Knowledge that Transforms

We seek to guide operations management (OM) research on the implementation of supply chain traceability systems by identifying business requirements and the factors critical to successful implementation. We first motivate the need for implementing traceability systems in two very different industries—cobalt mining and pharmaceuticals—and present business requirements and critical success factors for implementation. Next, we describe how we carried out thematic analysis of practitioner and scholarly articles on implementing blockchain for supply chain traceability. Finally, we present our results pertaining to the needs of different stakeholders such as suppliers, consumers, and regulators. The business requirements for traceability systems are curbing illegal practices; improving sustainability performance; increasing operational efficiency; enhancing supply‐chain coordination; and sensing market trends. Critical success factors for implementation are companies’ capabilities; collaboration; technology maturity; supply chain practices; leadership; and governance of the traceability efforts. These findings provide a nascent measurement model for empirical work and a foundation for descriptive and normative research on blockchain applications for supply chain traceability.

Earlier publications indicate that the price of young wines cannot be estimated accurately through weather conditions. Considering that a majority of fine wine is sold in the form of wine futures, even before the wine is bottled, determining realistic prices for these wine futures accurately is one of the most critical decisions. Surveys of leading wine merchants and industry experts exhibit significant departures from the realized market prices. We develop a pricing model for fine wines using weather, market, and expert reviews. The financial exchange for fine wines called Liv‐ex has already adopted our estimated prices and tagged them as “realistic prices.” Our approach combines temperature, rainfall, market fluctuations, and tasting expert scores and leads to accurate estimations that the wine industry has not seen before. Our study shows that higher temperatures, lower levels of precipitation, appreciation in the Liv‐ex 100 index as a market indicator, and higher barrel scores increase market prices. We conduct a comprehensive set of robustness checks and show that the mean absolute deviation of actual market prices from our estimated prices is substantially smaller than any academic benchmark. Our realistic prices help create transparency in this highly opaque market. When compared with the realized prices, our realistic prices guide buyers (e.g., distributors, restaurateurs, merchants) in their purchase decisions as they can determine whether a wine is underpriced or overpriced.

Illegal content uploads on social platforms have grown rapidly in recent years. While previous research has studied various enforcement efforts to remove illegal content, we consider such enforcement efforts in the setting where the content is sold through a platform firm. Furthermore, we consider the situation in which along with the subscription‐based legal content on the platform, there is free illegal content that may generate revenues through advertisements for the platform. Specifically, we analyze three scenarios of illegal content monitoring: (i) only the content developer monitors, (ii) only the platform firm monitors, and (iii) both the content developer and platform firm monitor the illegal content. We find that, under certain conditions, it is beneficial for the platform firm to exert a higher monitoring effort than the content developer even though the former may gain advertisement revenues through the display of illegal content. In addition, while it is expected that the content developer’s profit is highest when only it exerts the monitoring effort, we observe that the scenario where both players exert monitoring efforts results in a win‐win situation. We also analyze the social welfare under all the three illegal content monitoring scenarios.

The buy‐online‐and‐pickup‐in‐store (BOPS) service has been widely treated by retailers as an important omni‐channel initiative. However, few studies have attempted to quantify the impact of BOPS usage on subsequent purchase behaviors or examine the critical roles of offline stores in the value generation of BOPS. Thus, through 25,724 BOPS instances used by 16,202 unique customers via a hybrid retailer, this study investigated the impact of customers’ BOPS usage on their online and offline purchase frequency and purchase amount. The moderating effect of offline store factors was investigated based on data from a focal retailer consisting of 110 stores in four cities. Using a combination of propensity score matching and difference‐in‐difference (DID) identification, our research found the significant positive effects of BOPS usage on offline purchase frequency and online purchase amount. We also found nuanced moderating effects of offline store characteristics (i.e., store density, product variety, and competition intensity) in the influence of BOPS usage on purchase behaviors. Our study thus generates important theoretical and practical implications for omni‐channel operations.

Transaction cost economics (TCE) is one of the most widely referenced organization theories in operations and supply chain management research. Even though TCE is a broadly applicable theory of governance, one of its specific topics of interest—the make‐or‐buy decision—readily aligns with some of the central research questions on how firms manage supply chains. However, both general management and operations management researchers sometimes misunderstand and misapply TCE's aims, assumptions, and logic. A common mistake is to read TCE as a theory of competence or of power. While TCE relates to both, TCE is essentially a theory of efficient governance of transactions in particular and exchange relationships in general. Our purpose in this study is to review the intellectual and theoretical foundations of TCE, its primary aims, and its applicability as a theory of supply chain efficiency. To this end, we discover much common ground between TCE and research in operations and supply chain management. We close by discussing implications for future research, focusing on how operations and supply chain management researchers could contribute to broader academic conversations on management and governance.

Learning by doing is a fundamental driver of productivity among knowledge workers. As workers accumulate experience working on certain types of tasks (i.e., they become specialized), they also develop proficiency in executing these tasks. However, previous research suggests that organizations may struggle to leverage the knowledge workers accrue through specialization because specialized workers tend to lose interest and reduce effort during task execution. This study investigates how organizations can improve specialized workers’ performance by mitigating the dysfunctional effects of specialization. In particular, we study how other sources of task experiences from the worker's immediate manager as well as the organization itself help manage the relationship between worker specialization and performance. We do so by analyzing a proprietary dataset that comprises of 39,162 software service tasks that 310 employees in a Fortune 100 organization executed under the supervision of 92 managers. Results suggest that the manager role experience (i.e., the manager's experience supervising workers) is instrumental in mitigating the potential negative effect of worker specialization on performance, measured as task execution time. Such influence, however, is contingent on cases in which organizational task experience (i.e., the organization's experience in executing tasks of the same substantive content as the focal task) is limited. Taken together, our research contributes to multiple streams of research and unearths important insights on how multiple sources of experience beyond the workers themselves can help capture the elusive benefits of worker specialization.

We consider a discrete‐time infinite‐horizon inventory system with non‐stationary demand, full backlogging, and deterministic replenishment lead time. Demand arrives according to a probability distribution conditional on the state of the world that undergoes Markovian transitions over time. But the actual state of the world can only be imperfectly estimated based on past demand data. We model the inventory replenishment problem for this system as a Markov decision process (MDP) with an uncountable state space consisting of both the inventory position and the most recent belief, a conditional probability mass function, about the actual state of the world. Assuming that the state of the world evolves as an ergodic Markov chain, using the vanishing discount method along with a coupling argument, we prove the existence of an optimal average cost that is independent of the initial system state. For our linear cost structure, we also establish the average‐cost optimality of a belief‐dependent base‐stock policy. We then discretize the uncountable belief space into a regular grid and observe that the average cost under our discretization converges to the optimal average cost as the number of grid points grows large. Finally, we conduct numerical experiments to evaluate the use of a myopic belief‐dependent base‐stock policy as a heuristic for our MDP with the uncountable state space. On a test bed of 108 instances, the average cost obtained from the myopic policy deviates by no more than a few percent from the best lower bound on the optimal average cost obtained from our discretization.

A transition to electric vehicles (EVs) is widely assumed to be an important step along the road to environmental sustainability. However, large‐scale adoption of EVs may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low‐price periods, if large numbers of EV owners use smart charging to benefit from low prices. This effect is expected to be amplified when EV owners adopt smart decision support to assist them with optimal charging decisions. Therefore, energy policymakers are interested in advanced pricing schemes that can smooth demand or induce demand that comes as close as possible to a desired profile. We show, through simulations calibrated with real‐world data, that current approaches to electricity pricing are limited in their ability to induce desired demand profiles. To address this challenge, we present adaptive pricing, a method to learn from EV owner reactions to prices and adjust announced prices accordingly. Our method draws on the Green Information Systems principles and can assist grid operators in ensuring the reliable operation of the grid. We evaluate our results in simulations, where we find that adaptive pricing outperforms current electricity pricing schemes, yielding results close to the theoretically optimal ones. We test our method in inducing both flat and extremely volatile demand profiles, and we see that in both cases it manages to induce EV charging close to the ideal scenario under perfect information.

In response to highly unpredictable sudden‐onset natural disasters, efficient and effective management of disaster relief inventory (DRI) is essential. This study proposes a simple framework based on three fundamental DRI‐related decision themes, which are (1) who respond to DRI calling, (2) where to locate DRI, and (3) how to control DRI, aiming at identifying the research gaps between the realities’ calling and the literature’s consideration with a focus on DRI management. We review relevant literature for each decision theme, summarize the insights provided by the literature, assess the practical needs and procedures and present our detailed perspectives on the research gaps in practice. The chief implication from our observations and arguments is that scaling up a DRI response for catastrophic disasters and events which prepares the whole community for worst‐case scenarios has been highlighted in academics and practice but lack operational research in the area of relevant decision‐making tactics. Existing research concerning the DRI management issues of various coordination forms of disaster responders, the location‐allocation decision‐making determinants with DRI prepositioning and the DRI control policy in the face of various disaster uncertainties is still far from being fully understood, appropriately characterized and profoundly discussed. We recommend the potential future research areas with the implications of this review in the last conclusion section, wishing to provide researchers a better understanding of the needs in the real‐world.

Developing a complex new product requires the firm both to deconstruct that product into subsystems and to create an organizational structure aligned with the product architecture. However, empirical evidence indicates that misalignments do occur and are usually one of two general forms: a “hidden dependency,” which is a missing link between teams responsible for two interacting subsystems; or “spurious communications” between two teams that interact even though their respective subsystems are not linked. We model the product development process as a search on a rugged landscape and study how misalignments affect the performance of the process in both design quality and convergence time. We find that the effects are mediated by the organizational decision‐making structure, and also by the interaction pattern among product subsystems. For instance, with a modular design, a project with a hidden dependency yields higher quality design solutions than a project with spurious communications or an aligned project. However, hidden dependencies cause a longer convergence time. Further, in modular designs spurious communications do not impact quality or convergence time when compared with aligned projects. The effect in non‐modular product designs depends on the organizational decision‐making structure and managerial capability. When decisions are made in a centralized organization that employs a capable manager, spurious communications improve the design quality but could delay the convergence time. We trace the cause of these effects to errors committed by teams in rejecting superior designs, which make the search process more exploratory and covering a wider area of the search landscape.