This study examines the composition of attributes among members of the Production and Operations Management Society (POMS). We analyze POMS membership data from 2017 to 2023 and conference registration data from 2011 to 2024. Specifically, the study seeks to: (1) describe the current composition of POMS members and conference registrants in terms of gender, academic rank, and country of affiliation, and (2) provide recommendations for improving representation within professional societies.
Product returns are prevalent in practice. Many retailers provide lenient free return policies but with specific return window within which customers are allowed to return products. Motivated by this phenomenon, we consider a single-product online learning and pricing problem with stochastic product returns. A salient feature is that the demand function, depending on price and return window decisions, is initially unknown and must be learned on the fly. The retailer thus faces the classic exploration–exploitation trade-off. Moreover, we consider an inventory constraint, introducing an additional trade-off between earning revenue and managing inventory. We propose a modeling framework to integrate pricing and return window decisions, and develop a deterministic fluid model that serves as the full-information benchmark. To tackle the learning problem, we design a novel nonparametric learning algorithm that seamlessly integrates inverse stochastic gradient descent (SGD) and Upper Confidence Bound (UCB) methods. Under mild assumptions on demand and revenue functions, we establish a regret upper bound for our learning algorithm as O ( W T log T ) , where W denotes the number of return window candidates and T denotes the time horizon. This result aligns with lower bounds established in both online pricing and multi-armed bandit (MAB) literature. Numerical experiments are conducted to verify the effectiveness and robustness of our algorithm across various environments. From an operational standpoint, retailers can use our learning framework as a decision-support tool to identify the optimal price and return window.
Nafise Ghadiri Khorzoughi, Hadi Balouei Jamkhaneh, Hojat Ghimatgar
et al.
PurposeThe rapid evolution of Industry 4.0 (I 4.0) technologies has transformed supply chain (SC) operations, creating a need to redefine key performance indicators (KPIs) in quality management (QM). Addressing the lack of data-driven frameworks for evaluating Supply Chain Quality Management 4.0 (SCQM 4.0), this study identifies and prioritizes the most influential KPIs through the integration of machine learning (ML) techniques and managerial insights.Design/methodology/approachA mixed-method approach was employed. First, a systematic literature review (SLR) and expert interviews were conducted to identify relevant indicators. Second, a structured survey of 331 professionals from diverse industries was analyzed using seven supervised ML algorithms (SVM, KNN, RF, LDA, DT, RUSBoost and SVM 1-vs-All). The Random Forest (RF) algorithm achieved the highest accuracy and was applied to determine the final prioritization of KPIs.FindingsThe results indicate that indicators of digital innovation, supplier responsiveness, customer and supplier involvement, supplier resilience and customer satisfaction are the most critical drivers of SCQM 4.0 performance. The RF algorithm demonstrated superior predictive capability in modeling the relationships among multi-level indicators across upstream, internal and downstream dimensions.Practical implicationsThe findings provide managers with a structured, data-driven framework to enhance quality integration and performance within digitalized supply chains. Implementing ML-based analytics supports proactive KPI monitoring, evidence-based decision-making and continuous quality improvement under I 4.0 conditions.Originality/valueThis study offers one of the first empirical, ML-based frameworks for assessing SCQM 4.0. It bridges conceptual and operational perspectives by integrating data analytics with managerial expertise, thereby extending Quality 4.0 (Q 4.0) and SC 4.0 literature through a multi-level, performance-oriented lens.
Industrial engineering. Management engineering, Production management. Operations management
Pathogenic microbial contamination in broiler drinking water constitutes a critical factor in disease occurrence and transmission within large-scale farming operations. Effective drinking water quality management is essential for optimizing broiler health and production performance. Functional additives, such as hypochlorous acid water (HAW) and hydrogen-rich water (HRW), are often added to farm drinking systems. HAW inhibits bacterial growth, and HRW enhances antioxidant capacity in broilers. This study aimed to investigate the combined effects of these two functional additives and evaluate their impacts on bacteria and biofilm in the drinking water system, broiler production performance, antioxidant capacity, and intestinal environment. A total of 480 broilers were randomly divided into four groups (n = 120 each) and raised for 42 days with distinct aqueous solutions: Group A (tap water), Group B (HAW, 0.3 mg/L), Group C (HRW, 1200 ppb), and Group D (combined HAW and HRW, hydrogen-rich hypochlorous acid water [HRHAW], 0.3 mg/L + 1200 ppb). The results indicated that the sterilization rate of planktonic bacteria in drinking water exceeded 99.90 % after the HRHAW intervention, and the biofilm biomass decreased by 62.85 %. Compared with controls, HRHAW showed no significant impact on the feed–gain ratio but significantly improved breast meat tenderness (39.15 %) and antioxidant capacity (SOD: 6.52 %–27.54 %; GSH-PX: 8.54 %–50.97 %). Intestinal health was enhanced through oxidative stress mitigation and antibacterial effects. In summary, HRHAW successfully integrates the antioxidant benefits of HRW with the antimicrobial efficacy of HAW, significantly reducing the bacterial content in drinking water and enhancing the antioxidant capacity of broilers, thereby positively influencing broiler health.
With the rise of emerging risks, model uncertainty poses a fundamental challenge in the insurance industry, making robust pricing a first-order question. This paper investigates how insurers' robustness preferences shape competitive equilibrium in a dynamic insurance market. Insurers optimize their underwriting and liquidity management strategies to maximize shareholder value, leading to equilibrium outcomes that can be analytically derived and numerically solved. Compared to a benchmark without model uncertainty, robust insurance pricing results in significantly higher premiums and equity valuations. Notably, our model yields three novel insights: (1) The minimum, maximum, and admissible range of aggregate capacity all expand, indicating that insurers' liquidity management becomes more conservative. (2) The expected length of the underwriting cycle increases substantially, far exceeding the range commonly reported in earlier empirical studies. (3) While the capacity process remains ergodic in the long run, the stationary density becomes more concentrated in low-capacity states, implying that liquidity-constrained insurers require longer to recover. Together, these findings provide a potential explanation for recent skepticism regarding the empirical evidence of underwriting cycles, suggesting that such cycles may indeed exist but are considerably longer than previously assumed.
Christos Hadjichristofi, Spyridon Diochnos, Kyriakos Andresakis
et al.
The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.
Kesavan Manoharan, Pujitha Dissanayake, Chintha Pathirana
et al.
Purpose – Labour efficiency is the key component for the long-term sustainability of construction firms. Recent studies show that modernising organisational/managerial processes is necessary to raise labour efficiency in many emerging nations. Construction supervision is a crucial element in organisational/managerial practices, which provide blood circulation to the project operations by directing labour. Accordingly, this study aims to quantify the impacts of crucial organisational/managerial elements on the efficiency of labour in building construction projects based on the viewpoint of construction supervisors. Findings – A total of 28 factors were determined as critical, where lack of labour motivation, poor labour training facilities, poor performance evaluation practices, no labour rewarding mechanism and poor communication/cooperation between parties were judged to be the top five key issues in the list. The validity and reliability of the study findings were ensured through statistical tests and the experts' discussion outcomes. In view of the evolving challenges facing the industry, the results indicate that the organisational policies of construction enterprises in place addressing financial procedures, communication strategies, resource management and performance management practices must be enhanced. Research limitations/implications – The study findings will make a substantial contribution to reducing the disparity between organisation/management policies and labour practices towards changing how the sector operates to increase labour efficiency in construction projects. Originality/value – This study contributes to addressing the knowledge gap in the industry associated with the organisational protocols, especially to understand/predict how such elements are significant, how much they influence the efficiency of construction practices and what steps can be made to limit their effects on labour efficiency in construction. These could be crucial in modernising organisational policies and procedures for construction management.
Industrial engineering. Management engineering, Production management. Operations management
The article aims to present sustainable development reporting based on data obtained from Polish commercial banks, considering different approaches and scopes of presenting non-financial data, even though specific guidelines have been issued. The research procedure included a literature review of Polish and foreign literature and research using the case study method. The article presents examples of environmental, social and governance (ESG) activities reported by selected commercial banks in Poland in a case study. ESG activities are reported separately and presented as part of annual reports. Many of the banks’ activities presented in the survey can serve as a model for others, as not all banks have a clearly written ESG strategy. A positive effect of reporting ESG activities is the clarification of indicators, such as reducing greenhouse gas emissions, eliminating exposure to the extractive sector or increasing “green” financing. This article can contribute to showing role models for banks in three areas, i.e., environmental, social and corporate governance. As a result, the authors tried to propose solutions where sector organisations could compare themselves in non-financial areas.
Barsalou Matthew, Saraiva Pedro Manuel, Henriques Roberto
This paper explores Exploratory Data Analysis (EDA). Graphical methods are used to gain insights in EDA and these insights can be useful for forming tentative hypotheses when performing a root cause analysis (RCA). The topic of EDA is well addressed in the literature; however, empirical studies of the efficacy of EDA are lacking. We therefore aim to evaluate EDA by comparing one group of students identifying salient features in a table against a second group of students attempting to identify salient features in the same data presented in the form of a run chart, and then extracting relevant conclusions from such a comparison. Two groups of students were randomly selected to receive data; either in the form of a table or a run chart. They were then tasked with visually identifying any data points that stood out as interesting. The number of correctly identified values and the time to find the values were both evaluated by a two-sample t-test to determine if there was a statistically significant difference. The participants with a graph found the correct values that stood out in the data much quicker than those that used a table. Those using the data in the form of a table too much longer and failed to identify values that stood out. However, those with a graph also had far more false positives. Much has been written on the topic of EDA in the literature; however, an empirical evaluation of this common methodology is lacking. This paper confirms with empirical evidence the effectiveness of EDA.
Eduardo C. Garrido-Merchán, Sol Mora-Figueroa-Cruz-Guzmán, María Coronado-Vaca
This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The study includes a comparative analysis of DRL agent performance under standard Dow Jones Industrial Average (DJIA) market conditions and a scenario where returns are regulated in line with company ESG scores. In the ESG-regulated market, grants were proportionally allotted to portfolios based on their returns and ESG scores, while taxes were assigned to portfolios below the mean ESG score of the index. The results intriguingly reveal that the DRL agent within the ESG-regulated market outperforms the standard DJIA market setup. Furthermore, we considered the inclusion of ESG variables in the agent state space, and compared this with scenarios where such data were excluded. This comparison adds to the understanding of the role of ESG factors in portfolio management decision-making. We also analyze the behaviour of the DRL agent in IBEX 35 and NASDAQ-100 indexes. Both the A2C and Proximal Policy Optimization (PPO) algorithms were applied to these additional markets, providing a broader perspective on the generalization of our findings. This work contributes to the evolving field of ESG investing, suggesting that market regulation based on ESG scoring can potentially improve DRL-based portfolio management, with significant implications for sustainable investing strategies.
This is an exploratory study to analyze the permeation of the term/word “analytics” in the operations management domain by reviewing 2175 papers published in the Production and Operations Management journal (POMJ) since its inception in the winter 1992 until the summer of 2022. We identify the words that commonly precede the term “analytics” in these papers. We also identify the most common words that appear in the research papers in which the term “analytics” has appeared. This analysis allows us to reflect on the contexts in which analytics studies have focused and the influence they might exert in future studies. Results show that the usage of the term “analytics” has increased from less than 2% of articles published in POMJ during the period (2007–2016) to about 1 out of every 10 articles published from 2020 to the summer of 2022.
In the management of environment the Environmental Management Accounting (EMA) is essential for corporate or companies because corporate sectors are the main parties of environmental humiliation as they are existed in the environment and for protecting environment a branch of accounting is emerged which is called environmental management accounting. The objective of the study is to develop a compliance framework for EMA and appraise the ER practices in selected industries in Bangladesh. In conducting the study, 50 environmental sensitive industries were selected from DSE. A compliance checklist was developed on 75 aspects of EMA and ER under 13 groups. In developing the compliance index binary method is used i.e. 1= if ER practices; 0= if not practices. Further the level of EMR/ER practices have been evaluated in terms of selected independent variables of the company viz. total assets, total sales, return on equity and size of board. The study found that the environmental management accounting in the manufacturing companies is in poor level. The maximum compliance is 67% and the lowest is 20%. The TA, TS BS and SP have been considered to find out the explanatory variables. In most of the cases board size does not play significant role in the practice of EMA in the sampled firms.
Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and protocols for loanable funds (PLFs). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.
We are entering an era of great expectations towards our cities. The vision of “smart city” has been pursued worldwide to transform urban habitats into superior efficiency, quality, and sustainability. This phenomenon prompts us to ponder what role the scholars in operations management (OM) can assume. In this essay, we express our initial thoughts on expanding OM to the smart‐city scope. We review smart‐city initiatives of governments, industry, national laboratories and academia. We argue that the smart‐city movement will transition from the tech‐oriented stage to the decision‐oriented stage. Hence, a smart city can be perceived as a system scope within which planning and operational decisions are orchestrated at the urban scale, reflective of multi‐dimensional needs, and adaptive to massive data and innovation. The benefits of studying smart‐city OM are manifold and significant: contributing to deeper understanding of smart cities by providing advanced analytical frameworks, pushing OM knowledge boundaries (such as data‐driven decision making), and empowering the OM community to deliver much broader impacts than before. We discuss several research opportunities to embody these thoughts, in the interconnected contexts of smart buildings, smart grid, smart mobility and new retail. These opportunities arise from the increasing integration of systems and business models at the urban scale.
The UN Sustainable Development Goals are not optional. They are about survival. Reaching them requires focus and strong innovation. Operations Management is about designing innovative business models to allocate scarce resources. It therefore has the toolkit to help move the sustainability agenda, provided it strongly opts for studying relevant and pressing problems and proves its potential impact. This study argues that this requires innovation as well as a paradigm shift in how we look at contributions. The good news is that a growing number of OM academics select to work on important facets of sustainability. The bad news is that we stepped into this field relatively late so the impact of that work is still modest. We need to push the boundaries of our discipline to develop new context‐dependent knowledge on pressing problems, provide evidence of the validity of our findings, and translate them into practical and easy‐to‐use decision tools. This study uses closed‐loop supply chains and humanitarian operations to illustrate and support this thesis. OM needs innovation to substantially contribute to the sustainable development agenda and thereby safeguard its own sustainability as a discipline.
Humanitarian operations play a crucial role in alleviating human and social losses caused by natural disasters. The best way to know responders’ preparedness and ability to conduct efficient and effective humanitarian operations is to perform an evaluation. When evaluating humanitarian operations, the focus is mainly on their outcomes while the option of concentrating on the process is only mentioned, without examining in‐depth the subject nor providing specific tools for its analysis. This study tries to fill this gap by proposing and testing an outcome/process integrated approach for the evaluation of disaster operations management. The output analysis and the process analysis of disaster operations management are performed jointly by means of a questionnaire and a modeling tool, respectively. The integrated framework proposed has been applied to the emergency response of a small non‐profit organization to a flood. It has been shown that the two methods applied separately could give a distorted or partial picture of the operations under study, while the integrated framework proposed has proved to be effective, since it has brought to a deeper understanding of the processes. The approach can be used by practitioners to evaluate disaster operations management, and accurately and efficiently identify the key elements, strengths, and main weaknesses of relief operations.
Diego Gabriel Rossit, Jamal Toutouh, Sergio Nesmachnow
Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bahía Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.
Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the black-box nature of deep neural networks. In this paper, we propose an empirical approach to explain the strategies of DRL agents for the portfolio management task. First, we use a linear model in hindsight as the reference model, which finds the best portfolio weights by assuming knowing actual stock returns in foresight. In particular, we use the coefficients of a linear model in hindsight as the reference feature weights. Secondly, for DRL agents, we use integrated gradients to define the feature weights, which are the coefficients between reward and features under a linear regression model. Thirdly, we study the prediction power in two cases, single-step prediction and multi-step prediction. In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods. Finally, we evaluate a portfolio management task on Dow Jones 30 constituent stocks during 01/01/2009 to 09/01/2021. Our approach empirically reveals that a DRL agent exhibits a stronger multi-step prediction power than machine learning methods.