Journal of Operations Management最新文献

While the field experiment is a powerful and well-established method to investigate causal relationships, operations management (OM) has embraced this methodology only in recent years. This paper provides a comprehensive review of the existing OM literature leveraging field experiments. It also serves as a one-stop guide for future application of field experiments in the OM area. We start by recapping the characteristics that distinguish field experiments from other common types of experiments and organizing the relevant OM studies by topic. Corresponding to the commonly overlooked issues in field experiment-based OM studies, we then provide a detailed roadmap, ranging from experimental design and implementation to post-experiment analysis. We further outline the methodological and practical issues as well as corresponding solutions when applying field experiments. We conclude by identifying future research directions from an OM perspective.

Multiple organizations working jointly on shared activities in inter-organizational projects for a defined period of time are used increasingly to coordinate the supply of complex products, subsystems, and services across many industries. Despite the growth in inter-organizational networks as an organizational form, scholars have only recently begun to identify how lead organizations orchestrate the coordination of multiple parties with disparate goals, responsibilities, and capabilities. Prior work offers limited insights into the choice of network governance forms, and how coordination is undertaken by the network orchestrator to govern these networks. We conducted a longitudinal study of four networks to deliver vital services into a large project. We identified how the choice of network governance form was based on task complexity. A shared governance form was chosen for networks developed to deliver routine services, whereas a lead organization governance form was chosen for networks set up to deliver complex services. However, findings showed that the selection of an appropriate governance form was not sufficient for ensuring high performance. The network orchestrator's mode of coordination (formal or informal), the intensity of coordination (active or passive), and fit with the form of governance form (shared or lead organization governed) was important in driving performance.

The COVID-19 pandemic has created significant disruptions in both demand and supply. Our study makes use of such dramatic changes in demand and supply during the pandemic to examine resource dependence and power balancing/unbalancing issues in buyer–supplier relationships. Specifically, we investigate the effect of customer and supplier concentrations on firm resilience during the pandemic. Drawing on resource-dependence theory (RDT), we theorize that shifts in demand and supply in different pandemic stages influence the effect of customer and supplier concentrations on firm resilience by altering the power dynamics between focal firms and their concentrated customers and suppliers. Central to our theorizing is that the worsening power imbalance is more detrimental. Measuring firm resilience by loss and recovery (i.e., change) in productivity, our analysis of 23,440 Chinese listed firms' quarter observations from 2019 to 2020 shows that customer concentration is negatively related to firm resilience in the disruption stage but has no effect in the restoration stage. Supplier concentration is positively related to firm resilience in the disruption stage but undermines firm resilience in the restoration stage. These findings largely confirm our theoretical propositions. We discuss the theoretical and managerial implications.

Trust is considered essential to interfirm knowledge acquisition across borders. However, recent studies indicate that interfirm collaboration can thrive even in low trust situations. This study proposes that low trust can facilitate supplier knowledge acquisition (SKA) across borders if it is aligned with the other party's trust. Rather than high trust from a single party, trust congruence—similar levels of trust from buyers and suppliers regardless of their levels—may be more predictive of successful knowledge acquisition across borders. We further propose that different types of trust congruence have distinct effects on SKA across borders. Last, we propose that the influence of trust congruence on SKA is conditional on the presence of effective knowledge appropriability mechanisms (KAMs) and institutional distance between buyer and supplier home countries in cross-border transactions. Data are collected from 648 managers working for 162 matched dyads of manufacturing suppliers in China and buyers from 24 OECD countries. The findings support the propositions and suggest that supply chain managers should pay attention to aligning trust levels with their partners in cross-border transactions, and especially do so when lacking effective KAMs and when buyers and suppliers are from similar institutional environments.

Machine learning's (ML's) unique power to approximate functions and identify non-obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem-solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well-developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.

If there is some truth to the adage that justice must not only be done, but must be seen to be done, then surely it must be seen to be done in a timely manner. Yet court congestion and delays – which threaten to undermine the justice system – have become global phenomena with significant adverse implications for social welfare, economic development, and civil rights. This work describes an application of the Theory of Constraints (TOC) to the judicial system for the purpose of designing an intervention to alleviate court congestion – an intervention that won the 2012 Goldratt Foundation New Knowledge Award. In cooperation with the Jerusalem District Court in Israel, the judicial process was reviewed through the lens of TOC, and a set of operational changes was implemented to reduce case processing time. Data collected before and after this intervention indicate the potential of achieving lead-time reduction by applying TOC to judicial environments.

Demand planning is informed by demand forecasts, service level requirements, replenishment constraints, and revenue projections. “Demand forecasts” differ from “demand plans” in that forecasts only represent the distribution (or the most likely value) of product demand. Motivated by common forecasting practices in industry, our research examines whether forecasters recognize this difference between demand forecasts and demand plans. Based on a lab experiment informed by data from two large FMCG companies, we found that forecasters factor service levels into their demand forecasts, even when they are clearly instructed to predict the most likely demand and incentivized to minimize the forecast error. We establish that this result holds for students and practitioners alike, and show that this behavior is driven by the service level information, and not some other anchor. We use data from a recent industry survey to support the external validity of our key findings.

A chasm is growing between the advanced technologies available for improving manufacturing operations and those effectively used in practice. The vision of Industry 4.0 is to mobilize industry to seek out these possibilities for improvement and to close the gap between opportunity and reality. However, when compared with more established improvement opportunities such as lean manufacturing, the digitalization of manufacturing lacks in both paradigmatic examples and an understanding of how to achieve the benefits. This lack is a complication of concern: Without an appropriate operations strategy to capture the value of digitalization, manufacturing companies will be unable to focus on technological investments and operational changes. To address this concern, operations management academics must develop new theory through active engagement in the practice of digitalization in manufacturing. This research presents a paradigmatic example, based on engaged scholarship, focused on effectively combining novel object-interactive and conventional manufacturing syntax for benefiting from digitalization in internal operations and the wider supply chain. The contribution to literature is a novel operations strategy—hybrid digital manufacturing—for capturing the value of Industry 4.0 technologies.