Knowledge that Transforms

We consider a two‐stage principal–agent screening environment in a decentralized supply chain with retailers, distributors, and a supplier. The retailers possess private information regarding their local market profitabilities. The distributors can partially observe the retailers' profitabilities and are heterogeneous with regard to the precision of that information. The supplier determines the level of production, but knows neither the local market profitabilities nor the precision of the distributors' information. The supplier first allocates finished products to distributors, and the distributors then contract with local retailers with a capacity constraint. We find that due to the distributors' superior information, the quantity distortion on the retailers' side is mitigated, and the upstream information asymmetry subsequently affects the quantity allocation among the downstream retailers. The supplier may not benefit from contracting with the distributors. In addition, no distributor is excluded based on the heterogeneity of the information precision, even though some distributors do not have better information than the supplier. In the numerical examples, we further analyze how the local market heterogeneity and inventory costs affect the capacity allocation, the retailers' payoffs, and the supply chain profits. We document some counter‐intuitive quantity allocation rules that arise from the distributors' information advantage.

Decentralized decision making is a fact in the modern business world accompanied by extensive research that looks into its consequences for overall firm profits. We study the interactions of decentralized marketing and operations divisions in a corporation and explore their impact on overall firm profits in the case with and without coordination of the two decentralized units. We assume that the marketing department is responsible for the price that influences the demand (sales), and the operations department is responsible for the production rate. We allow for backlogging over time. We model the interdependence involving marketing and operations decisions as a non‐cooperative differential game, with the two divisions as strategically interacting players. We find that, without coordination, strategic interactions of marketing and production result in inefficiencies that can quantitatively be substantial. Next, we introduce a dynamic transfer pricing scheme as a coordination device and evaluate if it establishes efficient (first best and fully coordinated) outcomes. We show that if production and marketing play a game with pre‐commitment strategies, there exists a dynamic transfer price that efficiently (fully) coordinates decentralized decision making and hence results in Pareto‐efficient company profits. If the two decentralized divisions play a game without pre‐commitment, dynamic transfer prices can partially coordinate decentralized decision making but fail to fully eliminate overall inefficiencies arising from strategic interactions among decentralized divisions.

Although cross‐trained workers offer numerous operational advantages for extended‐hour service businesses, they must first be scheduled for duty. The outcome from those decisions, usually made a week or more in advance, varies with realized service demand, worker attendance, and the way available cross‐trained workers are deployed once the demands for service are known. By ignoring the joint variability of attendance and demand, we show that existing workforce scheduling models tend to overstate expected schedule performance and systematically undervalue the benefits of cross‐training. We propose a two‐stage stochastic program for profit‐oriented cross‐trained workforce scheduling and allocation decisions that is driven by service completion estimates obtained from the convolution of the employee attendance and service demand distributions. Those estimates, reflecting optimal worker allocation decisions over all plausible realizations of attendance and demand, provide the gradient information used to guide workforce scheduling decisions. Comparing the performance of workforce scheduling decisions for hundreds of different hypothetical service environments, we find that solutions based on convolution estimates are more profitable, favor proportionately more cross‐trained workers and fewer specialists, and tend to recommend significantly larger (smaller) staffing levels for services under high (low) contribution margins than workforce schedules developed with independent expectations of attendance and demand.

In this study, we consider the integrated inventory replenishment and transportation operations in a supply chain where the orders placed by the downstream retailer are dispatched by the upstream warehouse via an in‐house fleet of limited size. We first consider the single‐item single‐echelon case where the retailer operates with a quantity based replenishment policy, ( r , Q ), and the warehouse is an ample supplier. We model the transportation operations as a queueing system and derive the operating characteristics of the system in exact terms. We extend this basic model to a two‐echelon supply chain where the warehouse employs a base‐stock policy. The departure process of the warehouse is characterized in distribution, which is then approximated by an Erlang arrival process by matching the first two moments for the analysis of the transportation queueing system. The operating characteristics and the expected cost rate are derived. An extension of this system to multiple retailers is also discussed. Numerical results are presented to illustrate the performance and the sensitivity of the models and the value of coordinating inventory and transportation operations.

The value of demand information underlies many supply chain strategies that aim at better matching supply and demand. This study reports on the results of a laboratory experiment designed to estimate the behavioral value of demand information . Relative to the commonly assumed benchmark of a rational risk‐neutral decision maker, we find that decision makers are consistently willing to pay too much for the option to eliminate the risk of supply not matching demand. Contrary to intuition, we show that risk aversion does not explain this result. We posit that demand information provides behavioral value because it mitigates regret from ex post inventory errors.

We introduce a two‐period Stackelberg game of a supplier and buyer. We recognize that learning from manufacturing experience has many advantages. Consistent with the literature, we assume both the buyer and supplier realize reductions in their respective production costs in period 2 due to volume‐based learning from period 1 production. In addition, we introduce another learning concept, the future value, to capture the buyer's benefits of transferring current manufacturing experience for the design and development of future products and technologies. In contrast to the literature, we allow the supplier two mechanisms to impact the buyer's outsourcing decision: price and the investment in integration process improvement (IPI) that reduces the buyer's unit cost of integration. IPI may include the investment in new materials, specialized technology, or the re‐design of the integration process. Conditions are given whereby the buyer partially outsources component demand as opposed to fully outsourcing or fully producing in‐house. Furthermore, conditions are given characterizing when the supplier's price and investment in IPI are substitute strategies versus complements. Both analytic and numerical results are presented.

Changes in the global economy and technological advances are stimulating increased geographic distribution of new product design and development efforts. For large organizations that design and develop complex products, this geographic distribution has added a new layer of complexity to product development operations. In this empirical study of a large auto manufacturer, we examine the operational performance implications of splitting the design of vehicle subsystems across multiple geographic locations. Our results indicate that global distribution diminishes the chance of completing tasks on time and degrades subsystem design quality. Finally, by examining the interplay between subsystem centrality and global distribution, we found that higher centrality in the product architecture amplifies the impact of global distribution on subsystem error rates.

A fundamental aspect of designing systems with dedicated servers is identifying and improving the system bottlenecks. We extend the concept of a bottleneck to networks with heterogeneous, flexible servers. In contrast with a network with dedicated servers, the bottlenecks are not a priori obvious, but can be determined by solving a number of linear programming problems. Unlike the dedicated server case, we find that a bottleneck may span several nodes in the network. We then identify some characteristics of desirable flexibility structures. In particular, the chosen flexibility structure should not only achieve the maximal possible capacity (corresponding to full server flexibility), but should also have the feature that the entire network is the (unique) system bottleneck. The reason is that it is then possible to shift capacity between arbitrary nodes in the network, allowing the network to cope with demand fluctuations. Finally, we specify when certain flexibility structures (in particular chaining, targeted flexibility, and the “N” and “W” structures from the call center literature) possess these desirable characteristics.

We consider a periodic‐review inventory system with regular and expedited supply modes. The expedited supply is faster than the regular supply but incurs a higher cost. Demand for the product in each period is random and sensitive to its selling price. The firm determines its order quantity from each supply in each period as well as its selling price to maximize the expected total discounted profit over a finite or an infinite planning horizon. We show that, in each period if it is optimal to order from both supplies, the optimal inventory policy is determined by two state‐independent thresholds, one for each supply mode, and a list price is set for the product; if only the regular supply is used, the optimal policy is a state‐dependent base‐stock policy, that is, the optimal base‐stock level depends on the starting inventory level, and the optimal selling price is a markdown price that decreases with the starting inventory level. We further study the operational impact of such supply diversification and show that it increases the firm's expected profit, reduces the optimal safety‐stock levels, and lowers the optimal selling price. Thus that diversification is beneficial to both the firm and its customers. Building upon these results, we conduct a numerical study to assess and compare the respective benefit of dynamic pricing and supply diversification.

In this article, we analyze how retailers change their inventory investment behavior in response to macroeconomic shocks. We examine if service level, as measured by the ratio of stockout to inventory holding costs, can explain the differences in observed behavior across retailers. We use data on macroeconomic indicators and quarterly filings of US public retailers from 1985 to 2009 to estimate a dynamic model of short‐ and long‐term impact of macroeconomic shocks on inventory investment. Our results show that retailers with a high service level increase their inventory investment significantly more than those with a low service level during expansion shocks. Conversely, retailers with a low service level curtail their inventory investment significantly more than those with a high service level during periods of economic contractions. Thus, we show that the aggregate change in inventory investment documented in prior macroeconomics research is driven by different sets of retailers, as predicted by newsvendor logic. We draw implications of our findings to retailers as well as their suppliers.