Operations management research: an update for the 1990s

The operations management research agenda: An update

PurposeThus far, no study collects evidence from practitioners directly to investigate the characteristics of operations management (OM) research that appears to have impacts on OM practice, nor do we know how practitioners evaluate the managerial relevance of OM research. This paper aims to answer two interesting and important questions: how do practitioners judge the managerial relevance of OM research; and whether practitioners' criteria on managerial relevance can help OM researchers improve the relevance?Design/methodology/approachA panel of senior executives was asked to read the top 10 most downloaded papers from theJournal of Operations Managementand fill the designed questionnaire. Following Cronbach's cumulative theory‐building process through which progress is made by successively testing the efficacy of the measures, this research examined the diverse disciplines, consolidated relevant findings, and integrated them into a tractable, meaningful research framework.FindingsThis paper reveals that practitioners evaluate our OM research by three criteria: whether academic research is applicable or implementable (solution oriented), whether academic research provides novel insights or new perspectives to management (eye opening), and whether academic research helps practitioners recognize their situations (accessibility).Originality/valueWhile the awareness of managerial relevance in OM research has been growing, few systematic, quantitative‐oriented empirical studies of practitioners' attitude toward academic OM research exist in current literature. This paper directly explores practitioners' opinions on managerial relevance through quantitative analysis and identified several possible dimensions to pursue managerial relevance in OM research.

A couple of years ago, one of us was a co-author on a paper presenting the results of a nice little operations management (OM) research study. Although narrowly focused, it came to some important conclusions and insights regarding an important concept in our field, but we were at a loss about where to try to publish it. It wasn’t a massive study with multiple confounded variables that had to be teased out using sophisticated statistical techniques, nor did it present impressive, earth-shaking conclusions, so it seemed obvious that it wasn’t suitable for the top journals in our field. Nor, some editors informed us, was it appropriate for the engineering or math or management oriented, journals where OM researchers sometimes publish their work. It seemed we had run out of alternatives. Whereas the field of Finance has 46 or more journals that have the words Finance or Financial in their title, and somewhat the same is probably true for Marketing, Economics, and Management, in OM, there are only about five journals with the words Operations Management somewhere in their title. It thus seemed clear that there was a tremendous need, and opportunity, for additional journals in our field. Moreover, since the demise of the Production & Inventory Management Journal published by APICS (formerly, the American Production and Inventory Control Society) and the Notes section of the Journal of Operations Management, there has been no place for academic scholars to read about and publish small but high-quality OM research studies. Between just these two publications themselves, that means about 70 short OM research papers are not being published each year, or are perhaps being published but in less preferred or less appropriate journals. And since that time, the field has grown in both size and stature, while the pressure for publishing continues to increase. As a result, we decided to take matters into our own hands and initiate an OM journal dedicated to smaller, focused papers that held particular promise of advancing practice, since OM is basically an applied field. The journal would not compete with existing A-level journals in the field but would instead aspire to publish shorter, more sharply focused work that exhibited solid methodological rigor. The journal would entertain all OM topics and all forms of research: survey, math modeling, case, field, qualitative, simulation, ethnography, hermeneutics, metaanalyses, and other such reputable methodologies. However, total manuscript length would be limited to 20 pages (including figures and tables) and the turnaround time would be fast, with a target maximum of 8 weeks. We Oper Manag Res (2008) 1:1–5 DOI 10.1007/s12063-008-0009-8

Operations Management Research was started to promote research that advances both the theory and practice of operations management (Meredith and McMullen 2008). In this guest editorial, we discuss how operations management (OM) researchers could more systematically engage in the exploration of technological opportunity. In this respect, operations management research is an activity shaped by technological invention and innovation in use. Whereas OM researchers currently are still mainly interested in the performance of OM processes and practices adopted by large populations (e.g. of firms or customers), they should also attend to early adopters trying out new technology as well as those combining and configuring existing technologies in new ways. Expanding attention from studying how established practice transforms operational contexts in terms of efficiency and reliability to the development of innovative practice requires an integrative methodological approach for OM research. We outline such an integrative approach, based on a science-for-design approach. To systematically link practice and science, five methodological steps for the systematic formulation of research agendas and the development and testing of design principles are proposed. As such, we provide a framework that effectively connects practice and the scientific knowledge base in developing field-tested and grounded OM knowledge.

Objeto: El objetivo planteado es analizar la investigación en docencia en Dirección de Operaciones (DO) publicada en revistas de alto impacto del área, con objeto de estudiar tendencias y establecer futuros retos.\n\t\t\t\t Diseño/metodología/enfoque: Se ha realizado un estudio bibliométrico en 45 de las principales revistas JCR de DO y Management para el periodo 1995-2010. Los 129 artículos identificados sobre investigación en docencia en DO han sido analizados.\n\t\t\t\t Aportaciones y resultados: Existe una escasez de artículos sobre docencia en las principales revistas de DO y Management. El 62.2% de las revistas analizadas no han publicado ninguno, y las restantes (17) han dedicado tan solo el 0.4% del total de sus publicaciones a esta temática. Predominan los trabajos descriptivos y teóricos/conceptuales, en los que se abordan innovaciones docentes. Entre los retos identificados destacar la necesidad de desarrollar trabajos empíricos de calidad que permitan conocer\n\t\t\t\t el impacto real de las metodologías docentes en la mejora del rendimiento del alumno (en sus diferentes vertientes).\n\t\t\t\t Limitaciones: El análisis se ha restringido a 45 de las revistas más importantes de DO y Management basándonos en trabajos previos. Esta muestra puede considerarse representativa dados los objetivos del estudio planteado.\n\t\t\t\t Implicaciones prácticas: Los investigadores en docencia en DO reciben un análisis los trabajos publicados que les permitirá orientar sus futuras investigaciones en función de los resultados y retos planteados. A los docentes de DO se les ofrece una selección de artículos que pueden ayudarle en el desarrollo de su estrategia formativa. Las empresas se beneficiarán de este trabajo en la medida de que nuestros resultados pueden ayudar a que se ofrezca al mercado laboral directivos mejor formados.\n\t\t\t\t Valor añadido: La innovación en docencia es la base para la mejora de la formación y de la capacitación que obtengan los alumnos y futuros gestores de empresa. No se habían realizado investigaciones previas que estudiaran las publicaciones sobre docencia en revistas de DO y Management y pusieran en valor este importante campo de estudio.

International operations management and operations management research: a comparative analysis

The relative importance of journals used in operations management research A citation analysis

Statistical power in operations management research

Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research

Social media data contain rich information in posts or comments written by customers. If those data can be extracted and analysed properly, companies can fully utilise this rich source of information. They can then convert the data to useful information or knowledge, which can help to formulate their business strategy. This cannot only facilitate marketing research in view of customer behaviour, but can also aid other management disciplines. Operations management (OM) research and practice with the objective to make decisions on product and process design is a fine example. Nevertheless, this line of thought is under-researched. In this connection, this paper explores the role of social media data in OM research. A structured approach is proposed, which involves the analysis of social media comments and a statistical cluster analysis to identify the interrelationships amongst important factors. A real-life example is employed to demonstrate the concept.

This paper provides guidelines for the design and execution of survey research in operations management (OM). The specific requirements of survey research aimed at gathering and analysing data for theory testing are contrasted with other types of survey research. The focus is motivated by the need to tackle the various issues which arise in the process of survey research. The paper does not intend to be exhaustive: its aim is to guide the researcher, presenting a systematic picture which synthesises suitable survey practices for research in an OM context. The fundamental aim is to contribute to an increase in the quality of OM research and, as a consequence, to the status of the OM discipline among the scientific community.

Digitization and artificial intelligence are radically changing virtually all areas across business and society. These developments are mainly driven by the technology of machine learning (ML), which is enabled by the coming together of large amounts of training data, statistical learning theory, and sufficient computational power. This technology forms the basis for the development of new approaches to solve classical planning problems of Operations Research (OR): prescriptive analytics approaches integrate ML prediction and OR optimization into a single prescription step, so they learn from historical observations of demand and a set of features (co-variates) and provide a model that directly prescribes future decisions. These novel approaches provide enormous potential to improve planning decisions, as first case reports showed, and, consequently, constitute a new field of research in Operations Management (OM). First works in this new field of research have studied approaches to solving comparatively simple planning problems in the area of inventory management. However, common OM planning problems often have a more complex structure, and many of these complex planning problems are within the domain of capacity planning. Therefore, this dissertation focuses on developing new prescriptive analytics approaches for complex capacity management problems. This dissertation consists of three independent articles that develop new prescriptive approaches and use these to solve realistic capacity planning problems. The first article, “Prescriptive Analytics for Flexible Capacity Management”, develops two prescriptive analytics approaches, weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM), to solve a complex two-stage capacity planning problem that has been studied extensively in the literature: a logistics service provider sorts daily incoming mail items on three service lines that must be staffed on a weekly basis. This article is the first to develop a kERM approach to solve a complex two-stage stochastic capacity planning problem with matrix-valued observations of demand and vector-valued decisions. The article develops out-of-sample performance guarantees for kERM and various kernels, and shows the universal approximation property when using a universal kernel. The results of the numerical study suggest that prescriptive analytics approaches may lead to significant improvements in performance compared to traditional two-step approaches or SAA and that their performance is more robust to variations in the exogenous cost parameters. The second article, “Prescriptive Analytics for a Multi-Shift Staffing Problem”, uses prescriptive analytics approaches to solve the (queuing-type) multi-shift staffing problem (MSSP) of an aviation maintenance provider that receives customer requests of uncertain number and at uncertain arrival times throughout each day and plans staff capacity for two shifts. This planning problem is particularly complex because the order inflow and processing are modelled as a queuing system, and the demand in each day is non-stationary. The article addresses this complexity by deriving an approximation of the MSSP that enables the planning problem to be solved using wSAA, kERM, and a novel Optimization Prediction approach. A numerical evaluation shows that wSAA leads to the best performance in this particular case. The solution method developed in this article builds a foundation for solving queuing-type planning problems using prescriptive analytics approaches, so it bridges the “worlds” of queuing theory and prescriptive analytics. The third article, “Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management” proposes a novel prescriptive analytics approach to solve the two capacity planning problems studied in the first and second articles that allows decision-makers to derive explanations for prescribed decisions: Subgradient Tree Boosting (STB). STB combines the machine learning method Gradient Boosting with SAA and relies on subgradients because the cost function of OR planning problems often cannot be differentiated. A comprehensive numerical analysis suggests that STB can lead to a prescription performance that is comparable to that of wSAA and kERM. The explainability of STB prescriptions is demonstrated by breaking exemplary decisions down into the impacts of individual features. The novel STB approach is an attractive choice not only because of its prescription performance, but also because of the explainability that helps decision-makers understand the causality behind the prescriptions. The results presented in these three articles demonstrate that using prescriptive analytics approaches, such as wSAA, kERM, and STB, to solve complex planning problems can lead to significantly better decisions compared to traditional approaches that neglect feature data or rely on a parametric distribution estimation.

This work protocol form part of a three-phase publication (Marin-Garcia, 2019). Its objective is to establish a work procedure to answer these questions: 1) in which journals have articles about Operations Management with Partial Least Squares (PLS) been published?; 2) Do the results of previous reviews on this topic still prevail based on the very limited set of journals that it have been conducted (and before substantial modifications were made to report methods of PLS-based studies)?; 3) Do recent articles fulfil report recommendations; 4) What kind of measurement model has been considered for the constructs most frequently used in the selected articles?; 5) What are the usual R2 values in the cross-sectional studies represented in the selected articles?; 6) Within what statistical power range do the relations analysed with PLS fall?The article summarises current recommendations to select the analysis procedures and to report the research works that have used structural equations with PLS. We believe that this is an excellent contribution for researchers because it helps to improve the analyses and reports that derive from using PLS to, thus, increase the probabilities of them being accepted in relevant journals.Another contribution made by the present work, apart from establishing the aforementioned protocol, is to provide a list of the recent articles about operations management that have used PLS and the coding procedure to conduct our systematic review (to be subsequently published).

Following trends are not limited to the fashion and beauty industry. Many researchers in papers, master’s degree thesis, and PhD dissertations prefer to solve domain questions or problems with research methodologies previously used by others in the field, mainly, case study research in operations management and technology management studies. This paper is about to stress on adding design research methodology (DRM) as a very useful and practical methodology in management studies that can meet the main aim and concern facing researchers in management studies: produce and develop practical domain knowledge which meets the rigor and relevance functions of the research. This study unlocks all the aspects of DRM and represents a methodological perspective, and sheds light on the reasoning behind why and how DRM should be used more as a methodology in management studies in general, and operations and technology management specifically.

The objectives of the paper are to determine the state of operations management research in the 2000s from the standpoint of topics and methodologies to look for trends, and to examine implications for future research. We assessed the state of research in operations management by examining the research topics addressed such as manufacturing functions and performance, operations priorities and performance, operations practices and performance, organisational culture and performance and advanced technology and performance. In this paper, an attempt has been made to review the status of literature on basic operations management aspects and performance. This literature review paper is based on 104 peer reviewed journal articles from 2000 to 2015, i.e., Science Direct, Emerald, Google Scholar, Meta Press, Inderscience and Pro Quest. Our findings show some significant changes in the kind of research being performed in the 2000s. Based on this, possible research issues are also identified viz. sustainable manufacturing performances, sector specific best manufacturing practices, effect of organisational culture on manufacturing performance and relevance of operational performance to small and medium scale enterprises.