TY - JOUR
T1 - Agent learning in supplier selection models
AU - Valluri, Annapurna
AU - Croson, David C.
PY - 2005/4
Y1 - 2005/4
N2 - We use agent-based modeling to study the performance of a supplier selection model, originally proposed by Croson and Jacobides [Small Numbers Outsourcing: Efficient Procurement Mechanisms in a Repeated Agency Model, Working Paper #99-05-04 Department of Operations and Information Management, The Wharton School of the University of Pennsylvania (1999)], which displays a complicated reward and punishment profile under incomplete information, We document the dynamics and convergence to equilibrium of the interactions of a single buyer with a heterogeneous group of sellers, which results in both separation of sellers capable of producing high-quality goods from those incapable of doing so, and continuing incentives for high-quality-capable sellers to produce at the maximum quality possible, We model two methods of determining exploration reference points - an "auction-style" model focusing on probability of success and a "newsvendor-style" model focusing on profitability. Our simulation shows that (1) the tournament structure suffices to reach convergence at high-quality levels whenever the number of suppliers exceeds three, (2) punishment length and number of suppliers are substitutes, and (3) shorter punishments improve learning speed of convergence. Moreover, we show that it is strictly better for the buyer to transact with relatively few suppliers - a conclusion generated endogenously inside the model as a tradeoff between exploration and exploitation, rather than through assumptions that explicitly penalize supplier proliferation.
AB - We use agent-based modeling to study the performance of a supplier selection model, originally proposed by Croson and Jacobides [Small Numbers Outsourcing: Efficient Procurement Mechanisms in a Repeated Agency Model, Working Paper #99-05-04 Department of Operations and Information Management, The Wharton School of the University of Pennsylvania (1999)], which displays a complicated reward and punishment profile under incomplete information, We document the dynamics and convergence to equilibrium of the interactions of a single buyer with a heterogeneous group of sellers, which results in both separation of sellers capable of producing high-quality goods from those incapable of doing so, and continuing incentives for high-quality-capable sellers to produce at the maximum quality possible, We model two methods of determining exploration reference points - an "auction-style" model focusing on probability of success and a "newsvendor-style" model focusing on profitability. Our simulation shows that (1) the tournament structure suffices to reach convergence at high-quality levels whenever the number of suppliers exceeds three, (2) punishment length and number of suppliers are substitutes, and (3) shorter punishments improve learning speed of convergence. Moreover, we show that it is strictly better for the buyer to transact with relatively few suppliers - a conclusion generated endogenously inside the model as a tradeoff between exploration and exploitation, rather than through assumptions that explicitly penalize supplier proliferation.
KW - Agent learning
KW - Incomplete contracts
KW - Moral hazard
KW - Reinforcement learning
KW - Supplier management
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U2 - 10.1016/j.dss.2003.10.008
DO - 10.1016/j.dss.2003.10.008
M3 - Article
AN - SCOPUS:10444222622
SN - 0167-9236
VL - 39
SP - 219
EP - 240
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -