Service Operations Management Research Articles

Service Operations Management and Administrative Efficiency of a Technology University in GuangDong China

Service operation management has a significant impact on administrative efficiency. In a science and technology university in Guangdong Province, the diversity and complexity of service demand have a direct impact on the allocation of administrative resources. For example, with the increasing emphasis on scientific research services, more administrative resources are needed to optimize the process of scientific research equipment procurement and scientific research fund management. In terms of teaching services, if the school decides to carry out a new characteristic curriculum, the administration needs to allocate resources to arrange suitable classrooms and provide appropriate teaching equipment. This kind of demand change in service operation and management always tests the rationality and timeliness of resource allocation of administrative departments. If the administrative departments cannot efficiently adjust resource allocation according to service needs, it will lead to bottlenecks in service operation and affect the overall quality of teaching and scientific research services. The efficiency of administrative management also has a negative effect on service operation management. Efficient administrative management can bring positive promoting effect to service operation management.

Call for Papers: Manufacturing & Service Operations Management Special Issue on Responsible Retail Operations

Call for Papers: <i>Manufacturing &amp; Service Operations Management</i> Special Issue on Responsible Retail Operations

Frontiers in Operations: Optimal Genetic Testing of Families

Problem definition: Through the laws of inheritance, knowing an individual’s genetic status informs disease risk for family members, but current protocols for deciding whom to genetically test only consider one person at a time rather than design an optimal testing plan for the entire family. Methodology/results: We develop a Markov decision process framework for maximizing the net benefits of genetic testing that integrates a Bayesian network of genetic statuses, with a functional representation of cost-effectiveness. Our model provides a contingent sequence of family members to test one at a time, that is, a plan that dynamically incorporates new test results, revealed sequentially at random, to decide who next to test. In the general case, we show that optimal stopping follows a structure with two-sided thresholds, previously known only for individual testing. Although the optimal testing sequence, in general, is contingent on the family test results, in the special case of sibling-only tests we can identify this sequence a priori. Our numerical case study, which was conducted in a realistic BRCA1/2 testing setting, demonstrates that an optimal policy significantly improves cost-effectiveness over existing policies. Thus, our framework offers a promising and powerful new approach to genetic testing. Managerial implications: In an optimal policy, prioritizing testing family members who might otherwise not have been tested can lead to an overall improvement in familial health value, surpassing even the most cost-effective existing protocols. From a management perspective, healthcare organizations and insurance companies can potentially save costs by implementing this approach for such families. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: D. Adelman is grateful for financial support from Booth School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0057 .

Frontiers in Operations: Employees vs. Contractors: An Operational Perspective

Problem definition: We consider a platform’s problem of how to staff its operations given the possibilities of hiring employees and setting up a contractor marketplace. We aim to understand the operational difference between these two work arrangement models. Methodology/results: We consider a model where demand is not only stochastic but also evolving over time, which we capture via a state of the world that determines the demand distribution. In the case of employees, the platform controls the number of employee hours it uses for serving demand, whereas in the case of contractors, it sets the wage paid to them per utilized hour. We show that although the employee problem is equivalent to a standard newsvendor, the contractor one corresponds to an unusual version of the newsvendor model where utilization is the control variable. Managerial implications: This distinction makes the contractor model more flexible, allowing us to prove that it performs significantly better, especially if the order of magnitude of demand is unknown. Meanwhile, hybrid solutions that combine both employees and contractors have complex optimal solutions and offer relatively limited benefits relative to a contractor marketplace. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: This research was partially supported by the National Natural Science Foundation of China [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0029 .

2023 M&amp;SOM Meritorious Service Award

The continued success of Manufacturing &amp; Service Operations Management (M&amp;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&amp;SOM, editor-in-chief Georgia Perakis expresses her deepest gratitude to all those who served as reviewers for the journal in 2023. 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&amp;SOM grants the 2023 Meritorious Service Award to…

OYO Hotels USA: Coming to America

This case studies the growth of OYO Hotels (OYO) to illustrate the operational processes necessary to succeed in the service sector. The case allows for a discussion of employee- and customer-management systems, tech-driven solutions, and profit drivers. The material unfolds OYO's growth and its solution for making economy hotels discoverable and bookable online.The case raises a series of questions around OYO's business model, its ability to translate across global markets, and growth potential. It has been successfully taught in a second-year MBA class on the management of service operations.

Frontiers in Operations: Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic

Problem definition: The opioid epidemic is a crisis that has plagued the United States for decades. One central issue of the epidemic is inequitable access to treatment for opioid use disorder (OUD), which puts certain populations at a higher risk of opioid overdose. Methodology/results: We integrate a predictive dynamical model and a prescriptive optimization problem to compute high-quality opioid treatment facility and treatment budget allocations for each U.S. state. Our predictive model is a differential equation-based epidemiological model that captures the dynamics of the opioid epidemic. We use a process inspired by neural ordinary differential equations to fit this model to opioid epidemic data for each state and obtain estimates for unknown parameters in the model. We then incorporate this epidemiological model into a corresponding mixed-integer optimization problem (MIP) that aims to minimize the number of opioid overdose deaths and the number of people with OUD. We develop strong relaxations based on McCormick envelopes to efficiently compute approximate solutions to our MIPs that have a mean optimality gap of 3.99%. Our method provides socioeconomically equitable solutions, as it incentivizes investments in areas with higher social vulnerability (from the U.S. Centers for Disease Control’s Social Vulnerability Index) and opioid prescribing rates. On average, when allowing for overbudget solutions, our approach decreases the number of people with OUD by [Formula: see text], increases the number of people in treatment by [Formula: see text], and decreases the number of opioid-related deaths by [Formula: see text] after 2 years compared with the baseline epidemiological model’s predictions. Managerial implications: Our solutions show that policymakers should target adding treatment facilities to counties that have significantly fewer facilities than their population share and are more socially vulnerable. Furthermore, we demonstrate that our optimization approach, guided by epidemiological and socioeconomic factors, should help inform these strategic decisions, as it yields population health benefits in comparison with benchmarks based solely on population and social vulnerability. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0042 .

Design Science in business administration

Objective of the study: This article aims to analyse the intellectual structure of Design Science in Business Administration. It Identifies the most influential works and journals, the theoretical approaches for the generation of artifacts, and discusses the intellectual structure of the emerging literature on Design Science. Methodology: The research used bibliographic coupling and citation analyses in the Scopus and Web of Science databases. Originality/relevance: This research contributes to a better understanding of the design science in Business Administration. Main results: The articles are, in the main, theoretical, demonstrating that Design Science is still in an initial maturity phase. As for the empirical and theoretical (illustrated) articles, their respective authors indicate as artifacts: framework, method, and instantiation, in addition to Design Propositions, Design Principles, and Technological Rules. The articles that constitute DS's intellectual structure are predominantly in the Systems Information area and, to a lesser degree, in Service Design and Operations Management. Theoretical/methodological contributions: The article contributes to the scientific discussion on design science by identifying the main areas that use the paradigm to conduct research in Business Administration.

Frontiers in Operations: News Event-Driven Forecasting of Commodity Prices

Problem definition: Commodity prices have exhibited significant volatility in recent times, which poses an exogenous risk factor for commodity-processing and commodity-trading firms. Accurate commodity price forecasts can help firms leverage data-driven procurement policies that incorporate the underlying price volatility for financial and operational hedging decisions. However, historical prices alone are insufficient to obtain reasonable forecasts because of the extreme volatility. Methodology/results: Building on the hypothesis that commodity prices are driven by real-world events, we propose a method that automatically extracts events from news articles and combines them with price data using a neural network-based predictive model to forecast prices. In addition to achieving a high prediction accuracy that outperforms several benchmarks (by up to 13%), our proposed model is also interpretable, which allows us to identify meaningful events driving the price fluctuations. We found that the events frequently associated with major fluctuations in the price include “natural,” “hike,” “policy,” and “elections,” all of which are known drivers of price change. We used a corpus containing about 1.6 million news articles of a major Indian newspaper spanning 15 years and daily prices of four crops (onion, potato, rice, and wheat) in India to perform this study. Our proposed approach is flexible and can be used to predict other time series data, such as disease incidence levels or macroeconomic indicators, that are also influenced by real-world events. Managerial implications: Firms can leverage price forecasts from our system to design inventory and procurement policies in the face of uncertain commodity prices. Commodity merchants can also use the forecasts to design optimal storage policies for physical trading of commodities when prices are volatile. Our findings can also significantly impact policymakers, who can leverage the information of impending price changes and associated events to mitigate the negative effects of price shocks. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0641 .

Improving Farmers’ Income on Online Agri-Platforms: Evidence from the Field

Problem definition: To improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched online agri-platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and, more generally, how they can be leveraged to enable more efficient agricultural supply chains and markets. This paper describes work conducted in close collaboration with the state government of Karnataka, India, to design, implement, and assess the impact of a new two-stage auction on the state’s online agri-platform, the United Market Platform (UMP). Methodology/results: To ensure implementability and protect farmers’ revenue, the auction design is guided by practical operational considerations, as well as semistructured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By the end of May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%–94% profit gain) for over 10,000 farmers who traded in the treatment market. Managerial implications: The results from this paper offer tangible lessons on how innovative price discovery mechanisms could be enabled by online agri-platforms in resource-constrained environments. Importantly, the success of these designs critically depends on careful considerations of systemic operational and behavioral factors that affect trades in the physical markets. History: This paper has been accepted as part of the 2023 Manufacturing &amp; Service Operations Management Practice-Based Research Competition. Funding: Financial support from the Tata Center for Technology and Design at MIT and the National Science Foundation [Grant 1452875] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0437 .

Frontiers in Operations: Waiting Experience in Open-Shop Service Networks: Improvements via Flow Analytics and Automation

Problem definition: We study open-shop service networks where customers go through multiple services. We were motivated by a partnering health screening clinic, where customers are routed by a dispatcher and operational performance is measured at two levels: micro-level, via waits for individual services, and macro-level, via overall wait. Both measures reflect customer experience and could support its management. Our analysis revealed that waits were long and increased along the service process. Such long waits give rise to negative waiting experience and the increasing shape is detrimental as it is known to create perceived waits that are even longer. Our goal is hence to analyze strategies that shape and improve customers’ perceived experience. Methodology/results: Analytically, we use a stylized two-station open-shop network to show that prioritizing advanced customers, jointly with pooling (virtual) queues, can improve both macro- and micro-level performance. We validate these findings with a simulation model, calibrated with our clinic’s data. Practically, we find that an automated routing system (ARS), recently implemented in the clinic, had a negligible impact on overall wait—It simply redistributed waiting among wait-for-routing and wait-for-service. However, ARS renders applicable sophisticated priority and routing policies (that were infeasible under the manual routing practice), specifically the ones arising from the present research. Managerial implications: Our study amplifies performance benefits of accounting for individual customers’ system status in addition to station-level load information. We offer insights into the implementation of new technologies: Firms better plan for fundamental changes in their operation rather than harness new technology to their existing operation, which may be suboptimal due to past technical limitations. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: M. Chen acknowledges the support from the National Natural Science Foundation of China [Grant 72301280]. O. Baron acknowledges the support from the Natural Science and Engineering Research Council of Canada. J. Wang acknowledges the support from the City University of Hong Kong [Grant 11505421]. A. Mandelbaum is partially supported by the Israel Science Foundation [Grant 491/22]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0590 .

Frontiers in Operations: The Confidence Trap in Operations Management Practices: Anatomy of Man-Made Disasters

Problem definition: Reducing the likelihood of man-made disasters that cause harm to life, property, and the environment is a key societal goal. To that end, regulatory agencies are responsible for ensuring man-made disasters do not occur. In practice, to accomplish this task, regulators have to rely on and cooperate with operators. However, how much cooperation is optimal? In this study, we explore the prudent level of cooperation between operators and regulators to avoid man-made disasters and the role whistleblowers and other policy levers can play in maintaining it within a Goldilocks range. Methodology/results: We synthesize various theories and accounts of man-made disasters to construct a system dynamics model of the mechanisms that lead up to these unwanted outcomes. We employ simulations to uncover how changes in crucial parameters related to cooperation lead to different outcomes and influence the likelihood of the occurrence of man-made disasters. Managerial implications: We resist explanations for man-made disasters rooted in regulatory capture and offer instead a nuanced account rooted in excess cooperation between operators and regulators emerging as a result of everyday operational imperatives and constraints. Our findings indicate that absence of disasters leads operators and regulators to fall into a “confidence trap” that perpetuates limited regulatory oversight and excess cooperation and eventually leads to a disaster. To mitigate this tendency, we investigate, in particular, the role that timely whistleblowing and other policy levers can play in mitigating man-made disasters. We provide managerial and policy implications such as incentivizing and safeguarding whistleblowers, limits on the revolving door between operators and regulators, and more stringent operating and safety scientific standards. Overall, we offer a new frame and potentially fruitful frontier for the operations management community to explore. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.0034 .

Frontiers in Operations: Does Physician’s Choice of When to Perform EHR Tasks Influence Total EHR Workload?

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians’ workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028 .

Frontiers in Operations: Battery as a Service: Flexible Electric Vehicle Battery Leasing

Problem definition: The electric vehicle (EV) manufacturer NIO adopts a swappable-battery design and a battery-leasing business model known as battery as a service (BaaS). It recently introduced flexible battery leasing, which allows customers to temporarily up-/downgrade their primary leased batteries based on the needs for range. We investigate whether this business model innovation is viable, namely whether introducing flexible battery leasing in BaaS could benefit the manufacturer, the customers, and the environment compared with simple battery leasing. Methodology/results: Adopting a game-theoretical model, we find that introducing flexible battery leasing in BaaS can simultaneously improve the manufacturer profit as well as reduce the total customer cost and the total battery capacity. Such win-win-win outcomes generally occur for large high-capacity battery ranges and moderate high-capacity battery costs—both consistent with the ongoing trend in the EV industry and a model-calibration exercise. We further show that this key finding is robust for correlated regular and peak needs for range and when launching BaaS with flexible battery leasing and that if the manufacturer was to choose a high-capacity battery range for flexible battery leasing, it would choose one such that battery reallocation alone can meet all battery up-/downgrade demand without acquiring additional batteries. Managerial implications: Our findings confirm that flexible battery leasing can be a viable BaaS business model innovation and offer insights into when this may be the case. This insight strengthens the strategic support for EV manufacturers’ potential adoption of the swappable-battery design and the BaaS model, and it may inform their operating policies to implement flexible battery leasing. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0587 .

SMEs Halal Compliance in the Implementation of Service Operations Management

Possession of halal certification does not necessarily ensure that SMEs have high halal compliance. Halal compliance is important to ensure the quality of SME products, including their service products. This study analyzes SME halal compliance in the implementation of service operations management. Descriptive and verification methods are used to test the hypothesis using regression. Some SMEs in Indonesia which are in the food and beverage processing industry sector already have halal standard criteria, although some have not yet received halal certification. Other business sectors are not yet halal certified because they still feel it is not urgent. There is an influence between halal compliance and the implementation of service operations management. The service operations management system model refers to effective, efficient and productive fundamentals, which are efforts not to deviate from other than what has been prescribed by Islam.

ІМІТАЦІЙНЕ МОДЕЛЮВАННЯ МЕРЕЖІ МАСОВОГО ОБСЛУГОВУВАННЯ

In this work, simulation modeling of mass service network is investigated. The relevance of researching the topic lies in its potential to enhance efficiency and effectiveness across various service industries. In today's increasingly interconnected and technologically driven world, understanding how to optimize service delivery processes is paramount. By employing simulation modeling techniques, researchers and practitioners can simulate real-world scenarios, identify bottlenecks, test different strategies, and ultimately improve the overall performance of mass service networks. This research can have practical applications in areas such as transportation, healthcare, telecommunications, and customer service, where efficient management of large-scale service networks is essential for meeting demand, reducing costs, and enhancing customer satisfaction. Moreover, the insights gained from such research can inform policy-making decisions aimed at improving service accessibility, equity, and resilience in the face of disruptions, thus contributing to the advancement of both academic knowledge and practical applications in the field of service operations management. It is noted that simulation modeling can be considered as a methodology and not a theory of problem solving. One of the features of simulation models is the practically significant assumption that they can serve as a tool for analyzing the behavior of the system under the conditions determined by the experimenter. The elements of a mass service network are the input flow, the mass service system, and communication. The input flow is characterized by the interval of requests to the network, which can be a random or deterministic value, as well as the number of the mass service system to which the request is received. The state of the incoming stream is determined by the moment in time when the next request arrives in the mass service network. In the case when the time interval for the arrival of requirements is a random variable with a known distribution law, a random number generator is used to calculate it.

Decarbonizing OCP

Problem definition: We present our collaboration with the OCP Group, one of the world’s largest producers of phosphate and phosphate-based products, in support of a green initiative designed to reduce OCP’s carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its time-discounted investment cost, plus the cost of satisfying its remaining demand via the Moroccan national grid. OCP is currently designing its renewable investment strategy, using insights gleaned from our optimization model, and has pledged to invest 130 billion Moroccan dirham (MAD) (approximately 13 billion U.S. dollars (USD)) in a green initiative by 2027, a subset of which involves decarbonization. Methodology/results: We immunize our model against deviations between forecast and realized solar generation output via a combination of robust and distributionally robust optimization. To account for variability in daily solar generation, we propose a data-driven robust optimization approach that prevents excessive conservatism by averaging across uncertainty sets. To protect against variability in seasonal weather patterns induced by climate change, we invoke distributionally robust optimization techniques. Under a 10 billion MAD (approximately 1 billion USD) investment by OCP, the proposed methodology reduces the carbon emissions that arise from OCP’s energy needs by more than 70%, while generating a net present value (NPV) of 5 billion MAD over a 20-year planning horizon. Moreover, a 20 billion MAD investment induces a 95% reduction in carbon emissions and generates an NPV of around 2 billion MAD. Managerial implications: To fulfill the Paris climate agreement, rapidly decarbonizing the global economy in a financially sustainable fashion is imperative. Accordingly, this work develops a robust optimization methodology that enables OCP to decarbonize at a profit by purchasing solar panels and batteries. Moreover, the methodology could be applied to decarbonize other industrial consumers. Indeed, our approach suggests that decarbonization’s profitability depends on solar capacity factors, energy prices, and borrowing costs. History: This paper has been accepted as part of the 2023 Manufacturing &amp; Service Operations Management Practice-Based Research Competition. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0467 .

Patient Sensitivity to Emergency Department Waiting Time Announcements

Problem definition: Emergency department (ED) delay announcement systems are implemented in many countries. We answer three important questions pertaining to the operations and effectiveness of such systems by studying the public hospital network and ED waiting time (WT) announcement system in Hong Kong’s “universal” public healthcare system: (1) How many patients are aware of (and sensitive to) the ED WT announcements? (2) How sensitive are these patients to the announced WT? (3) How can the Hong Kong government improve the WT announcement system? Methodology/results: We study over 1.3 million patient visits to the 17 tier 1 public EDs. We structurally estimate the fraction of patients sensitive to the announced WT and their sensitivity to the announcements as well as patient characteristics that lead to higher sensitivity. In the patient’s ED choice decision, we estimate the trade-off between the travel distance to an ED and the expected WT at the ED. We find that 3.1% of the patients are sensitive to the announced WT, and they are willing to travel an additional 4.8 km to save one hour of waiting. Urgent patients are less likely to be sensitive to the delay announcement than less urgent patients, but those that are sensitive are more WT averse than their less urgent counterparts. Counterfactual analysis shows that the average actual WT and number of patients who leave without being seen can be reduced by 4.6% and 8.5%, respectively, by increasing the fraction of sensitive patients to 15.0% and, simultaneously, reducing the announced WT assessment window to one hour from the current level of three hours. Further improvement can be achieved by providing predicted WT information based on the current level of ED crowding or less extreme past performance—median WT rather than the currently used 95th percentile. Managerial implications: The Hong Kong government should utilize the two levers of the announcement system: the sensitive fraction of patients and information recency. Increasing the sensitive fraction can benefit the system when it is below a certain threshold level. However, administrators should exercise caution when the sensitive fraction becomes large and consider implementing additional measures to mitigate the negative effects of information delay. The sensitive group of patients can unfairly be punished for their proactiveness. Shortening the announced WT assessment window and providing predicted WT are possible alternatives that not only improve overall performance but also exhibit strong robustness to increases in the sensitive population. History: This paper has been accepted as part of the 2023 Manufacturing &amp; Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the Research Grants Council of the University Grants Committee through the Collaborative Research Fund [Grant C7162-20G] and General Research Fund [Grant 17502320]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0457 .

Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization

Problem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support. History: This paper has been accepted as part of the 2023 Manufacturing &amp; Service Operations Management Practice-Based Research Competition. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0455 .

Service Operations Mastery: A Holistic Toolset for Achieving Operational Excellence and Service Superiority

Effective operations management is central to achieving superior operational performance, whether in the manufacturing or service sector. While many studies have examined the importance of operations in manufacturing, less attention has been given to service operations management, particularly from the perspective of service operations managers. This study aims to bridge this gap by developing a comprehensive instrument for service operations management. By combining qualitative and quantitative approaches, we identified six key elements that underpin service operations management: Equipment Management, Human Initiatives, Service Delivery Control (SDC), Certifications, Technology Usage, and Service Delivery Design (SDD). Confirmatory factor analysis (CFA) confirmed the one-dimensionality of these factors, demonstrating their close interconnection. The instrument exhibited strong reliability and validity, enabling it to provide a structured framework for the empirical understanding of operations management in the service sector.