Knowledge that Transforms

This study is motivated by examples of outsourcing that are not readily explained by widely established economic theories. We extend recent literature that develops the idea that outsourcing can help firms avoid overinvestment by specifying more precisely the conditions under which this thesis is likely to apply. Our extension is realized through a two‐period game theoretic model in which the outsourcing and in‐house investments are driven by (1) the cost required to develop a product or process module, (2) competitive relevance, defined as the module's share in the production cost or the module's importance to the customer, and (3) modularity, defined as the extent to which generic investments in the module can approach firm‐specific investments in terms of the overall product/process performance. The analysis generates predictions about what types of insourcing, outsourcing, and non‐sourcing behaviors are likely to emerge in different parts of the parameter space. Outsourcing to a more concentrated industry upstream emerges at equilibrium when modularity is high, relevance low to medium, and development cost high enough that none or only a subset of focal firms wants to invest. While firms are forced to insource and overinvest due to a prisoner's dilemma when the development cost is sufficiently high relative to the module's relevance, we do not find outsourcing equilibria that solve this problem in a two‐period game with no commitment. This result implies that some form of tacit coordination in a multi‐period game may be necessary. We conclude the study with a discussion of empirical implications.

Profit‐maximizing firm owners who incentivize their managers with a bonus for process improvement create an intentional misalignment of their own objective and management attention. From the viewpoint of a single firm, such a local misalignment can never be profitable, but in this study we take a wider strategic perspective by investigating cost‐reducing process improvements of two firms competing in a Cournot market. We find that the use of a process improvement bonus (by firm A) can be profitable, by affecting the competitor's decision making. Informed about the reward structure at firm A, which provides an incentive for process improvement and thereby for increased production at that firm, the manager of the competing firm (B) is inclined to produce less if the owner of firm B only rewards profit. This leads to a higher profit for firm A. However, we also show that firm B's best strategy is to also offer a process improvement bonus, even if that firm is a cost laggard (with higher costs for process improvement), and that this leads to reduced profit for both firms in many situations unless one of them is sufficiently superior in its ability to improve processes. These results are robust for uncertain process improvement outcomes, multidimensional process improvement decisions, and information asymmetry in the owner–manager relationship.

We address an inventory rationing problem in a lost sales make‐to‐stock (MTS) production system with batch ordering and multiple demand classes. Each production order contains a single batch of a fixed lot size and the processing time of each batch is random. Assuming that there is at most one order outstanding at any point in time, we first address the case with the general production time distribution. We show that the optimal order policy is characterized by a reorder point and the optimal rationing policy is characterized by time‐dependent rationing levels. We then approximate the production time distribution with a phase‐type distribution and show that the optimal policy can be characterized by a reorder point and state‐dependent rationing levels. Using the Erlang production time distribution, we generalize the model to a tandem MTS system in which there may be multiple outstanding orders. We introduce a state‐transformation approach to perform the structural analysis and show that both the reorder point and rationing levels are state dependent. We show the monotonicity of the optimal reorder point and rationing levels for the outstanding orders, and generate new theoretical and managerial insights from the research findings.

How should a firm with limited capacity introduce a new product? Should it introduce the product as soon as possible or delay introduction to build up inventory? How do the product and market characteristics affect the firm's decisions? To answer such questions, we analyze new product introductions under capacity restrictions using a two‐period model with diffusion‐type demand. Combining marketing and operations management decisions in a stylized model, we optimize the production and sales plans of the firm for a single product. We identify four different introduction policies and show that when the holding cost is low and the capacity is low to moderate, a (partial) build‐up policy is indeed optimal if consumers are sensitive to delay. Under such a policy, the firm (partially) delays the introduction of its product and incurs short‐term backlog costs to manage its future demand and total costs more effectively. However, as either the holding cost or the capacity increases, or consumer sensitivity to delay decreases, the build‐up policy starts to lose its appeal, and instead, the firm prefers an immediate product introduction. We extend our analysis by studying the optimal capacity decision of the firm and show that capacity shortages may be intentional.

We study a firm's optimal transshipment problem considering the impacts of setup costs for transshipment and demand distribution shapes. We assume that the demand follows a three‐point distribution, which changes from a degenerate distribution, to a unimodal distribution, and to a bimodal distribution as the demand shape parameter increases. We find that as the demand shape parameter increases, the optimal transshipment strategy changes from no transshipment to transshipment, and finally to no transshipment. The firm would use two‐way transshipment when the shape parameter is relatively small, while it would use one‐way transshipment when the shape parameter is relatively large. When the optimal strategy is one‐way transshipment, the transshipment direction depends on the contribution margin as well as the demand shape, when the difference between the two demand uncertainties is small. Our study of a dual‐channel retail system shows that the additional benefit of two‐way transshipment is negligible when there are many retail stores.

Quality contracting is critical and challenging due to the many unique issues related to quality. In this study, we analyze the first‐mover right in quality contracting by considering two different strategies for the buyer: the quality requirement strategy (QR) where buyer moves first by posting quality requirement to suppliers and quality promise strategy (QP) where buyer voluntarily gives up the first‐mover right to suppliers to ask them to promise quality. We study which strategy (1) better encourages suppliers' quality improvement efforts and (2) leads to a higher expected profit for the buyer. To analyze the drivers behind the buyer's choice between QR and QP, we start with the basic model where buyer faces only one supplier who has the opportunity to make quality improvements. We then gradually add other business features such as information asymmetry and supplier competition, analyzing how each feature adds/changes the driving forces and how they interact in the buyer's decision between QR and QP. We consider both the case where the wholesale price is fixed (when the buyer has the power to dictate price or price is set by the market) and the case where the wholesale price is included as a variable (when price is part of the negotiation). We find that QP always leads to the first‐best quality efforts from the supplier(s) while QR limits their efforts. However, this does not guarantee higher expected profit for the buyer under QP. We provide insightful guidelines in buyer's choice between QP and QR. This research enriches the limited literature on quality contracting with quality improvement opportunity and asymmetric information.

The linear programming approach to approximate dynamic programming has received considerable attention in the recent network revenue management (RM) literature. A major challenge of the approach lies in solving the resulting approximate linear programs (ALPs), which often have a huge number of constraints and/or variables. Starting from a recently developed compact affine ALP for network RM, we develop a novel dynamic disaggregation algorithm to solve the problem, which combines column and constraint generation and exploits the structure of the underlying problem. We show that the formulation can be further tightened by considering structural properties satisfied by an optimal solution. We prove that the sum of dynamic bid‐prices across resources is concave over time. We also give a counterexample to demonstrate that the dynamic bid‐prices of individual resources are not concave in general. Numerical experiments demonstrate that dynamic disaggregation is often orders of magnitude faster than existing algorithms in the literature for problem instances with and without choice. In addition, adding the concavity constraints can further speed up the algorithm, often by an order of magnitude, for problem instances with choice.

We study and compare decision‐making behavior under the newsvendor and the two‐class revenue management models, in an experimental setting. We observe that, under both problems, decision makers deviate significantly from normative benchmarks. Furthermore, revenue management decisions are consistently higher compared to the newsvendor order quantities. In the face of increasing demand variability, revenue managers increase allocations; this behavior is consistent with normative patterns when the ratio of the selling prices of the two customer segments is less than 1/2, but is its exact opposite when this ratio is greater than 1/2. Newsvendors' behavior with respect to changing demand variability, on the other hand, is consistent with normative trends. We also observe that losses due to leftovers weigh more in newsvendor decisions compared to the revenue management model; we argue that overage cost is more salient in the newsvendor problem because it is perceived as a direct loss, and propose this as the driver of the differences in behavior observed under the two problems.

Seasonal demand for products is common at many companies including Kraft Foods, Case New Holland, and Elmer's Products. This study documents how these, and many other companies, experience bloated inventories as they transition from a low season to a high season and a severe drop in service levels as they transition from a high season to a low season. Kraft has termed this latter phenomenon the “landslide effect.” In this study, we present real examples of the landslide effect and attribute its root cause to a common industry practice employing forward days of coverage when setting inventory targets. While inventory textbooks and academic articles prescribe correct ways to set inventory targets, forward coverage is the dominant method employed in practice. We investigate the magnitude and drivers of the landslide effect through both an analytical model and a case study. We find that the effect increases with seasonality, lead time, and demand uncertainty and can lower service by an average of ten points at a representative company. While the logic is initially counterintuitive to many practitioners, companies can avoid the landslide effect by using demand forecasts over the preceding lead time to calculate safety stock targets.

This paper studies contract renegotiation in a stylized supply chain model. Two original equipment manufacturers (OEMs) sign fixed‐quantity contracts with a contract manufacturer (CM) prior to demand realization. Contract renegotiation after demand realization allows the OEMs to use capacity that is more or less than what they contracted for. We assume that the extra profit due to efficient allocation of capacity is allocated to the supply chain parties according to the egalitarian rule and investigate when an OEM's expected post‐renegotiation profit is maximized. We aim to understand how an OEM's expected post‐renegotiation profit is affected by her ability to negotiate a low wholesale price in the initial contract as well as the ability of the other OEM to do the same. Regardless of whether renegotiation is anticipated or not at the time of the initial contract, we find that an OEM, who had weak buyer power vis‐a‐vis the CM and was unable to negotiate a low wholesale price in the initial contract, may benefit more from renegotiation than a stronger OEM. In addition, we show that how the expected post‐renegotiation profit of an OEM changes with demand variance or anticipating renegotiation depends on the strength of the OEM's buyer power. Finally, we numerically test the robustness of our results in a supply chain with three OEMs and also identify when the OEMs prefer to leave the CM out of the renegotiation.