Marketing Working Papers

Supply chain modeling research identifies a variety of contracts to coordinate a decentralized linear supply chain of a supplier and a retailer; with both acting as risk neutral expected profit maximizers. We examine the effectiveness of such contracts when set by subjects acting as suppliers. Through these behavioral laboratory experiments we investigate whether prescribed outcomes correspond to observed behavior and therein, which models are most efficient in practice. Behavior is studied to elucidate the mitigating factors in decision making, such as risk perceptions, rejection, or altruism. We find that the contracts do not perform equally with respect to achieved supply chain efficiency, profit allocation, or risk sharing. The resulting supply chain efficiency is driven by individual factors such as risk aversion, different risk preferences for losses over gains, altruism, and rejection risk thresholds; and framing contexts such as the retailer’s right to set a minimum acceptable profit, the timing of payments according to the contract, or the nature of the sold good. Specifically, risk aversion reduces system efficiency as suppliers set simple wholesale price contracts and this effect is strongest in contracts framed as risks over gains and those which coordinate sales of innovative goods. The retailer's right to reject the contract improves efficiency, subject to the level of rejection risk a supplier is willing to set. Suppliers willing to incur more rejection risk make smaller gains in system efficiency and claim more of the profit despite the risk of rejection. Absent rejection risk, altruism drives the supplier’s allocation of system profits with increased altruism allocating an increased share to the retailer. Altruism suppresses risk aversion to improve system efficiency. Finally, a comprehensive conceptual framework on contract parameter setting of suppliers with different individual profiles generates a series of hypotheses, validated to a large extent by our experimental results.

Bucket brigade order picking is a method for retrieving orders from a storage rack where workers follow a fixed sequence and dynamically adjust to variability in work content along the rack. The method is simple and has been shown to provide superior performance in many applications. We develop an analytic model for predicting performance as a function of design and environmental parameters, and we use properties of the model in conjunction with simulation to offer insights into bucket brigade productivity.

Bucket brigade order picking is a method for retrieving orders from a storage rack where workers follow a fixed sequence and dynamically adjust to variability in work content along the rack. The method is simple and has been shown to provide superior performance in many applications. We develop an analytic model for predicting performance as a function of design and environmental parameters, and we use properties of the model in conjunction with simulation to offer insights for increasing bucket brigade productivity.