Problem Definition Research Articles

Private and Secure Distributed Deep Learning: A Survey

Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS) do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security and privacy challenges. This survey first structures the field of distributed learning into two main paradigms and then provides an overview of the recently published protective measures for each. This work highlights both secure training methods as well as private inference measures. Our analyses show that recent publications while being highly dependent on the problem definition, report progress in terms of security, privacy, and efficiency. Nevertheless, we also identify several current issues within the private and secure distributed deep learning (PSDDL) field that require more research. We discuss these issues and provide a general overview of how they might be resolved.

EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study.

Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle. This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems. This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops' insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development. In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care. The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.

Perspective matters in recovery: the views of persons with severe mental illness, family and mental health professionals on collaboration during recovery, a qualitative study.

Recovery from severe mental illness, including psychosis has been described as a personal and unique process, but it rarely is a journey undertaken without profound influences of significant others (family, mental health professionals). Diverging perspectives between persons with severe mental illness, family and professionals are frequent during the recovery process, notably in psychotic disorders. We aimed to explore processes of collaboration during recovery, to inform recovery supporting practices. Current qualitative study had a participatory design and was set within long-term mental healthcare for severe mental illness. We conducted semi-structured interviews and focus groups with persons with severe mental illness (most had a history of psychosis), family and professionals on their mutual contact during recovery. Using reflexive thematic analysis, we developed themes representing processes of collaboration during recovery. We described roles persons with severe mental illness, family and professionals attribute to each other in mutually influential terms of unconditional and meaningful contact (which takes time to establish) and problem-oriented aspects. Secondly, experienced differences over problem definition, "needing help" and consequently over the role parties attribute to one another, may result in negative interactions, in the area of having expectations; (not) informing; (not) having agency to change; experiencing (dis)agreement or struggle. unconditional, meaningful contact and knowing each other's perspective are important to fruitful interaction in a triad when perspectives on mental health problems diverge. Relationally centered and process oriented care with continuity of family and professionals involved are needed to advance recovery in severe mental illness, especially psychosis.

THE DESIGN OF A ROTATING MODULAR UPPER LIMB PROSTHETIC WRIST UNIT

This design study investigates a Prosthetic Wrist Unit for upper limb amputations using the Design Thinking methodology. It covers Problem Definition, Information Gathering, Concept Generation and Evaluation, Product Architecture, Configuration Design, Parametric Design, Detail Design, and Production. The study aims to create a small prosthetic wrist unit (50 mm diameter, 50 mm length, less than 125 gr), provides 360° rotation, connects easily with prosthetic hands, enables electric and data transfer, and is controllable via EMG sensors. This research contributes to prosthetic product design, enhancing the quality of life for amputees.

Emergency Care Efficiency vs. Quality: Uncovering Hidden Consequences of Fast-Track Routing Decisions

Problem definition: This work aims to examine the role of emergency department (ED) operational status related to congestion in fast-track (FT) routing decisions and the subsequent effects on patient outcomes. Methodology/results: In this paper, we utilize a two-year data set from two hospital EDs in Alberta, Canada, and adopt an instrumental variable approach to examine the effects of FT routing decisions on patient outcomes. Based on the empirical findings, we utilize a data-calibrated simulation to compare the performance of different routing policies. First, our study reveals that FT routing decisions are not purely clinically driven, and ED operational status is also associated with FT routing decisions. Second, being routed to FT can improve ED efficiency by reducing the average length of stay and left without being seen rates. However, this efficiency improvement comes at the cost of potential quality decline. In particular, being routed to the FT leads to an 8.2% increase in the 48-hour revisit rate for the high-complexity group and a 2.3% increase for the medium-complexity group. Third, we delve into the mechanisms behind observed patient outcomes and find that physicians in the FT area may prioritize expediting patient flow by simplifying patient diagnosis and treatment procedures. Consequently, the quality of care may be compromised for high- and medium-complexity patients. Finally, our simulation findings highlight the importance of selecting the “right” patients to be routed to the FT unit. To this end, the complexity-based classification method and dynamic routing policies emerge as promising avenues. Managerial implications: Our findings call for immediate attention from healthcare practitioners to carefully balance the trade-off between emergency care efficiency and quality, emphasizing the necessity of selecting the right patients for routing. Funding: This study is partially supported by the Hong Kong Research Grants Council [Grants GRF 11508921 and CRF C7162-20G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0440 .

Green Disposable Packaging and Communication: The Implications of Bring-Your-Own-Container

Problem definition: A growing number of firms are encouraging consumers to participate in “bring-your-own-container” (BYOC) behavior in which consumers bring their own reusable packaging to purchase and consume products, thus reducing single-use packaging waste. In this paper, we study the environmental implications of a firm’s BYOC implementation when considering its disposable packaging choice and communication strategy. Methodology/results: We build a stylized model to study a firm’s joint decisions on BYOC, disposable packaging choice, and communication and their implications on the environment. Our main results follow. First, allowing BYOC reduces the firm’s incentive to make fraudulent green claims about its disposable product packaging; however, BYOC implementation may harm the overall environment while improving the firm’s profit, thereby creating a new form of greenwashing. Second, the adoption of third-party certification for green disposable packaging is an effective remedy to mitigate the negative environmental impact of BYOC. In addition, the environmental implications of adopting third-party certification (either voluntarily or because of government mandates) depend on the relationship between the environmental qualities of green disposable packaging and reusable packaging. Whereas it always benefits the environment when the firm’s green disposable packaging has better environmental performance, adopting certification may negatively impact the environment if consumers’ reusable packaging is greener. Furthermore, we find numerically that offering a price discount for BYOC may encourage the firm to adopt certification because of increased profitability, thereby leading to the aforementioned environmental implications. Managerial implications: We offer operational insights on how firms should make joint decisions on BYOC, disposable packaging choice, and communication. We also generate insights on how governments should regulate firms’ green claims when firms start to allow BYOC. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0605 .

Subterranean eco-dystopia in Istanbul: the concreted segregation of Tarlabaşı

ABSTRACT The underground layers of cities are constantly concreted with urban transformation projects. The studies on built environment mainly focus on the morphological changes, related dispossessions and observable aspects of urban landscape. However, the environmental costs of transformations are not limited to the impacts of above-ground interventions. In Istanbul, the major impact of subterranean interventions is the destruction of water and soil ecosystems as well as its multi-layered heritage as the large-scale urban transformation decisions are primarily applied to historic neighbourhoods where the existing building stock was appropriated by the urban precariat. The urban transformation projects in historic neighbourhoods of Istanbul come into question with socio-economic dispossessions rather than environmental justice. In this manuscript, the main aim is to address the socio-ecological impacts of planning and architectural design decisions disregarding subterranean construction density, through the case of Tarlabaşı in Istanbul. Tarlabaşı represents one of the most emblematic case in Istanbul’s urban resistance history and evokes eco-dystopias, which may be attributed to the decisions that justify stigmatisation. With the context, conceptual frame and scope set by these problem definitions, while considering urban inequality a pre-condition of environmental justice, this manuscript examines the correlation between environmental policies, social stigmatisation and flexed environmental values in planning and architecture culture via local political ecology. The methodology employed in the study is based on qualitative analysis of the stages and legal decisions involved. Archival documents on the study area were used to identify the local variables contributing to the deterioration of socio-ecological unity for future discussions.

Conditional Community Search Based on Weight Information

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some complex search conditions based on Boolean expressions. For example, certain desired nodes should be contained in the communities, while certain undesired nodes cannot exist in the communities, which is called conditional community search. However, existing solutions for conditional community search often introduce some undesired nodes into the identified communities and exhibit relatively low search efficiency. To overcome these drawbacks, therefore, this paper investigates the problem of conditional community search based on weight information. First, we refine the original problem definition of conditional community search and outline the need for an improved algorithm for calculating the weights of the nodes. Then, we explore two novel algorithms for searching conditional communities based on calculated weight information. Finally, we conduct extensive experiments on several real-world datasets to verify the accuracy and efficiency of our proposed searching algorithms.

Site Reassignment for Mobile Outreach Teams: Investigating the Effectiveness of Decentralized Decision Making

Problem definition: United Nations Sustainable Development Goal (SDG) 3.8 states that health coverage should be universal by 2030. This is challenging in rural and poor areas. To address this challenge, mobile outreach teams of healthcare workers visit a fixed set of remote sites to provide healthcare services. Because of dynamics in demand and supply, once-rational site-to-team assignment decisions can become far from optimal over time. This paper considers the problem of reassigning sites to teams to maximize effectiveness. Solving this problem through a central planner does not fit the context: outreach teams commonly have a high degree of decision-making autonomy. We study a decentralized approach where subsets of teams collaborate in a series of team meetings to reassign sites. The key question for decision makers is whether and when such an approach is effective. Methodology/results: We propose a mixed-integer programming model for centralized site reassignment. We extend this model to represent the decentralized approach and develop a set of simple decision rules for this approach. We use empirical data from six country outreach programs of the nongovernmental organization MSI Reproductive Choices. Our results suggest that, when properly designed, decentralized decision making performs close to centralized decision making, and even outperforms it in the case of inaccurate information at the central level. The finding remains valid when demand and supply fluctuate, and is insensitive to the chosen objective. Managerial implications: Humanitarian organizations currently deploy mobile outreach teams to provide a wide variety of health services. We present several useful insights for decision makers in humanitarian organizations when making design choices, taking account of context. In particular, we show that decentralized site reassignment provides a feasible and effective alternative to centralized approaches in many contexts. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0437 .

Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem

Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem. Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .

Demand Equilibria in Spatial Service Systems

Problem definition: A service is offered at certain locations (“facilities”) in a geographical region. Customers can appear anywhere in the region, and each customer chooses a facility based on travel distance as well as expected waiting time. Customer decisions affect waiting times by increasing the load on a facility, and thus, they impact other customers’ decisions. The service provider can also influence service quality by adjusting service rates at each facility. Methodology/results: Using a combination of queueing models and computational geometry, we characterize demand equilibria in such spatial service systems. An equilibrium can be visualized as a partition of the region into service zones that form as a result of customer decisions. Service rates can be set in a way that achieves the best-possible social welfare purely through decentralized customer behavior. Managerial implications: We provide techniques for computing and visualizing demand equilibria as well as calculating optimal service rates. Our analytical and numerical results indicate that in many situations, resource allocation is a far more significant source of inefficiency than decentralized behavior. Funding: J. G. Carlsson was funded by the METRANS Transportation Consortium [Grant NCST-USC-RR-24-12] and the Office of Naval Research [Grant N00014-24-1-2277-P00001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0434 .

Note on Optimal Procurement Mechanisms for Assembly

Problem definition: We consider contract confidentiality in a decentralized supply chain, in which a single principal orders different components from different agents, each of which has private cost information. The principal may commit to publicly observed bilateral contracts or offer secretly observed contracts to each agent simultaneously. We also consider the problem of sequential contracting. Methodology/results: By correcting the main analysis in Hu and Qi [Hu B, Qi A (2018) Optimal procurement mechanisms for assembly. Manufacturing Service Oper. Management 20(4):655–666], we employ passive beliefs to study secret contracting. We show that there is a unique configuration of two-part tariffs under secret offers in both simultaneous and sequential contracting. We also extend the analysis to take into account ex post individual rationality (IR), which is arguably more relevant in this assembly setting. With ex post IR, we show that the traditional two-part tariffs should be augmented with payment adjustments. Managerial implications: We find that the efficiency achieved through public offers can be maintained with secret offers in this assembly setting contrary to conventional wisdom. Our analysis, thus, offers an explanation for why nondisclosure agreements are important and common in practice. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72192805]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0907 .

Information Dependency in Mitigating Disruption Cascades

Problem definition: Shocks that trigger supply chain disruptions inflict initial losses by damaging firms’ assets. The disruption can then cascade when an affected firm fails to deliver to its buyer, thereby interrupting the buyer’s operations, and continue thus across multiple levels (tiers) in the supply chain. To protect against such disruption cascades, firms can make ex ante investments in risk mitigation. These investments depend heavily on the operational characteristics of network participants and their interconnections. Gathering operational information can be challenging. Our aim is to shed light on the forces that govern information requirements for risk mitigation. Methodology/results: We introduce a game-theoretic model to characterize the equilibrium mitigation by firms in a decentralized arborescent network facing severe disruptions. We find that when the trigger shocks are nonconcurrent events, the equilibrium mitigation by a firm displays a limited vertical dependence on the operational attributes of suppliers that are farther away in tier (network) distance. Specifically, we show that information about a firm’s extended local neighborhood—up to its tier 2 suppliers—suffices to characterize its equilibrium mitigation. Allowing for concurrent shocks to simultaneously strike multiple firms increases the information requirement at partner firms that typically lie within two tiers downstream from the firms experiencing concurrent shocks. Managerial implications: Full supply chain visibility is costly. The literature offers little guidance on how to prioritize efforts to enhance visibility into the attributes of supply chain partners. Rather than a blanket call for greater visibility, our results proffer nuanced managerial prescriptions for the extent to which risk mitigation requires such visibility. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0408 .

Pride or Guilt? Impacts of Consumers’ Socially Influenced Recycling Behaviors on Closed-Loop Supply Chains

Problem definition: Social influenced emotions of pride and guilt have been identified by the environmental psychology (EP) literature as crucial drivers impacting recycling behavior, but they have mostly been overlooked in operations management (OM) research. In contrast, EP studies often ignore firms’ operational decisions. We analyze the impacts of both social influence and firms’ operational decisions to provide a comprehensive understanding of consumers’ recycling behaviors, which is essential for realizing remanufacturing’s full potential. Methodology/results: We consider a closed-loop supply chain consisting of a manufacturer selling a single product to a consumer community. Consumers’ recycling behavior depends on both the recycling reward offered by the manufacturer, as well as intrinsic and socially influenced pride (guilt) from recycling (not recycling). We develop an evolutionary game to model consumers’ recycling behavior and characterize the resulting equilibrium recycling rate, which is then integrated into the manufacturer’s decision problem. We characterize the manufacturer’s optimal strategy and the equilibrium recycling rate in four distinct regions defined by both the product’s overall difficulty of remanufacturing and the underlying strengths of consumers’ socially influenced pride and guilt. We show that in settings where the product has a moderately high difficulty of remanufacturing and consumers have stronger socially influenced pride than guilt, the manufacturer optimally induces an interior recycling rate. In such scenarios, there exist win-win pathways in using social influence–based interventions to increase both the manufacturer’s profit and the recycling rate. However, misalignment may occur when consumers substantially care for the product’s recyclability. Managerial implications: This study bridges sustainable OM and EP literature by analyzing how consumers’ socially influenced emotions of pride and guilt affect a manufacturer’s optimal decisions, profits, and the resulting recycling rate. We provide important insights for designing effective and efficient social influence–based interventions to improve recycling rates. Funding: W. Chen was supported by the National Natural Science Foundation of China [Grant 71902017], and C.-L. Tseng was supported by the University of New South Wales UNOVA Knowledge Hub. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0721 .

Guidance for establishing and applying tolerable upper intake levels for vitamins and essential minerals.

Vitamins and essential minerals are micronutrients that are required for the normal functioning of the human body. However, they may lead to adverse health effects if consumed in excess. A tolerable upper intake level (UL) is a science-based reference value that supports policy-makers and other relevant actors in managing the risks of excess nutrient intake. EFSA's principles for establishing ULs for vitamins and minerals were originally developed by the Scientific Committee on Food in 2000. This guidance from the EFSA Panel on Nutrition, Novel Foods and Food Allergens provides an updated framework for UL assessments. A draft was published in 2022 and underwent a 2-year piloting period. The present document incorporates revisions based on the experience gained through its practical implementation. It covers aspects related to the planning of the risk assessment (problem formulation and definition of methods) and its implementation (evidence retrieval, appraisal, synthesis, integration, uncertainty analysis). As in the previous framework, the general principles developed for the risk assessment of chemicals in food are applied, i.e. hazard identification, hazard characterisation, intake assessment, risk characterisation. Specific to nutrients are their biochemical and physiological roles and the specific and selective mechanisms that maintain the systemic homeostasis and accumulation of the nutrient in the body. Such considerations must also be taken into account when conducting risk assessments of nutrients.

Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

Multichannel Healthcare: Impact of Asynchronous Telemedicine Adoption on Patient Flow

Problem definition: This paper studies a multichannel healthcare system where physicians diagnose patients and prescribe treatment in-person or through asynchronous telemedicine (AT), a widely adopted yet relatively under-explored form of telemedicine. In collaboration with physicians at the Veterans Healthcare Administration (VHA), we examine the impact of introducing an AT channel on the existing in-person channel and on overall system performance. Methodology/results: VHA implemented AT at select clinics in the state of Georgia in 2012. Using a difference-in-differences design, we find that the introduction of the AT channel led to a sorting process whereby more complex patients were seen in the in-person channel. AT implementation led to a 20% increase in recommended visit time and an 8.5% increase in required clinical resources for in-person consultations. In addition, the adoption of AT resulted in higher throughput—more patients seen by the specialists per month across both channels. Using a fixed-effects model we find a reduction in average wait time for in-person referrals (37.5%), and for the most common medically necessary procedure (43%) despite an increase in the total number of consultations at the specialist clinic. We attribute the improved efficiency to early patient triage, better match between patient needs and treatment modality, and reduction of setup and switching costs in physicians’ workflow. Managerial implications: This paper contributes to our understanding of a rapidly expanding form of healthcare delivery: multichannel healthcare with in-person and AT channels. Our results suggest that healthcare managers and physicians can adopt AT to improve overall system efficiency. At the same time, they should take into account the additional impact of AT on the in-person channel when making capacity decisions and developing guidance on patient referrals. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0235 .

Faculty Building for Personalized Medicine and Care

Personalized medicine and care always places the patient at the center and requires psychosocial interprofessional skills. Using the example of personalized care, which is conceived as clinical, biopsychosocial work in an interdisciplinary, care-relevant manner, it is shown why comprehensive and differentiated abilities are required for this activity. According to grounded theory methodology, an interview-study with several experts in the field was carried out in a cyclical research design. As a result, problem definitions and solution concepts suggested to focus existing and development of new curricula or curriculum elements, to focus training processes and students’ qualification profiles for the respective personell-, organizational- development and faculty building. The qualification profile, definitions of learning objectives, as well as their effective implementation and didactic mediation possibilities are empirically reviewed and presented in an overview and exemplary manner. The training is designed to be practice-relevant, patient-centered with sufficient personal reflection skills, which are conveyed through teaching in continuous small groups. Selection procedures and internships take into account the requirements of psychosocial care and academic standards. Workplace-based examinations and case-oriented feedback develop the trainees into professional workers. A priori necessary basic competencies are presented and discussed in order to further elaborate empirical research and curricular strategies. Therefore the respective faculty building can be derived.

Trends in real-time artificial intelligence methods in sports: a systematic review

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings.

Unveiling Regulatory Operations: A Data Set of the Determinants, Process, and Outcomes of Product Defect Investigations by the U.S. Automotive Safety Regulator

Problem definition: The paucity of data on governmental regulatory agencies’ product safety defect investigations has restricted our knowledge about (1) the determinants of a regulator’s decisions to open or close an investigation, (2) the process it follows between opening and closing of an investigation, and (3) the outcomes of the investigation when it is closed. Methodology/results: The authors view a safety regulator’s opening and closing of a product defect investigation as a decision of interest to the operations management discipline. This data paper describes a rich, novel, and hand-collected data set of all investigations that the National Highway Traffic Safety Administration—the U.S. regulator for automobile safety—opened and closed against 187 manufacturers between 2009 and 2021. The authors provide two Microsoft Excel data files, one capturing data for the investigations opened and the other for the investigations closed. The data files enable researchers to address three sets of research questions. First, researchers can use the “Data on Investigations Opened” file to model the determinants of a regulator’s opening of a product defect investigation. Second, researchers can mine the textual variables from both files to identify the steps involved in the investigation process. They can also use the process variables included in the data to investigate the regulator’s efficiency in opening and closing investigations. Third, researchers can use the “Data on Investigations Closed” file to better understand when and why a regulator closes an investigation and the outcomes of the closed investigations. Managerial implications: The data files can also be valuable to nonacademic stakeholders (e.g., governmental organizations and regulators, journalists, liability lawyers, politicians, and safety advocates). The authors provide an open-access website that simplifies the use of the data for a nonacademic audience and allows them to draw insights from the data via graphs and tables. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.0705 .