above.

The modules of the lab will be a simulation-based testing environment with two essential

components: Visual Modeler and Simulator and Service Operations Optimizer.

Visual Modeler and Simulator. This component will be employed to create necessary

simulation models to simulate various interactive and collaborative transaction and

exchange scenarios in specific service supply chain implementations. This simulation

environment setting will help design and valuate high performance service operations.

17

Geographic

Information

System

(GIS)

Field Service

Planner

Field Service

Scheduler

Enterprise Resource Planning (ERP)

Equipment Usage Types of Parts

History Data Work Data

...

Business

Objectives

& Constraints

Figure 2. Conceptual Architecture of the Field Service Simulation Environment

Service Operations Optimizer. This mechanism is to assist in optimizing complex

stochastic problems in the systems via optimization modeling.

The simulation-based laboratory will help contribute to develop service-oriented supply

chain and optimization theories for the service industries. Meanwhile, the results will provide

guidelines to academic and business communities in the design of advanced supply chain

software for service sector. In addition, the research effort will advance new capabilities in

modeling, simulation, and optimization of service supply chain.

6. Conclusions

The ISES Lab facilitates implementing a service enterprise systems educational plan in two

aspects: (1) developing the after-sale service logistics and information management courses and

18

(2) implementing a new approach to enhancing the learning of modern service supply chain

optimization and service enterprise systems integration. When it comes to broader impacts, the

ISES Lab could help promote research that extends the application of optimization theories,

simulation and modeling techniques, and information technologies to an important economic

sector, the after-sale service sector. For instance, one of the research initiatives is the

development of fundamental theories of condition-based maintenance (CBM) and field service

management for improving the competitiveness and reliability of after-sale service enterprises.

Additionally, this research will open a new area for teaching service planning and scheduling,

service chain optimization, and e-service information systems design. It will provide the

foundation for introducing after-sale service logistics optimization in the supply chain

management course, which is becoming an important course in the curricula of many disciplines,

such as industrial engineering, production and operations management, operations research, and

management information systems.

This research work can be incorporated into the teaching of both operations management

and information systems at both the undergraduate and graduate levels. The educational plan

potentially leads to significant enhancement in the learning of service supply chain and logistics

management. The introduced courses will improve both theoretical and practical aspects of the

service enterprise systems curriculum. Involving students in research not only trains them

adequate problem-solving skills but also encourages them to continue graduate study. The

proposed ISES Lab also supports educational enhancement for cross-disciplinary programs by

incorporating various innovative learning methods for effectively engaging students in service

enterprise systems design and management. The philosophy to innovative technology

development consists of applying results from various sources. The proposed lab environment

19

facilitates collaboration among multidisciplinary members (majoring in IS, POM, etc.) allowing

the project teams to leverage their diverse experiences, knowledge, and skill sets. With the

special designed lab projects and assignments, teaching and learning are significantly enhanced

through the use of the scenario-based simulation exercises and discovery learning method. The

ISES Lab provides an interdisciplinary and collaborative learning environment allowing students

to greatly advance their cross-functional knowledge, problem-solving skills, and innovative

thinking in lean enterprise systems designwhich will well prepare them prior to their entering

the job marketplace.

REFERENCES

Ahitur, N., & Berman, O., Operations management of distributed service networks-A practical

quantitative approach. New York: Plenum Press, 1988.

Albrecth, W.S., and Sacks, R.J. (2000) The perilous future of accounting education, The CPA Journal, pp.17-23.

Albright, B., .Field service in tomorrow land,. Frontline Solutions, p52, January 2002.

Aldrich, C. (2003) Simulations and the Future of Learning: An Innovative (and Perhaps

Revolutionary) Approach to e-Learning, Pfeiffer.

Bandow, D. (2005) Lessons from Darwin: adaptation and career management for IT professionals, Proceedings of the 2005 ACM SIGMIS CPR Conference on Computer Personnel Research.

Benbasat I. (1984) 'An Analysis of Research Methodologies' in McFarlan (1984), 47-88.

Benbasat I., Goldstein D. & Mead M. (1987) 'The Case Study Research Strategy in Studies of

Information Systems' MIS Qtly 11,3 (September 1987) 369-386.

Bruner, J.S. (1961) The act of discovery, Harvard Educational Review, Vol. 31, pp.21-32.

Bruner, J.S. (1966) Toward a Theory of Instruction, Cambridge, MA: Harvard University Press.

Cash, J.I. & Lawrence, P.R. (Eds.) (1989) 'The Information Systems Research Challenge:

Qualitative Research Methods' Volume 1, Harvard Business School, 1989

20

Cavaye A.L.M. (1996) 'Case Study Research: A Multi-faceted Research Approach for IS'

Information Systems J. (1996) 227-242.

Corbitt, G. and Mensching, J. (2000) Integrating SAP R/3 into a college of business curriculum: Lessons learned, Information Technology and Management, Vol. 1, No. 4, pp.247-258.

Davis, E.W. and Spekman, R.E. (2003) The Extended Enterprise : Gaining Competitive

Advantage through Collaborative Supply Chains, Financial Times Prentice Hall.

DeSanctis G. (1990) 'Small Group Research in Information Systems: Theory and Method' in

Benbasat (1990), 53-82

Dingwall R. & Miller G. (1997) 'Context and Method in Qualitative Research' Sage, 1997

Dzubow, S. R., A methodology for the design and control of logistic service systems,

Unpublished doctoral dissertation, University of Pennsylvania, Philadelphia, PA, 1972.

Fripp, J. (1993) Learning Through Simulations: A Guide to the Design and Use of Simulations

in Business and Education, McGraw-Hill Trade.

Haghani, A., & Shafahi, Y., .Bus maintenance systems and maintenance scheduling: model

formulations and solutions,. Transportation Research Part A, vol. 36, pp. 453-482, 2002.

Gorgone, J.T., Davis, G., Valacich, J., Topi, H., Feinstein, D., and Longenecker, H. (2002) IS 2002: Model curriculum and guidelines for undergraduate degree programs in information

systems, http://www.spatial.maine.edu/SIEWEB/ IS_ModelCurriculum.pdf.

Gothe-Lundgen, M., Lundgen, J. T., &Persson, J. A., .An optimization model for refinery

production scheduling,. International Journal of Production Economics, vol. 78, issue 3, pp.

257- 272, 2002.

Hammer, D. (1997) Discovery Learning and Discovery Teaching, Cognition and Instruction, Vol. 15, No. 4, pp.485-529.

Hambleton, R. S., .A manpower planning model for mobile repairmen,. Journal of Operational

Research, vol. 33, pp. 621-627, 1982.

Henderson, B.A., Larco, J.L., and Martin, S.H. (1999) Lean Transformation: How to Change

Your Business into a Lean Enterprise, Oaklea Publishing.

Hersen M. & Barlow D.H. (1976) 'Single-Case Experimental Designs: Strategies for Studying

Behavior' Pergamon Press, New York, 1976

Haugen, D. L., & Hill, A. V., .Scheduling to improve field service quality,. Decision Sciences,

vol. 30, no. 3, pp. 783-804, 1999.

21

Hill, A. V., .An experimental comparison of dispatching rules for field service support, Decision

Sciences, vol. 23, no. 1, pp. 235-249, 1992.

Hill, A. V., March S. T., Nachtsheim, C. J., & Shanker, M. S., .An approximate model for field

service territory planning,. IIE Transactions, vol. 24, no. 1, 1992.

Jarvenpaa S.L. (1988) 'The Importance of Laboratory Experimentation in Information Systems

Research' Commun. ACM 31, 12 (December 1988) 1502-1504

Jarvenpaa S.L., Dickson G.W. & DeSanctis G. (1984) 'Methodological Issues in Experimental IS

Research' Mngt Sci 30, 5 (May 1984) 586-603

Jarvenpaa S.L., Dickson G.W. & DeSanctis G. (1985) 'Methodological Issues in Experimental IS

Research: Experiences and Recommendations' MIS Qtly 9,2 (June 1985) 141-156

Johnson, D. and Johnson, R. (1989) Cooperation and Competition: Theory and Research,

Interaction Book Company.

Johnson, S., Gerstenfeld, A., Zeng, A., Ramos, B., and Mishra, S. (2003) Teaching lean process design using a discovery approach, Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition.

Kennedy, M.N. (2003) Product Development for the Lean Enterprise: Why Toyota's System Is

Four Times More Productive and How You Can Implement It, Oaklea Press.

Kolesar, P., & Blum, E. H., .Square root laws for fire engine response distance,. Management

Science, vol. 19, pp. 1368-1378, 1973.

Law, A and Kelton W.D. (1999) Simulation Modeling and Analysis (Industrial Engineering and

Management Science Series), 3rd

edition, McGraw-Hill.

Lee, Y. H., Jeong, C. S., & Chiung, M., .Advanced planning and scheduling with outsourcing in

manufacturing supply chain,. Computers & Industrial Engineering, vol. 43, pp. 351-374,

2002.

Leutner, D. (1993) Guided discovery learning with computer-based simulation games: effects of adaptive and non-adaptive instructional support, Learning and Instruction, Vol. 3, pp.113-132.

Liu, J., Jiang, Y., & Zhou, Z., .Cyclic scheduling of a single hoist in extended electroplating

lines: a comprehensive integer programming solution,. IIE Transactions, vol. 34, issue 10,

pp. 905-914, 2002.

Panson, D. J., Dispatching strategies in a commercial service environment, Unpublished doctoral

dissertation, Clemson University, Clemson, SC, 1983.

22

Pongcharoen, P., Hicks, C., Braiden, P. M., & Stewardson, D. J., .Determining optimum Genetic

Algorithm parameters for scheduling the manufacturing and assembly of complex

products,. International Journal of Production Economics, vol. 78, issue 3, pp. 311-322,

2002.

Smith, S. A., .Estimating service territory size,. Management Science, vol. 25, pp. 301-311,

1979.

Towne, D. (1995) Learning and Instruction in Simulation Environments, Educational

Technology Publications.

Trauth, E., Farwell, D., and Lee, D. (1993) The IS expectation gap: industry expectations versus academic preparation, MIS Quarterly, Sep. pp. 293-307.

Tyndall, G., Gopal, C., Partsch, W., and Kamauff, J., Supercharging Supply Chains: New Ways

to Increase Value Through Global Operational Excellence, John Wiley & Sons, 1998.

Womack, J.P., and Jones, D.T. (2003) Lean Thinking: Banish Waste and Create Wealth in Your

Corporation, 2nd

ed., New York, NY: Free Press.

Yen, B. P., .Communication infrastructure in distributed scheduling,. Computers & Industrial

Engineering, vol. 42, pp. 149-161, 2002.

Yoon, S., & Ventura, J. A., .An application of genetic algorithms to lot-streaming flow shop

scheduling,. IIE Transactions, vol. 34, pp. 779-787, 2002.

Yu, L., Shih, H. M., Pfund, M., Carlyle, W. M., & Fowler, J. W., .Scheduling of unrelated

parallel machines: an application of PWB manufacturing,. IIE Transactions, vol. 34, pp.

921- 931, 2002.

Yun, Y., & Gen, M., .Advanced scheduling problem using constraint programming techniques in

SCM environment,. Computers & Industrial Engineering, vol. 43, pp. 213-229, 2002.