Editorial: Introducing Operations Management Research: Advancing practice through theory

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.

Operations management research: an update for the 1990s

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.

International operations management and operations management research: a comparative analysis

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.

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.

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.

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).

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.

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.

ABSTRACTDespite ambitious efforts in various fields of research over multiple decades, the goal of making academic research relevant to the practitioner remains elusive: theoretical and academic research interests do not seem to coincide with the interests of managerial practice. This challenge is more fundamental than knowledgetransfer, it is one of diverging knowledgeinterestsand means of knowledgeproduction. In this article, we look at this fundamental challenge through the lens of design science, which is an approach aimed primarily at discovery and problem solving as opposed to accumulation of theoretical knowledge. We explore in particular the ways in which problem‐solving research and theory‐oriented academic research can complement one another. In operations management (OM) research, recognizing and building on this complementarity is especially crucial, because problem‐solving–oriented research produces the very artifacts (e.g., technologies) that empirical OM research subsequently evaluates in an attempt to build explanatory theory. It is indeed the practitioner—not the academic scientist—who engages in basic research in OM. This idiosyncrasy prompts the question: how can we enhance the cross‐fertilization between academic research and research practice to make novel theoretical insights and practical relevance complementary? This article proposes a design science approach to bridge practice to theory rather than theory to practice.

The journal of Operations Management Research (OMR) is a rigorous journal that started its publication in 2008. It publishes short, focused research studies that advance both the theory and practice of operations management. Considering the relevant OMR’s contribution to the field of Operations Management in the last years, this study provides an overall assessment of the journal performance by conducting a retrospective review. To elaborate on OMR's temporal development in terms of publications, authors, affiliated institutions and countries, citation patterns, and conceptual structure, we extract publications from Scopus database for the period 2008–2020. We rely on bibliometric techniques in addition to bibliographic coupling, keyword analysis, and content analysis. 166 documents were analyzed using RStudio, VOSviewer, and Microsoft Excel. Findings emphasize OMR’s steady productivity growth (3.24%). Narrowly, Olhager J. is the most productive authors while Kalchschmidt M. and Stentoft J. are the most influential authors (H-index of 4). Furthermore, USA contributes to the highest number of publications while UK is the most influential country in terms of citations. Cranfield School of Management, UK stands as the top cited university. The analysis of the thematic evolution concludes to three main clusters: "Manufacturing and Supply chain Performance", "Six Sigma and Lean Management", and "Reshoring, Backshoring and Offshoring". This study recommends to further investigate the implications of the fourth industrial revolution and the sequels of COVID-19.

Statistical power in operations management research