Hasil untuk "Production management. Operations management"

T. Choi, Subodha Kumar, Xiaohang Yue et al.

In the Industry 4.0 era, automation and data analytics emerge as the major forces to enhance efficiency in operations management (OM). Disruptive technologies, such as artificial intelligence, robotics, blockchain, 3D printing, 5G, Internet‐of‐Thing, digital twins, and augmented reality, are widely applied. They potentially will bring a radical change to real world operations. In this study, we first explore several major disruptive technologies, examine the corresponding OM studies, and highlight their current applications in the industry. Then, we discuss the pros and cons associated with the use of these technologies and uncover the potential human–machine conflicting areas. After that, we propose measures which may be able to achieve human–machine reconciles in the coming Industry 5.0 era. A concept of “sustainable social welfare” which includes worker welfare, privacy, etc. is proposed and the roles played by policy makers are also discussed. Finally, a future research agenda, which covers topics in both the Industry 4.0 and Industry 5.0 eras, is established.

Ilya Jackson, Dmitry A. Ivanov, Alexandre Dolgui et al.

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research.

Aalok Kumar, S. Luthra, S. Mangla et al.

The global production and supply chain system is mostly disrupted due to widespread of the coronavirus pandemic (COVID-19). Most of the industrial managers and policymakers are searching for adequate strategies and policies for revamping production patterns and meet consumer demand. Form global supply chain perspectives, the majority of raw materials are imported from China and other Asian developing nations. The COVID-19 pandemic has broken the most of transportation links and distribution mechanisms between suppliers, production facilities and customers. Therefore, it is imperative to discuss sustainable production and consumption pattern in the post-COVID-19 pandemic era. Most of the prominent economies around the world enforced a total lockdown, and the focus has since shifted to surge in demand for essential products and services. This has led to a decline in demand for some nonessential products and services. The production and operations management challenges of the pandemic situations are discussed and adequately proposes policy strategies for improving the resilience and sustainability of the system. This paper also discusses the different operations and supply chain perspectives for handling such disruptions in the future.

V. Cannas, Maria Pia Ciano, Mattia Saltalamacchia et al.

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies’ competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

D. Ivanov

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins.

Marlon Dumas, Fredrik Milani, David Chapela-Campa

Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from human-driven to fully autonomous, thus redefining the boundaries of process automation and governance.

A. Dolgui, D. Ivanov

ABSTRACT The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability).

Chayasmita Deka, Chayasmita Deka, Mrinal Kanti Dutta et al.

Amidst escalating challenges concerning extreme climatic events, the transition to low-carbon lifestyles has emerged as a significant policy priority. To that end, adoption of low-carbon technologies like electric vehicles (EVs) is critical. This study is a novel examination of the socio-psychological mechanisms shaping intentions to adopt EVs in Assam, a fast-developing region in northeast India, characterized by collectivist cultural norms. While existing research has primarily focused on economic, technical, and volitional factors such as perceived behavioral control, environmental awareness and attitudinal variables, this study examines the combined effect of norm and fear-based drivers of intention to adopt EVs. Utilizing the Norm Activation Model (NAM) and the Protection Motivation Theory (PMT), this study identifies subjective norms and perceived vulnerability as the most significant norm-based and fear-based predictor of intention respectively. Structural equation modeling reveals a parallel rather than sequential operation of norm and fear-based constructs, with mediated intention pathways featuring a complex interplay of affect-cognition mechanisms shaping intention. Unlike findings in Western contexts, personal moral norms have less direct impact in shaping intention in a collectivist setting where social validation and group norms weigh higher. Awareness and environmental concern is also found to be ineffective unless it is accompanied with fear cues indicating personal vulnerability and a belief in the possibility of its mitigation. The findings highlight the need for localized, tailored, affect-filled communication strategies over nation-wide financial incentives alone to accelerate EV adoption. The limitations and directions for further research on evolving EV ecosystems are discussed.

Junyu Chen, Yang Shan, Shuai Li et al.

Abstract The long-term mining of lead-zinc ore bodies in Suichang Gold Mine has formed a high-stage, long-span columnar goaf group above 500 m elevation, posing significant risks of roof collapse, slope fragmentation, and ground pressure disasters. These hazards threaten the safety of deep mining operations and surface stability. To address these challenges, this study integrates field investigations, laboratory rock mechanics experiments, and FLAC3D numerical simulations to analyze the stability evolution of goafs before and after filling. Key innovations include the application of disaster chain theory to interpret goaf failure mechanisms and the optimization of cement-sand ratios for targeted filling (1:20 for 260–610 m levels and 1:8 for 528–540 m levels). Results demonstrate that cemented filling reduces vertical displacement by 2–4 cm, alleviates stress concentration (maximum compressive stress decreased by 0.5–1.0 MPa), and minimizes plastic zone expansion. Furthermore, the proposed interval mining sequence (first mining 300–390 m, followed by 260–290 m and 420–500 m levels) ensures both production efficiency and operational safety. This research provides a systematic framework for goaf management in complex mining environments.

Sandra Coello Suarez, V. Sanchez Padilla, Ronald Ponguillo-Intriago et al.

Parameter monitoring and control systems are crucial in the industry as they enable automation processes that improve productivity and resource optimization. These improvements also help to manage environmental factors and the complex interactions between multiple inputs and outputs required for production management. This paper proposes an automation system for broiler management based on a simulation scenario that involves sensor networks and embedded systems. The aim is to create a transmission network for monitoring and controlling broiler temperature and feeding using the Internet of Things (IoT), complemented by a dashboard and a cloud-based service database to track improvements in broiler management. We look forward this work will serve as a guide for stakeholders and entrepreneurs in the animal production industry, fostering sustainable development through simple and cost-effective automation solutions. The goal is for them to scale and integrate these recommendations into their existing operations, leading to more efficient decision-making at the management level.

Asid Ur Rehman, Vassilis Glenis, Elizabeth Lewis et al.

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorm return period-based designs to enhance resilience and adaptability to future climate extremes.

Xiahua Wei, Naveen Kumar, Han Zhang

Generative AI technologies, particularly Large Language Models (LLMs), have transformed information management systems but introduced substantial biases that can compromise their effectiveness in informing business decision-making. This challenge presents information management scholars with a unique opportunity to advance the field by identifying and addressing these biases across extensive applications of LLMs. Building on the discussion on bias sources and current methods for detecting and mitigating bias, this paper seeks to identify gaps and opportunities for future research. By incorporating ethical considerations, policy implications, and sociotechnical perspectives, we focus on developing a framework that covers major stakeholders of Generative AI systems, proposing key research questions, and inspiring discussion. Our goal is to provide actionable pathways for researchers to address bias in LLM applications, thereby advancing research in information management that ultimately informs business practices. Our forward-looking framework and research agenda advocate interdisciplinary approaches, innovative methods, dynamic perspectives, and rigorous evaluation to ensure fairness and transparency in Generative AI-driven information systems. We expect this study to serve as a call to action for information management scholars to tackle this critical issue, guiding the improvement of fairness and effectiveness in LLM-based systems for business practice.

Michele Azzone, Matteo Ghesini, Davide Stocco et al.

Climate-related phenomena are increasingly affecting regions worldwide, manifesting as floods, water scarcity, and heat waves, significantly impairing companies' assets and productivity. It is essential for asset managers to quantify the exposure of their portfolios to such risk. To this aim, we develop a framework based on the Vasicek model for credit risk that introduces downward jumps due to climate phenomena in a company asset's dynamics. These negative shocks are designed to mirror the negative effect of extreme climate events. The model calibration relies on companies' asset intensity and geographical exposure. We apply the new multivariate firm value model with jumps to assess the impact of climate-related extreme events on expected and unexpected portfolio losses. Our findings indicate that expected losses increase over time, with pronounced differences in exposure observed across sectoral indices. From an environmental policy perspective, these results suggest the need for additional capital buffers to offset losses arising from physical climate risks, particularly in sectors with high asset intensity.

Alexandre Dolgui, Dmitry A. Ivanov

The Internet of Behaviours (IoB) is a network designed and used for replicating, understanding, predicting, and influencing human behaviour through data analytics. This study aims to advance our understanding of how the IoB will impact supply chain and operations management (SCOM). Based on a combination of the existing IoB frameworks with industrial Metaverse and digital supply chain twins, we propose a framework for IoB in SCOM encompassing management, organisation, and technology dimensions. We conclude that the IoB will impact a multitude of SCOM areas such as supply chain design, demand forecasting, sales and operations planning, distribution and transportation, inventory and production management, sourcing strategy and purchasing, vehicle routeing, supply chain resilience and sustainability. We offer a structured future research agenda pointing to new research questions and topics stemming from IoB-driven real-time visibility, data analytics, and remote control. Methodology of SCOM can also be enriched by the IoB through a combination of artificial intelligence and digital technology with operations research and behavioural operations management.

Liping Zhou, Zhibin Jiang, Na Geng et al.

In the context of Industry 4.0, the manufacturing sector is moving from automation towards intelligence. The application of new generation information and communication technologies (ICTs) improves the interconnection and transparency of intelligent manufacturing (IM) systems, which will change how information interacts and work is done, thus changing how work should be managed. These changes require the following characteristics for IM production and operations management (POM): integration, flexibility and networking, autonomous and collaborative decision-making, learning-based operations management, self-optimisation and adaptability, and proactive decision-making. This paper presents the state of the art, current challenges, and future directions of IM-related POM research from the perspectives of these characteristics through a systematic literature review. Descriptive and thematic analyses of 208 research articles published between 2005 and 2020 are provided. The review and discussions focus on five research themes, i.e. value creation mechanisms, resource configuration and capacity planning, production planning, scheduling, and logistics.

S. Wamba, M. Queiroz, E. Ngai et al.

This editorial introduces the special issue “The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.’ We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented.

C. Glock, E. Grosse, M. Jaber et al.

Abstract The occurrence and characteristics of human learning and forgetting are extensively researched in many fields. Thus, the literature on learning curves is abundant. Within production and operations management, learning curves can describe the performance improvement of workers due to repetitions or experience, which makes them a useful tool for managerial decision making. Human learning is very relevant to manufacturing firms that are labor-intensive, especially where labor is costly. Also, technology and organization learning frequently occur in many organizations, even when manufacturing is not labor-intensive. It is not surprising, therefore, that, despite the plethora of prior work in learning curves, research in this important area continues to appear in the production and operations management literature. A comprehensive, systematic review of the literature, however, has never been conducted. This article aims at giving a general overview of learning curves in production and operations management. It maps this specific research area, synthesizes its research findings, highlights its key application areas and explores future research directions. This work achieves this goal by presenting the results of a systematic literature review on the applications of learning curves in production and operations management. First, a framework that includes typical learning curve models is developed. The fundamental characteristics of learning curves and their applications in production and operations management are then identified. This framework is used to categorize the literature. This study also presents a discussion of the most important and informative articles in each of the major categories, as well as highlighting future research opportunities.

W. Madushan Fernando, Amila Thibbotuwawa, H. Niles Perera et al.

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

О. P. Korzhova, D. S. Makashin, P. E. Popov et al.

The article focuses on the potential integration of the SFM digital control system into production. To achieve a more accurate implementation of the dSFM system, the article identifies its strengths and weaknesses. It evaluates and outlines the factors contributing to the successful implementation of the dSFM system in production. The article also analyses the traditional Lean manufacturing system, the analogue SFM system and its digital version. The study scrutinised the manner in which workers interact with the «System of Operations Management» with the purpose of refining its assimilation into manufacturing processes and enhancing employee output.

Muhammad Rofiqul Islam, Abdullah Al Mehdi

This study investigates the impact of Hofstede's cultural dimensions on abnormal core earnings management in multiple national cultural contexts. We employ an Ordinary Least Squares (OLS) regression model with abnormal core earnings as the dependent variable. The independent variables analyzed include Hofstede's dimensions: Power Distance Index (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance Index (UAI). Our findings reveal that individualism is positively associated with abnormal core earnings, suggesting that cultures characterized by high individualism may encourage practices that inflate earnings due to the prominence of personal achievement and rewards. In contrast, masculinity negatively correlates with abnormal core earnings, indicating that the risk-taking attributes associated with masculine cultures may deter earnings management. Interestingly, uncertainty avoidance is positively linked to abnormal core earnings, supporting the notion that managers tend to engage more in earnings management to minimize fluctuations in financial reports in cultures with high uncertainty avoidance. The relationship between power distance and abnormal core earnings is found to be non-significant, indicating no substantial effect in this context. These findings contribute to the literature on cultural influences in financial reporting, providing valuable insights for policymakers and multinational firms concerning the cultural contexts within which financial decisions and reporting occur.