Abstract The concept of a “digital twin” as a model for data-driven management and control of physical systems has emerged over the past decade in the domains of manufacturing, production, and operations. In the context of buildings and civil infrastructure, the notion of a digital twin remains ill-defined, with little or no consensus among researchers and practitioners of the ways in which digital twin processes and data-centric technologies can support design and construction. This paper builds on existing concepts of Building Information Modeling (BIM), lean project production systems, automated data acquisition from construction sites and supply chains, and artificial intelligence to formulate a mode of construction that applies digital twin information systems to achieve closed loop control systems. It contributes a set of four core information and control concepts for digital twin construction (DTC), which define the dimensions of the conceptual space for the information used in DTC workflows. Working from the core concepts, we propose a DTC information system workflow—including information stores, information processing functions, and monitoring technologies—according to three concentric control workflow cycles. DTC should be viewed as a comprehensive mode of construction that prioritizes closing the control loops rather than an extension of BIM tools integrated with sensing and monitoring technologies.
Despite the exponential growth of artificial intelligence (AI) research in operations, supply chain, and productions management literature, empirical insights on how organisational behavioural mechanisms at the human–technology interface will facilitate AI adoption in small- and medium-sized enterprises (SMEs), and subsequent impact of the adoption on sustainable practices and supply chain resilience (SCR) is under-researched. To bridge these gaps, we integrate resource orchestration and knowledge-based view theoretical perspectives to develop a novel structural model examining antecedents to SCR and AI adoption, considering AI adoption as a pivot for facilitating SCR. The structural equation modelling technique was employed on the data collected from 280 Vietnamese manufacturing SMEs’ operations managers. Our results demonstrate that leadership will drive AI adoption by creating a data-driven, digital and conducive culture, and strengthening employee skills and competencies. Furthermore, AI adoption positively influences CE practices, SC agility and risk management, which will help to achieve SCR. For managers, the importance of internal organisational employee-centric mechanisms to create value from AI adoption without impeding business value is highlighted. We reveal the enablers that will help in transforming SMEs to become resilient by deriving appropriate responses to unprecedented disruptions through data-driven decision-making leveraging AI adoption.
Abstract This paper systematically reviews the literature on the impact of geopolitical disruptions on supply chains to identify primary discourses, emergent themes and key gaps to set a future research agenda. The guiding research question is ‘how do geopolitical disruptions affect the configuration, flow, and management of global supply chains?’. The study applies a systematic literature review of 50 papers from the Association of Business Schools’ (ABS) ranked academic journals in the fields of operations, production, and supply chain management published between 1995 and 2022. Through an in-depth literature analysis, this paper demarcates geopolitical disruptions and the resulting impact on supply chains as a new subfield of research. The results indicate that the impact of geopolitical disruptions on supply chains can be mitigated through: (1) supply chain re-design including regionalisation, back-shoring, and moving away from just-in-time delivery models as well as (2) the implementation of emerging technologies, such as blockchain, 3D printing and artificial intelligence, to improve supply chain transparency and the development of modularised manufacturing. This paper is one of the first to define the current state of research and thinking on the impact of geopolitical disruptions on supply chains, laying a firm foundation for future research by setting a detailed research agenda based on identified gaps.
Amid the escalating environmental crises and economic disparities, Circular Economy (CE) has garnered recognition as a pragmatic mechanism for achieving Sustainable Development Goals (SDGs). In response, several supply chain organisations are integrating CE strategies into their business operations and production processes. Despite these developments and since the introduction of Business Charter for Sustainable Development by the International Chamber of Commerce in 1991, the academic corpus comprehensively connecting CE research themes, catalysts, deterrents, practices with the SDGs has remained limited. To bridge this gap, we present a systematic literature review (SLR) of CE research in operations, supply chain and production management encompassing a time span of 31 years (January 1991 – June 2022), by sourcing, screening, and analysing articles obtained from multiple research databases. Our thematic coding analysis generated ten research themes, and subsequently linking them with relevant SDGs. Additionally, we interweaved CE catalysts and deterrents, establishing a connection with the SDGs. This is further enriched with CE strategies aimed at equipping business practitioners to enhance sustainable business performance and contributing to specific SDGs. Lastly, we delineate CE knowledge data management and priority actions frameworks to aid organisations to enhance employee capability and actively leverage digital technologies for implementing CE strategies.
Online asset-selling businesses, such as used cars and real estate platforms, have experienced remarkable growth in recent years. Unlike general retail operations, which make decisions at the stock-keeping unit level, asset selling operates at the individual unit asset level. Practical operational constraints (e.g., infrequent price adjustments within a limited timeframe) set asset-selling platforms apart from general retail. Further complicating decision-making are real-world uncertainties, such as volatile demand and unknown latent value of the asset. We present a dynamic pricing framework that captures the salient characteristics of the asset-selling business while leveraging consumer online behavioral data to maximize the payoff of individual assets. We develop practical algorithms for solving the dynamic pricing problem, including a mean approximation (MA) algorithm that uses forecast mean values as proxies for future customer arrival rates and online learning algorithms that integrate learning of the latent value of an asset with dynamic pricing decisions. To evaluate these algorithms, we propose an asymptotic regime suitable for the online asset-selling business context, one that scales up customer demand arrival rate within a finite time horizon. The key findings are that, under mild conditions, the expected value of selling an asset is concave and increasing at a logarithmic rate with respect to demand rates and increasing no faster than a linear speed in the asset’s latent value. These properties allow us to derive the performance bounds of our policies. An extensive numerical study and a real-data calibrated case study demonstrate the practical value of our proposed algorithms, suggesting that those simple heuristics can achieve strong performance in the asset-selling environment. Moreover, our integration of the learning of an asset’s latent value with dynamic pricing decisions, alongside asymptotic analysis, provides a robust framework for data-driven decision-making and demonstrates the potential of consumer behavior data as a strategic asset for online asset-selling platforms.
The Malaysian textile industry is experiencing rapid development as a result of increased local demand, a robust export market, government backing, e-commerce expansion, and the expanding impact of social media. However, the industry also produces a significant amount of harmful wastewater, thus raising wastewater management as an environmental concern. The purpose of this review paper was to explore the industrial management of textile wastewater, including wastewater characteristics, treatment options, and sustainability initiatives. It analysed the present state of water sources in Malaysia, specifically focusing on the impact of wastewater from the textile industry. This was followed by a discussion of the trends in Malaysia's textile industry, the issues concerning the wastewater generated by this sector, and the use of industrial engineering and management in wastewater treatment. This concise review provides valuable information regarding future studies in wastewater treatment, with a specific focus on textile wastewater. Effective water resource management is crucial for ensuring sustainable industrial growth. Industrial management is an ongoing process that entails overseeing and coordinating all aspects of an organisation's production and operations.
Production management. Operations management, Business
Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions.
The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.
The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancing efficiency, and supporting informed decision-making. However, implementing AI within IT operations is not without its challenges, including issues related to data quality, the complexity of IT environments, and skill gaps within teams. The advent of Large Language Models (LLMs) presents an opportunity to address some of these challenges, particularly through their advanced natural language understanding capabilities. These features enable organizations to process and analyze vast amounts of unstructured data, such as system logs, incident reports, and technical documentation. This ability aligns with the motivation behind our research, where we aim to integrate traditional predictive machine learning models with generative AI technologies like LLMs. By combining these approaches, we propose innovative methods to tackle persistent challenges in AIOps and enhance the capabilities of IT operations management.
In the context of the rapid development of digital supply chain networks, dealing with the increasing cybersecurity threats and formulating effective security investment strategies to defend against cyberattack risks are the core issues in supply chain management. Cybersecurity investment decision-making is a key strategic task in enterprise supply chain manage-ment. Traditional game theory models and linear programming methods make it challenging to deal with complex problems such as multi-party par-ticipation in the supply chain, resource constraints, and risk uncertainty, re-sulting in enterprises facing high risks and uncertainties in the field of cy-bersecurity. To effectively meet this challenge, this study proposes a nonlin-ear budget-constrained cybersecurity investment optimization model based on variational inequality and projection shrinkage algorithm. This method simulates the impact of market competition on security investment by intro-ducing market share variables, combining variational inequality and projec-tion shrinkage algorithm to solve the model, and analyzing the effect of dif-ferent variables such as budget constraints, cyberattack losses, and market share on supply chain network security. In numerical analysis, the model achieved high cybersecurity levels of 0.96 and 0.95 in the experimental sce-narios of two retailers and two demand markets, respectively, and the budget constraint analysis revealed the profound impact of budget constraints on cybersecurity investment. Through numerical experiments and comparative analysis, the effectiveness and operability of this method in improving sup-ply chain network security are verified.
Sharif Al Mamun, Rakib Hossain, Md. Jobayer Rahman
et al.
A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring. Our probabilistic, interpretable models deliver reliable results: We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024. Formal tests of unconditional (Kupiec) and conditional (Christoffersen) coverage reveal that an LSTM baseline achieves near-nominal calibration. In contrast, a GARCH(1,1) model with Student-t innovations underestimates tail risk. Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations. Bayesian logistic regression improves recall and AUC-ROC for fraud detection, and a hierarchical Beta state-space model provides transparent and adaptive compliance risk assessment. The pipeline is distinguished by precise uncertainty quantification, interpretability, and GPU-accelerated analysis, delivering up to 50x speedup. Remaining challenges include sparse fraud data and proxy compliance labels, but the framework enables actionable risk insights. Future expansion will extend feature sets, explore regime-switching priors, and enhance scalable inference.
The objective of this article is to evaluate the structural changes that have occurred in the export of mechanical engineering from Ukraine. These changes have been influenced by significant transformations in the country's foreign economic policy, which have been primarily caused by a shift in the country's foreign policy as a result of military aggression from Russia, which has been a leading foreign trade partner for a considerable period of time. Methodology. The study is based on the authors' previous works, which are devoted to the study of the features of the export-oriented development of the country and the industry. In particular, the studies examine the strengthening of the innovative component in the export potential and the formation of mechanisms and strategies for ensuring the development of exports on a high-tech basis. The methodological basis of this work was also formed by the scientific studies of leading Ukrainian and foreign researchers devoted to the development of the export of mechanical engineering, problems and prospects of the industry in the implementation of foreign economic activity. In order to calculate structural changes in exports, a generalised methodology has been developed based on the systematisation of scientific papers that outline different approaches to assessing structural changes and disproportions. The main content of the proposed methodology is to form a system of indicators that allow for a comprehensive assessment of structural changes in the exports of a particular industry. To calculate and test the methodology, open statistical data on exports of mechanical engineering products were used. Results. The research findings indicated that, despite the pivotal role of the mechanical engineering sector in the national economy, the sector's performance in Ukraine has exhibited a discernible negative trajectory in terms of overall production and sales volumes, export volumes, and the patterns of expansion observed in export operations. In Ukraine, the contribution of mechanical engineering to the national economy is 8%, whereas in industrialised countries, this figure ranges between 30 and 50%. The long-term orientation of mechanical engineering enterprises towards the conventional Russian market has not provided the impetus for the innovative development of such enterprises. Objective changes in Ukraine's foreign economic policy related to Russia's military invasion have created a field of uncertainty for mechanical engineering companies. The search for partners in new foreign markets was rather slow and not always effective. All this led to structural changes in the export of mechanical engineering products. Calculations have shown the existence of imbalances in the structure of exports of mechanical engineering products. In particular, for a long time there was a predominance of heavy engineering products. Conversely, the calculations demonstrated that products with competitive advantages in foreign markets account for a relatively minor proportion of exports. This provides a rationale for a shift in strategy with regard to the expansion of exports in the mechanical engineering sector, with a focus on the increased export of competitive high-tech products. Practical implications. A complex of indicators was employed to calculate the structural changes in the export of mechanical engineering. This enabled the identification of those priority groups of mechanical engineering products with the greatest export potential. Value / Originality. The developed methodological approach, which integrates a set of indicators for the analysis of structural changes in exports, provides a foundation for the formulation of management decisions and the development of export strategies for the advancement of mechanical engineering enterprises within the context of an export-oriented economic model.
Thawee Nakrachata-Amon, Jumpol Vorasayan, Komkrit Pitiruek
et al.
Pork stands out as the most extensively produced and consumed meat globally. With advancements in technology, genetics, and management, the structure of the pig supply chain has transformed from the traditional birth-to-slaughter raising method to incorporate four primary specialized operations: breeding, farrowing, nursery, and fattening. Fattening, constituting approximately 70% of a market pig's entire life cycle, heavily relies on resources, notably in feed consumption. Despite the integration of feed production with pig farming in modern industrial setups through farming contracts, separate decision-making processes for production planning in both stages often result in overall inefficiency. This research proposes an optimization-based methodology to plan production for a vertically integrated setting of three supply chain echelons: a feed mill, fattening farms, and a slaughterhouse. Key coordinated decisions include creating production plans for specific feed formulations at the feed mill and organizing farming cycles at fattening farms to meet the demand of the slaughterhouse The aim is to optimize pig growth while minimizing the overall costs. The methodology includes a mixed-integer linear programming model for the pig supply chain, and a Lagrangian heuristic as method to make coordinated production plans. Computational experiments were conducted using diverse case-study data based on pig supply chains in Thatland. Compared with the results using a commercial software, Lingo's Simplex method, our proposed heuristic could find optimal solutions quicker for smaller problem instances and produce more effective feasible solutions within limited time frames for larger scenarios.
This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model performance. Two specialized GANs models, the Wasserstein Generative Adversarial Network (WGAN) and the Tail Generative Adversarial Network (Tail-GAN), were evaluated under the FE-GAN framework. The results demonstrate that FE-GAN significantly outperforms traditional architectures in both VaR and ES estimation. Tail-GAN, leveraging its task-specific loss function, consistently outperforms WGAN in ES estimation, while both models exhibit similar performance in VaR estimation. Despite these promising results, the study acknowledges limitations, including reliance on highly correlated temporal data and restricted applicability to other domains. Future research directions include exploring alternative input generation methods, dynamic forecasting models, and advanced neural network architectures to further enhance GANs-based financial risk estimation.
Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy challenges in 6G networks. In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first time, aiming to achieve efficient management of communication resources in a dynamic environment. Firstly, we construct a mathematical model for semantic communication in platoon systems, in which the DeepSC model and MU-DeepSC model are used to semantically encode and decode unimodal and multi-modal data, respectively. Then, we propose the quality of experience (QoE) metric based on semantic similarity and semantic rate. Meanwhile, we consider the success rate of semantic information transmission (SRS) metric to ensure the fairness of channel resource allocation. Next, the optimization problem is posed with the aim of maximizing the QoE in vehicle-to-vehicle (V2V) links while improving SRS. To solve this mixed integer nonlinear programming problem (MINLP) and adapt to time-varying channel conditions, the paper proposes a distributed semantic-aware multi-modal resource allocation (SAMRA) algorithm based on multi-agent reinforcement learning (MARL), referred to as SAMRAMARL. The algorithm can dynamically allocate channels and power and determine semantic symbol length based on the contextual importance of the transmitted information, ensuring efficient resource utilization. Finally, extensive simulations have demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE, SRS, and communication delay in C-V2X platooning scenarios.
Juan De Anton, Juan J Senovilla, Jose M Gonzalez-Varona
et al.
Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure
We develop several innovations to bring the best practices of traditional investment funds to the blockchain landscape. Specifically, we illustrate how: 1) fund prices can be updated regularly like mutual funds; 2) performance fees can be charged like hedge funds; 3) mutually hedged blockchain investment funds can operate with investor protection schemes, such as high water marks; and 4) measures to offset trading related slippage costs when redemptions happen. Using our concepts - and blockchain technology - traditional funds can calculate performance fees in a simplified manner and alleviate several operational issues. Blockchain can solve many problems for traditional finance, while tried and tested wealth management techniques can benefit decentralization, speeding its adoption. We provide detailed steps - including mathematical formulations and instructive pointers - to implement these ideas and discuss how our designs overcome several blockchain bottlenecks, making smart contracts smarter. We provide numerical illustrations of several scenarios related to our mechanisms.