Knowledge that Transforms

Language system diversity is a source of individual differences. Research on human cognition has established that writing direction influences non-linguistic mental schemata such as spatial orientation. However, there is little empirical evidence of its impact on task performance. We examine whether task performance in manual order-picking is higher when the in-aisle travel direction follows the writing direction of order pickers. We conducted this study in cooperation with a German brick-and-mortar grocery retailer, allowing us to employ a unique real-world data set comprising 3,200,534 storage-location visits by 113 order pickers, 61 of whom had a left-to-right and 52 a right-to-left writing direction. Our statistical analyses suggest that order-picking task performance improves when the in-aisle travel direction follows individual writing direction. This creates a path to diversity-inspired operations management that treats efficiency and the diversity and inclusion of human workers as equally important for optimization.

Motivated by recent developments aimed to address last-mile delivery challenges in rural areas of Indonesia and China, we develop a theoretical model to study how to lower participation barriers to the e-commerce market. We analyze how stage stations lower the cost of last-mile delivery by leveraging the preexisting social fabric in rural communities, including local stores, as well as social technologies such as chat services and virtual groups. Specifically, we examine two models, a decentralized model with stage stations run by independent agents, and a centralized model, similar to the one pioneered by Alibaba Taobao. We find that when the delivery cost in rural areas is high, the decentralized model can lower the participation cost and increase both platform profit and social welfare. In general, the centralized model outperforms the decentralized one. One reason is that the decentralized model suffers from a double marginalization problem. The centralized model has more flexibility, for example, in determining the pricing policies for both sellers and buyers. It could offer free service to rural customers if the participation of fresh rural customers can attract many additional sellers. However, a centralized model may not be feasible in many countries. Therefore, we explore whether the platform can implement a coordination mechanism for decentralized stage stations. We find that our proposed coordination mechanism can improve the performance of the decentralized model. Our results have important implications for how e-commerce platforms can leverage stage stations and social technologies to lower participation barriers for rural customers, thereby creating a more inclusive development model.

We consider the information design problem of a demand-maximizing firm launching a product of unknown quality to a market consisting of customers who have heterogeneous prior beliefs about quality. The firm publicly discloses information about quality to all customers. These customers can subsequently opt to acquire additional information about the product at a cost from sources beyond the firm’s control. Our study is motivated by the common practice of firms conducting public pilot tests or soliciting reviews from opinion leaders before launching a new product to inform potential customers about its quality. To analyze this problem, we construct a game-theoretic model of Bayesian persuasion between the firm and its customers. We characterize the firm’s optimal information policy and show that it can range from fully disclosing quality to exaggerating or downplaying quality to not disclosing quality at all depending on market characteristics. We delineate the impact of market heterogeneity and access to additional information on the optimal information disclosure policy of the firm. Our analysis provides managerial guidance for firms in designing information provision strategies and operationalizing them for different market characteristics.

Due to the strategic and adaptive nature of adversaries, the deployment of new technologies is common practice in the arenas of security and defense (e.g., new baggage scanners at airports). History has shown that the deployment of these technologies has often been disclosed to the public, allowing malicious actors to potentially understand which venues are defended, and how. There is limited research examining how information disclosed about the deployment of new security measures can impact the beliefs and decisions of adversaries. Studying these beliefs and decisions is critical in obtaining insights into adversarial behavior, which can inform the allocation of defensive resources and the design of related information disclosures. This article aims to address this gap by studying how people—who are motivated to attack one among multiple targets—respond when receiving information from a defender regarding the deployment of new security measures at those targets. We address whether attackers (i) believe the information they receive from the defender and (ii) choose to attack after learning that new security measures may be deployed at their target(s) of interest. We find that attackers’ beliefs regarding where new security measures are deployed, and their decisions to attack particular targets, are impacted by the information they receive from the defender and by their understanding of the defender’s target valuations, highlighting the importance of strategic information disclosure in counterterrorism operations. In an experimental setting with two targets, when attackers ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>975</mml:mn> </mml:math> ) knew that the defender valued one target much more than the other, they had a stronger belief that security measures would be deployed at the higher-value target, even when the defender announced that only the lower-valued target was protected with enhanced security. We also identify important factors that increase the likelihood of deterrence (i.e., when adversaries decide not to attack), which is a major goal in counterterrorism operations. Overall, this study provides novel insights into information disclosure and adversarial decision making in security and defense contexts, contributing to the operations management and behavioral decision analysis research in this domain.

Under a relational contract, the value placed on expected future business must outweigh the short-term temptations to deviate for the buyer–supplier relationship to persist. Operational and relational factors that influence this trade-off have been explored, however, there is a considerable lack of research on the moderating effects of supplier and market characteristics. We offer insights into how supplier service models and market dynamics impact suppliers’ decisions to renege on the relational contract. Limited access to transactional and contractual data has restricted previous exploration. We overcome this limitation with a detailed dataset in the for-hire truckload transportation sector. We find that a third-party brokerage service model is better able to overcome operational demand challenges and maintain service due to lower capacity constraints and pooling effects as compared to asset-based providers. Furthermore, when the overall market is capacity-constrained, long-term relationships become less of a deterrent for suppliers to reject business. In addition, during tightly constrained markets, suppliers respond with higher rejection rates to short-term demand surges but not to historical demand variability.

In practice, it is common, especially for online retailers, to bundle different products together during procurement to save transportation and handling costs. It is important to understand and theorize how to manage dynamic procurement by taking advantage of joint ordering in the presence of joint and individual setup costs. In this article, we characterize the structure of optimal policy for a periodic review multiproduct inventory system with multiple setup costs, including a joint setup cost and an individual setup cost for each product. By proposing the notion of [Formula: see text]-quasi-convexity, we show that an optimal procurement policy for such a system follows the so-called ([Formula: see text]) policy when demands increase stochastically over time: order up to [Formula: see text] for states in the region [Formula: see text], do not order for states in the region [Formula: see text], and order certain quantities for states in neither [Formula: see text] nor [Formula: see text]. To better understand the optimal policy, we provide the bounds for the optimal order-up-to levels and the boundary sets of the optimal policy. Under the convex single-period inventory costs, we also provide a lower bound deterministic system which can be asymptotically optimal as the coefficient of variations decreases to zero. Leveraging these operational insights, we propose five simple heuristic policies: the independent ([Formula: see text]) policy, vector ([Formula: see text]) policy, linear interpolation ([Formula: see text]) policy, the deterministic approximation, and the weighted deterministic approximation policy. Extensive numerical experiments indicate that the last three heuristics perform well. In particular, the weighted deterministic approximation policy, whose average performance gap is < 1%, dominates the others in almost all our numerical experiments. Finally, we show that how our results can be extended to systems with more complex setup cost functions, such as time-varying, set-based, and quantity-dependent setup costs.

We assess how a retailer can manage inventories and labor under an extreme condition such as a temporary negative demand shock due to a pandemic or an economic turmoil. This analysis incorporates labor market frictions whereby firms incur deadweight costs associated with hiring and firing employees, as well as the option to furlough labor. We examine the impact of these frictions on a retail firm's optimal operating policies around inventory level, furlough, and layoffs. We find that labor market frictions condition the inventory and the level of employment in two ways. First, they lead to underinvestment in inventories, which limits recovery and employment in the postshock period. Second, high labor market frictions motivate the firm to conservatively downsize workforce during the negative demand period leading to higher employment, when compared to downsizing without friction. A significant contribution of our study lies in delineating the optimal furlough decisions and quantifying the impact of the furlough option on inventory and labor decisions. We demonstrate the conditions under which it is optimal for the retailer to either (i) fully downsize labor and leverage the furlough option in the labor market, or (ii) maintain excess labor while also opting to furlough a portion of the workforce. During an extreme event such as a temporary negative demand shock, our results highlight the need for a coordinated effort when implementing governmental subsidy policies on alleviating labor and inventory reductions by accounting for labor market frictions and furlough support.

This paper studies the challenges involved in production planning in coproduction systems, specifically the production of semiconductor chips for light-emitting diodes (LEDs). The production output in this industry is characterized by a stochastic distribution over the targeted production metric; thus the whole range of production is not suitable for a specific application. We formulate a novel stochastic profit optimization problem with random production output and random demand—based on information gleaned from interactions with a large integrated LED manufacturer—and determine the optimal production parameter setting and the batch size analytically; we solve the problem exactly in the special case of a single customer specification, and approximately in the case of an arbitrary number of customer specifications. We find that the optimal production setting depends on the sharpness of the density function governing the production output distribution and the range of parameter settings that are acceptable to customers. We show analytically that even under perfectly symmetric conditions, the optimal production setting is not necessarily symmetrically located with respect to the output range. We complement our analytical results with a Monte Carlo simulation of an augmented model with service level constraints. Our simulation results show that the approximate model that we develop serves as an excellent proxy for the intractable exact model, and illustrates the interplay between production output randomness, demand randomness, service levels, and production yield.

We study the U.S. Coin Supply Chain from both the demand and supply side perspectives to improve coin circulation in the economy. More specifically, we provide an operating policy for Depository Institutions (DIs) to improve their efficiency in packaging, distributing, coordinating, and managing the inventory of coins. We further propose a new policy (via a Rewards Program) to increase coin circulation in the economy and optimally determine a reward price, which, if implemented by the Federal Reserve System (FRS), can incentivize DIs to process the coins themselves and/or frequently deposit excess coins at the FRS. We identify the structures of DIs’ best response to the reward price and FRS’s optimal pricing policy. The results and insights developed in this study can be beneficial to both the FRS and DIs in increasing the efficiency of the nation’s coin circulation and thereby reducing their operating costs. Additionally, to estimate the societal benefit from the Rewards Program, we formulate the coin flow for an entire FRS region as an optimization problem from the supply side perspective and quantify the societal costs (and benefits) with and without the reward. We show via numerical experiments that both the FRS and Dis reduce their operating costs as a result of the program and produce a surplus benefit for the society. The reward price that maximizes this surplus is determined optimally. We also propose a robust optimization framework to help DIs and the FRS to manage their inventory under uncertain demand while attaining their cost reduction goals.

We study the impact of government financing designed to promote clean technology advancement. Government financing offers loans for financially constrained firms that manufacture and market clean-technology products. We build an analytical model to explore the impact of such government financing in the presence of market uncertainty. Our analysis shows that compared to prevalent commercial financing schemes such as bank financing and equity financing, government financing encourages firms to pursue more aggressive operational strategies by elevating technology levels and production volumes. While those aggressive measures yield environmental benefits, they also expose firms to increased bankruptcy risk. To address this issue, we propose a risk-mitigation strategy that manages bankruptcy risks within the bounds of a moderate environment target. Our work sheds light on the often-overlooked risk tied to government financing for clean technology development and offers insights into some of the high-profile bankruptcies of firms that received such financing.