Emerging Topics in Supply Chain Management: Product Substitution, Demand Ambiguity, and Environmental and Social Responsibility

Chengzhang Li, Purdue University

Abstract

This study examines several emerging topics in supply chain management including the dynamic product substitution, the joint optimization of price and order quantity with demand ambiguity, and the implementation of the environmental and social responsibility (ESR) programs. We provide below a brief description of the results obtained for the specific problems considered in this study. In the first problem discussed in Chapter 2, we present a dynamic model, in which the firm replenishes product inventories from uncertain sources and dynamically allocates available products to meet the uncertain demands with the flexibility of substitution. To address the analytical challenge associated with multi-product management, we develop an approximation algorithm that leverages the value of substitution, while allowing separability of the future profit among the products. Through extensive numerical analysis, we demonstrate that our approximation yields good performance measured by the percentage profit gap against an upper bound problem. We show that substitution can generate significant benefits when the supply capacities are moderate, the supply and demand uncertainties are high, or the replenishment cycle is short. In the second problem discussed in Chapter 3, we study the problem of jointly optimizing the price and order quantity for a perishable product in the presence of demand ambiguity. We employ the minimax regret decision criterion to minimize the worst-case regret, which is defined as the difference between the optimal profit that could be obtained with perfect information and the realized profit using the decision made with ambiguous demand information. We characterize the optimal pricing and ordering decisions under the minimax regret criterion and compare their properties with those in the classical models that seek to maximize the expected profit. We compare the minimax regret approach with two other approaches that are commonly used under demand ambiguity, namely the max-min robust approach and the regression-based data-driven approach. In the demand ambiguity setting, we show that the minimax regret approach avoids the high degree of conservativeness that is often incurred in the max-min approach. In the data-driven setting, we show via a numerical study that the minimax regret approach outperforms the classical regression-based approach when data is scarce, when the demand has high volatility, or when the demand model is misspecified. In the third problem discussed in Chapter 4, we focus on the problem of administering ESR programs throughout a complex supply network. We apply a bilateral bargaining framework to analyze to what extent an ESR initiator should directly engage higher-tier suppliers, as opposed to delegating the assurance of ESR compliance to its first-tier suppliers. We show that the eventual structure of negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as a monotone function of the negotiating parties’ relative bargaining power. We find that the ESR initiator tends to delegate ESR compliance negotiation to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers for ESR compliance can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones.

Degree

Ph.D.

Advisors

Lu, Purdue University.

Subject Area

Information Technology|Economics|Information science|Management|Marketing|Operations research

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