LittleField Technology case

I N F O R M STransactions on Education

Vol. 7, No. 3, May 2007, pp. 201–222issn 1532-0545 07 0703 0201 informs ®

doi 10.1287/ited.7.3.201©2007 INFORMS

A Simulation Exercise to Illustrate the Impact ofan Enterprise System on a Service Supply Chain

James L. Ritchie-DunhamPsychology Department, Harvard University, Cambridge, Massachusetts 02138,

[email protected]

Douglas J. Morrice, Edward G. Anderson, Jr., James S. DyerDepartment of Information, Risk, and Operations Management, Red McCombs School of Business,

The University of Texas at Austin, Austin, Texas 78712 [email protected],[email protected], [email protected]

In this paper, we present a computer-based simulation exercise designed to help students understand theimpact of an enterprise system on business performance in a service supply chain. The particular servicesupply chain simulated in the exercise is a wireless telecommunications firm. In this exercise, students performsimulations to experience managing the supply chain of the telecommunications firm with and without anenterprise system. The simulator tracks their business performance. Then the results are used as the basis ofdiscussion in a subsequent debriefing session. We describe the educational goals of the simulation exerciseand how the exercise can be structured in order to achieve these goals. The latter is illustrated by the use ofthe simulation exercise in a master’s level supply chain management course in the Red McCombs School ofBusiness at the University of Texas at Austin. The simulator includes realistic details. In fact, it is based onthe extensive consulting experiences of the first author with two North American telecommunications firms.We describe the simulator in detail under the various scenarios, explain how it was validated, and provide thesimulator equations in system dynamics format in Appendix B.

1. IntroductionMost large organizations have implemented or areconsidering implementing some form of an enterprise-wide information system (ES). Proponents of theseESs suggest they provide many benefits over thelegacy systems they replace, such as better systemsintegration, standardization of data and processes,end user visibility across the business enterprise, andimproved decision support functionality (Davenport2000, Ettlie 2000, Gattiker and Goodhue 2000, Mabertet al. 2000, McAfee 2000). ESs cost millions of dollarsand take many months to implement fully (O’Leary2000). Thus, when evaluating different ESs, decisionmakers need to understand the potential benefits andcosts. In practice, quantifying the impact of an ES(both costs and benefits) may be difficult due to lackof good benchmarking data and the challenge of iso-lating the impact of an ES from other potentially con-founding factors.This paper describes a computer-based simulation

exercise designed to teach students about the poten-tial impact of a successfully implemented enterprise

system on business performance in a service sup-ply chain. The system dynamics simulator allowsstudents to experience the value added by runningthe supply chain of a wireless telecommunicationsfirm under two different scenarios: with and with-out an ES. The simulator tracks the performance ofdecision-makers as they manage the supply chainof the simulated telecommunication firm under oneof the aforementioned scenarios. Thus, this exerciseisolates the impact of an ES implementation. Theresults from these exercises are compared to determinewhether or not the proposed ES benefits influence thebottom line under the use of a typical manager. Itis not always clear if an ES will help managers rundynamically complex systems (Sharda et al. 1988).Therefore, besides providing a hands-on managementtraining experience, this exercise also provides anopportunity for a rich discussion of the issues withwhich companies, the academic literature, and thepopular press are wrestling. A paper containing apreliminary proposal for this simulation exercise was

201

mailto:[email protected]




Ritchie-Dunham et al.: Simulation Exercise to Illustrate the Impact of an Enterprise System on a Service Supply Chain202 INFORMS Transactions on Education 7(3), pp. 201–222, © 2007 INFORMS

presented at the 2000 Winter Simulation Conference(Ritchie-Dunham et al. 2000).We have designed the simulation exercise to be a

three to six-hour self-contained module that can beused as part of a longer course in supply chain man-agement or information management, or as a stand-alone executive education short course. In §5, wedemonstrate its use over two 75-minute sessions in amaster’s level supply chain management class in theRed McCombs School of Business at the Universityof Texas at Austin. The students performed the sim-ulation in the first session. The second session wasspent discussing the simulation results and how theseresults related to actual company experiences and tothose studied in the academic literature.We selected a service sector firm, in particular,

a wireless telecommunications firm, as the basis forthis simulation exercise for four reasons. First, ourstudents and corporate partners have requested moreservice sector related exercises be added to our cur-riculum (Anderson and Morrice 2000) because themajority of our graduates find employment in theservice sector. Second, games and other exercisesare more common for physical goods supply chains(Littlefield Technologies, Bates 2002; e.g. The MITBeer Distribution Game, Senge 1990). Third, wire-less telecommunications is a business with which thestudents can easily identify. They find the applica-tion area interesting making it easier to motivate theexercise. While there is the potential downside thatthis specific example might provide a narrow expe-rience for the students, we do not believe this tobe the case because the simulator contains elements(both financial and operational) common to firmsin many other industries, such as those involved inother aspects of information technology services, con-sulting, retail services, third party logistics, and callcenters.The final reason for selecting a wireless telecom-

munications firm as the case example is that thefirst author has extensive consulting experience in thetelecommunications industry. In fact, the simulator isbased on system dynamics simulation models devel-oped and used for strategic policy analysis at twoNorth American telecommunications firms. It is wellvalidated, comprehensive, and easy to understand(see Appendix B for simulator equations).Throughout the paper we often use the term ES

synonymously with Enterprise Resource Planning(ERP) systems especially when citing some of themore recent academic literature. We have consciouslydecided to use ES because we think it is more generalthan a specific business term used in current prac-tice (namely, ERP). Additionally, we believe that thesignificance and purpose of our simulation exercise

transcends the current issue of implementing Enter-prise Resource Planning (ERP) systems. The businessvalue of information technology to the enterprise hasbeen a topic of interest in the literature for the last15–20 years with more recent literature focusing onthe value of specific applications such as ERP sys-tems (Hitt et al. 2002). Based on the results of thestudy by Akkermans et al. (2003), this topic is likelyto be of continuing interest to business executives andacademics for some time into the future especially inspecific application areas such as supply chain man-agement. Therefore, the overall worth of this exerciseis to help students understand the impact of the afore-mentioned principles of integration, standardization,visibility, and improved decision support on businessperformance in service supply chains.It is important to note that the simulation exer-

cise does not include all the details and complexitiesinvolved in implementing an ES. These are accountedfor in the simulator by ES implementation costs. Thiswas done to simplify the exercise and narrow thefocus down to exploring the potential benefits of asuccessful ES implementation. In other words, theexercise focuses on before and after the ES implemen-tation but not the transition between these two states.It is our experience that such focusing is necessaryin order to mitigate the risk of making the exercisetoo complex to yield any meaningful results. To pro-vide a proper context for the students and balance thediscussion, we emphasize to them that they shouldview the results of this exercise as the potential forES implementation at its best. Then we discuss casestudies and other literature illustrating ES implemen-tations that have been costly and challenging (Griffithet al. 1999, Hong and Kim 2002, Songini 2004). Thelatter serves as a launch pad for a discussion on whatfactors can lead to a successful ES implementation.The rest of the paper is organized in the follow-

ing manner. Section 2 details the nature of opera-tions of a wireless telecommunications firm and howthe system dynamics simulator captures that nature.Section 3 presents the enterprise systems. Section 4outlines the validation of the simulator. Section 5provides an example of the simulation exercise con-ducted with master’s students at the University ofTexas at Austin. Section 6 contains a concludingdiscussion.

2. The Wireless TelecommunicationsSimulator

The simulator presented here was abstracted from amore complex system dynamics simulator that wasdeveloped by one of the authors and used by twonational telecommunication firms for strategic deci-sion making. The original simulator was simplified

Ritchie-Dunham et al.: Simulation Exercise to Illustrate the Impact of an Enterprise System on a Service Supply ChainINFORMS Transactions on Education 7(3), pp. 201–222, © 2007 INFORMS 203

to make it easier for subjects to control during theexperiment. Both the original and simplified simula-tors were developed with experts in wireless telecom-munications and supply chains (see Appendix A). Thesimulator parameters were then scaled to protect theconfidentiality of the telecommunications firms. Thisscaling did not affect the dynamics of the simulator.

2.1. Wireless Telecommunications OperationsWe briefly describe how wireless telecommunicationswork and how a telecommunications firm provideswireless services. The principles of wireless telecom-munications are straightforward. A person uses hercell phone, which acts as a radio transceiver, to con-nect to a nearby base station (see Figure 1). To ensureconstant and continuous cell phone coverage whilethe user is moving, the base stations form a networkof overlapping cells. These base stations manage,send, and receive signals from the cell phones inits geographic area to a mobile telecommunicationsswitching office (MTSO). The MTSO places calls fromland-based telephones to wireless customers, switchescalls between base stations as the cell phone travelsacross cell boundaries, and authenticates wirelesscustomers before they make calls. Additionally, theMTSO connects the network to public networks suchas Southwestern Bell and other cellular networks,which connect with land-based phones. A cellularnetwork is constructed in stages by adding more basestations to increase network coverage and quality.Figure 2 summarizes how a telecommunications

firm provides wireless services, as modeled in the sim-ulator. Starting in the lower-left corner, the firmmakesInvestment Decisions that allocate financial resourcesto: (1) Infrastructure (base stations), (2) HumanResources (training and hiring people), and (3) theService Supporting Information Technology (to sup-port call center services). These three resources influ-ence different dimensions of Customer Satisfaction,which influences the number of customers in the Cus-tomer Base and impacts the organization’s Financials.The Financials influence further investment decisionsand the cycle repeats itself.

Figure 1 Overview of How Wireless Telecommunications Work

The simulator incorporates external and internalcomponents of the firm’s supply chain, as denoted byboxes in Figure 2. External components include thesuppliers (within Infrastructure) and the CustomerBase. Internal components include Human Resources,Infrastructure, Service Supporting Information Tech-nology, and Financials. This section briefly explainsthe model logic. Appendix B provides a full descrip-tion of the simulator’s equations, ordered by theboxed components in Figure 2.

2.2. External ComponentsIn the Infrastructure component of the supply chain,as expressed in the simulator (see Figure 3), the firmplaces orders for base stations with the suppliers. Themonthly ordering capacity is based on the annualbudget for base stations and their average cost. Sincethe firm competes with other firms also installingbase stations, the suppliers’ daily building capacityavailable to the firm is a function of the supplier’sannual building capacity and the firm’s market share.This supplier building capacity is a function of itsinitial building capacity, its growth due to changesin our firm’s demand, and its growth due to over-all demand, the latter being a function of growth inthe population demanding wireless services and mar-ket share. The supplier’s growth in building capacityis moderated by its growth response time—how longit takes to shift its capacity once it sees a shift indemand. The building of base stations converts themfrom Capacity in Process to Base Stations, which arein service. The base station turn around is a ratioof the capacity in process and the rate of buildingbase stations with a lower bound constraint on min-imum construction time. After an average life, theBase Stations are retired. The number of Base Sta-tions and their daily capacity determines the NetworkDaily Capacity. Capacity Utilization is then a func-tion of this Daily Capacity and the Total Daily Usage,which is a function of the number of Customers andtheir average usage. Capacity Utilization influencesNetwork Quality, which influences the Customers’Perceived Call Quality (see Customer Satisfactioncomponent of Figure 3). The other component of per-ceived call quality is Network Coverage, which is aratio of the number of active Base Stations and thenumber of base stations required to cover 100% of thepopulation.On the other end of the supply chain, in the Cus-

tomer Satisfaction component, the firm provides ser-vices to customers for a fee. Satisfaction is measuredas a utility function of the price paid for a perceivedlevel of call quality and customer service, relative toboth the competitive offering in the marketplace andthe customer’s disposable income dedicated to wire-less telecommunication services. Each of the factors


Figure 2 Overview of How a Telecommunications Firm Is Setup to Provide Wireless Services

that determine customer satisfaction is determinedendogenously, as explained in the following section.The firm’s retention rate is the ratio of customer sat-isfaction to the competiton’s customer satisfaction.In the Customer Base component, the rate at whichcustomers are gained from or lost to the competi-tion is a function of the retention rate and the cus-tomer’s delay in acting i.e., how long it takes themto respond to changes in relative customer satisfac-tion based on experiences and contracts (see Figure 3).The firm gains new customers as a product of thenumber of new users added to the population in eachtime period and the firm’s new user market share.The population of wireless customers is assumed togrow at a constant rate over the seven-year simula-tion period, with the customer’s average disposableincome for wireless services held constant.

2.3. Internal ComponentsThe Human Resources component describes thedynamics of human resource skills as employees arehired or fired, and as they are trained or their skillsare made obsolete by changing technologies (see Fig-ure 4). Employees have either relevant skills or obso-lete skills. Relevant Skilled People are hired intothe firm and may leave the firm by attrition or bydownsizing. They also convert to Obsolete SkilledPeople when their skills become obsolete, which isa function of the Industry Technology Change Rate.

As Obsolete Skilled People, they leave by attrition orby downsizing, or they are trained to become Rele-vant Skilled People. The rate of people being trainedis influenced by training effectiveness, which is afunction of the training budget per employee withobsolete skills, as benchmarked against industry stan-dards. Training effectiveness is also influenced byHR’s ability to effectively schedule the right trainingwhen needed by the right people. The HR ServiceIndex, a determinant of perceived Customer Service(in Figure 3), is a function of the actual customer toemployee ratio and the benchmark service level thatcan be provided at different ratios of customers toemployees.The Service Supporting Information Technology

component depicts the dynamics of the informationsystems that support the call centers. The firm investsits budget in new SSIT, which is retired a speci-fied time later. The effective IT$ per employee is aratio of the amount of SSIT available to the num-ber of call center employees, which is moderatedby the information processing quality. As informa-tion processing quality increases, the effective IT$per employee increases for the same investment inSSIT. The effective IT$ per employee is benchmarkedagainst industry standards to determine the degreeto which the employees have sufficient IT to facili-tate their ability to see the information necessary toaddress customer needs and act on them. Thus, the


IT Facilitation Index compares the effective IT bud-get per call center employee to a benchmark level.The IT Facilitation Index along with the HR ServiceIndex influence Customer Satisfaction through per-ceived customer service (see Figure 3).

Figure 3 Stock-Flow Model of External Components

The Financials component (see Figure 2) calculatesthe financial statements based on the status and flowof resources in the firm. This component is not shownin a diagram (see Appendix B for formulations).

3. The Enterprise SystemsThis section explores how enterprise systems influ-ence the management of a complex system, like thewireless telecommunications firm overviewed above.As organizations determined that information tech-nology might facilitate their business, they createddiscrete information systems for that specific pur-pose. As the number of these systems multipliedand the size of organizations grew, the complexityof these loosely interfaced systems increased. To linkthese systems more tightly, software packages beganto offer integrated systems, such as material require-ments planning, which provided multiple functionalapplications with a common database. (For a systemdynamics study of these early systems, see Morecroft1983.) These systems eventually incorporated a fullsuite of supply chain applications to the manufac-turing systems. These systems, which began as backoffice support, now support the front office across thesupply chain (Davenport 2000). These enterprise sys-tems are very complex, in their integration of manyfunctional applications and best practices, as well astheir need to evolve with the organization over time(Markus and Tanis 2000). This complexity of inter-connected functional applications and their associatedorganizational issues makes successful implementa-tion of these systems very difficult. Organizationsinvest in these complex systems because of thepromise of reduced cycle time, faster informationtransactions, improved financial management, abilityto offer electronic commerce, and making tacit pro-cess knowledge explicit (Davenport 2000, Hitt et al.2002, Markus and Tanis 2000). It is assumed that thesebenefits will convert eventually into higher economicreturn (Andersen 1999).

3.1. Simulators of the Firm With andWithout an ES

To allow students to experience the value added byapplying a successfully implemented ES to a firm, wecreate two versions of the simulator described in §2 torepresent the following scenarios: the firm with an ESand the firm without an ES. We consider the simulatorof the firm without an ES first because virtually allfirms start here before installing an ES.In the simulator of the firm without an ES,

information is maintained in independent (or frag-mented) information systems corresponding to thefirm resources. Strategic-level performance indica-tors focus on financials and operations. Financial


Figure 4 Stock-Flow Model of Internal Components

indicators include economic value added (EVA) andthe components of EVA, as well as capital invested,debt to equity ratio, and current budgetary alloca-tions. Operational indicators include average monthlyorders, inventory, number of employees, marketshare, and number of customer complaints. To emu-late independent systems, the simulation lags theinformation by a three-month delay and provides itin financial and operational terms.The simulator of the firm with an ES provides addi-

tional benefits, listed by component in Table 1. In

the Financials component, the administrative over-head is lower with an ES because much of thedata processing is automated (Davenport 2000). Inthe Human Resources component, the ES tracks therelevant skills of thousands of employees, more effi-ciently identifying those employees requiring train-ing and scheduling their availability for specificcourses. Additionally, by providing employees withrelevant, integrated information from across the firm,the ES reduces the skills required to find and processthe required information.


In the Infrastructure component, the ES’s capi-tal asset management software monitors system-wideutilization of the base stations, spreading usage moreevenly over the network and planning infrastruc-ture preventive maintenance more optimally. Thisreduces the overhead for service technicians waitingto respond to failed systems. In the Service Support-ing Information Technology (SSIT) component, the ESintegrates information across many areas, giving theemployees the right information they need. The ini-tial cost of the ES is included in the initial cost ofthe SSIT, which is determined by the average costof implementing an ES for a firm in this industrywith a given level of revenues (Mabert et al. 2000,2001; Meta Group 1999). On-going ES expendituresare determined by the students during simulation. Inthe Investment Decisions component, the ES providesreal-time, standardized information that is integratedacross the whole firm.The benefits associated with an ES listed in Table 1

were derived primarily from the experience of theexpert panel. While specific results for a telecom-munications firm are difficult to find in the litera-ture, there are some studies from other industries thatcorroborate the benefits in Table 1. Robinson (2005)provides anecdotal support from several, mostly man-ufacturing case studies (see also http://www.bpic.

Table 1 Impacts of Enterprise System on a Wireless Telecommunications Firm

co.uk/cases.htm). Hitt et al. (2002) contains resultsfrom an extensive study that empirically establishesthat firms that adopt ERP will show greater perfor-mance. From a survey of business executives, Gefenand Ragowski (2005) show that it is easier for expertsto perceive the business value of ERP at a specific ITmodule level than at an overall enterprise level. Thissupports our work because our expert panel was onlyrequired to assess the benefits at the module-specificlevel. Overall enterprise value emerges from the sim-ulation exercise.Perhaps the most relevant work to our current

study is found in McAfee (2002), which contains adetailed longitudinal analysis of the benefits derivedfrom an ERP implementation at a manufacturing firm.While the specific metrics used by McAfee do notdirectly correspond to our metrics, we can make com-parisons based on broader categorizations. Specifi-cally, all the percentage improvement metrics in thelast column of Table 1 can be categorized as qual-ity and reliability of service improvements (train-ing effectiveness, network quality, average life, andright information) or labor efficiencies (administra-tive costs, skill level requirements, technician over-head). McAfee (2002) has two metrics that correspondto quality and reliability of service: daily fractionof orders shipped late and daily standard deviation

http://www.bpic.co.uk/cases.htm

http://www.bpic.co.uk/cases.htm


of lead time of orders shipped. Across these twometrics, he reports improvements of anywhere from34–89 percent which compares to our improvementsin the range of 14–100 percent. McAfee (2002) alsoreports sustained head count reductions of 15–53 per-cent post-ERP which also compares to our 14–50 per-cent range of improvements.The output generated by the simulator with the

ES takes advantage of the fact that an ES provides amore integrated environment for data collection andinformation generation (Davenport 2000). Therefore,wherever possible, the ES version of the simulatorprovides more specific and relevant information thanthe simulator without ES. Additionally, there is noinformation lag or delay in the simulator with the ES.Table 2 provides a comparison of the type of informa-tion generated by each simulator.

4. Simulator ValidationTo increase confidence that the simulator was work-ing correctly, we ran it through a full suite of widelyaccepted validation tests (Sterman 2000).Critics of simulator-based theoretical and exper-

imental research suggest that the results (whether“good” or “bad”) are pre-determined by the equa-tions in the simulator—a criticism that applies to allmodels, simulated, or not. Thus, researchers have theburden of validating and verifying their models. Toaddress this criticism, many researchers opt to usewell-known simulators, often accompanying a text-book and used in the classroom, to show that they

Table 2 Output Information Provided in the Simulators of the Firm With and Without an ES

entered no specific biases (Reagan-Cirincione et al.1991, Segev 1987). There is reason to suspect that theirresults are still a function of the particular formulationof the simulator. With this consideration, our researchmakes explicit the simulator to be used, along withthe underlying assumptions (see Appendix B for acomplete listing of the simulator equations). Addi-tionally, we provide a brief overview (see Table 3) ofthe full suite of well-documented and accepted meth-ods we used to validate and verify the simulator (seeRitchie-Dunham 2002 for details).The vast simulation literature provides guidance

on what to validate and how, defining validation asconfirmation that a computer simulator, within thedomain it applies, satisfies the range of accuracy forthe application for which it is intended (Forrester1961, Sargent 1999). Validation involves three keycomponents of a simulation: the simulator concept,the simulator operations, and the simulator data(Sargent 1999).Simulator concept validity focuses on the reason-

ableness of the theory and assumptions underlyingthe simulator. We applied standard concept valida-tion tests (see Table 3) from the system dynamics lit-erature (Sterman 2000). The simulator passed all thetests, including boundary adequacy, structure assess-ment, dimensional consistency, parameter assessment,extreme conditions, integration error, behavior repro-duction, behavior anomaly, and family membership.Simulator operational validity centers on the accu-

racy of the simulator’s behavior, for its intended


Table 3 System Dynamics Simulator Validity Tests

purpose. We ran the simulator multiple times withrandom policies, measuring the range of possibleresponses, and outcomes. The expected performanceof random allocation should be neutral or negative,since the common belief is that it takes thought to besuccessful (i.e., make a profit). The results from apply-ing these random policy runs to the simulator seemedreasonable to the expert panel, providing a range ofoperational believability in the simulator.Simulator data validity concentrates on the use of

adequate and correct data. We obtained data for theinitial values of the stocks, parameters affecting thepolicy within the flow, initial values of the converters,and time series with knowledge from the expert paneland data from two firms in the industry.

5. An Illustration of the SimulationExercise

This section illustrates how we used the simula-tor pedagogically in a master’s-level course. Weconducted the simulation exercise as a two-sessionmodule in a master’s level supply chain manage-ment class in the Red McCombs School of Businessat the University of Texas at Austin. The class had

39 second-year master’s students, mostly MBA, withthe remainder from a joint business/engineering pro-gram. Each session lasted 75 minutes. In the firstsession, the students used the simulator to makestrategic supply chain resource allocation decisionsfor the simulated wireless telecommunications firmevery six months over a seven-year period. The sec-ond session consisted of a discussion of the resultsfrom the simulation exercise and how these resultscompared to actual company experiences and aca-demic research. While the two-session structure forthis exercise may have its downsides (the sessionswere actually on different days), it does have theadvantage of providing time for the instructor to ana-lyze and organize the simulation results for presenta-tion and discussion in the second session.As stated in the Introduction, the educational goal

of the simulation exercise is to have the students expe-rience the value added of making strategic decisionsin a firm’s service supply chain with and withoutan enterprise system. In a pilot test of the simulator,we found students tended to go straight to the sim-ulator without reading the case and instructions. Toencourage the students to read the case and instruc-tions and develop their thoughts first, we had the


Table 4 A Flow of the Simulation Exercise

students start the experience, as we describe in Table 4,by opening a web page, where we: (1) used formsto capture demographic data, (2) presented the casestudy materials seen in Appendix C, and (3) gave theinstructions about how to open and run the simula-tor seen in Appendix D. Then the students clicked alink that opened the simulator that had been installedon their computer. These materials were also avail-able to the students for reference within the simulator.All materials provided to the students are providedin the Appendices, including links to the simulationmodel, in Appendix E. To promote both focused effortand exploration, the students were asked: (1) to dotheir best to maximize the value they created forthe simulated company, and (2) to explore differ-ent strategic hypotheses about how to achieve thebest supply-chain-wide consequences. The studentswere instructed to ask at any time if they had anyquestions.

5.1. First SessionAt the beginning of this exercise, all students receivedthe same information. Specifically, they were givendetails about the case and simulation procedures. Noprior preparation on ES was given. Students workedindividually. The students started by entering demo-graphic information about themselves, for example,work experience. Several screens in the simulatorwalked them through the case description and exerciseinstructions (see Appendices C and D). Each studentwas randomly assigned to either the with ES or with-out ES simulation scenarios. After this, they ran thesimulation, in which they were provided the informa-tion shown in Table 2 and allowed to allocate financial

resources to: (1) ordering base stations, (2) hiringhuman resources, (3) firing human resources, (4) train-ing human resources, and (5) acquiring service sup-porting information technology. The debriefing wasconducted in a subsequent session. A flow of theentire exercise is given in Table 4. Table 4 also in-cludes the time it takes per section of the exercise andthe materials required. These times were validatedduring the exercise by recording the actual time takenby each participant in each section.We tracked the economic value created by each stu-

dent in the simulator using the widely used measureMarket Value Added (MVA) (Ehrbar 1998). Althougheach scenario ran for seven simulated years, we usedthe MVA at the end of the fifth year as the perfor-mance measure in order to avoid end game effects.

5.2. Second (Debriefing) SessionThe debriefing session opened with a general discus-sion about the students’ experience with the simu-lation exercise. From their comments and questions,we found that the students were most interested in:(1) mapping the ES impacts on the business flowof materials and information; (2) understanding theinfluence of these ES impacts on organizational per-formance; (3) understanding how ES influence thequality of strategic decisions; (4) seeing the wholefirm and its supply chain; (5) focusing on a serviceapplication; and (6) the learning approach (i.e., usinga computer simulation exercise). The issues surfacedin the discussion we led in the debriefing session andfrom comments on written evaluations.Next, we focused on the student’s experience of

the simulator exercise. What did they notice? What


seemed to influence their performance? What didthey want to achieve and how were they helped orhampered by the information they were provided?We have noticed common responses to these ques-

tions. The students typically notice that: (1) it isharder to create value for the firm than they thoughtafter reading the case study; (2) delays in investing ininfrastructure slow down market growth; and (3) theyhave to invest in infrastructure, human resources, andtraining to keep customers satisfied, so they can main-tain market share with the rising market demand.They tend to notice that their performance is mostinfluenced by: (1) when they invest in infrastructure;(2) the information they have about what is going on;and (3) how much they sustained their investment ingrowth of infrastructure and human resources. Whendiscussing what they wanted to achieve, two campsemerge, focusing on either: (1) rapid growth of eco-nomic value added, at all costs; or (2) sustainablegrowth in financials and customer satisfaction. Theytypically comment that having what appeared to bereal time data helped see the consequences of theiractions more clearly, while seemingly delayed infor-mation hampered seeing these consequences. Also,having integrated information helped them see thelinkages across the supply chain, versus seeing onlywhat was happening in each function.We then present the statistical results to compare

their experiences of having or not having an ES. Herewe focus on comparing the experiences and results thestudents had in the two groups (“with ES” and “with-out ES”). Results are presented in the form of sim-ple descriptive statistics and as a regression analysis.The regression analysis included fifth year MVA asthe response, the simulator scenario as a predictor,and a number of other factors controlling for differ-ent types of work experience amongst the participants(e.g. years of managerial work experience, and yearsof experience in the telecommunications industry). Weexplore whether the differences in results between thegroups make sense and why. Note, while it may seemdesirable to have each student do both scenarios (i.e.,with and without an ES), we have found that it isnot an absolute necessity when this type of post gamecomparison of the results is used because patterns arequite likely to emerge from the aggregated data inthe statistical analysis. A single scenario per studentis not only more economical in terms of time, it alsoavoids additional complications such as the need tocorrect for a learning effect.The analysis and comparison of the results segues

nicely into a conversation about the impacts of anES on a firm, discussing the impacts of ES on busi-ness flows of materials and information, organiza-tional performance, strategic decision making quality,and seeing the whole firm and its supply chain. Here

we introduce a discussion on studies found in the aca-demic literature and the popular press. This includesa discussion of the mixed results and possible rea-sons for these mixed results. This discussion includesnegative ES impacts on business performance, suchas the loss of flexibility, loss of competitive advan-tage, training difficulties, cost overruns, and otherimplementation difficulties (Griffith et al. 1999, Hongand Kim 2002, Songini 2004). Additionally, we discussthe results emerging in carefully designed ES-impactstudies (Hitt et al. 2002, McAfee 2002), that findpositive business performance from better systemsintegration, standardization of data and processes,end user visibility across the business enterprise, andimproved decision support functionality. Such a dis-cussion provides the opportunity to talk about howhard it is for researchers to assess the impact of ESon business performance from empirical data sets rel-ative to a carefully designed simulation experiment.We also take the opportunity to discuss complexitiesnot captured by the simulation exercise such as thelearning curve effect documented by McAfee (2002).

6. Concluding DiscussionWe have presented a simulation exercise to help stu-dents assess the impact of a successfully implementedES on the service supply chain of a telecommu-nications firm. We have found that the simulationexperience and the subsequent debriefing providea valuable learning experience for both studentsand instructors. Our overall experience with suchexercises is that the students find them to be ofgreat educational value (Anderson and Morrice 2000,Bates 2002).The over-arching principles that guided the devel-

opment of this simulation exercise were instructionalbrevity and ease of use because we only have a lim-ited amount of time to cover such topics in our supplychain and information management courses. Fromdiscussions with other instructors, executive educa-tion consultants, and textbook authors, we recognizedthat this was not unique to our program. The exer-cise requires a standard PC withMicrosoft Windows, anexecutable version of the simulator and an isee Playerfrom ISeeSystems http://www.iseesystems.com/ torun iThink simulation models (see Appendix E).As we mentioned, the simulation exercise can be

completed in three to six hours of class time. Wepresented a three-hour example of a simulation exer-cise that works well in our supply chain manage-ment course. Many other configurations are possible.For example, with more class time, each studentcould participate in multiple simulation scenariospossibly providing an even richer educational expe-rience. We have done such things with other simula-tion exercises. However, when students perform more

http://www.iseesystems.com/


than one simulation, care must be taken to accountfor learning effects (and the corresponding statisticalbiases) if the simulation results are to be used in asubsequent debriefing session (Anderson and Morrice2000).Although the statistical results discussed in §5.2

are rich in educational value and provide a basisfor excellent discussion, much more could be donewith the data collected from the simulation exercise.The simulator tracks the performance of each studentover time. Analysis of this data provides the opportu-nity to discuss dynamical decision making behaviorand how this impacts business performance. There isa strong and growing literature in this area due tothe interest in supply chain dynamics (Sterman 2000).Based on our experience, we do need to add one word

Appendices

Appendix A: Expert Panel for Simulator

of caution at this point. Including a statistical anal-ysis of the simulation dynamics requires more time,care, and statistical sophistication on the part of theinstructor and the students (it generally requires someknowledge of time series analysis). Nevertheless, thisanalysis can be valuable because it often helps toexplain the patterns observed in the more “static” sta-tistical analysis mentioned in §5.2.

AcknowledgmentsWe thank the AE and three reviewers for feedback that sig-nificantly increased the teaching value of this paper. Ourresearch was supported in part by a grant from the SAPAmerica University Alliance program, a fellowship from theUniversity of Texas at Austin, software from ISee Systems(previously High Performance Systems), and funding fromthe Institute for Strategic Clarity.


Appendix B: Simulator Equations

This section provides the equations, in system dynamics format (Sterman 2000) of the simulator used in the experiment ascoded in the software iThink 7.0.1 (ISeeSystems 2001). Due to space considerations, this section does not include the lines ofcode for the experimental manipulations used to alter whether the subjects had access to an enterprise system or not. Fora complete description of the simulator with full documentation of each equation, see Ritchie-Dunham (2002).


Appendix C: On-Line Case Study Description

The students were presented the following case study on-line in the simulator.


Figure 1 A Cell in a Cellular Network

Consultant ReportThe map below describes the business cycle for LejacordWireless, in general terms, for the supply chain external andinternal components.External Components. Following the logic of the external

value chain, in the Customer Satisfaction component, thefirm provides services to customers for a fee. The customerchooses to continue with the firm, if satisfied with the ser-vice provided for the price paid. Satisfaction is measuredrelative to both the competitive offering in the marketplaceand the customer’s disposable income dedicated to wirelesstelephone services. Satisfaction is measured as a utility func-tion of the price paid for a perceived level of call qualityand customer service. We assume demand for wireless ser-vices will continue its strong growth, with the customer’saverage disposable income remaining about the same. Inthe Customer Base component, relative Customer Satisfac-tion affects movement of customers between the firm andthe competition.Internal Components. Following the logic of the internal

value chain, the Financials component takes the revenuesfrom customers and operating costs from the internalresources and calculates the financial statements. TheInvestment Decisions component applies your capital allo-cation decisions to technology, human resources, and ser-vice supporting IT. In the Technology component, suppliersadd base stations to the basic infrastructure, providinga level of network coverage and quality. The HumanResources component describes the dynamic of humanresource skills as employees are hired or fired, and as theyare trained or their skills are made obsolete by changingtechnologies. The Service Supporting Information Technol-ogy component depicts the dynamics of the informationsystems that support the call centers.

Cellular Network DescriptionAlthough different technologies exist, all cellular networkshave the same basic structure. A cell, consisting of a base

Figure 2 A Network of Cells

station (tower) with a certain range of coverage, representsthe basic unit in the cellular network. Figure 1 depicts acell with the tower at the center and a circular region ofcoverage.Each cell has a maximum capacity measured in chan-

nels. The number of channels determines the number oftelephone connections that can be handled simultaneouslyin a base station’s coverage area. To increase coverage andcapacity, a wireless telecommunications company constructsa network of cells (see Figure 2). Since adjacent cells overlapslightly, the cells must be designed so that the same chan-nels in different cells are not adjacent. Otherwise, differenttelephone connections using the same channel in differentcells might interfere with one another.A Mobile Telephone Switching Office (MTSO) represents

the heart of a cellular network. The MTSO interfaces withall the base stations in the network through landline cableconnections. Additionally, it connects the network to PublicSwitched Telephone Networks (PSTN) such as Southwest-ern Bell and other cellular networks. Therefore, all tele-phone traffic between the cellular network and the PSTN orother networks passes through the MTSO.A cellular network is constructed in stages by adding

more cells to increase coverage and capacity. The construc-tion of each cell requires base station installation and con-nection of the base station to the MTSO through a landlinecable connection. If construction crews are available, theconstruction of a cell takes 30 days and costs $300,000. Thecell is not functional until construction is complete.The subjects were provided the following summary of

the case study, inside the simulator, to refer to during theexperiment.


Appendix D: On-line Simulator Instructions

The subjects were presented the following instructions about running the simulator on-line. All subjects were given thesame set of instructions.


Appendix E: Running the SimulationTo run the simulation, download an executable versionof the simulator http://archive.ite.journal.informs.org/Vol7No3/Ritchie-DunhamMorriceAndersonDyer/lejacordwireless.exe (also available at http://www.instituteforstrategicclarity.org/what_is.htm#Cases) anddownload and install an isee Player from ISeeSystems http://www.iseesystems.com/ to run iThink simulation models.The isee Player can be downloaded for free from http://www.iseesystems.com/softwares/iseeruntime/default.aspx.

ReferencesAkkermans, H. A., P. Bogerd, E. Yucesan, L. N. v. Wassenhove. 2003.

The impact of ERP on supply chain management: Exploratoryfindings from a European delphi study. Eur. J. Oper. Res. 146284–301.

Andersen. 1999. Enterprise Business Solutions: The Andersen Consult-ing Survey of Chief Executive Officers. Andersen Consulting Insti-tute for Strategic Change.

Anderson, E. G., D. J. Morrice. 2000. A simulation game for service-oriented supply chain management. Production Oper. Manage-ment 9 40–55.

Bates, J. 2002. Littlefield technologies: I love this game! Texas Busi-ness Weekly, http://www.texasbusinessweekly.com/media/paper480/news/2002/11/06/News/Littlefield.Technologies.I.Love.This.Game-315651.shtml (last accessed on November 6,2002).

Davenport, T. H. 2000. Mission Critical: Realizing the Promise of Enter-prise Systems. Harvard Business School Press, Cambridge, MA.

Ehrbar, A. 1998. EVA: The Real Key to Creating Wealth. John Wiley &Sons, Inc., New York.

Ettlie, J. E. 2000. Technology and weak appropriation conditions:The adoption of resource planning systems. Paper presented atthe Institute for Operations Research and Management Science,Salt Lake City, Utah.

Forrester, J. W. 1961. Industrial Dynamics. Productivity Press, Cam-bridge, MA.

Gattiker, T., D. Goodhue. 2000. Understanding the plant level costsand benefits of ERP: Will the ugly duckling always turn intoa swan? Proc. Thirty-Third Annual Hawaii Internat. Conf. SystemSci.

Gefen, D., A. Ragowsky. 2005. A multi-level approach to measuringthe benefits of an ERP system in manufacturing firms. Inform.Systems Management 22(1) 18–25.

Griffith, T. L., R. F. Zammuto, L. Aiman-Smith. 1999. Why newtechnologies fail. Indust. Management (May–June) 29–34.

Hitt, L. M., D. J. Wu, X. Zhou. 2002. Investment in enterpriseresource planning: Business impact and productivity measures.J. Management Inform. Systems 19(1) 71–98.

Hong, K.-K., Y.-G. Kim. 2002. The critical success factors forERP implementation: An organizational fit perspective. Inform.Management 40 25–40.

ISeeSystems. 2001. Ithink (Version 7.0.1), Hanover, NH.Mabert, V. A., A. Soni, M. A. Venkataramanan. 2000. Enterprise

resource planning survey of US manufacturing firms. Produc-tion and Inventory Management J. 41(2) 52–58.

Mabert, V. A., A. Soni, M. A. Venkataramanan. 2001. Enterpriseresource planning: Measuring value. Production and InventoryManagement J. 42(3/4) 46–51.

Markus, M. L., C. Tanis. 2000 The enterprise systems experience—From adoption to success. R. W. Zmud, ed. Framing the Domainsof IT Management: Projecting the Future Through the Past. Pin-naflex Educational Resources, Inc., Cincinnati.

McAfee, A. 2000. The impact of enterprise information systemson operational effectiveness: An empirical investigation. Ph.D.thesis, Harvard Business School, Boston.

McAfee, A. 2002. The impact of enterprise technology adoption onoperational performance: An empirical investigation. Produc-tion Oper. Management 11(1) 33–53.

Meta Group 1999. ERP implementation study reveals costs, bene-fits. APICS—The Performance Advantage. October.

Morecroft, J. 1983. Concepts, theory, and techniques: A systems per-spective on materials requirement planning. Decision Sci. 14(1)1–18.

O’Leary, D. 2000. Enterprise Resource Planning Systems: Systems LifeCycle, Electronic Commerce, and Risk. Cambridge UniversityPress, New York.

Reagan-Cirincione, P., S. Schuman, G. P. Richardson, S. A. Dorf.1991. Decision modeling: Tools for strategic thinking. Interfaces21(6) 52–65.

Ritchie-Dunham, J. L. 2002. Balanced scorecards, mental models,and organizational performance. Ph.D. thesis, Department ofManagement Science and Information Systems, University ofTexas at Austin, Austin, Texas.

Ritchie-Dunham, J. L., D. J. Morrice, E. G. Anderson, J. Scott. 2000.A strategic supply chain simulation model. J. A. Joines, R. R.Barton, P. A. Fishwick, K. Kang, eds. Proc. 2000 Winter Sim-ulation Conf., Institute of Electrical and Electronics Engineers,Piscataway, New Jersey.

Robinson, P. 2005. Business Excellence: The Integrated Solution to Plan-ning and Control. BookSurge Publishing, North Charleston, SC.

Sargent, R. G. 1999. Validation and verification of simulation mod-els. P. A. Farrington, H. B. Nembhard, D. T. Sturrock, G. W.Evans, eds. Proc. 1999 Winter Simulation Conf., Institute of Elec-trical and Electronics Engineers, Phoenix, Arizona.

Segev, E. 1987. Strategy, strategy-making, and performance in abusiness game. Strategic Management J. 8 565–577.

Senge, P. M. 1990. The Fifth Discipline. Doubleday Currency, NewYork.

Sharda, R., S. Barr, H. McDonnell, C. James. 1988. Decision supportsystem effectiveness: A review and an empirical test. Manage-ment Sci. 34(2) 139–159.

Songini, M. L. 2004. Ford abandons oracle procurement system:Switches back to mainframe apps. Computerworld 7(August 23).

Sterman, J. D. 2000. Business Dynamics: Systems Thinking and Model-ing for a Complex World. Irwin McGraw-Hill, Boston.

cite:here

cite:here

http://archive.ite.journal.informs.org/Vol7No3/Ritchie-DunhamMorriceAndersonDyer/lejacordwireless.exe



http://www.instituteforstrategicclarity.org/what_is.htm#Cases

http://www.instituteforstrategicclarity.org/what_is.htm#Cases



http://www.iseesystems.com/softwares/iseeruntime/default.aspx

http://www.iseesystems.com/softwares/iseeruntime/default.aspx

http://www.texasbusinessweekly.com/media/paper480/news/2002/11/06/News/Littlefield.Technologies.I.Love.This.Game-315651.shtml



LittleField Technology case

Documents

Transcript of LittleField Technology case