Service Operations Management Research Articles

Hybrid modeling methodology for integrating customers’ behaviors into system simulation to improve service operations management

Service quality is significant for improving customer satisfaction and enhancing competitive advantages of service business. Due to the dynamic nature of service activities, system simulation has become popular for researching service operations management during the past few years. However, most previous studies about service operations system simulation use only one single modeling paradigm, making it still challenging for exploring individual consumers’ behaviors and evaluating the relative efficiency of different alternative strategies for service operations improvement. In this context, a hybrid modeling methodology framework is proposed in this study for integrating customers’ behaviors into service operations system simulation. Specifically, we designed an algorithm for simulating consumers’ behavioral decisions based on cognitive psychology. And then, individual consumer's decision behavior was modeled as an agent, and integrated into a hybrid simulation model developed based on DES (Discrete Event Simulation) coupled with ABS (Agent-Based Simulation) paradigms. Moreover, we involve cost-benefit analysis for further exploring different alternative operation improvement strategies based on simulation experiments. We then design and implement a prototype simulator based on the methodology framework and apply it in a real case study to demonstrate its functionalities and utility. The results successfully obtained indicates that reducing the reserved customers’ numbers blindly cannot always improve customers’ satisfaction, and improving the service quality to all customers is thus necessary, rather than being concerned with only the satisfactions of reserved (VIP) customers. The findings also reveal the dynamic influence mechanism of service intervention time to giving-up customers’ numbers, indicating a promising future for applying the methodology framework in improving service quality and customer satisfaction. This work offers new perspectives and valuable demonstration on promoting future strategic researches about service operations management improvement with the integration of customers’ behaviors and service operations system simulation though emerging hybrid modeling paradigm.

Using Mobile Device Data to Understand the Effect of Stay-at-Home Orders on Residents’ Mobility

Problem definition: This study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1014 .

Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption

Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.1020 .

Predicting delays in service operations

Delays constitute a key challenge in the management of service operations, causing substantial quality and cost issues. Delays in one service event can cause delays in another service event and so on, which creates challenges in the management of complex services. Assuming a lower-triangular matrix formalism, we develop a novel approach to modelling such chains of delays in complex service operations such as health care and software development. This approach can enable service managers to identify, understand, predict and control delays. Our research provides a novel theoretical contribution to the literature on service delays.

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: M. Samorani’s work was partly funded by the 2020 Leavey Research Grant. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0999 .

From product to service quality: the role of managerial mindsets

This study is a response to calls for a more comprehensive treatment of service operations management amid concerns over the prevalence of poor service quality. Building upon the ideas of total quality management as an ethos of continual learning and quality improvement, this study aims to elucidate different managerial mindsets towards service quality and the resulting impact on the organisational learning environment. Using a qualitative case study methodology of a sample of UK-based service organisations, the findings point to a trichotomy of managerial mindsets towards service quality each connected to different types of organisational learning. These include (i) outcome mindset with a focus on short-term learning, (ii) process mindset with a focus on intermittent learning, and (iii) process-outcome mindset with a focus on continuous learning. We suggest several propositions for further empirical testing and highlight practical implications of the findings.

OM Forum—A Vision of Responsible Research in Operations Management

Are we contributing positively to the society at large by research that we conduct in the field of Operations Management? Currently, the answer is probably closer to “no” than to “yes.” We often do not realize it, but there is very real cost of conducting our research nonresponsibly. In this invited OM Forum contribution, which is based on my Manufacturing and Service Operations Management Distinguished Fellow lecture given at the University of Texas at Dallas in 2018, I discuss what I believe it means to conduct research responsibly, I offer some specific steps to improve the relevance of what we do, and I argue that this approach gives our field a new opportunity to make broader impact by expanding the horizon of our work. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

To support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71871207, 71991464/71991460, and 71921001]. Supplemental Material: Data and the online appendix are is available at https://doi.org/10.1287/msom.2021.0994 .

Understanding the Value of Fulfillment Flexibility in an Online Retailing Environment

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations

AboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeManufacturing & Service Operations ManagementVol. 23, No. 4 Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations Guillaume Roels , Bradley R. Staats Guillaume Roels , Bradley R. Staats Published Online:12 Aug 2021https://doi.org/10.1287/msom.2021.1021 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 23, Issue 4July–August 2021Pages 745-1004, C2 Article Information Metrics Information Received:May 27, 2021Accepted:May 27, 2021Published Online:August 12, 2021 Copyright © 2021, INFORMSCite as Guillaume Roels , Bradley R. Staats (2021) Introduction to the Manufacturing & Service Operations Management Special Issue on People-Centric Operations. Manufacturing & Service Operations Management 23(4):758-760. https://doi.org/10.1287/msom.2021.1021 PDF download

The Forty-year History of Revenue Management: Bibliometric Analysis

This paper presents research trends, leading publishers, influential articles, and shifting concerns in the field of Revenue Management for over forty years based on bibliometric analysis. Bibliometric data was retrieved from Web of Science core collection with a well-defined strategy. The data was processed using Network Analysis Interface for Literature Studies Project scripts. Subject-wise and year-wise research trends were presented. The shifting concerns in RM in terms of topic, method, and domain were highlighted using keyword analysis. In general, RM showed an increasing number of published papers with exponential manner every year. The research core in RM covered the three major decisions in RM including pricing, quantity control, and structural decision. It was highlighted that RM’s concern has shifted from single-firm decision to be more consumer- and competition-centric. The data showed that the needs of empirical study and more advanced quantitative methods for complex and real-time problems were urged. In addition, the adoption of RM was extended for industries with semi-flexible capacity. The top influential publishers were Decision Sciences, Operations Research, Management Science, and Management Science Manufacturing & Service Operations Management.

Listening to online reviews: A mixed-methods investigation of customer experience in the sharing economy

Understanding customer experience is an essential part of service operations for sustaining business in the sharing economy. We investigate the relationship among customer experience, perceived value, and customer loyalty from a stimulus-organism-response (S-O-R) perspective. Using a dataset of 4166 listings in a leading Chinese accommodation-sharing platform and text mining and econometric methods to analyze online customer reviews, we find that customer experience manifests in the physical environment and human interaction dimensions. The results show a positive association among customer experience, perceived value, and customer loyalty. Notably, the physical environment and human interaction are equally important in influencing customers' value judgments about their consumption experience. Moreover, perceived value has a stronger positive effect on attitudinal loyalty than on behavioral loyalty. This study adds new insights into the customer experience-perceived value-customer loyalty path by showing that the physical environment and human interaction have the same importance in affecting perceived value and identifying the subtle difference between attitudinal and behavioral loyalty influenced by perceived value in the accommodation-sharing economy. Furthermore, these findings provide managerial insights for service operations management and marketing strategy planning.

Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

Problem definition: Translate data from electronic health records (EHR) into accurate predictions on patient flows and inform daily decision making at a major hospital. Academic/practical relevance: In a constrained hospital environment, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63,432 admissions at a large academic hospital (50% female, median age 64 years old, median length of stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination, and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques that is equally accurate, interpretable, frugal in data and computational power, and production ready. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: The research was funded by Beth Israel Deaconess Medical Center. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0971 .

Managing inventory at GoUNESCO

Learning outcomes This case calls out the following inventory management learning objectives in a project context: to understand the project categories of inventories and their drivers for planning purposes, to learn how to manage different categories of inventory and to learn the importance of effective allocation of inventory at different project locations. Case overview/synopsis GoUNESCO is a start-up formed with the objective of providing a platform for laypersons to engage with places of heritage value. Among others, one of the key components of this platform is “go heritage runs,” in short GHR. In the past three years, the popularity of runs organized under the brand GHR has been increasing, both in terms of locations and the number of participants in each location in successive years. Inventory planning and management for projects is tricky. Different categories of inventory have different implications of shortages or excesses. The case dwells into inventory planning and management of t-shirts and medals, to drive home this point. Complexity academic level The case can be positioned in service operations management or supply chain management courses while covering inventory management topics for both Master of Business Administration (MBA) and executive MBA students. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 11: Strategy.

2020 M&SOM Meritorious Service Award

The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, Editor-in-Chief Georgia Perakis would like to express her deepest gratitude to all those who served as reviewers for the journal in 2020. Among all reviewers, some individuals have distinguished themselves by reviewing several manuscripts and with each manuscript by writing a fair, critical, and constructive review in a timely fashion. In recognition of their outstanding service provided to support the journal’s scholarly mission, M&SOM grants the 2020 Meritorious Service Award to…

Acknowledgment to Editors and Reviewers (2020)

The continued success of Manufacturing & Service Operations Management (M&SOM) relies on the support of authors and reviewers. On behalf of M&SOM, I would like to express my sincere appreciation to the department editors, guest editors, associate editors, guest associate editors, and reviewers who provided expert counsel and guidance on a voluntary basis. The following list acknowledges those who served from January to December 2020. Through their efforts, the journal was able to provide submitting authors with timely, thoughtful, and constructive reviews. I hereby acknowledge their service for the journal, and gratefully appreciate their contributions for our profession. Georgia Perakis, M&SOM Editor

Effect of Live Chat on Traffic‐to‐Sales Conversion: Evidence from an Online Marketplace

Over the years, online retailers have increasingly embraced live chat to improve customer service and facilitate transactions. We examine how online sellers’ use of live chat influences their online traffic conversion into sales. Like brick‐and‐mortar stores, a fundamental task for online stores is to convert traffic into purchases, an important problem in retail service operations management. We argue that live chat can increase online sales conversion by performing the functions of informing and persuading. We explore how the two roles of live chat interact with existing information regarding sellers and their products, such as product descriptions, ratings, price, and reviews, as these types of information also influence consumers’ perceived product quality and help mitigate uncertainties in product fit to their needs. We apply a random‐coefficient model in a Bayesian hierarchical framework and test the model using a panel dataset from Taobao, the leading C2C platform in China. Our results suggest that live chat has a positive impact on conversion and that the strength of this positive effect depends on seller and product characteristics. The positive effect is stronger when product information on web pages is less comprehensive (where live chat manifests an informative role), and when the perceived value of the product is higher (where live chat manifests a persuasive role) with either a higher product rating or a lower product price. Our results provide relevant and useful implications for online merchants and platform owners.

Ayushman Bharat: Service Adoption Challenges in Universal Healthcare System

The Ayushman Bharat, the universal healthcare scheme in India, faced service adoption challenges after its launch in 2018. It was an enigma for the top management in Ayushman Bharat regarding slower service adoption of a free mass healthcare coverage scheme by the target population. The case focuses on the service adoption challenges from patient and physician perspectives while implementing a universal healthcare system. It provides insights to policymakers, physicians, service operations managers, and healthcare administrators regarding managing the universal healthcare system’s implementation challenges in a developing country context. Research questions/Objective: This study aims to understand service adoption challenges in a universal healthcare system setting. The study explores the following research questions: How is service adoption theory applied in a universal health coverage program? What should be the integrated marketing communication plan to improve the awareness about a universal healthcare program? Links to theory: The study uses service adoption theory. It analyzes service adoption challenges for the universal healthcare system in India called Ayushman Bharat. It also uses literature on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Phenomenon studied: The case study uses pan India patient and physician data to explore service adoption issues in Ayushman Bharat - a universal healthcare scheme in India. Case context: The primary data collected through the field (hospital) visits and interaction with patients and physicians of Ayushman Bharat form the basis of this case study. Findings: The study emphasizes on performance expectancy, ease in availing of the service, positive social influence, and facilitating conditions for service delivery of Ayushman Bharat. The Ayushman Bharat scheme’s performance expectancy means how being a healthy individual would contribute to better performance at the workplace. The effort expectancy is the level of ease an eligible Ayushman Bharat scheme can avail the service at the empanelled hospital. The social acceptance of the Ayushman Bharat scheme by friends, peers, and people in the vicinity would create a positive social influence. The facilitating conditions in the Ayushman Bharat scheme are the government’s capacity to provide organizational and technological infrastructure to support this universal healthcare program. Discussions: The use of service adoption theory and the UTAUT model to enhance the adoption of the universal healthcare system in India have been discussed in the case study.

Statement of the Manufacturing & Service Operations Management Journal

Statement of the <i>Manufacturing &amp; Service Operations Management</i> Journal

An Analysis on the Impact of Firm Size on Profitability of Listed Deposit Money Banks in Nigeria

This paper examines the impact of firm size on the profitability of listed Deposit Money Banks (DMBs) in Nigeria, carried out based on the historical panel data analysis. To achieve this objective; an ex-post factor research design was employed. Data were generated from the annual reports and accounts of the sampled quoted Deposit Money Banks (DMBs) from 2005 – 2014. Fixed-effect and random-effect Generalized Least Square (GLS) regression technique was used as tool of data analysis. The findings establish that the independent variable (firm size) has insignificant positive effect on the DMBs’ profitability proxies represented by ROA and ROE. It was concluded that Firm Size does not have significant impact on the profitability of the listed DMBs in Nigeria. The paper recommends that DMBs should maintain optimum firm size through effective management of service operations which is crucial for controlling labor cost by using the smallest possible amount of inputs which include labor and other operating cost to bring out maximum result toward improving the corporate profitability significantly.