Editorial Statement

Department Structure

The field of Operations Management has broadened in several new and exciting directions in recent years. The journal is explicitly embracing some of these new directions while continuing to welcome work in traditional areas. To effectively do this, the journal has added 2 new departments and increased the scope of the three former departments of the journal. In particular, in addition to the flagship department of Manufacturing and Supply Chain Operations, a new department named Services, Platforms and Revenue Management has replaced the department of Service Operations. Platforms have emerged in recent years in many areas as evident from the prominence of firms such as Amazon and Airbnb. This department welcomes high quality papers in these three areas. The new department entitled Environment, Health and Society embraces research in areas related to sustainability and the environment as well as health and healthcare. This new department also aims to publish high impact research that addresses different societal as well as public policy issues that have become central to the field of OM. The department of Innovative Operations is now renamed Operational Innovation to signal its receptiveness to research in innovation more broadly in different areas not only of the OM field but also beyond. These could range from rethinking the operations and introducing innovation in an R&D process or a marketing process, among others, in the non-profit or for-profit space. Finally, to reflect the big impact of data and analytics in OM, we have established a new department to showcase research related to the Analytics in OM (this is also the name of the new department). The area of Analytics defined as “the scientific process of transforming data into insights for making better decisions” spans many (if not all) areas of OM research. Papers in this area can embrace data, models, algorithms and computation in many diverse applications. Those can include traditional areas such as supply chains, transportation and logistics, but also less traditional areas such as online markets, health, healthcare and public policy, to name a few. Finally, the journal is starting a new initiative called Practice Platform. The goal of this initiative is to bring practitioners closer to the OM community and expose OM researchers to exciting new directions in different industries where OM research can have an impact. This initiative will not accept submissions of papers at this stage; rather, practitioners will write short papers by invitation only that describe new problems and directions that will provide new opportunities to the community for important and impactful research. Our hope in this initiative is to facilitate more interaction between academics and knowledgeable practitioners. Our long-term goal through this initiative is to bring academia and practice in the OM field closer. Readers can find details about each department in the editorial statements of each department.

Publication Criteria

The journal plans to continue publishing high impact papers that study important and relevant questions in the field of OM. The aspiration of the journal is to be the preferred outlet for papers in the area of OM, broadly defined. Overall, the journal encourages papers that take different and diverse methodological and/or empirical approaches in OM research. In fact, the papers published in the journal can present theoretical, empirical, analytical and/or computational contributions broadly related to the OM field. Overall, the journal welcomes papers that make contributions that are descriptive, predictive and/or prescriptive as they relate to the field of OM and these can be at the strategic, tactical or operational level. Published papers can trade off the presence of data and mathematical rigor in both traditional and new problem areas in the field through traditional or new models and methods, but also should have an impact in OM practice and beyond.

In addition, we will strive for fast acceptance, preferably in two rounds of review, with two reviewers for each submitted paper. The criteria we will consider for accepting papers will trade off between the following five criteria.

1. Are there serious errors in the analysis that are not fixable in preferably two rounds?
2. Is the question/topic addressed in the paper poorly motivated about its relevance to operations management and originality?
3. Is the result/analysis in the paper incremental and does not have the potential to be generalizable?
4. Is the paper poorly executed in terms of assumptions and approaches, with insufficient depth of analysis?
5. Is the paper poorly written in terms of structure (logical flow, transitions) and exposition (notations, grammatical errors, typos, etc.)?

We believe a paper accepted for publication should strive for high standards but at the same time realize that a paper is not meant to be "the last paper" written in the area it addresses. We will keep on working on being constructive, positive and thoughtful in our reports. This is important to all of us and is important for the whole community.

Department Mission Statements

AI in Operations

Department Editors: Maxime C. Cohen, McGill University; and Tinglong Dai, Johns Hopkins University

It has become clear that AI is revolutionizing the business world as we know it. Recent advances in generative AI and agentic AI are changing how operational predictions and decisions are formulated, executed, monitored, and improved. These increasingly autonomous systems can synthesize information, augment and enrich data, generate solutions, test what-if scenarios and counterfactuals, interact with humans through natural language, and—when integrated with tools—plan and take actions in complex workflows. Accordingly, the Department seeks rigorous and impactful research that explains when, how, and why these technologies create operational value (or fail to do so), and that advances the science and practice of designing, implementing, and governing AI-enabled operational systems.

Our understanding of AI in Operations—both in methods and applications—is broad, with an emphasis on work where generative and agentic AI meaningfully reshape decision processes, organizational design, and operational performance. We welcome papers that develop new theoretical frameworks, empirical evidence, behavioral foundations, and computational approaches, including but not limited to research on: (i) AI-enabled planning and control that integrates learning with optimization and simulation; (ii) "language-to-decision" systems that democratize advanced analytics and decision tools for practitioners; (iii) AI lifecycle operations (data pipelines, labeling, training, deployment, monitoring, retraining, and incident response), multi-AI agent systems, and the resource and governance constraints that shape them; (iv) human–AI collaboration in operational settings, including trust calibration, bias and discrimination, override policies, accountability, incentives, and workforce adaptation; and (v) evaluation, transparency, and best practices that improve reliability and reproducibility, especially for agentic systems that act through tools. We also welcome papers that rigorously examine the workforce and societal implications of adopting and deploying modern AI systems in operational contexts. The Department embraces a variety of methods, including analytical modeling, optimization, stochastic models, econometrics and causal inference, field and lab experiments, and computational/algorithmic work. Given AI's cross-cutting nature, we are open to interdisciplinary contributions and papers that bridge traditional OM domains and emerging AI-enabled operational contexts.

Given the pace at which AI is evolving, we will strive to engineer an expedited review process for timely, high-impact papers, providing constructive feedback that shortens the review cycle and maximizes impact. To accomplish this, we may experiment with a new, modern (optional) approach to the editorial process with the goal of making the review process faster, fairer, more consistent, more transparent, and more constructive.

Analytics in Operations

Department Editors: Melvyn Sim, National University of Singapore; and Huseyin Topaloglu, Cornell University

Combined power of algorithms, data and computation has enabled analytics to be indispensable for solving challenging problems in operations such as, among others, the sharing economy, online markets, public policy, healthcare, transportation logistics, and supply chains. Accordingly, the Department seeks papers that use mathematical modeling and analysis to drive decision-making in data-rich environments. We are particularly interested in papers that have strong methodological contributions with a clear path to making an impact in the practice of data-driven decision-making.

Our understanding of analytics, in terms of both methodology and application areas, is broad. We welcome papers with focus on optimization, stochastic analysis, and data-intensive methods. Application areas of interest range from business applications in online platforms, retail and operations to societal applications in healthcare and politics. The department looks for papers that excel in both their methodological contributions and their ability to incorporate large-scale data into the decision-making process, as opposed to papers that purely use standard tools to test hypotheses on the data. We value demonstrating the effectiveness of the proposed methodology on real-world data, but do not put it forth as a requirement for all papers submitted to the department. Nevertheless, the submitted work should have a clear path to contributing to the science of data-driven decision-making. While management insight is appreciated, we understand that a novel algorithmic approach may not immediately translate into management insight.

Environment, Health and Society

Department Editors: Atalay Atasu, INSEAD; Özlem Ergun, Northeastern University; and Lauren Lu, Dartmouth College

We are interested in publishing innovative and impactful papers that contribute to improving health, environmental and societal outcomes by informing practices in business, government or non-governmental organizations. We look for papers that can provide insights that managers and policy makers need to address key challenges in a fast-changing and interconnected world. Of particular interest is work relevant to the UN Sustainable Development Goals, including but not limited to health, health equity, food safety and security, clean energy, climate action, responsible consumption and production, decent work, education and gender equality. We are also interested in work that expands the traditional boundaries of operations management by considering the societal externalities and equity implications of technology and operations management practices. We are open to a variety of approaches, including but not limited to algorithmic, behavioral, empirical, experimental, and modeling studies.

Manufacturing and Supply Chain Management

Department Editors: Wedad Elmaghraby (revisions only), University of Maryland; Feryal Erhun, Cambridge University; Dorothee Honhon, University of Texas at Dallas; and Mahesh Nagarajan, University of British Columbia

The Manufacturing and Supply Chain department aims to publish the best research in established and emerging areas of operations management. The core values of the department include intellectual depth, innovative thinking and value to applications. The department welcomes papers with rigorous and innovative analysis and papers with the potential for translatable actions and impact in practice. We aim to promote interdisciplinary research that is consistent with our core values.

We welcome papers that use a wide variety of methodologies, both descriptive as well as prescriptive in nature including but not limited to optimization, applied probability, game theory, econometrics, algorithm design, behavioral and decision science and simulation. We encourage work in emerging areas of operations that have historically been understudied and are of increased importance in today's global economy.

Operational Innovation

Department Editors: Ryan Buell, Harvard Business School; Felipe Caro, University of California, Los Angeles; and Kamalini Ramdas (revisions only), London Business School

We take a broad view of innovation as being about new ways of creating social and economic value. We are in particularly interested in publishing research that examines directions in which operational thinking and decision-making can unleash value through more transformational (as opposed to incremental) innovation. This includes research on:

• New processes for knowledge discovery and dissemination
• New processes to develop and manage AI and ML for real problem solving
• New business models for products and services
• New ways of managing innovation to spur transformational value creation

We are open to all methodologies. We welcome analytical and empirical methods (including experiments). We are particularly focused on research that is both rigorous and has direct practical implications.

Services, Platforms and Revenue Management

Department Editors: Mor Armony, NYU Stern School of Business; Jun Li, University of Michigan; Marcelo Olivares (revisions only), University of Chile; and Robert Swinney, Duke University

The Department of Services, Platforms, and Revenue Management seeks manuscripts that offer enduring knowledge to improve our understanding of the design, performance, and their key drivers of service systems, platform management, and revenue management. The services or platforms of interest could involve interactions among businesses and/or consumers, or be internal to an organization.

We value novel research questions, impactful contributions, and rigorous execution. A strong motivation derived from real world applications is necessary, along with a clear contribution to knowledge and/or a demonstration of managerial relevance. We embrace a variety of methods, including analytical modeling, empirical methods, lab and field experiments, and numerical methods (simulation, machine learning). Given the cross-functional nature of services, we are very much open to interdisciplinary contributions that use or integrate methods and/or knowledge from different fields (e.g., social sciences and engineering).

Industries of interest could range from physical to information-based services, the decisions could range from strategic to operational, and the scope of analysis could range from macroscopic (e.g., industries, organizations) to meso and microscopic (e.g., teams, individuals). Topics include: wait time and capacity management, pricing and revenue management, service contracting, digital platform management and scaling, marketplaces, e- and m-commerce, process design and digitalization, service quality management, servicization and productization, access and location, network management, customer/employee experience management, people operations and organizational design, service design and innovation, peer-to-peer services, crowdsourcing, service globalization, sustainable and/or ethical issues in services.

Relative to the other departments for which there is potential overlap (e.g., healthcare, analytics, innovation), we value contributions that are more practice-driven than methodological and that are generalizable beyond a single domain of application.

Special-Category Papers

In addition to regular research papers, the journal considers selected submissions in three special categories to augment its core editorial mission. To differentiate from regular research papers, a special-category paper is published with a banner that identifies its category.

OM Forum

An OM Forum manuscript offers responsible views on the history, status and future prospects of OM research and managerial practice. Contributions may be short or long, and may comment on earlier material appearing in the journal. In contrast to survey papers, OM Forum offers general opinions and perspectives on the field, rather than detailed coverage of specific research results. The goal of the OM Forum category, in short, is to provide an outlet for thought leadership and critical discourse within the OM research community.

OM Forum contributions are by invitation of the Editor-in-Chief. However, proposals for topics or nominations of potential authors may be submitted directly to the Editor-in-Chief. Papers are selected based on the interest and importance of the topic to the OM research community, the authoritativeness of the author(s), and the quality of exposition. Submissions are reviewed as appropriate to provide constructive feedback to authors.

Data Set

To facilitate data-driven research, the journal publishes short papers (1–4 pages) that merely describe a potentially useful data set. A Data Set paper describes how a data set was collected, provides a description of the variables in the data set, explains the construction of the data set, and offers potential uses for the data. These papers do not provide analysis of the data. Data Set papers are critically reviewed by an Associate Editor and at least two reviewers to ensure that the data set could be of potential use to OM research. A data set could provide a standard set of problems for algorithmic testing and comparison, or a data set could provide use in an econometric analysis, among other potential uses. The actual data are stored on the M&SOM website. Research papers that utilize data are also strongly encouraged to post their data on the M&SOM website. However, because the data set in such a paper should be described in the regular research paper, there is no need for the authors to create an additional Data Set paper. In other words, this category provides researchers with an opportunity to share their data with the community even if they do not provide research results.

Survey

The journal publishes authoritative research surveys of interest to its readership. Such surveys must meet the criteria of relevance and importance of regular research submissions.

Authors interested in submitting a survey paper must first contact the Editor-in-Chief with a short proposal. The Editor-in-Chief will then consult with an associate editor on the proposal. If the survey topic is deemed of sufficient potential interest to the OM community, a submission is encouraged and the paper undergoes the same peer review process as a regular research paper.

OM Practice

The journal remains very interested in "OM Practice" papers, namely, papers that report on innovative implementations of OM research to real problems or that rigorously document existing practice and demonstrate how current modeling approaches succeed or fail in practice. The expectation is that for publication these papers should make contributions of archival quality. Therefore, the review process for such papers is the same as for a regular manuscript. The criteria for publication are: importance of the problem, issue or phenomenon to the OM research community; relevance of the research to addressing the problem or understanding the phenomenon; rigor and novelty of the work; and quality of exposition. Hence, there is no need to designate such submissions as a "Special Category" paper. Also, we encourage OM researchers submit their OM practice papers to the biennial M&SOM Practice-Based Research Competition.