The 2007 Procurement Challenge: A Competition to Evaluate Mixed ProcurementStrategies

A. SardinhaSchool of

Computer Science,Carnegie Mellon

University

M. BenischSchool of

Computer Science,Carnegie Mellon

University

N. SadehSchool of

Computer Science,Carnegie Mellon

University

R. RavichandranSchool of

Computer Science,Carnegie Mellon

University

V. PodobnikFaculty of Electrical

Engineering andComputing,

University of Zagreb,Croatia

M. StanDepartment of

Computer Science,University ”Politehnica”

of Bucharest,Romania

Abstract

This article discusses the design of the 2007 “SupplyChain Management - Procurement Challenge” (SCM-PC), a competition designed by the first three authors toevaluate the performance of mixed procurement strate-gies. Specifically, the SCM-PC Challenge revolvesaround a PC assembly scenario, where trading agentsdeveloped by different teams compete for componentsrequired to assemble different types of PCs. The Chal-lenge requires the agents to manage supply chain riskthrough combinations of long-term, quantity-flexibleprocurement contracts and one-off procurement con-tracts for different components. Collectively the au-thors represent the top three entries in the 2007 Pro-curement Challenge. They present the strategies theirteams developed for the competition, compare their per-formances, and discuss lessons learned from the compe-tition.

IntroductionSupply chain management involves planning, implement-ing and controlling the buying and selling of raw materials,work-in-process inventory and finished goods. Traditionally,this process has been static, depending primarily on long-term relationships between existing trading partners. The in-creasing adoption of more flexible and dynamic relations hasthe potential to make markets more efficient by establishingbetter matches between suppliers and customers. However,as relationships become more flexible the decisions involvedin supply chain management will become more complex dueto both the shear number of factors that have to be taken intoaccount while making these decisions, and the uncertaintiesin the markets. The role of technology to aid in supply chainmanagement decision making has thus become inevitable.In the short term, technology can be used passively to pro-vide insights to supply chain managers. In the longer term,technology can adopt a more active role by making decisionsin an autonomous manner.

The TAC SCM CompetitionThe TAC SCM competition was established to simulatemany of the challenges imposed by dynamic markets, while

Copyright c© 2008, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

keeping the rules simple enough to entice researchers fromvarious fields to submit entries. The rules of the game mimicmany of the real world market forces, in order to be able totransfer the results of the game into practical managerial in-sights. In this competition, the participants make a softwareagent that plays the role of a PC manufacturer that needsto make decisions regarding procurement of raw materials,production of computers, and sale of these finished goods toconsumers.

The Procurement ChallengeOver the years, the annual TAC SCM competition has be-come more and more competitive with contributions fromtop universities around the world. In order to push the sci-ence to the extreme, it is necessary to continuously changethe rules of the game. While redesigning a game, one hasto strike a very delicate balance between making a game tooboring for incumbents, and too difficult for new entrants. InTAC SCM, we achieve this balance by introducing new chal-lenge games that mimic smaller but more complex goals.

We discuss one such challenge in this paper - the procure-ment challenge where agents compete in securing profitablecontracts for procuring raw materials from the suppliers. Bylimiting the agents’ control to just the procurement of rawmaterials we are able to better analyze best practices in amore controlled setting. In (Andrews et al. 2007), the au-thors show that in the “baseline” TAC SCM game, the topagents make purchases with longer lead times. This is sim-ilar to the real world managerial insight that supply chainrisks can usually be mitigated by adopting long-term pro-curement options versus one-off contract purchases. In or-der to further understand the tradeoffs between the uncer-tainty in demand in the long-term and uncertainty in supplyin the short term, we introduced the option of negotiatinglong-term, quantity-flexible procurement contracts in addi-tion to the one-off procurement contracts that are present inthe “baseline” TAC SCM game.

We present relevant background work in the next section,and then present an extended description of the procurementchallenge in following section. We then describe the ap-proaches of the top three agents in the 2007 competition,and then present a detailed analysis of the results of the ac-tual competition. We finally conclude by enlisting some in-sights we have gathered from this work that may be useful

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in real world supply chains.

Related WorkThe work presented in this paper is closely related to threelines of research: i.) the development of other trading agentcompetitions, ii.) reports about supply chain trading agentdesign and analysis, and iii.) work on optimizing procure-ment decisions through analytical methods and simulations.

Related Work on Trading Agent CompetitionsThe original Trading Agent Competition (TAC) was firstconceived in 1999 (Wellman and Wurman 1999) as a wayto encourage research on automated trading in a competi-tive academic environment. The first tournament was held inJuly 2000, and centered around a travel scenario (Wellmanet al. 2001). The travel game is now called TAC Classicand involves agents bidding against one another on the var-ious components of travel packages to satisfy the demandsof their simulated clients. The annual competitions led tothe growth of a community centered around the topic ofautomated trading and significant developments in the un-derstanding of the phenomena surrounding this topic (manyof these developments are summarized in (Wellman, Green-wald, and Stone 2007)).

Building on the success of the original Trading AgentCompetition, a new game scenario was introduced in 2003that focuses on the challenges of automating a supply chainentity (Arunachalam and Sadeh 2005). The supply chaingame, called TAC Supply Chain Management (TAC SCM),involves agents playing the role of PC manufacturers whobuy components and sell assembled PCs to simulated cus-tomers. TAC SCM has cultivated a research communityfrom over 60 different institutions and 20 different coun-tries, and continues to produce research that is relevant toreal-world supply chain entities (e.g. (Benisch, Andrews,and Sadeh 2006; Kiekintveld et al. 2007)).

In 2007 the TAC community introduced three new com-petitions. One was a completely new scenario called theTAC Market Design game (Cliff et al. 2007). This game re-quires entrants to develop automated market rules for match-ing simulated buyers and sellers. The other new compe-titions were challenges based on the same scenario as theTAC Supply Chain Management game. The TAC SCM chal-lenges isolate specific components of the problem faced byagents competing in the full game. The TAC SCM Predic-tion Challenge (Pardoe 2007) focuses on the task of predict-ing and forecasting the stochastic supply and demand facedby a supply chain trading agent. The TAC SCM Procure-ment Challenge is the topic of this paper and was designedto isolate the procurement decisions faced by agents in thefull TAC SCM game.

Related Work on Trading Agent Design andAnalysisThe agent descriptions in this paper follow a long line ofwork describing successful agents for the TAC SCM sce-nario. For example, Benisch et. al. 2006 (Benisch et al.

2006) provides an in depth description of the different mod-ules composing the CMieux agent. In Kiekintveld et. al.2004 (Kiekintveld et al. 2004) the DeepMaize team de-scribes how their agent dynamically coordinates sales, pro-curement and production strategies in an attempt to stayprofitable. In (He et al. 2006) the SouthamptonSCM teampresents their agent’s strategy based on fuzzy reasoning.In (Pardoe and Stone 2004) the TacTex team describes ma-chine learning techniques that were used to predict bid pricesof other agents and offers considerable insight into the over-all strategy behind their first-place agent in (Pardoe andStone 2006) and (Pardoe and Stone 2007). Podobnik, Petricand Jezic describe the CrocodileAgent agent in (Podobnik,Petric, and Jezic 2006), (Petric, Podobnik, and Jezic 2007a)and (Petric, Podobnik, and Jezic 2007b), and PhantAgent isdescribed by Stan et. al. in (Stan, Stan, and Florea 2006).The Botticelli team (Benisch et al. 2004) shows how theproblems faced by TAC SCM agents can be modeled asmathematical programming problems, and offers heuristicalgorithms for bidding on RFQs and scheduling orders. TheRedAgent team (Philipp W. Keller 2004) presents an inter-nal market architecture with simple heuristic-based agentsthat individually handle different aspects of the supply chainprocess.

Related Work on Supply Chain OptimizationThe third line of related work involves optimizing supplychain decisions in a non-competitive setting by analyzingabstracted models of purchasing decisions or running sim-ulations. Analytical techniques have come largely from themanagement science and operations research communities.A good overview of the work in this space is given by Lariv-iere (Lariviere 1999). This work typically attempts to char-acterize optimal replenishment policies (e.g. (Anupindi andBassok 1999)) under various stochastic assumptions aboutsupply and demand. The most closely related paper to ourwork is by Martinez-de-Albeniz and Simchi-Levi (de Alb-eniz and Simchi-Levi 2005). They address the problem ofoptimizing order quantities from a portfolio of flexible long-term contracts and spot market procurement opportunitieswhen supply conditions are deterministic (e.g. suppliers donot refuse business and never default).

There have been a number of other simulation tools de-veloped to analyze different aspects of supply chain per-formance. These simulations include software to evaluatedifferent ways of re-engineering the supply chain (Swami-nathan, Smith, and Sadeh 1998), determine the impact ofdifferent information exchange protocols on supply chainperformance (Swaminathan, Sadeh, and Smith 1995), andunderstand the “bullwhip effect,” (Lee, Padmanabhan, andWhang 1997) (i.e. the amplification of demand fluctuationsas they travel through a supply chain).

TAC SCM Procurement ChallengeThe TAC-SCM Procurement Challenge was introduced in2007 to provide a competition platform designed to iso-late the procurement decisions faced by manufacturer agentsin the baseline TAC-SCM game (Arunachalam and Sadeh

49

2005; Collins et al. 2006). This challenge game also extendsthe space of procurement options available to the manufac-turer agents by allowing them to enter long-term contractswith suppliers agents.

The TAC SCM Procurement Challenge (or “SCM-PC”)was designed to promote the development of supply chaintrading agents that are capable of effectively coordinatingtheir procurement decisions. The game revolves around apersonal computer (PC) assembly supply chain consistingof competing PC manufacturer agents, their component sup-plier agents and their customer agents. This challenge re-quires agents to manage supply chain risk by negotiatinglong-term, quantity-flexible procurement contracts and sup-plementing these contracts with one-off procurement con-tracts.

The SCM-PC game features three agents competing forsupply contracts from ten different suppliers. Each game hasone hundred simulated days, and each day lasts ten secondsof real time. A server simulates the customers and suppliers,and provides banking, production, and warehousing servicesto the individual agents. The agents receive messages fromthe server on a daily basis informing the state of the game,such as the current inventory of components, and must sendresponses to the same server until the end of the day indicat-ing their actions, such as component orders to the suppliers.At the end of the game, the agent with the highest sum ofmoney is declared the winner.

The long-term contracts are negotiated when the gamestarts, and each contract stipulates a minimum and max-imum weekly quantity the agent commits to purchasing.Each day, the agents may also decide to procure additionalcomponents by negotiating one-off contracts. Each agenthas an identical factory, where it can produce any type ofcomputer. The factory is simulated by the game server, andalso includes a warehouse for storing components and fin-ished computers. A daily production and delivery scheduleare also generated for the agent, and orders are only pro-duced and delivered if the required components are avail-able.

Customer Demand, Production and DeliveryOn each day, the agents receive the exact same set of ordersfrom customers representing one third of the total demand.Each order consists of a product type, a quantity, a due dateand a price per unit. The customer demand is generatedaccording to the same Poisson distribution as the baselineTAC-SCM game, with an average that is updated on a dailybasis by a random walk. The server attempts to produce anddeliver orders in a greedy fashion by giving priority to or-ders with higher revenue. When an order reaches the topof the queue the server checks whether or not each agent hasenough components to produce it. Those agents with enoughcomponents exchange them for the revenue associated withthe order.

Long-term ContractsThe long-term contracts are used to distribute risk betweensuppliers and agents. Each contract consists of a minimum(Qltsmin) and maximum (Qltsmax) weekly quantity the agent

commits to purchasing, an execution price (pexec) that theagent has to pay for each unit it actually purchases from thesupplier, and a unit reservation price (pres) that the agenthas to pay independently of how much it actually orders.To ensure that each game presents a mix of long-term andone-off contract options, we assume that each component isavailable from a supplier that only offers long-term contractsand another one that only offers one-off contracts.

When the game starts, the agents have the option of ne-gotiating the long-term contracts for each component, andthese contracts are awarded based on second price auctions.The server first announces a reserve price (ρ) for each auc-tion, and then waits ten seconds for the agents to submittheir bids. Each bid consist of a requested maximum weeklyquantity and an execution price that the agent is willing topay for each component. The minimum weekly quantity andthe reservation price are not specified by the agents in thebids, but are calculated by the suppliers as follows:

• Qltsmin = Qltsmax/(1+α), where α changes from one gameto another and is announced at the start of each game.

• pres/(pres + pexec) = β, where β also changes fromone game to another and is announced at the start of eachgame.

The long-term contract supplier allocates 100% of itsweekly nominal capacity (Cnomweek) to the bidding agents.Quantities are allocated based on the requested maximumweekly quantities, starting with the bid that has the highestexecution price. Each agent’s long-term contract has an exe-cution price that is computed as the next highest price belowits own bid (”second highest price” rule). The allocation pro-ceeds until there are no bids left or until the long-term sup-plier has run out of capacity (based on its weekly nominalcapacity). In the latter situation, the last manufacturer agentto receive a contract may end up with a maximum weeklyquantity that is less than what it had requested.

At the beginning of any given week, each agent decideshow much to actually order under its long-term contracts. Ifthe total quantity requested by the agents exceeds the sup-plier’s capacity, the supplier computes the ratio of demandit can satisfy based on its actual capacity. Each agent thenreceives a quantity that is proportional to this ratio, so thatall agents with long-term contracts are treated equally andreceive the same fraction of their actual demand that week.

One-off ContractsEvery day, manufacturer agents can send requests for quotes(RFQs) to suppliers with a given reserve price, quantity, typeand delivery date. A supplier receives all RFQs on a givenday, and processes them together at the end of the day tofind a combination of offers that approximately maximizesits revenue. On the following day, the suppliers send backto each agent an offer for each RFQ with a price, a possiblyadjusted quantity, and a due date. A detailed description ofthe one-off contract negotiation is presented in (Collins et al.2006).

The suppliers have a limited capacity for producing acomponent, and this limit varies throughout the game ac-cording to a mean reverting random walk. Moreover, sup-

50

pliers also limit their long-term commitments by reservingsome capacity for future business. The pricing of compo-nents is based on the ratio of demand to supply, and higherratios result in higher prices. Each day the suppliers estimatetheir free capacity by scheduling production of componentsordered in the past and components requested that day aslate as possible. The price offered in response to an RFQ isequal to the requested components base price discounted bya function proportionate to the supplier’s free capacity be-fore the RFQ due date. The manufacturer agents normallyface an important trade-off in the procurement process: pre-order components for the future where customer demand isdifficult to predict, or wait to purchase components and riskbeing unsuccessful due to high prices or availability.

TAC SCM-PC AgentsThis section describes the approaches of the top threeSCM-PC agents: PhantAgent (University “Politehnica”of Bucharest), CMieux (Carnegie Mellon University) andCrocodileAgent (University of Zagreb). Each of theseagents use a different combination of long-term and one-off contract strategies. One of the primary differences be-tween the agents is the way that future demand is predictedand how this prediction is used to create long-term procure-ment strategies. Another primary difference is how one-offcontracts are handled, with PhantAgent and CrocodileAgentusing repeated queries with fixed lead times and CMieuxvarying its lead times between queries.

PhantAgent

PhantAgent divides its decision making process into threedifferent sub-problems: calculating needed components,handling long-term contract procurement, and generatingone-off contract orders. Each of these problems is solved us-ing relatively simple heuristics which we will now describein detail. Many of the heuristics used in PhantAgent rely onexternal parameters which can be optimized by analyzinghistorical performance. However, due to limited availabil-ity of historical data prior to the 2007 SCM-PC competitionthese parameters were set largely by hand.

Calculating Needed Components At the beginning ofeach day, PhantAgent estimates the number of componentsit will need from the current day to the end of the simula-tion. In order to determine this number, PhantAgent firstestimates the number of components it expects to have ininventory on each of the remaining days. The expected in-ventory levels are estimated by iterating through each day,adding component arrivals that are due and subtracting theestimated component usage. The main difficulty in this pro-cess involves determining a good estimate of each day’scomponent usage. We will refer to the usage of componentj on the current day d as Q(d,j). To estimate Q(d,j) PhantA-gent combines two heuristic values:

• The first heuristic value, E[Qj ], assumes a fixed usageeach day based on the expected demand as described inthe simulation parameters.

• The second, Q̄(d,j), is a moving average of componentusage from the past 10 days.

Both of these heuristic values have certain weaknesses.The problem with using E[Qj ] is that it is not flexible todemand variations and fails to account for fluctuations indemand throughout the game. By ignoring such fluctuationsthe agent will often either run out of components or be leftwith excess inventory when the simulation nears its end. Theproblem with using Q̄ is that demand at the beginning of thesimulation can be significantly different than the demand atthe end. Thus, the long lead time orders placed at the begin-ning of the game based on the demand at that time may notmatch the demand when the components arrive. To avoidthese problems PhantAgent uses a weighted average of bothheuristic values, whereE[Qj ] is weighted more heavily dur-ing the beginning of the game. The formula is given in Equa-tion 1.

Q̂(d,j) =D − dD

E[Qj ] +d

DQ̄(d,j) (1)

where:D - the total number of days in the simulation.

The result is then slightly scaled down to avoid excessinventory towards the end of the game due to changes indemand when the long lead time requests were made.

Handling Long-term Contract Procurement PhantA-gent prioritizes availability over price in the long-term con-tracts. In order to capitalize on high selling prices duringtime of low availability, it was empirically determined thatbidding the average one-off contract prices from the pastseveral games enabled the agent to reliably procure the quan-tity it desired.

Throughout the game PhantAgent exercises the option toincrease weekly order quantities if there is a need for com-ponents and the one-off contract suppliers are charging morethan the long-term contract prices.

Generating One-off Contract Orders For one-off con-tract requests, PhantAgent uses all 5 RFQs each day to re-quest components with fixed lead times (for SCM-PC in2007, values used were {2, 3, 10, 25, 45} days). The agentadjusts its requested quantities according to current marketconditions, so on some occasion it might not use some of theRFQs. This fixed lead time strategy usually allows the agentto procure components consistently throughout the simula-tion while paying a price that is close to the average paid byany agent.

Requests with very short lead times (such as lead timesof 2 and 3 days) are treated independently of the otherRFQs and are used primarily to maintain a steady stockof components. These requests have been observed tovary significantly in price from one day to another (duringlow and high demand periods, the prices of RFQs withvery short lead times are usually very low and very highrespectively). Handling these daily variations is the mainconcern here and for this the reserve price mechanism isused to only accept offers with good prices.

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Generating Long Lead Time Requests with One-off Con-tracts Since PhantAgent uses fixed lead times for all one-off contract requests, the main decision regarding long leadtime requests is choosing appropriate order quantities. Thegeneral principle governing this decision is to make longlead time orders only if they are expected to be better orequal to orders with short lead times. In most cases the or-der with longer lead times have the lowest prices. However,orders with shorter lead times towards the end of the gameare important due to the scaling down applied to the esti-mated component usage. Order quantities are chosen simplybased on the difference between the expected usage and thecomponents already expected from prior procurement.

CMieux

The strategy used by CMieux for the long-term contractnegotiation and procurement is described in the next sub-section. The following sub-section then presents CMieux’snegotiating strategy for the one-off contracts, which is es-sentially the same used in (Benisch et al. 2006).

Long-term Contract Negotiation and Procurement Thenegotiation of the long-term contracts is conducted on thefirst day of each game, and each agent receives a reserveprice pres, an α and a β before it computes a bid for eachcomponent. The main challenge the agent faces when com-puting a bid, composed of a desired maximum weekly quan-tity and a bid price, is the high uncertainty it has about thecustomer demand and the prices of components in the one-off contract markets. Thus, the strategy used by CMieuxrelies on average one-off contract prices (P̄one−off ) fromprevious games to compute a bid price for pexec. The for-mula is given in Equation 2.

p = max(P̄one−off , ρ+ increment) (2)

The first value of the max function in Equation 2 com-putes a bid price (p) for low reserve prices (smaller than athreshold, P̄one−off ). Thus, when reserve prices are low theagent is willing to buy components from the long-term con-tract suppliers for a price equal to the average one-off con-tract price. The second value of the max function in Equa-tion 2 computes a bid price for high reserve prices (whenρ ≥ P̄one−off ), and sets p to be very close to ρ.

The requested maximum weekly quantity in each bid isconsidered a parameter in the model that must be adjustedto reflect the amount of risk the agent is willing to take withthe long-term contracts. Empirically, it was determined thaton average the loss from unsold inventory in low customerdemand games outweighed the profits from sales in higherdemand games. This problem occurred due to the very lowflexibility (approximately 15%) between the minimum andmaximum weekly quantities, which was not large enough tocover all the different customer demand scenarios. Thus, amore conservative strategy was adopted and the requestedmaximum weekly quantity was adjusted to suit low demandgames.

One-off Contract Procurement The module is designedto rapidly adapt to changing market conditions and exploitgaps in the one-off contract market to ensure that its procure-ment prices tend to fall below its competitors. Each day, theprocurement module performs two tasks: i.) it attempts toidentify a particularly promising subset of current supplieroffers, and ii.) it constructs a combination of RFQs to besent to suppliers that balances the agent’s component needswith identified gaps in current supplier contracts.

Accepting Supplier Offers The module accepts supplieroffers using a rule-based decision process. The agent beginsby selecting offers that are satisfactory based on price, quan-tity and due date using historical data. In an effort to keep theagent’s reputation as high as possible, the agent first acceptsoffers that satisfy the quantity and due date requirements ofthe corresponding RFQ (“full offers”). Next, if still needed,satisfactory offers with relatively large quantities (“partialoffers”), or early due dates (“earliest complete offers”) arealso accepted.

Sending Supplier Requests In order to determine theamount of components needed, the procurement modulecomputes the difference between the inventory required tomaintain production levels specified by the customer orders,and the projected inventory from the long-term and one-offcontract suppliers for the remainder of the game. How-ever, CMieux does not need to procure this entire differenceeach day. The components are not needed immediately, thusit can divide the purchasing of components across severaldays. This will not only enable the agent to aggressivelyprocure components within a specific scheduling window,but also allow the agent to buy some of the components itneeds well in advance, when they are likely to be cheapest.

The process of computing what specific requests to sendto suppliers is then decomposed by component type. Foreach component type, the procurement module generatesseveral sets of lead times and searches for the set with thehighest utility. This utility is computed by approximatingthe sum of the utility of the components they request andsubtracting their forecast prices. The sets of lead times withthe greatest utility for each component are sent as RFQs tothe appropriate suppliers. The reserve price of each RFQ isset to be the average utility of the components it includes.

CrocodileAgentCrocodileAgent’s architecture is based on incorporating ageneric intelligent software agent model (Podobnik, Trzec,and Jezic 2007) into the IKB framework (Vytelingum et al.2005), a three layered agent-based framework for designingstrategies in electronic trading markets. The following sec-tions describe the approach for handling long-term contractnegotiation and one-off contract procurement.

Negotiating Long-term Contracts At the start of thegame the CrocodileAgent negotiates long-term contractsfor each component. After the suppliers announce reserveprices for each component CrocodileAgent calculates themaximum weekly quantity it will request. The requestedquantity, Q̂j , for each component j is a linear function of

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its reserve price (ρ, where ρ is represented as a fraction ofthe component’s base price). The reserve price may assumeany value in the interval [ρmin, ρmax], so Q̂j is calculated asshown in Equation 3.

Q̂j = (ρmax − ρ

ρmax − ρmin)Qmaxj + (3)

(1− ρmax−ρρmax−ρmin

)Qminj

where:Qminj , Qmaxj - the minimum and maximum quantity

requested for component j.

After the maximum weekly quantities have been deter-mined, CrocodileAgent submits a long-term contract bidfor each component with price equal to the reserve price(p = ρ).

Each week CrocodileAgent chooses an actual quantityto order (Qorderj ) for each long-term contract based on theamount of components it has in inventory (Nj). This quan-tity increases linearly from a fixed constant σ% to 100% ofthe maximum quantity in the long-term contract (Qltsmax) asthe number of components in inventory decreases. The for-mula is given in Equation 4.

Qorderj = min(Qltsmax× (4)N+

j −σ%×N−j +(σ%−100%)×Nj

N+j −N

−j

, Qltsmax)

where:N−j , N+

j - minimum and maximum inventory level forcomponent j,

Negotiating One-off Contracts CrocodileAgent breaksup the procurement problem with one-off contracts into twodifferent sub-problems: strategy on the first day (day 0) andreplenishment of components during the rest of the game.A close examination of the baseline TAC SCM Game rules(Collins et al. 2006) (which also defines the SCM-PC one-off contract supplier model) suggests that procurement ofcomponents at the very beginning of the game (day 0 pro-curement) can provide components with low prices through-out the game (because there is no prior component demand).Although the concept of day 0 procurement strategy hassome similarities with long-term contract negotiation, thesetwo procurement strategies are totally independent becauseeach component is available from two different suppliers:one that only offers long-term contracts and one that onlysells components with one-off contracts.

Day 0 Strategy with One-off Contracts The goal of us-ing the day 0 strategy is to acquire components for the be-ginning of the game and (if possible) buy cheap componentswith longer lead times. CrocodileAgent sends a fixed set offive RFQs on day 0. The parameters for day 0 procurementwere determined by conducting a series of experiments.

Component Purchase During the Game with One-offContracts Throughout the game CrocodileAgent uses theone-off contracts to fill in gaps in its inventory not covered

by the long-term contracts or day 0 strategy. At the begin-ning of each day, the agent calculates the quantity of eachcomponent that has been previously ordered but not yet de-livered (Qoutstandingj ). This quantity is multiplied by a fixeddistance factor, so that orders with longer lead times havea smaller weight than orders with shorter lead times. Ifthe quantity in inventory plus the outstanding quantity fora component is less than a fixed threshold, a more aggres-sive strategy is used where five RFQs are sent to the supplierwith short lead times and relaxed reserve prices. Otherwise,RFQs are sent to suppliers with fixed lead times so that itcan replenish its inventory without exceeding a maximumamount.

The one-off contract suppliers sometimes offer bargainson very short lead times (e.g. lead times of 2 or 3 days). Inorder to capitalize on these bargains, CrocodileAgent sendsRFQs with short lead times and low reserve prices duringthe first few days of the game and as long as its outstandingcomponents are not more than a certain percentage above anacceptable maximum.

2007 TAC SCM-PC Results and AnalysisThis section presents the results of the final rounds of the2007 TAC-SCM Procurement Challenge. The final roundswere held at the Twenty-Second Conference on Artificial In-telligence (AAAI-07). They featured twelve games, threegames for each combination of three agents out of the fourfinalists. The final standings are presented in Table 1, thevalue in the third column is the average profit accumulatedby each agent over the course of the nine games it played in.

Table 1: Final Standings for the 2007 SCM-PC

Games Number AverageAgent Played of Games Score

(out of 12) WonPhantAgent 9 4 8,731,513

CMieux 9 6 7,405,743CrocodileAgent 9 2 6,399,115

Warrior 9 0 4,200,440

In addition to the overall competition results we also per-formed a finer pairwise comparison of the top three agentsto account for the fact that they did not all participate in thesame games. Table 2 presents the performance of pairs ofagents in all of the games involving them both. For the topthree agents there are three distinct pairs and each pair par-ticipated in six common games. As can be seen, the pairwiseresults provide a different ranking with CMieux ahead of theother two agents and PhantAgent ahead of CrocodileAgent.It is also worth noting that these results are consistent withthe number of games won by each agent throughout the fi-nals, with CMieux winning 6 out 9 games, PhantAgent 4 outof 9 games and CrocodileAgent 2 out of 9 games.

The discrepancy between the overall rankings and pair-wise rankings can be explained by the varying demand con-ditions faced by agents in different games. PhantAgent

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achieved a higher overall score than CMieux because it par-ticipated in one game with a high customer demand, and wasable to successfully take advantage of this opportunity.

Table 2: Final “Pairwise” Standings for the 2007 SCM-PC

Agent Games Number of AveragePlayed Games Won Score

CMieux 6 4 7,149,838PhantAgent 6 2 6,788,197

Agent Games Number of AveragePlayed Games Won Score

CMieux 6 4 8,286,761CrocodileAgent 6 1 4,385,217

Agent Games Number of AveragePlayed Games Won Score

PhantAgent 6 3 10,027,071CrocodileAgent 6 1 7,096,601

We will now present analysis of several important aspectsof the game as well as graphs that illustrate the effect ofthe strategies adopted by the top three agents. The follow-ing sub-sections describe the sales volume of each agent, theone-off and long-term contract mixes, and the average pro-curement costs.

Customer Orders and DeliveriesTo measure the sales volume of the top three SCM-PCagents, we calculated their realized demand percentage, orthe fraction of the total possible demand that they were ableto satisfy. Figure 1 presents a pairwise comparison of theaverage realized demand (with 95% confidence intervals) ofthe top three agents. As in Table 2, the values shown foreach pair in Figure 1 are calculated using only the games thatpair participated in. CrocodileAgent had the highest averagerealized demand amongst all agents. However, the overlap-ping confidence intervals show that there was no statisticallysignificant difference between any of the agents.

Figure 1: Average Realized Demand

Quantity Ordered from the SuppliersThe average number of components ordered by each of thetop three agents from the one-off and the long-term contractsuppliers with 95% confidence intervals is presented in Fig-ures 2 and 3. These graphs show that all three of the topagents procure a substantial amount of components from themore stable long-term market, but tend to buy significantlymore from the one-off contract market. While long-termcontracts provide some amount of flexibility in the weeklyorders, they are negotiated when the agents have no infor-mation about the customer demand. It is not surprising thatthese agents chose to rely more on one-off contracts sincethey can be negotiated on a daily basis giving them moreflexibility to adapt to varying market conditions.

Figure 2: Average Number of Components Ordered fromthe One-off Contract Suppliers

Figure 3: Average Number of Components Ordered fromthe Long-term Contract Suppliers

Component PricesFigure 4 presents the average weighted prices of componentspurchased by each agent in both the long-term and one-offcontract markets combined (with 95% confidence intervals).The graph shows that CMieux’s procurement prices weresignificantly better than the other two agents when comparedacross both markets.

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Figure 4: Average Weighted Prices of Components fromLong-term and One-off Contracts

When we examine the one-off and long-term contractmarkets separately we see that CrocodileAgent was able toget significantly lower prices for long-term contracts thanthe other two agents. CMieux’s long-term prices were aclose second to CrocodileAgent with an average differenceof 1.69%. In the one-off contract market CMieux has a sig-nificant edge over the other two agents.

CrocodileAgent’s low long-term procurement costs canbe explained by the fact that it bids the lowest possible pricefor all of the long-term contracts (as described in the previ-ous section). However, the fact that CMieux had the low-est overall procurement costs suggests that CrocodileAgentwas not able to procure enough from the long-term marketto overcome CMieux’s better prices in the one-off contractmarket. CMieux’s dynamic one-off contract strategy for op-timizing RFQs each day was more effective than the fixedprocurement strategies of the other agents. The additionalflexibility provided an advantage leading to the lowest over-all average procurement costs.

Conclusions and Future WorkThis paper began with a description of the Supply ChainTrading Agent Competition Procurement Challenge (SCM-PC). In addition to isolating the procurement decisions facedby agents in the “baseline” Supply Chain Trading Competi-tion, the SCM-PC rules extended the purchasing options toinclude quantity flexible long-term contracts that are negoti-ated once at the start of the game.

We then described the approaches of the top three SCM-PC agents from the 2007 competition: PhantAgent (Uni-versity “Politehnica” of Bucharest), CMieux (Carnegie Mel-lon University) and CrocodileAgent (University of Zagreb).These agents were shown to differ primarily in the waysthey predicted future demand and the flexibility they em-ployed in their one-off contract procurement. In particular,PhantAgent and CrocodileAgent used repeated queries withfixed lead times, while CMieux varied its lead times betweenqueries.

Finally, we presented a detailed analysis of the resultsfrom the actual competition. The results showed that the

agents used long-term procurement contracts to procure abaseline inventory, but purchased the bulk of their compo-nents with one-off contracts. This suggests that the existenceof a flexible one-off contract market enabled the agents tomitigate the risk typically associated with long-term com-mitments.

One potential short-coming of the results from the 2007SCM-PC competition is that the agents and agent designershad very little historical experience to learn from. This lackof historical data may have made the game less dynamic andslightly inefficient. In the future we plan to provide toolsallowing agents to analyze information from previous gamelogs.

Another future change will involve improving the com-petition structure itself. Our analysis of the results sug-gested that CMieux was purchasing components signifi-cantly cheaper than the other two agents, while maintain-ing similar service levels. However, due to an imbalance ingame conditions CMieux placed behind PhantAgent in over-all average profit. A closer look at the reasons for this dis-crepancy revealed a flaw in the competition structure. Dueto the high variance in customer demand between gamesthe agents should not have been compared across gamesin which they did not all compete. Our attempts to con-trol for this after the fact (by comparing performance be-tween agents only in games where they were both present)appeared to correct the discrepancy.

We originally proposed the TAC SCM Procurement Chal-lenge in order to better analyze best practices in procure-ment in a more controlled setting. Largely, we believe wehave made significant progress towards this goal and havegained important insights about automated supply chain pro-curement markets. In 2008, we have decided to allow eachsupplier to offer both one-off and long-term contracts, ratherthan restricting each supplier to offer only one type of con-tract. This is consistent with practices found in many actualsupply chains and also somewhat more challenging.

AcknowledgmentsThe platform for the procurement challenge is based on thebaseline Supply Chain Trading Agent Competition gameoriginally designed by Carnegie Mellon’s e-Supply ChainManagement Laboratory in collaboration with the SwedishInstitute of Computer Science (SICS). The first three au-thors, who are with the e-Supply Chain Management Labo-ratory, would like to thank SICS for sharing its code and al-lowing them to customize it for the Procurement Challengedescribed in this paper. This work has been supported bythe National Science Foundation under ITR Grant 0205435as well as by a grant from SAP Research.

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