Operations Management Research Research Articles

Data Analytics in Operations Management: A Review

Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management in three major areas—supply chain management, revenue management, and healthcare operations—and highlight some exciting directions for the future.

Developing a Guideline for Setting Targets for National Performance Management Measures to Assess Performance of the National Highway System in South Carolina

This paper presents a framework for establishing targets for national system performance measures for reliability. The paper outlines step-by-step procedures followed using the National Performance Management Research Data Set and provides a possible range of estimates for future years’ targets for South Carolina highways. This paper focuses only on the percentage of person-miles traveled on both Interstate and non-Interstate national highway system. The framework presented in this paper is reproducible for other state Departments of Transportation, and accounts for construction projects that might influence the future predicted target number(s).

Anesthesia Information Management Systems: Evolution of the Paper Anesthetic Record to a Multisystem Electronic Medical Record Network That Streamlines Perioperative Care

Initially devised in the 1890s, the traditional anesthetic record comprises physiological changes, crucial anesthetic or surgical events, and medications administered during the perioperative period. The timely collection of quality data facilitates situational awareness and point-of-care clinical decision making. The burgeoning volume and complexity of data in conjunction with financial incentives and the push for improved clinical documentation by regulatory bodies have prompted the transition away from paper records. Anesthesia Information Management Systems (AIMS) are specialized electronic health record networks that allow the anesthesia record to interface with hospital clinical data repositories, resulting in improvements in quality of care, patient safety, operations management, reimbursement, and translational research. Like most new technological advances, adoption was slow at first due to the challenges of integrating complex systems into daily clinical practice, questions about return on investment, and medicolegal liability. Recent technological advances, coupled with government incentives, have allowed AIMS adoption to reach an acceleration phase among US academic medical centers; widespread utilization of AIMS by 84% of US academic medical centers is expected by 2018-2020. Adoption among nonacademic US and European medical centers still remains low; information concerning Asian countries is limited to literature describing only single–hospital center experiences.

Coupling National Performance Management Research Data Set and the Highway Performance Monitoring System Datasets on a Geospatial Level

Integration of various datasets is crucial given the emphasis placed on holistic reporting of performance measures of various variables related to road transportation by the Moving Ahead for Progress in the 21st Century (MAP-21) Act. None is more confounding than the merger of geospatial datasets, which is necessary, for example, to combine vehicle travel time and volume information for road segments. Such a merged dataset is released through the National Performance Management Research Dataset (NPMRDS). The NPMRDS is supposed to exclusively cover the National Highway System (NHS) and Strategic Highway Network (STRAHNET) sub-selected from the Highway Performance Monitoring System (HPMS). However, one finds that the coverage is not perfect. There are not only many extra road segments included in the NPMRDS, but also some NHS/STRAHNET roads segments are not fully covered by corresponding NPMRDS segments. Further, one finds very little literature about the method Texas Transportation Institute uses to orchestrate the conflation. Therefore, it was endeavored to create a conflation algorithm which might perform better. The benchmark for the proposed algorithm is the identification of the segments wrongly conflated during the creation of the NPMRDS geospatial dataset. The proposed methodology uses a combination of five measures of similarity between the HPMS and NPMRDS segments. The proposed method successfully identifies significant numbers of mismatched segments: about 5% excess NPMRDS segments, and about 3% HPMS segments without NPMRDS counterpart.

Avoiding bias and fallacy in survey research: A behavioral multilevel approach

AbstractMuch like researchers in general management and organization theory interested in organization‐level phenomena, operations management researchers theorize and analyze entire operational systems, such as factories, supply chains, airports, or hospital wards. In discussing threats to measurement validity, we tend to focus on motivational factors such as social desirability, acquiescence, and other self‐serving biases. In this paper, I seek to complement these discussions by examining the broader question of how individual‐level responses can provide information on system‐level traits. In an empirical illustration, I apply multilevel factor analysis to examine the factor structure of organizational commitment in a sample of 2,355 informants embedded within 265 manufacturing plants. The conclusion is that understanding the multilevel essence of theoretical concepts is crucial and that confounding levels may lead to inferences that are not only biased but—much worse—fallacious.

Towards the use of bond graphs for manufacturing control: Design of controllers

The integration of control theory methods with operations management techniques enables to treat the dynamic facet of production and supply chain problems. In this paper, we revisit a bond graph/mathematical model devoted to depict the dynamics of multi-workstation production systems, and propose closed-loop simulation models with state feedback controllers, designed according to the pole placement and LQR methods. A real case was explored and simulations with the introduction of a disturbance in the production system were carried out. The results revealed that the model can provide prescriptive capacity adjustments and can help to define appropriate reference levels for the work in process in the system. It presents a disturbance-oriented behaviour that has advantages over pure push or pull systems commonly used in Production Planning and Control (PPC). In contrast to previous publications referring to the model, where the emphasis is set on the modelling process itself, the contribution of this paper concerns the design of the controllers, an aspect that is still left open for this kind of model. This design of the controllers has some peculiarities, since the state model of the production system does not fit the standard form. The presented approach is also relevant to the readership of the operations management (OM) and operational research (OR) fields for two reasons: (1) the use of control theory in OM provides good prescriptive solutions for dynamic production environments and the quality of these solutions is directly related to the design of the controllers; (2) the models generated by the integration of control theory and optimization, in some scenarios, present advantages over purely static optimization models.

Looking forward in operations management research

As we take over the Editor in Chief roles of the Operations Management Research journal, we want to look very briefly back and then cast our thoughts and aspirations for the field and the journal forward over the next few years and indeed decade.

OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research

A new generation of healthcare operations management (HOM) scholars is studying timely healthcare topics (e.g., organization design, design of delivery, and organ transplantation) using contemporary methodological tools (e.g., econometrics, information economics, and queuing games). A distinguishing feature of this stream of work is that it explicitly incorporates behavior, incentive, and policy considerations arising from the entanglements across multiple entities that make up the complex healthcare ecosystem. This focus is a departure from an earlier generation of research that primarily centered on optimizing given operations of a single entity. This paper provides an introduction to this burgeoning field and maps out research opportunities. We start with identifying key entities of healthcare delivery, financing, innovation, and policymaking, illustrating them on a healthcare ecosystem map (HEM). Next, we explore the HOM literature examining the interactions among various entities in the HEM. We then develop a taxonomy for the recent HOM literature (published in Manufacturing & Service Operations Management, Management Science, and Operations Research between 2013 and 2017), provide a tool-thrust graph mapping methodological tools with research thrusts, and situate the HOM literature in context by connecting it with perspectives from medical journals and mass media. We close with a reference to technological innovations that have the potential to transform the healthcare ecosystem in future decades.

Formative measurements in operations management research: Using partial least squares

The partial least squares (PLS) approach to structural equation modeling (SEM) appears across a wide array of business research publications, including those in operations management (OM). However, the authors’ summary of PLS use in the OM literature suggests some concerns and issues. First, the debate on the use of PLS-SEM is intensifying instead of being mediated despite the increasing use of PLS-SEM. Second, a lack of clarity exists among OM researchers about the use of reflective and formative measurements for constructs. Third, the validation of formative measurement is not routinely conducted in studies, which supports the need to summarize and illustrate the validation procedure of formative measurement. Without addressing these questions, the rigor involved in selecting reflective versus formative measures, especially in the OM field, is compromised. This research summarizes the procedures for choosing and validating formative measurement. The authors provide an illustrative OM example to demonstrate how the specific steps are applied. Through proactive selection and judicious operationalization of the measurement model and appropriate comparisons of the overall research model effectiveness based on criteria such as the R2 of the dependent variable OM, researchers provide a tool to help them extend existing theoretical frameworks and explore new theories.

Evolution of Operations Management Research Topics and Methods in the Digital Economy

This forum paper examines the past and the future of Operations Management (OM) research. First, we investigate the evolution of OM research from 1997 to 2018 by using machine learning tools to analyze all OM papers published in five journals (JOM, MS, M&SOM, POM, and OR), and find that the number of information/financial flow-focused OM research papers has increased steadily over the years. Second, we present three research topics motivated by the US-China trade war and the Covid-19 pandemic, and postulate that future OM research is likely to involve all three flows: material, information, and financial flows. Finally, we argue that, to achieve operational efficiency, resilience, and sustainability in the Industry 4.0 era, firms should build (or strengthen) three new capabilities: Connectivity, Clarity, and Continuity. As firms develop new ways to build these new capabilities, more innovative OM research ideas will ensue.

Data Analytics in Operations Management: A Review

Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management, in three major areas -- supply chain management, revenue management and healthcare operations -- and highlight some exciting directions for the future.

OM Forum—New Opportunities for Operations Management Research in Sustainability

Operations management (OM) research has made several important contributions to environmental sustainability over the last two decades. In this article, we point to a new opportunity on the horizon—the circular economy movement, which is gaining significant traction in practice. We introduce the concept and highlight the building blocks behind it. We also explain why this trend in practice can provide an opportunity for the OM community. To do so, we describe four companies that have been positioned as prime examples of implementing the circular economy concept and discuss the operational challenges faced by them. We draw on them to identify several promising and open questions for OM research and explain why an operations perspective is critical in this context.

Designing visual management in manufacturing from a user perspective

Many organisations use daily meetings, whiteboards and an information system for employee intra-communication. While Operation Management research is often management centred, Human Centred Design, instead, takes a user’s perspective. This research aims to reflect upon and describe a method, applied in practice in a double case study within manufacturing, on how to (re-)design meetings and visual management boards, and what type of information and key performance indicators are most relevant for the personnel. The paper proposes a lean Kata-improvement inspired design method, which takes the personnel’s perspective on design of daily visual management.

Vignettes-based research in behavioural operations management

Vignettes-based research in behavioural operations management

Efficiency Frontier Identification on the Context of Operations Strategy – A Study on Representative Constructs and Variables

The efficiency frontier is a function that indicates the maximum level of attainable performance result by a given set of inputs. The frontier is estimated based on the observation population of companies’ inputs and outputs, considering a representative sample. On the operations strategy context, inputs can be understood as the competitive priorities, with should be managed to lead through a superior organizational performance. Some methods for calculating technical efficiency are proposed in literature and all of them requires inputting and output variables. This paper presents a process for defining variables based on statically analysis of the round 4 of High-Performance Manufacturing (HPM) data. The HPM project seeks to identify the practices adopted by high performance organizations and applies a survey with companies in 18 countries. The survey includes 1597 questions in 12 categories, culminating in a huge set of variables. Multivariate Data Analysis techniques are performed to establish a representative set of variables. As a result, we present the constructs to evaluate each input and output performance. This is an important contribution for the development of a Decision Support Systems for Manufacturing based on the frontier estimation techniques, which could be seen as a representative innovation to operations management and production research.

Blockchain for Supply Chain Traceability: Business Requirements and Critical Success Factors

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.

A Content Analysis on Efficiency Frontier Identification and Operations Strategy

Operations strategy and efficiency frontier are two topics widely covered in Operations Management literature in its own development path. However, when we survey both topics in an integrative way it is not possible to identify a wide range of papers. Considering this research gap, an important contribution to operations management research area is given, approaching the two concepts in a complementary manner. Through a comprehensive systematic literature review, we find out 19 papers fully related to operations strategy and efficiency frontier identification, which composes the portfolio of analysis. This paper focuses on promoting a content analysis of this portfolio seeking to identify research gaps in this stream of study and therefore making it possible to strengthen it. As a result, a mapping of the research stream as well as future work opportunities is presented in the perspective of operations strategy approach, context, completeness analysis, and research proposal. Based on the content of the portfolio papers set, the relation between operations strategy and efficiency frontier concept are clarified and represented.

Highlights in Customer-driven Operations Management Research

The evolution from mass-produced to mass-customized and even personalized products, services, and product-service bundles leads to increasing complexity of operations management. These new realities challenge companies in both business-to-business and business-to-customer markets to compete not only on the traditional basis of cost, quality, and delivery time but also on the capabilities in managing this increasing operational complexity. The objective of this paper is to identify key challenges and opportunities in managing operations in complex customer-driven manufacturing covering the value network, requirements engineering, product configuration, and the production systems, as well as the opportunities for handling these through digitally-enabled methods and tools for materializing the Industry 4.0 vision of efficient lot-size-one productions.

Universal-deterministic and probabilistic hypotheses in operations management research: a discussion paper

Based on Karl Popper’s approach to science, this conceptual article aims at promoting a discussion on important issues debated in the OM literature. Our focus is on the different types of hypotheses used in OM investigations and the implications of this for the formulation and testing of theory with an emphasis on case study research. Given that Popper’s methodology calls for an adequate testing of scientific propositions (universal-deterministic or probabilistic), we illustrate how case study research can be used to conduct a severe test of a scientific theory. It is also explained how case studies can be utilized to propose universal-deterministic hypotheses that should then be tested. Our investigation further discusses why it is important to clearly acknowledge the differences between the two types of hypotheses. Otherwise, inadequate research designs, deficient policy advice, and other similar problems, can arise. Topics for future research and discussion are also offered.

Applying a longitudinal interpretive multi-case research method to study the employee impact of operations management systems in a micro firm setting

While many operations management journals are now publishing interpretive studies, there is still a need for more material on how to carry out such work under the interpretive gestalt. By offering a research account of a longitudinal interpretive multi-case study, this paper seeks to contribute to the literary gap in ‘how to do it [interpretive research]’. The purpose of this paper is to document a longitudinal operations management study in three micro-firms, where the catalyst for this study is the implementation of an electronic operations management system in each case. The paper chronicles the authors’ experience in a four-year multi-case study, and as such provides insight into design, development and execution of longitudinal interpretive operations management research, and the building of a theoretical model. While blueprints are not the purpose of such reflections, they can provide insight and advice to researchers, which is the underlying goal of this paper.