Hasil untuk "Production management. Operations management"

P. Helo, Yuqiuge Hao

Abstract With the development and evolution of information technology, competition has become more and more intensive on a global scale. Many companies have forecast that the future of operation and supply chain management (SCM) may change dramatically, from planning, scheduling, optimisation, to transportation, with the presence of artificial intelligence (AI). People will be more and more interested in machine learning, AI, and other intelligent technologies, in terms of SCM. Within this context, this particular research study provides an overview of the concept of AI and SCM. It then focuses on timely and critical analysis of AI-driven supply chain research and applications. In this exploratory research, the emerging AI-based business models of different case companies are analysed. Their relevant AI solutions and related values to companies are also evaluated. As a result, this research identifies several areas of value creation for the application of AI in the supply chain. It also proposes an approach to designing business models for AI supply chain applications.

D. Ivanov, Christopher S. Tang, A. Dolgui et al.

ABSTRACT While Industry 4.0 has been trending in practice and research, operations management studies in this area remain nascent. Our intent is to understand the current state of research in Industry 4.0 in different disciplines and deduce insights and opportunities for future research in operations management. In this paper, we provide a focused analysis to examine the state-of-the-art research in Industry 4.0. To learn about researchers’ perspectives about Industry 4.0, we conducted a large-scale, cross-disciplinary and global survey on Industry 4.0 topics among researchers in industrial engineering, operations management, operations research, control and data science at the 9th IFAC MIM 2019 Conference in Berlin in August 2019. By using our survey findings and literature analysis, we build structural and conceptual frameworks to understand the current state of knowledge and to propose future research opportunities for operations management scholars. Glossary of Abbreviations AGV: Automated guided vehicle; AI: Artificial intelligence; APS: Advanced planning system: a wide variety of software tools and techniques, with many applications in manufacturing and logistics (including the service sector); BDA: Big data analytics; CAS: Complex adaptive system: a system composed of many interacting parts that evolve and adapt over time; CIM: Computer integrated manufacturing; CPFR: Collaborative planning, forecasting and replenishment; CPS: Cyber-physical system: a seamless integration of computation and physical components; DAMCLS: Decision analysis, modelling, control and learning systems; ERP: Enterprise resource planning; FMS: Flexible manufacturing system; I4.0: Industry 4.0; IFAC: International Federation of Automatic Control: a federation is concerned with the impact of control technology on society; IME: Industrial and mechanical engineering; IoT: Internet-of-Things; IT: Information technology; M2M: Machine-to-machine; MAS: Multi-agent system: a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver; OR: Operations research; RFID: Radio frequency identification: a technology that uses electromagnetic fields to automatically identify and track tags attached to objects; RMS: Reconfigurable manufacturing system: a manufacturing system that can change and evolve rapidly in order to adjust its productivity capacity and functionality; OM: Operations management; T&T: Track and trace system; VCA: VOS viewer co-occurrence analysis: a software tool for visualising bibliometric networks; VMI: Vendor-managed inventory.

A. Dolgui, D. Ivanov

5G technology enables end-to-end connectivity in real time at a highly granular level along with the associated end-to-end visibility through the Internet-of-Everything. While some potential benefits of 5G for digital supply chain and operations management have been declared, literature is still silent about theoretical underpinning and structured conceptualisation of application areas, underlying implementation challenges, and the role of 5G in future transformations of value creation. This paper aims to offer some directions of how to close this research gap. We organise the discussion around five major capabilities of the digital supply chain and smart operations which can be enhanced by 5G, i.e. intelligence, visibility, transparency, dynamic networking, and connectivity. We delineate possible future research topics related to 5G in different areas of Industry 4.0-driven, digital supply chain and operations management which can be useful for researchers and practitioners alike when seeking to understand the impact of 5G on both short-term and long-term time scales. Our analysis encompasses both operational processes (e.g. transformations of manufacturing and warehouse operations by end-to-end connectivity of devices) and strategic perspectives (e.g. transformations of business models and supply network structures through end-to-end real-time visibility and connectivity of industry, public infrastructure, and consumers). Finally, cost-benefits trade-offs are discussed.

Sunil Mithas, Zhi-Long Chen, T. Saldanha et al.

Emerging technologies such as artificial intelligence, blockchain, additive manufacturing, advanced robotics, autonomous vehicles, and the Internet of Things are frequently mentioned as part of “Industry 4.0.” As such, how will they influence operations and supply chain management? We answer this question by providing a brief review of the evolution of technologies and operations management (OM) over time. Because terms such as “Industry 4.0” do not have a precise definition, we focus on more fundamental issues raised by Industry 4.0 emerging technologies for research in OM. We propose a theory of disruptive debottlenecking and the SACE framework by classifying emerging technologies in terms of the functionalities they enable: sense, analyze, collaborate, and execute. Subsequently, we review the nascent but rapidly growing literature at the interface between digital technologies and OM. Our review suggests that one way to assess the value of Industry 4.0 technologies can be via their influence on adding revenues, differentiating, reducing costs, optimizing risks, innovating, and transforming business models and processes. Finally, we conclude by proposing an agenda for further research.

Hongyan Sheng, Taiwen Feng, Ling Liu

Although digital transformation has aroused the interest of scholars and practitioners, how to reach a win-win situation between economic and carbon performance in the context of carbon neutrality has not been well addressed. Based on organizational information processing theory, this study investigates how digital transformation affects economic and carbon performance via low-carbon operations management practices (LOMP) and the moderating role of CEO ambivalence. We test research hypotheses using hierarchical regression analysis by collecting data from 297 Chinese manufacturing firms. Our results reveal that all three dimensions of LOMP mediate the impact of digital transformation on carbon performance, and low-carbon products mediate the impact of digital transformation on economic performance. In addition, CEO ambivalence weakens the impacts of digital transformation on three dimensions of LOMP. The findings extend the digital transformation and LOMP literature and provide theoretical guidance for managers to achieve the goals of economic development and carbon reduction.

D. Ivanov, A. Dolgui

Supply chain (SC) and operations management has been developed for decades under the fundamental assumption of continuous availability (with some temporary fluctuations and disruptions) of resources to satisfy demand. Under extreme shocks and long-lasting disruptions, SCs and operations face long-term shortages of components, energy, capital, and labour, as well as rapidly rising prices. These long-term resource shortages and risks of hyper-inflation pose novel and unexpected challenges potentially leading to global ripple effects. In this conceptual paper, we first analyse and categorise the available literature on SC and operations management with resource shortages. We then systemise some managerial implications of resource shortages and rising inflation, conceptualising some potential research directions which appeared or may appear in the settings of shortage economy. We conclude that the shortage economy provides a distinct and specific context which goes beyond the conventional problems under constrained or disrupted resources – i.e. the long-term resource shortages with simultaneous disruptions in labour, material, energy, and capital availability. This paper could be useful for researchers and practitioners alike to systemise potential impacts of a shortage economy on SCs and operations, and to navigate adaptation and recovery processes in a structured way to help society cope with deep uncertainties.

Rui Pedro Marques, Dorabella Santos

This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable insights, enhancing transparency, improving forecasting, optimizing production and inventory, reducing waste, and enabling circular economy practices. Complementarily, OR provides methodological rigor through optimization models, simulation, and multicriteria decision-making, enabling organizations to balance economic, environmental, and social objectives in supply chain design and operations. The findings reveal that BI and OR jointly contribute to 11 of the 17 United Nations Sustainable Development Goals (SDGs), demonstrating their strategic relevance for global sustainable development. This paper’s contribution is twofold: it consolidates fragmented academic research through an integrative framework clarifying how BI and OR reinforce sustainability within SCM, and it provides practitioners with evidence of how these tools can generate both operational efficiency and a competitive advantage while meeting environmental and social responsibilities. Future research should focus on bridging existing gaps in the literature and advancing the practical applications of these technologies.

Yue Shen, Feng Yang, Jianbang Wu et al.

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM.

André de Mendonça Santos, Maíra Pinto Oliveira

O desenvolvimento da Indústria 4.0 promoveu grandes mudanças na logística, impulsionando a automação, digitalização e integração dos processos operacionais. Com base neste contexto, este estudo tem como objetivo avaliar a aplicação das tecnologias da Indústria 4.0 em uma empresa de transporte e serviços localizada em Feira de Santana-BA, analisando os desafios e as possibilidades de sua implementação. Com isso, foi realizada uma pesquisa qualitativa, composta por pesquisa bibliográfica, entrevista com gestor e visita técnica à empresa. A análise foi conduzida por meio da matriz SWOT da empresa, permitindo a identificação de fatores internos e externos que influenciam a adoção dessas tecnologias. A partir deste diagnóstico, foram propostas estratégias para otimizar os processos em termos de logística, reduções diretas das limitações operacionais e novas oportunidades a serem exploradas dentro do contexto da Logística 4.0.

Raj Kumar Jat, Shubham Durgude, Vijay Singh Meena et al.

What are the key factors influencing yield in winter maize cultivation under adverse climatic conditions? How can on-farm experimentation reveal innovative strategies to improve production in these challenging environments? Four year (2020-21-to-2023-24) on farm experimentation at 160 farmers in the districts of Purnia and Katihar were consider for study. The key factors evaluated for maize yields encompassed sowing windows, varietal performance, topography, seed treatment, earthing up, planting methods, spacing, tillage practices, irrigation, and nutrient management. Data was collected using a structured questionnaire that was validated by visiting on-farm experimentation at fields. Results indicated that the optimal sowing window for high yields was October 25th to November 7th, with high-yielding varieties Grover 4455 and Srikar 1818 showing the best performance. Topography showed a preferential distribution of yield towards upland areas. The variety P3355 demonstrated consistent performance, appearing across both high and medium yield categories. Higher frequencies of high yields in seed-treated plots were nevertheless obtained, with 62% high yields obtained in treated plots against 48% obtained in plots without treatment. At earthing up is one of the critical practices in flat bed system (FBS), and it contributed much to higher yields (χ²=17.86, p=0.003), but in raised bed system (RBS), which allow superior yields intrinsically. This trial showed that optimum spacing of 50 cm row-to-row and 22 cm plant-to-plant, coupled with moderate tillage operations of 4-10, with a median of 6, resulted in increased yields. Efficient irrigation management, where high-yielding plots received balanced nutrient applications of 243.85-165.51-106.74 NPK kg/ha, was a critical factor in realizing high yields. Principal component analysis (PCA) underlined the role of integrated agronomic practices in maximizing maize production. It provides actionable insight to farmers with respect to maize yield improvement for economic resilience and sustainable agriculture. Overall, this study identified optimal sowing windows, high-yielding varieties, and integrated agronomic practices that significantly enhance winter-maize production under adverse climatic conditions, offering actionable insights for sustainable agriculture.

John Reyes, Josefa Mula, Manuel Díaz-Madroñero

Abstract A lean supply chain (LSC) is a set of organizations directly linked by upstream and downstream value streams between processes that work collaboratively to reduce costs and waste. Currently, supply chains (SCs) have been put to the test as the world has had to face a series of unprecedented disruptions in demand and supply caused by the COVID-19 pandemic. In this paper, a detailed study of constructs and multistructural components was carried out to develop a conceptual reference model that merges Industry 4.0 (I4.0) digital technologies with lean manufacturing tools to reduce waste and minimize costs in the lean supply chain planning (LSCP) context. The main theoretical contribution of this conceptual proposal is to establish a structured relation among the lean, agile, sustainable, resilient and flexible paradigms to improve SC performance by implementing I4.0 enabling technologies. The proposed conceptual model, dubbed as LSCP 4.0, is applied and validated with a case study in a large footwear company. It can help decision-makers and researchers to improve the planning and management of digital SC production processes, even with unexpected disruptions.

Nur Sunar, Jayashankar M. Swaminathan

Many parts of the world are experiencing extreme weather events, energy poverty, food insecurity, and lack of access to basic healthcare. Moreover, concerns over socioeconomic, gender, and racial inequalities are growing. These socially relevant issues are ripe for analysis and improvement using an operations management lens. In this paper, we review some of the relevant research advancements made in the last decade, and identify future research directions on these important topics. In particular, we focus on papers related to sustainable planet (renewable energy, environmentally and socially responsible operations, regulation‐driven operations), agriculture, and public health. For future research directions, we discuss the role of innovative business models and disruptive technologies, such as artificial intelligence (AI) and blockchain, in addressing these pressing issues.

MA Nan, CAO Shanshan, BAI Tao et al.

[Significance]The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.[Progress]Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.[Conclusions and Prospects]To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.

Ghi Tran Nha, Trung Nguyen Tan, Long Nguyen Thanh et al.

This study is conducted to explain entrepreneurial support resources of firms based on social network theory in developing countries, the case of Vietnam. Partial Least Squares Structural Modeling (PLS-SEM) was conducted with a sample size of 220 entrepreneurs in SMEs. The results supported the positive link between formal and informal networks and entrepreneurial firm performance. Second, the study explored the partial mediating role of access to entrepreneurial resources between formal networks, informal networks, and entrepreneurial firm performance. In addition, the results also provide practical value to entrepreneurs in actively building relationship networking in the entrepreneurship ecosystem. Finally, the study proposed some implications for entrepreneurs, limitations, and further research.

Giuseppe Piras, Francesco Muzi, Claudia Zylka

The integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) is a growing reality in the building production sector. Through this integration, it is possible to improve the efficiency of management, maintenance, use and planning of conservation operations, providing an integrated and dynamic vision of the built environment. Simultaneous exchange of BIM-GIS elements in a shared environment facilitates information access and optimizes processes like requalification, activity planning, safety and sustainable urban design. Two alternative strategies are proposed for the multidisciplinary approach, using advanced technologies to acquire, process and manage detailed and georeferenced data. The first one is an open-source environment to guarantee flexibility, customization and accessibility. The second option, in a closed-source environment, provides advanced functionalities and dedicated support. Both require careful planning, detailed analysis and collaboration between the disciplines of architecture, engineering and geoinformatics. The study transcends theoretical analysis by exploring practical implications for real-world systems integration, examining their advantages, limitations and potential synergies in terms of flexibility, security and sustainability. This will enable a more efficient and comprehensive management of the architectural heritage and the built environment, contributing to its preservation and enhancement in the context of the digital transition in a future perspective of smart cities.

Reza Zanjirani Farahani, N. Asgari, Luk N. Van Wassenhove

Textile waste is one of the most pollutant items globally, being strongly affected by fast fashion (FF) products. Public pressure has made many FF firms voluntarily collect a small fraction of their preowned items and export them to developing countries for reuse. However, some developing countries are launching import bans on second‐hand clothes. In addition, FF firms may soon be forced by extended producer responsibility legislation to collect more preowned items for reuse and recycling. To date, they do not have sufficient capacity to deal with this. Charities have been the key collectors and recyclers of unwanted clothes. Therefore, charities could help FF firms increase their capacity in this reverse supply chain (SC). However, we hardly witness such a collaboration for two main reasons: (i) charities prefer to sell high‐quality preowned items in the primary market to generate the highest possible revenue and FF firms may fear cannibalization, (ii) many charities believe that FF firms generate quantities of low‐quality items that require collection and sorting while being difficult to sell in the primary market. Charities also face competition from many small for‐profit organizations selling FF preowned items. While charities have the support of volunteers, they tend to be less efficient. This work urges Operations Management (OM) researchers to suggest innovative business models to help (i) FF firms and charities collaborate to solve the abovementioned issues, and (ii) charities to improve their traditional practices for competitiveness. This study is primarily a position paper highlighting some challenges and introducing interesting research problems. Although the paper is not a research paper, it follows a qualitative research method to collect and analyze the required supporting documents to justify arguments and statements. We collected primary and secondary data from the textile reverse SC members to familiarize the OM community with this context. The current changes in the textile reverse SC offer many great opportunities for impactful OM research.

Robyn M. Underwood, Brooke L. Lawrence, Nash E. Turley et al.

Abstract Honey bee colony management is critical to mitigating the negative effects of biotic and abiotic stressors. However, there is significant variation in the practices implemented by beekeepers, which results in varying management systems. This longitudinal study incorporated a systems approach to experimentally test the role of three representative beekeeping management systems (conventional, organic, and chemical-free) on the health and productivity of stationary honey-producing colonies over 3 years. We found that the survival rates for colonies in the conventional and organic management systems were equivalent, but around 2.8 times greater than the survival under chemical-free management. Honey production was also similar, with 102% and 119% more honey produced in conventional and organic management systems, respectively, than in the chemical-free management system. We also report significant differences in biomarkers of health including pathogen levels (DWV, IAPV, Vairimorpha apis, Vairimorpha ceranae) and gene expression (def-1, hym, nkd, vg). Our results experimentally demonstrate that beekeeping management practices are key drivers of survival and productivity of managed honey bee colonies. More importantly, we found that the organic management system—which uses organic-approved chemicals for mite control—supports healthy and productive colonies, and can be incorporated as a sustainable approach for stationary honey-producing beekeeping operations.

Varun Tripathi, S. Chattopadhyaya, A. Mukhopadhyay et al.

In industry 4.0, shop floor management teams are increasingly focused on developing an unprecedented strategy to avoid financial losses and address the challenges and problems encountered in operations management. In the present scenario, the management teams use various process optimization approaches for operational control, including lean manufacturing, smart manufacturing, the internet of things, and artificial intelligence. The process optimization approach is used to maximize productivity within limited constraints on the shop floor. The present research aims to develop a smart production management system and suggest an efficient process optimization approach to enhancing industrial sustainability by identifying problems and challenges encountered in the complex shop-floor conditions in industry 4.0. The developed production management system has been prepared by classifying the challenges and problems found in the previous research work and organizing brainstorming sessions. The developed management system has been validated by a comprehensive investigation of a case study of an earthmoving machinery manufacturing unit. The analysis showed that the developed system could enhance operation excellence and industrial sustainability in industry 4.0 by optimizing the utilization of resources on the shop floor within limited constraints. The authors of the present article strongly believe that the developed production management system will improve operational excellence and would be beneficial for industry personnel and researchers in controlling operations management in shop floor management of heavy machinery manufacturing, including industry 4.0.

Sunil Mithas, Yanzhen Chen, Yatang Lin et al.

Empirical research in operations management (OM) has made rapid strides in the last 30 years, and increasingly, OM researchers are leveraging methods used in the econometrics and statistics literature to assess the causal effects of interventions. We discuss the two key challenges in assessing causality with observational data (i.e., baseline bias, differential treatment effect bias) and how dominant identification approaches such as matching, instrumental variables, regression discontinuity, difference‐in‐differences, and fixed effects deal with such challenges. We surface the key underlying assumptions of different causal estimation methods and discuss how OM scholars have used these methods in the last few years. We hope that reflecting on the plausibility and substantive meaning of underlying assumptions regarding different identification strategies in a particular context will lead to a better conceptualization, execution, evaluation, dissemination, and consumption of OM research. We conclude with a few thoughts that authors and reviewers may find helpful in their research as they engage in discourse related to causality.

Yiğit Kazançoğlu, Melisa Ozbiltekin‐Pala, Muruvvet Deniz Sezer et al.

PurposeThe aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).Design/methodology/approachTen different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.FindingsThe results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.Research limitations/implicationsThe interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.Originality/valueThe main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.