I N F O R M STransactions on Education

Vol. 10, No. 3, May 2010, pp. 105–112issn 1532-0545 !10 !1003 !0105 informs ®

doi 10.1287/ited.1100.0045©2010 INFORMS

The Mexico-China Sourcing Game:Teaching Global Dual Sourcing

Gad Allon, Jan A. Van MieghemKellogg School of Management, Northwestern University, Evanston, Illinois 60208

{g-allon@kellogg.northwestern.edu, vanmieghem@kellogg.northwestern.edu}

We describe a three-hour class on global dual sourcing built around a game that demonstrates the challengesin making operational decisions, and transfers recent academic insights to the classroom. Student teams

manage a firm with access to a responsive but expensive supply source (Mexico) and a cheap but remote source(China). Each team must determine a sourcing strategy to satisfy random demand that is revealed throughoutthe game. In each period, teams place orders to both sources and manage two assets: inventory and their bankaccount. The goal is to maximize each team’s value (final bank balance). During the debriefings, we analyze thepolicies used by different teams along both financial and operational metrics, present the optimal strategy, andsummarize the experiential learning points.

Key words : dual sourcing; strategic sourcing; experiential learning; inventory management; total landed cost;simulation game

History : Received: July 24, 2009; accepted: March 1, 2010.

1. IntroductionIn many retail settings, and business in general, firmscan source goods from multiple suppliers. We inves-tigate a typical choice between two suppliers: One islow cost but has long lead times whereas the otherprovides quick response but at higher cost. This clas-sical problem is faced by many companies and moststudents understand its relevance. However, effectivemanagement of dual sourcing is surprisingly chal-lenging and conveying its complexity through tra-ditional means such as a case study or a lecture isdifficult: Solving a dual-sourcing case requires ana-lytic tools that are not readily available to students orinstructors. A lecture on dual sourcing runs the riskthat students may not appreciate the added level ofcomplexity in day-to-day, as well as strategic, dualsourcing relative to single sourcing.We have developed a sourcing game as an expe-

riential learning tool that addresses the above short-comings. In this game, students play the role ofsourcing managers who must make strategic alloca-tion decisions as well as place day-to-day orders totwo suppliers. During this process, they experiencethe operational, financial and service related conse-quences of their decisions.The sourcing game is designed with four pedagog-

ical objectives in mind:(1) to develop intuition on possible sourcing strate-

gies and to appreciate simple, robust policies to guidesourcing decisions;

(2) to highlight the role of working capital estima-tion in the concept of total landed cost;(3) to appreciate the added complexity of manag-

ing a supply portfolio over single sourcing;(4) to understand the futility of guessing and over-

reacting to demand.The game has been used with MBA and execu-

tive education students and although some knowl-edge of basic inventory models is useful, it is not nec-essary. In the remainder, we will describe sequentiallythe three-hour class we have taught in three parts:Part A introduces global sourcing and related theory,Part B describes the game, and Part C summarizesthe debriefing phase as well as a discussion of recentacademic research. We conclude this paper by sum-marizing student reactions and possible extensions.

2. Class Part A: Introduction toGlobal Sourcing

This section describes the first part of the classthat introduces global sourcing and related theory(about 30 to 45 minutes of class time).

2.1. The Motivating CaseAs preparation for the class, the students are askedto read the Mexico-China minicase in Van Mieghem(2008). The case describes a $10 billion high-tech U.S.manufacturer of wireless transmission components.The company was at a crossroads regarding its global

105

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual Sourcing106 INFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS

network; the case focuses on its two assembly plants,one in China and the other in Mexico. Although theChinese facility enjoyed lower costs, ocean transporta-tion made its order lead times 5 to 10 times longerthan those from Mexico. With highly uncertain prod-uct demand—coefficients of variations of monthlydemand for some products were as high as 1.25—solesourcing was unattractive: Mexico was too expensiveand China too unresponsive.The firm had to decide how it could best utilize

these two sources. At the strategic level, this amountsto properly allocating product demand to each source.Strategic allocation refers to the expected cumula-tive demand allocated to each source over the plan-ning horizon. At the tactical level, the firm had tochoose a dynamic ordering policy that implementsthat strategic allocation at lowest cost. In practice,specifying strategic allocations and ordering policiesare key tasks of any sourcing strategy—be it globalor domestic—because it affects costs and suppliermanagement.

2.2. Discussion on PracticeTo connect the game to real sourcing practices, andif students have prior experience with similar set-tings, we start with an open discussion covering twoquestions:(1) What policies have you used in practice? This

discussion typically reveals some of the followingpractices:

(a) The allocation of products to plants is basedsolely upon historic allocations (e.g., new productswithin a family are allocated to plants in the exactsame fashion as existing products in that familyregardless of supply and demand characteristics).

(b) The allocation of products to plants is basedupon internal politics (e.g., “pet” production facilitiesget higher volume, lower complexity products to keeputilization high and cost structure low).

(c) The allocation of products to plants is basedupon basic understanding of costs (e.g., China’scost base is lower—thus source full requirementsoffshore).

(d) Product allocations follow a simple primary-secondary allocation (e.g., if primary locationcannot fulfill demand—requirements cascade to thesecondary location).

(e) Product-to-plant allocation decisions are rare-ly revisited or adjusted (e.g., allocation decision is a“life sentence”).(2) What data or metrics are used to make sourcing deci-

sions? This discussion follows the practices above andhighlights the typical cost, lead times, and service riskmetrics. The instructor then can ask the natural ques-tion: How can we combine these various componentsinto a single metric? This brings us to the concept oftotal landed cost.

2.3. The Concept of Total Landed Cost (TLC)To address the motivating case, the natural first step isto compare the two extreme solutions: single sourcingfrom Mexico versus single sourcing from China. Thisrequires accounting for the difference in cost of goodssold (COGS), shipping costs, duties, and working-capital requirements.1 For that purpose we review theconcept of total landed cost (TLC), which representsthe end-to-end cost to transform inputs at the sourceto outputs at destination. The TLC captures not onlythe traditional cost of goods sold but also accountsfor supply chain costs such as transportation, cus-toms, duties, and taxes, as well as required workingcapital carrying costs to achieve desired service lev-els and protect against supply and demand risks (seeFigure 1).We will refer to all but working capital cost com-

ponents as the sourcing cost. Computing the sourcingcost is tedious yet straightforward. In contrast, work-ing capital is driven by pipeline and safety inventory,which depend on lead times, volatility, and servicelevels.

2.4. Computing TLC for Single SourcingWorking capital and inventory requirements are eas-ily estimated for single sourcing using readily avail-able standard inventory formulae. Before moving todual sourcing and to the game, we remind the stu-dents of the concept of safety stock and compute thetotal landed cost for each of the locations separately.This computation reveals that single sourcing fromChina appears to be the favorite solution.Asking the students’ reaction to this analysis gen-

erates a discussion of the merits of sourcing fromMexico and of the expected benefits of dual sourcingin a quest to combine the strengths of both sources.This also leads to a discussion on the risk of using asingle source. We usually discuss two types of sup-ply risks: stochastic lead times and supply disruptions(such as natural disasters strikes.) This naturally leadsto the question of how to design an effective dual-sourcing policy, which is the transition point to thegame.

1 It should be noted that working capital includes the pipelineinventory as well as the safety stock. The pipeline inventorydepends on the transformation process, which takes place betweenthe moment of ordering and the moment of receipt of the order. Ifpart of the lead time is caused by inflexibility in production becauseof set-up times, then there is no per se investment in material dur-ing the lead-time period. Thus, in order to accurately assess theworking capital requirements one needs to know the precise timingof material inflow to build a sequence of cash flows over time.

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual SourcingINFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS 107

Figure 1 Total Landed Cost (TLC) Is the Total Cost Incurred From Source to Destination

Material Labor/service Overhead

Illustrative

Typical COGS Supply chain+ service

68%9%

8%

100%7% 3% 3% 2%

TLCOutboundfreight

Customs,duties, and taxes

Inboundfreight

Workingcapital

3. Class Part B: The Mexico-ChinaSourcing Game

This section covers the main part of the class bydescribing the different stages of the game (about 1 to112 hours of class time).

3.1. Game Setup (Mostly Done in Advance ofClass)

The idea behind the game is for students to act assourcing managers of a firm making periodic order-ing decisions for a new product. Demand is highlyuncertain and a probabilistic forecast is provided.Setting: A class with as few as 5 or as many as

60 students is divided into 5 to 10 groups.Props: Each student group needs (at least) one lap-

top computer. Each group receives a log-in code forthe website hosting the game.2 The instructor needsa laptop that is connected to the Internet and to aclassroom screen projector.Setup: Similar to the Mexico-China minicase, each

team represents an identical company that is intro-ducing a new product that can be sourced from Chinaor Mexico. Each team manages two assets: cash andinventory.The demand distribution for the product is known

and shown to all groups in advance. (The specific dis-tribution that we have used is gamma with mean 10and standard deviation 15, reflecting a highly volatiledemand.) The actual demand realization is identicalfor all groups and projected onto the classroom screendynamically over time (see Figure 2).Each team can place an order to Mexico and an

order to China in each period; sources differ in costand in lead times. Specifically, orders placed to Mex-ico have a sourcing cost of $8,000 per unit and arriveat the beginning of the subsequent period. Ordersplaced to China have a sourcing cost of $7,000 perunit but arrive only after four time periods. The

2 For access to the game, please contact the authors.

product is launched in the fourth period, allowingthe groups to build pipeline inventory from bothsources before sales start. The product sells for $10,000per unit. The starting version of the game assumesthat any unit demanded but not available on handis backlogged for a cost of $20 thousand per unitand each team plays in a separate yet identicalmarket.All teams start with zero money in the bank and

can borrow to finance inventory. Any bank balance(both negative and positive) incurs a 1% interest rateper period. All team bank balances are projectedon the classroom screen continuously (which allowsteams to compare their position to other teams andcreates a sense of competition and excitement). Anyleftover inventory at the end of the game is liqui-dated at zero value (although the instructor can easilychange the salvage value).The objective for the teams is to maximize their

firm value, which is their bank balance, at the end ofthe game. The termination time of the game is deter-mined by the instructor but is not told to students inadvance to prevent end-of-horizon effects.

3.2. Kickoff and Brainstorming (Between 15 and30 Minutes of Class Time)

The goal of this stage is to allow each group todevelop a sourcing strategy. Each group is givenan Excel spreadsheet that allows students to simu-late and test their strategies. Specifically, the spread-sheet shows how the pipeline inventory, sales, andbank balance evolve given user-entered orders anddemand.Groups are asked to think through how they would

react (in terms of placing orders), given a newdemand realization and a certain inventory status.To steer their thinking, each group is also askedto estimate the fraction of orders they will placeto China and to Mexico and hand this in to theinstructor.

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual Sourcing108 INFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS

Figure 2 During the Game, the Time as Well as Corresponding Demand and Bank Balances Are Projected on the Classroom Screen

Results for Period 24

Page reloads automatically

Period: 600 KTotal team value

Instructor options: Periods Results Edit demand/periods Edit teams Data download MASTER RESET

480 K

360 K

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3.3. Playing the Dual-Sourcing Game (Between45 to 60 Minutes of Class Time)

The game starts with a four-period prelaunch stageduring which students can fill their pipeline beforesales start. Demand and sales start following theprelaunch stage. During each period, all groups areinformed simultaneously of the same period demandas shown in Figure 2. (Showing all students the samedemand stream creates a positive class atmosphere,manifested by students shouting “YES!” or “Oh Jeez”etc., where all students together experience the game.)In each period, the following typical inventory

actions take place:(1) Teams observe the period demand (which is

zero during prelaunch).(2) Sales are determined automatically as the min-

imum of period demand and on-hand inventory (see

Figure 3 Screenshot of a Team’s Web Browser Interface Where Students Can Place Orders and Track Their Performance

Figure 3 for a screenshot of the team’s Web browserinterface).(3) Previous orders are received automatically.(4) Teams place orders to the two sources.(5) Each team’s pipeline inventory status and its

bank balance are updated automatically for the nextperiod.The instructor terminates the game at a time of his

or her choosing. (We have typically played 30 to 35periods, for about 50–60 minutes.) It is critical not toinform students of the termination period in advanceto prevent end-of-horizon effects.

4. Class Part C: Debrief andConnection to Recent Research

This section describes the last part of the class duringwhich we debrief the game insights, discuss recent

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual SourcingINFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS 109

Figure 4 A Dashboard That Summarizes Financial (Top Row) and Operational (Bottom Row) Metrics of All Teams Is Projected on the ClassroomScreen During the Debriefing of the Game

Game Over. Final Results.

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Instructor options: Periods Results Edit demand/periods Edit teams Data download Log outMASTER RESET

academic research, and summarize takeaways (about30 minutes of class time).

4.1. Debriefing the GameAfter ending the game, the instructor asks differ-ent groups (including the winning team, the runner-up, and a low performer) for their sourcing strategy.Teams are encouraged to share their thoughts andexperiences on the benefits of their strategy and whatthey would do differently next time.During that discussion, every argument is vali-

dated against metrics compiled during the game. Theinstructor shows a dashboard (Figure 4) that summa-rizes the performance of each team along financialand operational metrics:(1) Financial metrics include the value of each

team’s firm (bank values at the end of the game),costs (total number of units sourced), and revenues(the cumulative number of units sold).(2) Operational metrics include each team’s actual

strategic allocation (fraction of total units sourcedfrom each location), average on-hand inventory, andservice level measured by the fill rate (total numberof units sold divided by the total demand).We are also able to show the actual orders placed

over time from each source for each group.The discussion leads to the observation that differ-

ent teams may perform equally well financially andemploy different strategies. The groups usually differwith respect to average on-hand inventory as well asthe fraction of units sourced to China versus Mexico.The common thread for all successful groups is theability to execute the strategy and not change it in theface of lower (or higher) than expected demand orinventories. Determining the features of a good dual-sourcing policy and the guidelines provided by aca-demic theory are the focus of the next topic.

4.2. Recent Academic Research: TBS and DualIndex Policies

The objectives of the game include answering thefollowing two questions: (1) What features do effec-tive dual-sourcing policies share? and (2) how can wedetermine near-optimal policies and average sourcingallocation?Effective dual-sourcing policies take into account

not only the on-hand inventory and the currentdemand but also the entire pipeline status, as well asthe entire demand forecast. Research has shown thatthe structure of the optimal policy is very complex.Therefore, the dual-sourcing literature has tradition-ally focused on determining sophisticated dynamicpolicies that approach optimal performance. Typi-cally, these policies are characterized by one or twotarget inventory levels (base-stock levels) and keeptrack of one or two inventory positions (indices).For example, Veeraraghavan and Scheller-Wolf (2006)show that a dual-index policy with two target levelsperforms close to optimally, which represents state-of-the-art dual-sourcing research.Unfortunately, determining the target levels of

sophisticated policies such as the dual-index policyrequires sophisticated computational work (optimiza-tion via simulation). In addition, under these poli-cies, the associated strategic sourcing allocation canbe determined only through simulation. For a strate-gic sourcing manager, it would be desirable to have asimple policy with simple guidelines that determinethe strategic allocation during the planning phasewithout significantly compromising performance.Recently, Allon and Van Mieghem (2010) have pre-

sented such a policy that is used in practice: the tai-lored base-surge (TBS) policy. Under this policy, aconstant (standing) order is placed to the low-costsupplier in each period. The responsive source is usedonly to bring the total inventory (on-hand + pipeline)

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual Sourcing110 INFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS

Figure 5 A Tailored Base-Surge Policy Orders a Constant FractionFrom the Low-Cost Source and Orders From the ResponsiveSource to Respond to Surges

Volume

Surge demand

Base demand

Time

Supplier 2, cost efficient butless flexible

Supplier 1 with key competencyfocused on responsiveness

to a single target level. The TBS policy thus echoes afundamental tenet in strategy: it aligns the orderingpatterns with the core competencies of the suppliers(Figure 5). The constant base allocation allows Chinato focus on cost efficiency whereas Mexico’s quickresponse is utilized only dynamically to guaranteehigh service.Besides its simplicity, the TBS policy directly deter-

mines the strategic allocation, which equals the stand-ing order divided by the average demand. Janssenand de Kok (1999) study a similar policy to the TBSand show using numerical study that a firm shouldallocate close to 90% of its demand to the cheapersource. Allon and Van Mieghem (2010) optimized theTBS policy analytically and present a simple square-root formula to specify the near-optimal strategicallocation:

Base allocation" 1#!

!

h

2"c#$

where h is the unit holding cost, "c is the unit sourc-ing cost differential, # is the demand rate, and ! isthe supply-demand volatility.3 Simple formulae alsoexist for the corresponding target inventory level andcost. When focusing only on demand volatility, theformula simplifies further:

Base allocation" 1#!

h

2"cCOVD$

where COVD is the coefficient of variation of thesingle-period demand distribution.The formula also provides insight by identifying

the key drivers of dual sourcing and quantifying theirinteraction. Specifically, the allocation to the low-costsupplier is high when(1) the key monetary trade-off "c/h is large, which

implies a large cost advantage or a small cost of cap-ital (small commercial risk);(2) the expected demand rate # is high;

3 Specifically, ! 2 is the sum of the squared coefficients of variationof the interdemand times and the China supply times.

(3) the supply-demand volatility ! is small. Thisarises with stable or high-volume products (in thematurity phase of the product life cycle) and requiresstable (level) production in China. This factor alsoshows how the sourcing allocation should change asa product moves through its product life cycle.Students are asked to compute the strategic alloca-

tion using the square-root formula for the parametersof the game, which is easy:• h= about $7,000$1%/period= $70/period,• "c= $1$000$• COVD = 1%5$

so that the near-optimal base allocation is approxi-mately 1#%

&0%07/2'$1%5= 1# 0%28= 0%72.(As a homework assignment, the instructor can ask

for a similar computation for each of the differentSKUs in the minicase.)In the class we also compare the performance of

different classical policies for a finite horizon prob-lem, similar to the game. Table 1 compares the profitsof four strategies: single sourcing from Mexico, sin-gle sourcing from China, the tailored base-surge pol-icy, and a dual base-stock policy. For each policy, wehave optimized via simulation over the policy param-eters. For single-sourcing policies we computed theoptimal base-stock policy. For the tailored base-surgepolicy we used the formula given in class, and forthe dual base-stock policy we used, again, simulation-based optimization to compute the optimal thresholdlevels. For each policy we present both the value afirm would have obtained using that specific policyand the specific demand sample path played in thegame, as well as the expected value corresponding tothe demand distribution.We also compare these policies with the case in

which the firm has perfect demand information. Withperfect demand foresight, the optimal policy is sim-ple: order the exact demand quantities four periodsearly from China. The comparison with perfect infor-mation highlights the dramatic cost of uncertainty.As one may expect, the dual base-stock policy out-

performs the other policies, in expectation. However,on the specific sample path played in the game, theTBS generates a higher profit. It is important to note

Table 1 Comparison of Four Sourcing Strategies

Optimal target Actual value ! ExpectedStrategy inventory levels game demand ($) value ($)

Single source Mexico 23 557 422Single source China 48 423 569Tailored base-surge 18, standing order 616 547

to China= 5Dual base-stock 10, 45 514 586Perfect information Order exact demand 2!321 1!878

+ single four periods earliersource China from China

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual SourcingINFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS 111

that, in expectation, single sourcing from China in thisgame would result in higher profits than the TBS pol-icy. However, our analytic model of the TBS does notcapture dynamic control from China, because it essen-tially assumes the lead times are too long to allow forclosed-loop control. Therefore, a single-sourcing pol-icy that does allow for dynamic control may do betterthan the TBS policy. (Also note that the significant dif-ferences between the expected value of a policy andthe profit under the specific realization of demand areattributable to the high volatility of demand.)

5. Summary and ExtensionsThis section describes how we conclude the class andsummarizes the contributions of the game and possi-ble extensions, as well as student reactions.

5.1. Summarizing Key Takeaways of the GameDual sourcing is a surprisingly complex manage-ment problem with potentially high rewards. Thefirst step in determining an effective dual-sourcingpolicy is to consider its entire source-to-destinationcost. Although it is straightforward (yet tedious) tocompute most components of this total landed cost,the impact on inventory and thus working capital isimportant yet difficult to assess under dual sourcing.Effective dual sourcing uses suppliers with very

different strengths. In this game, we combined alow-cost (but slow) supplier with a fast (but expen-sive) source. Effective dual-sourcing policies are alsotailored to the strengths of each source. The tai-lored base-surge policy has a standing order withthe low cost supplier (allowing that supplier to levelthe workload and reduce costs further) and ordersfrom the fast source only to react to demand surges.Such a policy allows the firm to reduce total cost byplacing the majority of orders to the low-cost sup-plier while guaranteeing high service levels by plac-ing occasional, small orders from the responsive, yetexpensive supplier.Although determining optimal dual-sourcing poli-

cies is computationally complex (and requires simula-tion), it is valuable—and often sufficient in practice—to have some guiding formulae to support dual-sourcing decisions. The square-root formula providesa simple starting point when determining strategicsourcing allocations.Successful dual sourcing often requires having a

strategy and sticking to it. Having a suboptimal pol-icy is typically better than continuously changing thedecision process. For example, a strategy should allowthe manager to react to high demands by increasingorders; however, reducing the target inventory level(base-stock) when observing a few low-demand peri-ods usually leads to low performance.

The game also provides a background to illustratethe benefits that companies have experienced aftertransitioning from sole to dual sourcing:• Significant air freight reduction—by moving to

dual sourcing some companies have seen 70% +reductions in air freight.• Faster response to changes in demand, as

opposed to missing demand. Under single sourc-ing from a low cost source, by the time a demandchange was recognized, it was too late in the seasonto respond.• Improved ability to manage new product intro-

ductions. When products were sole sourced froman offshore low-cost supplier, six to nine months ofproducts were in the pipeline before the company rec-ognized poor sell-through, which left them with sig-nificant obsolete inventory.

5.2. Possible Customizations and Extensions ofthe Game

The game can easily be extended along several dimen-sions, in order of suggested importance and ease ofimplementation:Customization: The game software currently allows

the instructor to change the names and locationsof the sources. For example, the fast source can bedomestic and two locations could be France andPoland. The instructor can also modify the demandrealization and any other parameters.Operational extensions: Changing the backlogging

assumption in the game and allowing for lost salesis a straightforward modification to the game. So isadding a physical holding cost to the current finan-cial opportunity cost or a fixed ordering cost. In addi-tion, it is easy to modify the software and allow forstochastic lead times (but we found that this makesthe game much harder to understand for studentswithout offering any new insights).Financial extensions: It is straightforward to have a

higher interest rate for borrowing than saving. Onecan also introduce a default threat by adding a con-straint on maximal borrowing.Nonstationary parameters: For example, instead of

assuming i.i.d. demand, the demand distributioncould reflect the different stages of the product lifecycle of introduction, growth, maturity, and decline.The same can be done with prices, costs, exchangerates, and interest rates.Competitive market: For example, any demand

unmet by one team spills over to the other teams.This adds another layer of complexity, even for a sim-ple single-period, single-source model where one canchoose different excess demand splitting rules; seeLippman and McCardle (1997).

Allon and Van Mieghem: The Mexico-China Sourcing Game: Teaching Global Dual Sourcing112 INFORMS Transactions on Education 10(3), pp. 105–112, © 2010 INFORMS

Design-marketing-sales decisions: For example, teamscan boost demand by spending money on marketingcampaigns and sales incentives. This would necessar-ily imply that teams no longer observe a commondemand. Another extension could be to include prod-uct and supply network design, similar to the GlobalSupply Chain Management Simulation of Enspire Learn-ing.4 This simulation focuses on the design of theproducts and the supply network and allows the play-ers to modify the sourcing allocation in a limited man-ner. In contrast, our game is simpler and focuses onmanaging an existing supply network in a dynamicsetting.Multiple items or multiple stages: Although we do not

suggest this, one could extend to multiple productsor to a multistage supply chain along the lines of thefamous Beer Game. In essence, this would lead to aBeer Game with dual sourcing.The game can also be used to conduct experi-

ments to test research hypotheses. For example, teamsmay stick closer to their original plan and policy ifthey experience the game asynchronously or are notinformed of other teams’ bank balances.Although this shows how the game can be

extended, we strongly advise to play the game in itscurrent simple format because it allows students tomap their decisions to subsequent results and thusenhances the learning from the game.

5.3. Instructor Experience So FarSo far, the authors have played the game in about10 business school class settings: with full-time MBAstudents in an operations strategy elective, with fac-ulty members at a research conference, and in severalexecutive education courses. Next we will play it withour doctoral students. We believe the game wouldalso work well with graduate, as well as advancedundergraduate, students in engineering.The response has been uniformly positive. Exec-

utives appreciate the realism of the game and the

4 http://www.enspire.com/simulations/gscms. Also distributed byHarvard Business Publishing under Prod. #: 6107-HTM-ENG.

complexity inherent in dual sourcing. After playingthe game using only intuition, the class enhances theirperceived value of the academic research and sim-ple guideline formulae. Students often suggest thatthe game should be played with a cross-functionalteam, representing their marketing, sales, financial,and operational groups. Such experience would con-vey the importance of interfunctional coordinationand collaborative forecasting.From an instructor’s perspective, the game is easy

to explain, has minimal requirements (it needs only afew laptops connected to the Internet), and is simpleto set up. A single instructor can easily run the gamewith as few as five students or as many as 60 studentswithout needing additional assistants or diminishingthe effectiveness of the game. The game not only suc-cessfully achieves the pedagogical objectives, it alsohighlights the value of academic research for a realis-tic and important business problem.

Supplementary MaterialFiles that accompany this paper can be found anddownloaded from http://ite.pubs.informs.org.

AcknowledgmentsWe are grateful to Cort Jacobi and Ruchir Nanda of DeloitteConsulting for ongoing collaboration and insightful discus-sions on dual sourcing.

ReferencesAllon, G., J. A. Van Mieghem. 2010. Global dual sourcing: Tai-

lored base-surge allocation to near and offshore production.Management Sci. 56(1) 110–124.

Janssen, F., T. de Kok. 1999. A two-supplier inventory model. Inter-nat. J. Production Econom. 59(1–3) 395–403.

Lippman, S. A., K. F. McCardle. 1997. The competitive newsboy.Oper. Res. 45(1) 54–65.

Van Mieghem, J. A. 2008. Operations Strategy: Principles and Practice.Dynamic Ideas, Belmont, MA, 230–232.

Veeraraghavan, S., A. A. Scheller-Wolf. 2006. Now or later: A simplepolicy for effective dual sourcing in capacitated systems. Oper.Res. 56(4) 850–864.