A. B. L. D. S. Jabbour, J. Luiz, Octaviano Rojas Luiz
et al.
Abstract This article examines and conceptualizes the implications of the adoption of circular economy (CE) business models on operations management (OM) decision-making processes, in the areas of product design, production planning and control, and logistics/supply chains. A systematic literature review was conducted in order to identify and examine the following key areas: (1) the new demands faced by OM decision-making regarding changes in capability, work procedures, intra- and inter-organizational relationships and technologies; (2) the specific changes OM decision-making must make in order to support CE business models (based on the ReSOLVE framework); and (3) guidelines which will help designers and operations and logistics/supply chain managers develop the necessary skills to meet society and the global market's emerging demands. The findings of this research will allow operations managers to foresee the unfolding needs for capacity building in CE, and scholars can build on the results of this article to develop new research themes. Furthermore, this is the first article to describe the ways in which OM knowledge can support the transition towards the circular economy based on the perspective of dynamic capabilities.
David Alejandro Pinzon, Gina Amado, Jader Rodriguez
et al.
The cultivation of blueberry (<i>Vaccinium</i> spp.) has undergone an unprecedented global expansion, driven by its nutraceutical value and the diversification of production zones across the Americas, Europe, and Asia. Its consolidation as a strategic crop has prompted intensive scientific activity aimed at optimizing every stage of management from propagation and physiology to harvest, postharvest, and environmental sustainability. However, the available evidence remains fragmented, limiting the integration of results and the formulation of knowledge-based, comparative production strategies. The objective of this systematic review was to synthesize scientific and technological advances related to the integrated management of blueberry cultivation, incorporating physiological, agronomic, technological, and environmental dimensions. The PRISMA 2020 methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was applied to ensure transparency and reproducibility in the search, selection, and analysis of scientific literature indexed in the Scopus database. After screening, 367 articles met the inclusion criteria and were analyzed comparatively and thematically. The results reveal significant progress in propagation using hydrogel and micropropagation techniques, efficient fertigation practices, and the integration of climate control operations within greenhouses, leading to improved yield and fruit quality. Likewise, non-thermal technologies, edible coatings, and harvest automation enhance postharvest quality and reduce losses. In terms of sustainability, the incorporation of water reuse and waste biorefinery has emerged as key strategies to reduce the environmental footprint and promote circular systems. Among the main limitations are the lack of methodological standardization, the scarce economic evaluation of innovations, and the weak linkage between experimental and commercial scales. It is concluded that integrating physiology, technology, and sustainability within a unified management framework is essential to consolidate a resilient, low-carbon, and technologically advanced fruit-growing system.
Nicola Mercanti, Monica Macaluso, Andrea Marianelli
et al.
The reduction or elimination of sulphur dioxide (SO<sub>2</sub>) in winemaking represents a major technological and sustainability challenge due to the central antimicrobial and antioxidant roles of this additive. This study evaluated the technological feasibility and chemical stability of a no-added-SO<sub>2</sub> vinification protocol applied under controlled winery conditions over four consecutive vintages, compared with a conventional sulphite-based protocol. The no-added-SO<sub>2</sub> protocol integrated closed-circuit operations, controlled inert gas management, temperature-regulated fermentation, strict hygiene practices, the addition of grape seed extracts as alternative antioxidant agents, and real-time monitoring of CO<sub>2</sub> production and O<sub>2</sub> availability via a smart tank. Across all vintages, wines produced using the no-added-SO<sub>2</sub> protocol showed regular alcoholic and malolactic fermentations and volatile acidity values consistently below the sensory perception threshold (1.2 g/L). Total SO<sub>2</sub> levels ranged between 0.3 and 86 mg/L and free SO<sub>2</sub> ranged between 0.4 and 16 mg/L, attributable exclusively to endogenous yeast production. Multivariate analysis confirmed that vintage was the dominant factor affecting most compositional parameters, particularly phenolic and anthocyanin profiles, whereas sulphur dioxide management represented a secondary but clearly identifiable source of variability. These findings indicate that sulphur dioxide-free vinification is technically feasible when supported by precise process control and continuous real-time monitoring. Rather than a universal replacement for conventional sulphite management, the no-added-SO<sub>2</sub> protocol should be regarded as a complementary and technologically contingent tool for sustainable SO<sub>2</sub> reduction within a precision oenology framework.
Vehicular Ad Hoc Networks (VANETs) support the information dissemination among vehicles, Roadside Units (RSUs), and a Trust Authority (TA). A trust model evaluates an entity or data or both to determine truthfulness. A security model confirms authentication, integrity, availability, non repudiation issues. With these aspects in mind, many models have been proposed in literature. Furthermore, many information dissemination approaches are proposed. However, the lack of a model that can manage traffic incidents completely inspires this work. This paper details how and when a message needs to be generated and relayed so that the incidents can be reported and managed in a timely manner. This paper addresses this challenge by providing a traffic incident management model to manage several traffic incidents efficiently. Additionally, we simulate this model using the VEINS simulator with vehicles, RSUs, and a TA. From the experiments, we measure the average number of transmissions required for reporting a single traffic incident while varying the vehicle density and relaying considerations. We consider two types of relaying. In one series of experiments, messages from regular vehicles and RSUs are relayed up to four hops. In another series of experiments, messages from the regular vehicles and RSUs are relayed until their generation time reaches sixty seconds. Additionally, messages from the official vehicles are relayed when they approach an incident or when the incident is cleared. Results from the simulations show that more vehicles are informed with four-hop relaying than sixty-second relaying in both cases.
Artificial intelligence (AI) is increasingly embedded into service firms’ operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations.
Abstract Transparency, visibility, and disintermediation are some of the prospects of the aspiring blockchain technology in the business-to-business context. The digital transformation and Industry 4.0 trends also facilitate blockchain applications in operations management (OM) and manufacturing. However, scientific contributions and successful industrial applications in this area are still scarce and mainly at a proof-of-concept stage. The empirical research in this article is based on an expert interview study to uncover and analyse the potential and barriers to the adoption of blockchain technology in OM and manufacturing from within the industry. Semi-structured interviews with industry experts are employed to elaborate on promising practices for the industry to efficiently promote blockchain adoption and meaningful research directions for scholars. Findings include unexplored potential regarding distributed production networks and collaboration, expected evolutionary steps of IoT, disintermediation leading to new business models like tokenisation, and short-term rather than long-term relationships. Current barriers include staff difficulties, legal uncertainties, missing infrastructure and standardisation, and unclear governance structures. Improving smart contract security and interoperability of private and public protocols will enable further dissemination of the technology. Managers and academic scholars can address these findings and new propositions of this study in future application development and implementation.
Masud Ramezanian Kaykanloo, Asgar Khademvatani, Hossein Ali Akhlaghi Amiri
Abstract This study investigates the optimization of condensate recovery in a retrograde gas reservoir, where production efficiency is hindered by complex interactions between subsurface and surface processes. Accurate modeling of these interactions is essential for reliable production forecasting and economic assessment. This research compares the efficacy of two simulation methodologies: (1) standalone reservoir modeling and (2) integrated modeling encompassing the reservoir, wells, pipelines, and surface facilities under various gas reinjection and production scenarios. Key economic metrics, including Net Present Value (NPV) and Modified Internal Rate of Return (MIRR), are employed to assess scenario feasibility and identify optimal recovery strategies. The findings demonstrate that integrated modeling significantly enhances production forecasts' accuracy by capturing interdependencies often neglected in standalone models. Specifically, optimized gas reinjection in the integrated model resulted in a 15% increase in condensate recovery and improved reservoir pressure maintenance, thereby facilitating sustained productivity. Economically, integrated simulations yielded an NPV up to 10% higher than the standalone approach under optimal reinjection conditions, indicating enhanced economic resilience to market fluctuations. Through this methodology, the study provides a more comprehensive framework for evaluating technical and economic performance in gas condensate reservoir management, offering refined tools for informed decision-making in complex field operations.
Markus Fallmann, Lukas Stanger, Martin Fischer
et al.
This paper presents the development and implementation of a broadly applicable Energy Management System (EMS) based on model predictive control (MPC) to optimize energy consumption in a real-world industrial food processing plant. The EMS, formulated as a Mixed-Integer Linear Programming (MILP) optimization problem, is designed to minimize energy use and switching operations- defined as the number of equipment on/off transitions per unit of energy delivered (switches/MWh) - while ensuring sufficient heating and cooling for production. The control structure is built upon a two-tiered MPC framework. At the higher level, an MPC algorithm optimizes energy efficiency over a 24-hour horizon, taking into account the production schedule, predicted energy demands, and the operation of thermal storage and heat pumps. The lower-level controller, with a faster sampling rate, focuses on short-term disturbance rejection and immediate system adjustments. The system was evaluated over 14 days of real-world economic plant operation, with results showing significant improvements in efficiency and in reducing switching operations and thus wear. On the cold process side, switching operations have been reduced while maximizing control performance under tight temperature constraints. On the hot side, the EMS achieved a remarkable 8 % increase in efficiency and 36 % reduction of switching operations.
As the strategy for building a robust agricultural nation gains momentum and agricultural science and technology advances, the quality of agricultural products has seen significant improvement, accompanied by an increase in the economic income of agricultural producers and operators. Therefore, the fair and reasonable implementation of the revenue distribution of the agricultural supply chain is of great significance in improving the quality of agricultural products and ensuring the stable operation of the supply chain. The article focuses on the three main bodies of the agricultural supply chain, namely production and price co-integration enterprises, logistics service enterprises and sales enterprises, and utilises the matrix semi-tensor product to establish the Shapley value revenue allocation model of the interval cooperation game, so as to make the revenue allocation of the governance of agricultural products' quality in the digital supply chain more reasonable and scientific. Finally, numerical examples verify the Shapley value model, demonstrating that this revenue allocation scheme, when applied, can boost the overall supply chain's revenue through cooperative agricultural product quality management, elevate agricultural product quality and market competitiveness, and foster collaboration to ensure the stability of supply chain operations.
Today, products are no longer isolated artifacts, but nodes in networked systems. This means that traditional, linearly conceived life cycle models are reaching their limits: Interoperability across disciplines, variant and configuration management, traceability, and governance across organizational boundaries are becoming key factors. This collective contribution classifies the state of the art and proposes a practical frame of reference for SoS lifecycle management, model-based systems engineering (MBSE) as the semantic backbone, product lifecycle management (PLM) as the governance and configuration level, CAD-CAE as model-derived domains, and digital thread and digital twin as continuous feedback. Based on current literature and industry experience, mobility, healthcare, and the public sector, we identify four principles: (1) referenced architecture and data models, (2) end-to-end configuration sovereignty instead of tool silos, (3) curated models with clear review gates, and (4) measurable value contributions along time, quality, cost, and sustainability. A three-step roadmap shows the transition from product- to network- centric development: piloting with reference architecture, scaling across variant and supply chain spaces, organizational anchoring (roles, training, compliance). The results are increased change robustness, shorter throughput times, improved reuse, and informed sustainability decisions. This article is aimed at decision-makers and practitioners who want to make complexity manageable and design SoS value streams to be scalable.
To conceptualize living systems based on the processes that create them, rather than their interactions with the environment, as in systems theory. Maturana and Varela (1969) at the University of Chile introduced the term autopoiesis (from Greek self and production). This concept emphasizes autonomy as the defining feature of living systems. It describes them as self-sustaining entities that preserve their identity through continuous self-renewal to preserve their unity. Furthermore, these systems can only be understood in reference to themselves, as all internal activities are inherently self-determined by self-production and self-referentiality. This thesis introduces the Fuzzy Autopoietic Knowledge Management (FAKM) model, which integrates the system theory of living systems, the cybernetic theory of viable systems, and the autopoiesis theory of autopoietic systems. The goal is to move beyond traditional knowledge management models that rely on Cartesian dualism (cognition/action) where knowledge is treated as symbolic information processing. Instead, the FAKM model adopts a dualism of organization/structure to define an autopoietic system within a sociotechnical approach. The model is experimentally applied to a manufacturing company in the Maule Region, south of Santiago, Chile.
In the modern world, the development of Artificial Intelligence (AI) has contributed to improvements in various areas, including automation, computer vision, fraud detection, and more. AI can be leveraged to enhance the efficiency of Autonomous Smart Traffic Management (ASTM) systems and reduce traffic congestion rates. This paper presents an Autonomous Smart Traffic Management (STM) system that uses AI to improve traffic flow rates. The system employs the YOLO V5 Convolutional Neural Network to detect vehicles in traffic management images. Additionally, it predicts the number of vehicles for the next 12 hours using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). The Smart Traffic Management Cycle Length Analysis manages the traffic cycle length based on these vehicle predictions, aided by AI. From the results of the RNN-LSTM model for predicting vehicle numbers over the next 12 hours, we observe that the model predicts traffic with a Mean Squared Error (MSE) of 4.521 vehicles and a Root Mean Squared Error (RMSE) of 2.232 vehicles. After simulating the STM system in the CARLA simulation environment, we found that the Traffic Management Congestion Flow Rate with ASTM (21 vehicles per minute) is 50\% higher than the rate without STM (around 15 vehicles per minute). Additionally, the Traffic Management Vehicle Pass Delay with STM (5 seconds per vehicle) is 70\% lower than without STM (around 12 seconds per vehicle). These results demonstrate that the STM system using AI can increase traffic flow by 50\% and reduce vehicle pass delays by 70\%.
Aref Ayati, Mohammad Mahdi Hashemi, Mohsen Saffar
et al.
Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field.
This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.
How should financial institutions hedge their balance sheets against interest rate risk when managing long-term assets and liabilities? We address this question by proposing a bond portfolio solution based on ambiguity-averse preferences, which generalizes classical immunization and accommodates arbitrary liability structures, portfolio constraints, and interest rate perturbations. In a further extension, we show that the optimal portfolio can be computed as a simple generalized least squares problem, making the solution both transparent and computationally efficient. The resulting portfolio also reduces leverage by implicitly regularizing the portfolio weights, which enhances out-of-sample performance. Numerical evaluations using both empirical and simulated yield curves support the feasibility and accuracy of our approach relative to existing methods.
Owing to its multidisciplinary nature, the operations management (OM) and information systems (IS) interface distinguishes itself from the individually focused perspective of both fields. The number and depth of contributions in this department can help both disciplines advance to better address important theoretical and practical challenges of the business world. In this paper, we study the characteristics of problems at the interface between OM and IS, and review past work that has been instrumental in setting the tone and direction of research at this interface. We extend our discussion to provide directions for future research at the OM and IS interface in the domains such as smart city management, healthcare, deep learning and artificial intelligence, fintech and blockchain, Internet of Things and Industry 4.0, and social media and digital platforms.
Although much research has been carried out to examine various contextual issues and moderating factors for successful R&D investments, very little research has been conducted to explore the role of a firm's operational and process characteristics. In this study, we explore how firms could possibly enhance the financial returns of R&D investments through quality management, using Six Sigma implementation as an example, and efficiency improvement, using the stochastic frontier estimation of relative efficiency as a proxy. Based on data from 468 manufacturing firms in the United States over the period 2007–2014, we construct a dynamic panel data model to capture the effects of R&D investments on firms’ financial returns in terms of Tobin's q. Using the system generalized method of moments estimator, our results indicate that the financial returns of R&D investments are significantly enhanced when firms adopt Six Sigma and improve efficiency in operations. Our additional analyses further suggest that such an enhancement effect through quality and efficiency improvements is more pronounced under high operational complexity as approximated by labor intensity and geographical diversity. Instead of considering innovation activities and process management as contradictory functions, we show that quality and efficiency improvements indeed support firms’ R&D investments, leading to higher financial returns.
Over the past two decades, there has been explosive growth in the application of robust optimization in operations management (robust OM), fueled by both significant advances in optimization theory and a volatile business environment that has led to rising concerns about model uncertainty. We review some common modeling frameworks in robust OM, including the representation of uncertainty and the decision‐making criteria, and sources of model uncertainty that have arisen in the literature, such as demand, supply, and preference. We discuss the successes of robust OM in addressing model uncertainty, enriching decision criteria, generating structural results, and facilitating computation. We also discuss several future research opportunities and challenges.
The role of national culture interactions is important in operations management and supply chain management decisions. Yet, cross‐cultural research in this field is limited. Our goal in this study is to review relevant research, to raise awareness about the critical role of national culture among operations management and supply chain management researchers, and to offer directions for future research. To achieve this goal, we report our research findings in three major categories: (i) Operational Decisions (innovation, and research and development, quality management, workforce management, performance measurement, and risk, security and disaster management); (ii) Supply Chain Management (buyer–supplier interactions, governance mechanisms, outsourcing, and offshore operations); and (iii) Interdisciplinary Topics (entrepreneurship, investments, joint ventures, and mergers and acquisitions). We also suggest methodological considerations for future research by those interested in studying national culture.