Decision Sciences Journal of Innovative EducationVolume 5 Number 2July 2007Printed in the U.S.A.

TEACHING BRIEF

Parallel Interaction Supply Chain Game:An Extension of the Beer Game

Pedro M. ReyesHankamer School of Business, Baylor University, One Bear Place #98006, Waco, TX76798-8006, e-mail: pedro reyes@baylor.edu

INTRODUCTION

As the subject of supply chain management (SCM) continues to grow, so too thechallenges of teaching its concepts. While the Beer Game has become one of themost popular simulations used in the classroom to help students understand theobstacles to effective SCM, it is possible to extend its original purpose to teachvarious topics in SCM. In my courses over the past few years, the basic Beer Gamehas evolved from a simple linear supply chain to a more complex parallel interactionsupply chain. This extension was initially designed to illustrate the rationing andgaming as a cause of the bullwhip effect, but has recently evolved to also serveas a means to introduce the students to various SCM topics. These topics (such asspeculation/postponement, risk-pooling, control systems, and technology in supplychain integration) result from the students’ final debriefing session (Phase 5) wherethe students are brainstorming ideas for solving the operational issues discovered.

Instead of a 1-day overview of the SCM course, I provide the students with ahands-on introduction to SCM. The strategy that I use is a five-phase approach andspans five 80-minute classroom time slots. This multiphase approach is designedto first introduce the students to operational issues in SCM and then leads them toan overview of the tactical and strategic concepts that will be explained throughoutthe semester. Phase 1 serves as an introduction to the operational issues in SCM. InPhase 2, a version of the basic Beer Game is simulated. Phase 3 then focuses on aconceptual discussion the operational efficiencies observed and the bullwhip effectand concludes with a brief introduction to the extended simulation. In Phase 4,a parallel interaction supply chain game is simulated to introduce the studentsto the rationing and gaming effects that are not explicitly seen in the basic BeerGame. Finally, in Phase 5, by debriefing, we discuss the possible tactical andstrategic solution to those operational issues. This hands-on multiphase approachto introduce the students to the SCM curriculum has served to be beneficial forreinforced learning throughout the semester.

INSTRUCTOR INSTRUCTIONS

Phase 1: Introduction to Operational Issues in SCM

In the first phase, the students are provided with an overview of the challengesand obstacles related to SCM. This phase includes an introduction to SCM and

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the important elements: purchasing, operations, distribution, and integration. Theprimary objective is to make the students conscious of how the basic operationsmanagement decisions (inventory, capacity, process, and quality) are interrelatedat the SCM level.

Phase 2: Simulation of the Basic Beer Game

In Phase 2, I have the students participate in a modified version of the basic BeerGame. While no preparation by the students is required, I assign an optional readingassignment after Phase 1 that serves as a link to Phase 2 (see Appendix A1) and ishelpful in explaining the rules and procedure. We assume the reader is familiar withthe basic Beer Game (Sterman, 1989) and therefore refer the reader to AppendixA2 for a brief explanation of the modifications. At the end of the simulation Icollect the data collection forms, which I use to create graphs used in Phase 3.Although not shown here, the graphs are similar to those found in Sterman (1989).The students are encouraged to share their observations of the simulation amongthemselves outside of class in preparation for Phase 3.

Phase 3: Debriefing and Discussion of the Basic Beer Game

In this phase, I use the students’ data from Phase 2. I find that by using their dataand graphs leads to healthier discussion. The first discussion is on the observedoscillations and bullwhip effect. This is then followed by the students’ perceivedcauses of the bullwhip effect and distorted information. SCM inefficiencies arefurther discussed.

Phase 4: Simulation of the Parallel Interaction Supply Chain Game

In this phase, I introduce the students to the parallel interaction supply chain game asan extension to the basic Beer Game. Initially this was designed to see how rationingand gaming causes distorted information in the supply chain and leads to thebullwhip effect. However, this has evolved to demonstrating the parallel interactionsfound in a supply chain. A network design for this simulation consists of twocustomers (Ci), two retailers (Ri), one distribution center (D), two manufacturers(Mi), and three vendors (Vi) (see Appendix A3). Lego building blocks are used torepresent the materials being assembled.

The playing procedure follows the basic Beer Game played in Phase 2 (referto setting up the game in Appendix A4). Customer demand for two homogeneousproducts is controlled with the use of random numbers. I generate two demandstreams, both with the same average demand (e.g., λ = 6). Product substitutionat the retail store is not allowed; therefore, any unsatisfied customer orders at theretail store is considered lost sales. Each retailer (Ri) inventories the two products:beginning with 15 units each of red-white and blue-white units. There is a maximumshelf space capacity of 30 units, but the shelf space allocation for each productremains at 15 units each. We assume the distribution center is owned by the samefirm as the retail store. The distribution center also inventories both products, witha maximum warehouse capacity of 60 units.

The two competing firms (Mi) produce the two different products—M1 pro-duces red-white units and M2 produces blue-white units. The manufacturers have

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capacity to assemble the Lego pieces (or produce) 15 units each period. The threevendors (Vi) provide the raw materials to the manufactures: V1 supports M1 withred raw material, V3 supports M2 with blue raw material, and V2 produce a white(base) component and supports both M1 and M2. We could assume infinite capacityfor both V1 and V3 and limit V2, but I impose capacity constraint of 30 units forall Vi. The results are about the same. Data collection is required (forms will varydepending on player, see example in Appendix A5). This simulation is played ata slow pace to make sure all of the transactions are being recorded and typicallyruns for about 1 hour. Costs are recorded in this simulation: holding cost = $1 andout of stock cost = $2 for each period. At the end of the simulation I collect thedata collection forms, which I use to create graphs used in Phase 5.

Phase 5: Debriefing of Phase 4 and Introduction to VariousSCM Concepts

In this final phase I use the graphical results from Phase 4 to discuss the dynamicsof parallel supply chains (see Appendix A6 for outcomes of game). An importantlesson not easily seen in the traditional Beer Game is the effects of rationingand gaming. This parallel interaction supply chain game was initially designed tohelp the students observe the effects of rationing and gaming (a few examples areprovided in Appendix A7). This is observed in the Ri and D interactions and Mi

and V2 interactions. This extension of the basic Beer Game is also used to discussthe inventories inefficiencies caused by parallel interactions in the supply chain.For example, mismatched inventories found at Mi due to V2’s allocation, whichstalls production.

Of particular interest is how V2 allocates raw material to both manufacturers(and similarly the distribution center allocate products to both retailers) reactingand supporting various demand rates. With exception to a few spikes in demand,both manufacturers’ demand stream to vendor two was relatively about the same.However, while manufacturer one was receiving at or near 100% fulfillment, man-ufacturer two was constantly receiving less allocation. This could explain why theretail stores could maintain adequate stock levels for product two.

This parallel interaction supply chain game (in my classes) has been usefulto provide a hands-on overview of various SCM topics. During this debriefing, thestudents are asked to brainstorm ideas for solving these operational issues, whichthey identify in layman’s terms and I translate into the various topics that will becovered during the semester. Such topics include demand planning, risk pooling atthe company-owned distribution center, and the position of the decoupling point.Control systems such as just-in-time, enterprise resource planning, and vendor-managed inventories are also discussed; and I add speculation/postponement as aviable strategy. Finally, we discuss the uses of technology and SCM integration—point-of-sale information, radio frequency identification (RFID) tags, electronicdata interchange (EDI), and e-commerce.

DISCUSSION

The value that I have found in this parallel interaction supply chain game is that itpresents itself to be a more realistic representation of SCM and is highly effective in

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helping students understand the complexity and dynamics of parallel supply chains.By having the students participate in a hands-on introduction to the SCM coursethey deduce the topics to be studied during the semester and enhance the reinforcedlearning as we progress from identifying the operational issues and examining thepotential tactical and strategic solutions.

Students’ comments have included: “Overall, I think it was a good experiencebecause we saw the inefficiencies of a complex supply chain . . . when constrainedby resources;” “Using our own data and not the book’s, the bullwhip effect iseasily seen;” “The gaming aspect was probably what I gained the most from inthis simulation;” and “The variations included in the second simulation, as well asthe added knowledge on supply chains that I now have made this simulation muchmore interesting than the first.”

This multiphase is an approach that I have been using for the past 3 years.Before that, I had used the parallel interaction supply chain game toward the endof the semester to apply and highlight what had been presented throughout thesemester. I changed my approach to stimulate interest early and then recall thegame for reinforced learning throughout the semester.

Excel spreadsheet forms, past experiment results, and student comments areavailable from the author by e-mail on request for those interested in using thisgame.

REFERENCES

Lee, H., Padmanabhan, V., & Whang, S. (1997a). The Bullwhip effect in supplychains. Sloan Management Review, 38(3), 93–102.

Lee, H., Padmanabhan, V., & Whang, S. (1997b). Information distortion in a supplychain: The Bullwhip effect. Management Science, 43, 546–558.

Reyes, P., Raisinghani, M., & Singh, M. (2002). Global supply chain managementin the telecommunications industry: The role of information technology in in-tegration of supply chain entities. Journal of Global Information TechnologyManagement, 5(2), 48–67.

Sterman, J. (1989). Modeling managerial behavior: Misperceptions of feedbackin dynamic decision making experiment. Management Science, 35, 321–339.

APPENDIX

A1: Optional Reading Assignment

Prior to Phase 2 Lee, H., Padmanabhan, V., and Whang, S. (1997a); Lee, H.,Padmanabhan, V., and Whang, S. (1997b); Sterman, J. (1989)

Prior to Phase 5 Reyes, P., Raisinghani, M., and Singh, M. (2002)

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A2: Modifications to the Basic Beer Game Playing Environment

Sterman’s StockManagement Experiment Modification Purpose

Linear game consist of foursectors: retailer,wholesaler, distributor,and factory

I rename these four sectors:retailer, distributor,manufacturer, and vendor

In order to include asupplier of raw materialsto the manufacturer(factory) withoutincreasing the number ofsectors.

Customer demand beginsat a constant four casesand then increases to aconstant eight cases.

I use a predetermineddemand stream for thecustomer demand, whichI generate using arandom numbergenerator with Excel(e.g., λ = 6)

This allows for varyingcustomer demand, andalso controlled with anaverage demand (which Ican adjust for eachgame).

Game is initialized inequilibrium, includingpipeline inventories.Customer demand isknown to be four casesper week, and eachplayer orders four casesper week.

Opening inventories ateach level is set at twotimes λ, and does notinclude safety stock. I donot start the game withpipeline inventories.Customer demand (λ) innot announced.

Students are forced to use ademand forecastingmethod learned in theprerequisite OperationsManagement course.

Two week time lag fororder processing andorder delivery.

The lead-time for orderprocessing is one week(not two), but I maintainthe two-week deliverytime lag.

Current informationtechnology makes a oneweek order processinglead-time more realistic.

Backorders are allowed atall levels.

Backorders are allowed atall levels except theretailer.

Any unfilled orders at theretailer are consideredlost sales.

No apparent capacityconstraints.

I do not impose anycapacity constraints, but Ido instruct the vendor tofollow a build-to-forecastinventory policy.

Players are inherentlyconstrained by theirsupplier’s ability to fulfillorders, except for thevendor. In this case, thevendor is thereforeconstrained by theirability to forecastdemand. Hence, thevendor is may only buildwhat was forecasted ineach period.

Costs are recorded at eachlevel.

Students are reminded ofrelated holding andout-of-stock costs, butare not recorded in thisfirst simulation.

Prefer the student focus onservicing the customer.Strategic objectives areto be discussed in thesecond simulation.

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A3: The Parallel Supply Chain Interaction Game Network

A4: Setting Up the Parallel Interaction Supply Chain Game

1. Prior to the start of the game generate two demand streams for each of the customers. Igenerate random numbers using the tools function in Excel. Hence, C1 and C2 willeach have two demand streams—one for each of the two homogeneous products.

2. Assign students to play the different roles of the game.3. Instruct the students that this game follows the playing procedure from the basic Beer

Game (from Phase 2).4. Distribute the Lego game pieces to begin the game as follows:

• Each retailer (Ri): 15 of each product• The distribution center (D): 30 of each product• Each manufacture (Mi): 15 of each product• Each vendor (Vi): 20 of their respective raw material

5. Constraints for each player (per period)• Each retailer (Ri): maximum shelf-space capacity of 30 units• The distribution center (D): maximum warehouse-space capacity of 60 units• Each manufacture (Mi): maximum production capacity of 30 units• Each vendor (Vi): maximum production capacity of 30 units

A5: Data Collection Form for the Parallel Interaction Supply Chain Game(Retailer)

Player: Retailer

Receive Place Customer Customer InventoryInventory Order Demand Order Filled On Hand

Week Red Blue Red Blue Red Blue Red Blue Red Blue

123...50

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A6: Outcomes for the Parallel Interaction Supply Chain Game

1. How the “rationing” and allocation rules when demand exceeds capacity influencesthe other channel members behavior (and resulting decisions).

2. How the “gaming” behavior influences the behavior of the other channel members.3. How the miss-matched inventories affect the flow of materials and information in

the supply chain.4. Demand planning techniques.5. How capacity influences supply chain behavior.

A7: Graphical Examples of Typical Results from the Parallel InteractionSupply Chain Game

Retail 1 Orders Placed and Receipts for Product 2

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P2_Ordered P2_Received

Retail 2 Orders Placed and Receipts for Product 1

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P1_Ordered P1_Received

Retail 2 Orders Placed and Receipts for Product 2

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P2_Ordered P2_Received

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Distributor Demand and Shipped Orders for Product 1

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D_P1 S_P1

Distributor Demand and Shipped Orders for Product 2

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D_P2 S_P2

Vendor 2Demand and Orders Shipped to Manufacturer 1

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D_M1 S_M1

Vendor 2Demand and Orders Shipped to Manufacturer 2

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D_M2 S_M2

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Pedro M. Reyes received his PhD in Operations Management, an MBA in Op-erations Management, an MS in Information Systems, and a BS in Mathematicsfrom the University of Texas at Arlington (UTA). He was recognized by the UTACollege of Business as a Lawrence Schkade Research Fellow, received the Han-kamer School of Business Young Researcher Award, and is an Affiliate of SloanIndustry Studies. His research interest consists of the use of RFID in supply chainoperations.