Problem definition: A key question in socially responsible supply networks is as follows: When firms audit some, but not all, of their respective suppliers, how do the degree centralities of the suppliers (i.e., the number of firms to which they supply) affect their auditing priority from the viewpoint of the firms? To investigate, we consider an assembly network consisting of two firms and three suppliers; each firm has one independent supplier that uniquely supplies to that firm and one common supplier that supplies to both. Academic/practical relevance: Most supply networks are characterized by firms that source from multiple suppliers and suppliers that serve multiple firms, thus resulting in suppliers who differ in their degree centrality. In such networks, any negative publicity from suppliers’ noncompliance with socially responsible practices—for example, employment of child labor, unsafe working conditions, and excessive pollution—can significantly damage the reputation of the buying firms. To mitigate this impact, firms preemptively audit suppliers although resource and time considerations typically restrict the number of suppliers a firm can audit. Consequently, it becomes important to understand the impact of the degree centralities of the suppliers on the priority with which firms audit them. Methodology: Game-theoretic analysis. Results: Downstream competition between the firms drives them away from auditing the supplier with higher centrality, that is, the common supplier, in equilibrium, despite the fact that auditing this supplier is better for the aggregate profit of the firms. We show that this inefficiency is corrected when the firms cooperate (via a stable coalition) to jointly audit the suppliers and share the auditing cost in a fair manner. We also identify conditions under which joint auditing improves social welfare. Managerial implications: We have two main messages: (i) individual incentives can lead firms to deprioritize the auditing of structurally important suppliers, which is inefficient; (ii) the practice of joint auditing can correct this inefficiency.

W hen firms invest in a shared supplier, one key concern is whether the invested capacity will be used for a competitor. In practice, this concern is addressed by restricting the use of the capacity. We consider what happens when two competing firms invest in a shared supplier. We consider two scenarios that differ in how capacity is used: exclusive capacity and first-priority capacity. We model firms' investment and production decisions, and analyze the equilibrium outcomes in terms of the number of investing firms and capacity levels for each scenario; realized capacity is a stochastic function of investment levels. We also identify conditions under which the spillover effect occurs, where one firm taps into the other firm's invested capacity. Although the spillover supposedly intensifies competition, it actually discourages firms' investment. We also characterize the firms' and supplier's preference about the capacity type. While the non-investing firm always prefers spillovers from the first-priority capacity, the investing firm does not always want to shut off the other firm's access to its leftover capacity, especially when allowing spillover induces the other firm not to invest. The supplier's preference depends on the trade-off between over-investment and flexibility.

This paper investigates mechanisms by which a powerful original equipment manufacturers procures multiple inputs for assembly from suppliers with privately informed costs, either simultaneously or sequentially. The optimal mechanisms always lead to matching purchase quantities of the inputs. Thus, quantity–payment contracts that implement the optimal mechanisms are contingent across suppliers (i.e., each supplier’s contract terms contain other suppliers’ private costs as variables), making the implementation impractical. To address this issue, we propose alternative implementations of the optimal mechanisms by menus of two-part tariff contracts that are noncontingent. In addition, optimal simultaneous and sequential procurement mechanisms for assembly are shown to be revenue-equivalent for all parties despite their different asymmetric information structures. Our findings suggest that procurement managers need not strategize contracting sequences for assembly, but should rather focus on achieving the best pricing with each supplier and coordinating purchase quantities.

We study a firm's optimal strategy to adjust its capacity using demand information. The capacity adjustment is costly and often subject to managerial hurdles which sometimes make it difficult to adjust capacity multiple times. In order to clearly analyze the impact of demand learning on the firm's decision, we study two scenarios. In the first scenario, the firm's capacity adjustment cost increases significantly with respect to the number of adjustments because of significant managerial hurdles, and resultantly the firm has a single opportunity to adjust capacity (single adjustment scenario). In the second scenario, the capacity adjustment costs do not change with respect to the number of adjustments because of little managerial hurdles, and therefore the firm has multiple opportunities to adjust capacity (multiple adjustment scenario). For both scenarios, we first formulate the problem as a stochastic dynamic program, and then characterize the firm's optimal policy: when to adjust and by how much. We show that the optimal decision on when and by how much to change the capacity is not monotone in the likelihood of high demand in the single adjustment scenario, while the optimal decision is monotone under mild conditions and the optimal policy is a control band policy in the multiple adjustment scenario. The sharp contrast reflects the impact of demand learning on the firm's optimal capacity decision. Since computing and implementing the optimal policy is not tractable for general problems, we develop a data-driven heuristic for each scenario. In the single adjustment scenario, we show that a two-step heuristic which explores demand for an appropriately chosen length of time and adjusts the capacity based on the observed demand is asymptotically optimal, and prove the convergence rate. In the multiple adjustment scenario, we also show that a multi-step heuristic under which the firm adjusts its capacity at a predetermined set of periods with exponentially increasing gap between two consecutive decisions is asymptotically optimal and show its convergence rate. We finally apply our heuristics to a numerical study and demonstrate the performance and robustness of the heuristics.

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