Reshoring Decisions in Supply Chains and Industry 5.0 Optimization: AI Based Sustainable Decision Support Model

Reshoring Decisions in Supply Chains and Industry 5.0 Optimization: AI Based Sustainable Decision Support Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reshoring Decisions in Supply Chains and Industry 5.0 Optimization: AI Based Sustainable Decision Support Model Muhammet Mustafa Akkan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252236/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Global supply chains face increasingly uncertain phenomena and reshoring decisions have become a strategic necessity. This paper presents an artificial intelligence-based decision support model for optimizing reshoring processes in connection with sustainability and Industry 5.0 principles. The developed model supports multidimensional decision-making processes in supply chain management by using big data analytics, machine learning and optimization techniques. The proposed framework evaluates critical factors such as lead time, cost, operational risks, environmental impact, and resilience in an integrated approach. Combining different data sources, the model allows decision makers to determine the most appropriate reshoring strategies by conducting dynamic scenario analyses. This approach, which adopts the human-machine collaboration approach of Industry 5.0, not only increases economic and operational efficiency, but also contributes to the principles of sustainable production and supply management. With the model developed in the study, it is aimed to make significant contributions to academic literature and industrial applications by presenting a new perspective on supply chain management and optimization of reshoring decisions. supply chain reshoring artificial intelligence optimization industry 5.0 sustainability Figures Figure 1 Figure 2 Figure 3 1 Introduction Global supply chains have recently undergone a significant transformation process by being exposed to various economic, political and social fluctuations (Giammetti et al., 2021). Supply chain disruptions and geopolitical tensions, especially with the COVID-19 pandemic, have forced companies to create more resilient and flexible structures (Lee et al., 2022). In this context, reshoring, which means moving the parts of production activities previously carried out in offshore locations to home countries or nearby geographies, has become a strategic choice for companies (Tsai & Urmetzer, 2023). The main challenges encountered in reshoring processes can be listed as high costs, low flexibility, supply chain interruptions and long delivery times (Longauer, Hauck & Vasvari, 2023; Charpin, 2021). Industry 5.0 offers various innovative approaches to overcome these problems. Industry 5.0 has emerged as a new industrial paradigm that goes beyond the digitalization and automation provided by Industry 4.0 and puts the human factor at the center (Akundi et al., 2022 ; Zhen & Yao, 2024 ) and aims to increase supply chain resilience and sustainability by combining human creativity and problem-solving capabilities with advanced technologies (Kabir, Khan & Kabir, 2024 ). In the literature, the potential of advanced technologies brought by Industry 5.0 in supply chain management is emphasized. Digital twin technologies have shown every phase of manufacturing and can thus help with early-stage problem identification (Marrone et al., 2023 ; Sawik, 2023 ). Furthermore, documented are cases of artificial intelligence-based analytical systems supporting decision optimization. Blockchain technology simultaneously improves supply chain transparency and traceability while IoT devices help to enable continuous data collecting, thereby enhancing operational efficiency (Li, 2020 ; Ali et al., 2022 ). These developments will enable businesses to apply a more efficient reshoring plan with a competitive edge as well. The main objective of this study is to propose an innovative model that integrates Industry 5.0 principles and enables effective implementation of reshoring strategies in supply chains. Within the scope of the research, firstly, a literature search was conducted in the Web of Science database, and it was seen that there was no study on ‘reshoring and industry 5.0 optimization in supply chains’. In this framework, the phenomena of ‘reshoring optimization in supply chains’ and ‘industry 5.0 optimization in supply chains’ have been examined and analyzed separately, and a theoretical framework has been created based on this information. In the following, the main building blocks of the proposed model are identified and the implementation steps of the model are explained. This method is suitable for making companies more competitive by increasing the efficiency of the entire supply chain network. Therefore, in this context, the research is an important and valuable study in that the phenomenon of reshoring and Industry 5.0 optimization in supply chains are examined together. The rest of the paper is organized as follows: In the next section, the existing literature is reviewed in detail and the salient areas are highlighted. In Section 3 , the relationship between reshoring and Industry 5.0 phenomena and supply chain is expressed. Section 4 explains the methodology of the research and elaborates the conceptual framework. Section 5 describes the development of the model in full detail, and the requirements of the proposed artificial intelligence-oriented model are presented. Section 6 contains the general conclusions, contributions, limitations and future research directions of the model. 2 Literature review The literature on reshoring optimization in supply chains shows that various academic studies have been conducted on production, reshoring and restructuring of supply chains, especially in recent years. One study by Gray et al. ( 2017 ) investigates why US Small and Medium-sized Enterprises (SMEs) relocate their production activities back into high-cost countries. The study analyses decision making in case studies and system dynamics modelling of four SMEs and points out that the lessons learnt from the previous offshoring decisions are an important factor. The decision depends on a mix of factors, such as high freight costs, quality problems and the rising need for flexibility. Exploring the rebirth of the Belt and Road Initiative in the global supply chain under the post-COVID-19 era, Lee & Song ( 2023 ) bring nine research agendas for improving port efficiency and logistics networks, such as port governance improvement, green transport corridors establishment, and hub of logistics distribution. Tsai & Urmetzer (2023) provides a systematic literature review on production location change, presenting a three-stage framework for the decision-making process and highlighting critical gaps for future research. With a three-stage decision framework, gaps in the understanding of regional impacts were expressed. It emphasized that institutional learning and innovation capacity are important determinants. Freund et al. ( 2024 ) analyzed the US effort to reshape its trade policies with China and analyzed the changes in supply chains caused by this process. The research found that US tariffs reduced imports from China and increased imports from nearby geographical regions, but there was no evidence of significant reshoring. It was stated that imports from neighboring countries increased and this situation created new loyalties. Lampon & Rivo-Lopez (2021) examined the impact of technology intensity on turnaround decisions in the European manufacturing sector and found that turnaround decisions are made with cost-oriented strategies in low-tech industries and with innovation and proximity-oriented strategies in high-tech industries. Strategic commitment and quality improvement motivations come to the fore in reshoring decisions. Similarly, a study by Bettiol et al. ( 2023 ) identified the strategic alternatives of Italian manufacturing firms after offshoring and suggested that these strategies could be supported by a framework. Researchers identified six ways that improve long-term resilience and are cost-effective. These strategies include product innovation and selective sourcing. Another study by Ali et al. ( 2022 ) proposes an integrated mineral supply agreement to bridge the infrastructure gap for decarbonization. This agreement will be able to reduce costs while increasing supply chain resilience. Sawik ( 2023 ) developed a scenario-based stochastic model to optimize reshoring decisions under supply chain disruptions and showed that these decisions provide cost reduction and flexibility. Implementing reshoring methods can improve resilience, but they require considerable backing from the government. According to the findings of a study conducted by Yu & Kim ( 2018 ) on the fashion business, reshoring is more lucrative in cases when there is uncertainty regarding demand. The study analyzes the financial efficiency of offshore and reshoring scenarios. In addition, Marrone et al. ( 2023 ) conducted a review that provided decision criteria for partial nationalization of pharmaceutical supply chains. More specifically, they emphasized the fact that this strategy has the potential to decrease supply risks. Longauer, Hauck & Vasvari (2023) examined making or buying strategies in multi-stage manufacturing processes and the role of the learning effect on reshoring decisions. Using dynamic optimization models, this study has shown that reshoring decisions are effective, especially when cross-stage learning improves efficiency. The stochastic optimization model of Sawik ( 2025 ) uses a scenario-based approach for economic reshoring under random influences. Gur & Dılek (2023) conduct at literature review and case study focused on China's strategic rise in the international system by analyzing various impacts faced regionally and globally. Park et al. (2021) conducted a literature review and case study to examine innovation, current trends and impact of current trends on agriculture and food sector. Charpin (2021) examined the strategies used to understand and manage the effects of political risk in global supply chain management. Finally, Giammetti et al. (2021) examined the economic effects of regulations and deglobalization processes in European countries and conducted scenario analyses with input-output modelling. The results show that some regions will emerge from this process as winners and others as losers. 2.1 Industry 5.0 optimization in supply chains In the literature on supply chain optimization in the context of Industry 5.0, numerous ways for rearranging production and supply chains, as well as the ramifications of these techniques in diverse industrial settings, have been studied in recent years. These techniques have been discussed in different industrial settings. Akundi et al. ( 2022 ) have identified the current research trends in human-machine interaction in Industry 5.0. They assert that these trends are centered on topics such as sustainability, artificial intelligence (AI), and digital transformation. Kabir, Khan, and Kabir ( 2024 ) modeled the hierarchical structure of supply chain 5.0 capabilities and examined the interrelations between these capabilities. Expert opinions were collected using the Delphi method and the order of priority of these competencies was determined. Zhen & Yao ( 2024 ) examined the integration of digital twin and blockchain technologies into supply chain management within the scope of Industry 5.0 and revealed that these technologies provide transparency and operational efficiency. Iakovou et al. ( 2024 ) examined the key issues and potential solutions in the realm of solar recycling via the lens of next-generation reverse logistics networks. A case study was carried out by the authors to prove the model's usefulness. Li ( 2020 ) assessed the strategies in the US-Mexico border region that are designed to enhance local production within the context of Industry 5.0. She underscored the importance of infrastructure development and supplier selection in the success of these strategies. Technological adaptation is of paramount importance during this process. Andres et al. ( 2024 ) analyze the practical advantages of supporting technologies for Logistics 5.0. Sharma et al. (2024) employed a comprehensive methodology integrating AHP, ELECTRE, and DEMATEL to identify, rank, and devise feasible solutions for addressing the challenges associated with the adoption of Industry 5.0. Dossou, Mozos, and Pawlewski (2024) simultaneously sought to develop a tool for evaluating supply chain performance regarding sustainability, digitization, and optimization. The researchers delineated three primary axes: sustainable transformation, digital transformation, optimum transformation, and established performance metrics for each axis. The document outlines a framework for a performance assessment tool designed to assess and enhance supply chain performance. In their study, Ahmed et al. ( 2023 ) utilized a synthesis of Pareto analysis and the Bayesian Best-Worst Method to evaluate the impact of Industry 5.0 requirements, informed by artificial intelligence, on enhancing supply chain flexibility within the garment and footwear sectors in Bangladesh in the post-pandemic era. Strategic insights were obtained with the purpose of improving supply chain resilience. These insights were obtained by determining the relative relevance of AI-based Industry 5.0 requirements and constructing a hierarchical ranking of those requirements. In their study, In the realm of AI-based fault diagnostics, Leng et al. ( 2024 ) underlined the need of using machine learning and artificial intelligence algorithms. They also performed comparative study of many algorithms. Dabo & Hosseinian-Far ( 2023 ) investigated the significance of Industry 5.0 in the shift to the circular economy and the need of reverse logistics and to maximize reverse logistics operations by combining binary logistic regression with decision tree approaches. Nicoletti & Apolloni (2024) emphasized the effects of basic models on green logistics. Kertens et al. (2024) conducted a performance evaluation of Internet of Things (IoT) service providers in the context of Industry 5.0 through case studies. Yu & Sun ( 2024 ) aimed to design a sustainable reverse logistics network under uncertainty conditions by developing a multi-objective model that balances economic, environmental and social objectives and compared various models. Lo ( 2023 ) utilized multi-criteria decision-making methods to create a data-driven and non-human factor-dependent decision support system, with the objective of improving supplier evaluation processes. Colimbi et al. (2024) compared various methods for anomaly detection in e-industrial applications and examined the potential of large language models. Chidozie et al. ( 2024 ) evaluated the impacts of Industry 4.0 and 5.0 on supply chain sustainability and the role of digital transformation. 2.2 The role of AI based approaches A review of the literature on reshoring, supply chain and optimization show that there is very little focus on AI-based applications and approaches, which is the most important supporter of Industry 5.0. In most of the studies, theoretical analysis, case studies and traditional modelling methods are at the forefront. Sawik’s ( 2023 ) study employed stochastic mixed integer programming methods to analyze and mitigate risks in reshoring decisions, with the ultimate aim of optimizing cost. In contrast, an assessment of studies on supply chain and Industry 5.0 optimization challenges suggests a frequent use of AI-based methodologies within the research scope. In pioneering research, Sharma et al. (2024) used the Deep Q-Learning algorithm to predict state-action values, whereas Leng et al. ( 2024 ) used the Random Forest (RF) algorithm to make solid predictions by rigorous examination of data feature connections. SVM makes it simpler to locate hyperplanes that, when used properly, might properly determine information borders. Dabo & Hosseinian-Far ( 2023 ) perform view evaluation plus comprehensive textual evaluations utilizing all-natural language handling strategies. Through their research, Ahmed et al. ( 2023 ) explored the application of mixed artificial intelligence systems in conjunction with the Bayesian Best-Worst Method. These systems are very efficient in integrating symbolic logic and neural networks to arrive at proper solutions in complex decision-making situations. Hussien et al. ( 2023 ) used deep learning and alternate direction algorithms for the analysis of feedback mistakes and delays. In their paper, Zhen & Yao ( 2024 ) used genetic algorithms and particle swarm optimization (PSO) to solve engineering optimization problems. 3 Reshoring and supply chain relations Reshoring is the movement of supply chains and manufacturing from previously relocated overseas sites to one near the company's headquarters or local markets. This idea is usually supported in response to events including supply chain interruptions, fast changing consumer expectations and growing logistics costs (Gray et al., 2017 ; Tsai & Urmetzer, 2023). Reshoring strategy is clearly one of the key choices businesses have to keep their competitive edge and boost their operational resilience. In this regard, several elements have been successful in implementing reshoring plans. These elements are investigated in the literature under four key headings: cost, risk management, strategic advantages and political and legal elements (Gray et al., 2017 ; Yu & Kim, 2018 ; Longauer, Hauck & Vasvari, 2023). 3.1 Cost factors While cost benefits are a major determinant of offshore plans, reshoring decisions are driven by growing labor wages in the nations of manufacture and more logistics expenses brought on by distance. Reshoring has becoming increasingly appealing as labor prices rise, particularly in traditional offshore nations like China. 3.2 Supply chain risks Global crises, such as the COVID-19 pandemic, have led to disruptions in supply chains and caused companies to review their resilience strategies. Factors such as geopolitical risks, natural disasters and trade wars have accelerated the adoption of reshoring strategies (Lampon & Rivo-Lopez, 2021; Li, 2020 ). 3.3 Strategic and competitive advantages The return to local production allows companies to respond faster to customer demands, improve product quality and provide a higher level of service (Yu & Kim, 2018 ). Especially fast delivery times and flexible production capabilities are among the factors that strengthen the competitive advantage of reshoring strategies. 3.4 Political and legal factors Government incentives, tax advantages and sustainability requirements are other important factors influencing reshoring decisions. Policies favoring local production encourage companies to adopt such strategies and facilitate decision-making (Freund et al., 2024 ; Tsai & Urmetzer, 2023). The effects of reshoring practices on supply chains are discussed, particularly in the context of supply chain structure, costs, risk management and service level. First of all, reshoring allows supply chains to become shorter and more localized. This reduces logistics costs and complexity in the supply chain (Lampon & Rivo-Lopez 2021; Longauer, Hauck & Vasvari, 2023). Secondly, the establishment of local production facilities can lead to an increase in fixed costs, while shorter logistics chains can lead to savings in transport and storage costs. Moreover, by lowering inventory costs, faster response to consumer needs helps to improve financial performance (Yu & Kim, 2018 ; Li, 2020 ). On the other side, a change from global to local supply chains could assist lower geopolitical uncertainty and global trade hazards. Local manufacture also helps to strengthen supply networks (Tsai & Urmetzer, 2023; Sawik, 2025 ). By reducing quality control procedures, reshoring finally helps to guarantee more constant product quality. Furthermore, faster responses to customer needs help to raise the degree of service (Gray et al., 2017 ; Yu & Kim, 2018 ). 4 Industry 5.0 and supply chain relations The concept of Industry 5.0 signifies a novel industrial revolution, wherein technological innovations are amalgamated with human values and sustainability objectives (Ahmed et al., 2023 ; Kabir, Khan & Kabir, 2024 ). This paradigm is predicated on a model of collaboration between human beings and robotic technologies. Key components of Industry 5.0 include AI, the IoT, big data analytics, additive manufacturing, blockchain, digital twins and metaverse supply chain technologies (Hussien et al., 2023 ; Yu & Sun, 2024 ; Fernandez-Miguel et al., 2024). The objective of Industry 5.0 is to optimize the utilization of resources, reduce waste, and promote sustainable production and consumption patterns by leveraging circular economy principles (Hussien et al., 2023 ). It is anticipated that the related elements will improve supply chain management systems in terms of efficiency, flexibility, and transparency. On the other hand, supply chains often have complex structures and require the efficient integration of different stages (Nicoletti & Apolloni, 2024). Industry 5.0 presents effective solutions for alleviating this issue. First of all, IoT devices make it possible to monitor all assets in the supply chain with sensors. This can help make better decisions in areas such as inventory management, shipment tracking and machine maintenance (Andres et al., 2024 ). Big data analytics analyses real-time and historical data to identify trends, predict demand and optimize processes. Additive manufacturing creates customized components on demand, reducing waste and shortening supply chains (Fernandez-Miguel et al., 2024). Blockchain technology offers transparency and traceability along the supply chain. The use of this technology ensures trust in data from logistics to production (Dossou, Mozos & Pawlewski, 2024). Digital twins are digital replicas of physical systems that allow to simulate and optimize supply chain’s operations. Digital twins are virtual models of physical systems that enable the simulation and optimization of supply chain operations. The metaverse supply chain creates immersive virtual environments for visualizing and collaborating on supply chain processes (Chidozie et al., 2024 ; Fernandez-Miguel et al., 2024). In addition, Industry 5.0 is focused on reducing environmental impact and increasing sustainability. Renewable energy sources and the use of technology to reduce carbon emissions support environmental responsibility in supply chains (Colimbi et al., 2024). 4.1 Supply chain optimization with Industry 5.0 The optimization procedure in the supply chain covers price decrease, optimizing making use of readily available sources plus raising shipment rate (Leng et al., 2024 ). Industry 5. 0 offers a range of advancements that add boosting the performance of the supply chain procedure. IoT-based sensing units as well as instantaneous information analytics enable business to anticipate the need as well as get used to supply plus result appropriately. This is vital for both setting you back conserving as well as enhancing client contentments (Dabo & Hosseinian-Far, 2023 ). AI powered projecting designs can determine future need cycles coupled with possible traffic jams. This is an essential procedure for source preparation as well as stock monitoring (Lo, 2023 ). AR innovation can lessen mistake prices by executing real-time advice for staff members. VR can be used for critical preparation by restoring supply chain situations (Akundi et al., 2022 ). Industry 5.0 is not only a technology transformation for supply chains, but also a step towards a humane and sustainable future. In this new era, companies can create more flexible, efficient and responsible supply chains by adopting technological innovations (Kertens et al., 2024; Ahmed et al., 2023 ). 5 Research methodology and conceptional framework This section outlines the methodological approach, detailing the conceptual framework developed to integrate AI and Industry 5.0 principles, enabling the effective implementation of reshoring decisions in supply chains. The methodology integrates situation analysis, scenario development, simulation phase, industry 5.0 integration and performance evaluation and monitoring phase to improve the accuracy of reshoring decisions. The framework proposed in this study is structured to enable effective reshoring decisions in supply chains and provides important outputs for firms, supply chain stakeholders and policy makers. Within the scope of the research, Web of Science database was searched with the keywords ‘supply chain’, ‘reshoring’, ‘industry 5.0’ and ‘optimization’, but no study was found. Upon this, the keywords ‘supply chain’, reshoring’ and ‘optimization’ were used and 18 articles were obtained. The analysis methods, variables and factors used in the related studies were used as a guide on how to achieve optimization. Secondly, the keywords ‘supply chain’, industry 5.0’ and ‘optimization’ were searched. The evidence from these research and Industry 5.0 optimization in supply chains and the methods used are expressed in descriptive form. Thus, the ways in which Industry 5.0 components can be integrated with reshoring decisions have been determined and a guideline for the creation of the model has been established. Figure 2 shows the conceptual framework of the research. This framework provides a structured overview of how to integrate the components of reshoring and industry 5.0 optimization in supply chains. 6 Model development The AI-driven analysis and decision framework implemented in the study is designed to evaluate options for the effective implementation of reshoring operations in supply chains, to use various data sources in an integrated manner, to provide Industry 5.0 optimization and to assist decision-making processes. Within the scope of the study, firstly, the relevant literature has been analyzed in detail and the methods and variables/factors used in the studies have been expressed in an explanatory manner. From this point of view, it has been determined which factors should be taken into consideration by firms when making reshoring decisions. The methods and variables used in the studies examined within the scope of designing the model are given in Table 1 . Table 1 Methods and Variables/Factors Used in the Analyzed Studies Authors & Date Methods Variables & Factors Gray et al., 2017 Case studies, system dynamics modeling, and heuristic decision-making analysis Decision type: Reshoring or offshore Drivers: Labor costs, total cost of ownership, and transportation cost Decision environment: Level of uncertainty and executive experience Lee & Song, 2023 Literature review Impacts of Covid 19: Stalled projects and logistics chain disruptions Logistics performance: Port efficiency and green logistics corridors Political Risk: China-US trade dispute, regional tensions Tsai & Urmetzer, 2023 Systematic literature review Reasons for relocation: Cost advantage, flexibility and sustainability Internal factors: Strategic firm resources and dynamic capabilities External factors: Political risks, market proximity Freund et al., 2024 Difference-in-differences econometric approach Import share: Change in import share by country Strategic sectors: High technology products Trade policy variables: Tariffs and trade disputes Lampon & Rivo-Lopez, 2021 Econometric analysis Endogenous factors: Innovation capacity, technological level of production processes External factors: Logistics and labour costs, security of supply Types of strategy: Cost-oriented and innovation-oriented strategy Bettiol et al., 2023 Multiple case study analysis Decision-making factors: Drivers, inhibitors and facilitators Implementation process: Scope, timing and management Performance outcomes: Operational efficiency, customer satisfaction and cost reduction Ali et al., 2022 Conceptual research Supply chain factors: Security of raw material supply and processing capacity External factors: Geopolitical risks and environmental regulations Carbon reduction strategies: Recycling, energy efficiency and localization Sawik, 2023 Stochastic mixed integer programming and scenario-based optimization Ripple effect: Disruptions spread throughout the supply chain Reshoring costs: Capital expenditure, operating costs Service level: Average and worst-case performance Yu & Kim, 2018 Computer simulations comparing financial metrics under different sourcing scenarios Resource selection: Offshore and reshore scenarios. Performance metrics: Gross margin, stock turn ratio and service level Types of error: Volume and variety error Marrone et al., 2023 Scoping review of academic literature and thematic analysis Types of criteria: Local production capacity, logistics costs Risk management: Inventory build-up and diversification strategies Stakeholder views: Industry representatives and public authority Longauer, Hauck & Vasvari, 2023 Dynamic optimization modeling Production stages: Learning potential and process complexity at different stages Cost elements: Labour costs, equipment costs, production error rates Learning effect: Cost-reducing learning rate depending on the production volume at each stage. Types of strategic decisions: Partial or full internalization, outsourcing Sawik, 2025 Stochastic mixed integer programming models Reshoring decisions: Localization of main suppliers, replenishment portfolios from reserve suppliers. Performance metrics: Total cost, service level, business viability and resilience Policy factors: Government incentives (subsidies for capital expenditure), local labour subsidies Risk metrics: Scenario-based worst-case performance, supply chain disruptions due to ripple effects Iakovou et al., 2024 Situation analysis and Resource-Task-Network based model Reverse logistics network performance: Recycling rate and logistics costs Policy factors: Regulatory gaps and optimization of recycling infrastructure Sustainability: Resource utilization efficiency and waste minimization Li, 2020 Policy analysis Production capacity. Local production potential and existing infrastructure Supply chain security: Logistics network structure and critical material supply Policy factors: Incentives and regional co-operation strategies Giammetti et al., 2021 Input-output analysis, scenario analysis and hypothetical inference method Production network structure: Local and international intermediate goods flows Regional shortages: Direct and indirect effects of production disruptions Types of scenarios: Complete disengagement from globalization, refocusing on Europe and a return to past production patterns Charpin, 2021 Literature review Types of nationalism: Economic nationalism and national hostility Risk factors: Trade wars government interventions and local requirements Supply chains: Import bans, tariffs and costs Gur & Dilek, 2023 Literature review and case studies Economic size, diplomatic relations, security strategies and levels of regional co-operation. Park et al. 2021 Literature review and case studies Production quantities, quality parameters and economic effects Akundi et al., 2022 Literature review and text mining Main themes related to Industry 5.0 Kabir, Khan & Kabir, 2024 Delphi, ISM-DEMATEL Supply chain 5.0 capabilities: personalization, real-time data, cloud supply chain, connectivity, transparency, agility, flexibility, sustainability and proactive approach Zhen & Yao, 2024 Case study and mediation analysis Sustainable supply chain management, digital transformation applications Andres et al., 2024 Literature review Big data analysis, sustainability, decision-making processes Sharma et al. 2024 AHP, ELECTRE and DEMATEL Critical barriers and solutions for Industry 5.0 adoption Dossou, Mozos & Pawlewski, 2024 AI-based methods and optimization algorithms Sustainable transformation: Economic, social, environmental and circular economy Digital transformation: Creating a digital twin of the company's supply chain Optimal transformation: Improving the internal organization of the supply chain Ahmed et al. 2023 Bayesian Best-Worst Method AI-based requirements of Industry 5.0 Dabo & Hosseinian-Far, 2023 Binary logistic regression and decision trees Factors affecting reverse logistics networks Nicoletti & Apolloni, 2024 Basic models (FMs) Current status and expectations of Logistics 5.0 Kertens et al. 2024 Hedonic double boundary estimation Inputs: Price and training cost Outputs: After-sales service, reliability, storage Yu & Sun, 2024 Fuzzy Mixed-Integer Linear Programming, Monte Carlo simulation and Discrete event simulation Recycling logistics network design, demand and capacity requirements Lo, 2023 VC-DRSA, CRITIC and CTOPSIS Supplier evaluation criteria: Sustainability, digital transformation, real-time data sharing and organizational culture transformation Colimbi et al. 2024 Large language models Visual and language models for anomaly detection Chidozie et al. 2024 Conceptual review Factors affecting supply chain performance Hussien et al. 2023 Deep learning and alternate direction algorithms Feedback errors and delays Leng et al. 2024 Literature review and machine learning techniques (LSTM-RNN) Closed-loop supply chain management and remanufacturing processes 6.1 Data collection and assessment In order to make effective reshoring decisions in supply chains, it is necessary to use various data sources that include production and demand, economic, political, technological and potential risk factors. Table 2 provides an overview of the primary data sources, categorized as production and demand data, supply chain data, financial data, technological data, political and legal situation and potential risks. Each category brings together a large data set of different factors, providing key insights for effective reshoring decisions. Production and demand data in this context are of critical importance for understanding the basic resources and potential of the companies. The obtained data regarding supply chain operations provide an important overview in terms of the efficiency, quality and safety of the operations in progress, and a future course can be drawn with economic analysis to be made with financial data. Technological data, especially when evaluated within the framework of Industry 5.0 and AI-based transformation process, clearly reveals the current situation of companies and provides guidance on the issues that need to be developed. Political and legal situation data provide a framework for the regulatory implications of reshoring, while potential risks help to clearly account for uncertainties in decision-making. Table 2 Data Sources and Types Category Data Source Types of Data Measurement & Frequency Manufacturing & Demanding Data Internal corporate source data, market research, public reports Local production capacity, local labour capacity Weekly/monthly logs Production lines data Production error rate, performance of production lines, inventory level, production capacity per labour force Real time/daily Sales and CRM data, market research Current demand, potential demand Quarterly/annual logs Supply Chain Data Supply chain operations Quality of logistics infrastructure Quarterly/annual updates Lead times and security Real time/daily Logistics chain disruptions, supply chain dependency Weekly/monthly Port/station activity Weekly/monthly Financial Data Internal corporate source data Labour costs, equipment costs, investment costs, transport and freight costs Weekly/monthly Total cost of ownership, gross margin Quarterly/annual logs Macroeconomic variables GDP, industrial production, business activity Quarterly/annual logs Technology Data Inventory department reports Computers, servers, IoT devices, network equipment Quarterly/annual IT department reports AI-based technology and application usage, integration of IoT devices, adoption level of big data analytics tools, cyber security applications Real time/daily IT department reports ERP, SCM, CRM applications data Weekly/monthly Human resources department reports Employees competence in the use of technology (training, technology skills) Quarterly/annual Political and Legal Situation Local government regulations Tax reductions and reshoring incentives, local labour support Real time/weekly Potential Risks Geopolitical risks Wars, regional tensions, tariffs, trade wars Weekly/monthly reports Political risks Tariffs and trade wars Weekly/monthly reports Environmental and sustainability risks Natural disasters, fire, flood, earthquake, pandemic, production and waste management Monthly/quarterly reports Economic risks Uncertainty of return on investment Monthly/quarterly reports Operational risks Local source of critical components, availability of skilled labour, modernization of production lines Weekly/monthly reports Optimizing reshoring operations in supply chains with Industry 5.0 and AI-based methods and building a decision-making model is based on data collection and pre-processing. In this research, data sources are divided into six categories: production and demand data, supply chain data, financial data, technological data, political and legal situations and potential risks. Each type of data processed is key to making the right decision. Manufacturing and demand data plays a critical role in improving a company's operational efficiency and responding more effectively to market needs. Indicators such as local and offshore production capacity, production capacity per workforce and local workforce capacity allow optimizing production processes (Gray et al., 2017 ; Ahmed et al., 2023 ; Sharma et al., 2024). While in determining defect rates in production processes may identify opportunities to eliminate waste in operational improvements and balance supply chain operational costs (Sawik, 2023 ; Nicoletti & Apolloni, 2024). Demand data generated on currently and potentially saleable products from sales and CRM systems helps in forecasting and thus provide an understanding of the expectations of customers to optimize production planning (Marrone et al., 2023 ; Akundi et al., 2022 ). Further, demand fluctuations can be predicted more accurately when combined with inputs from market research and both local and offshore production strategies can be suitably shaped (Chidozie et al., 2024 ; Gray et al., 2017 ; Yu & Kim, 2018 ). Analyzing the operational data is crucial for achieving the advancement of resource efficiency, adaptable production processes, and flexibility in conditions to accommodate market flexibility (Hussien et al., 2023 ). Despite escalating competition for the organization, consumer happiness yields greater profitability. Supply chain information is a vital resource of evaluation, consisting of logistics facilities top quality, preparation, safety and security, logistics chain disturbances, port/station effectiveness plus supply chain dependency. In-depth evaluation of this information is essential in boosting the performance of the supply chain along with avoiding prospective disturbances. For example, the quality of logistics infrastructure can help reduce costs and increase customer satisfaction by shortening shipment times (Lee & Song, 2023 ; Ali et al., 2022 ; Lo, 2023 ). Lead times and safety data build resilience and flexibility in the supply chain by enabling better management of risks (Dabo & Hosseinian-Far, 2023 ; Giammetti et al., 2021). Additionally, information such as logistics chain disturbances plus port/station task can be made use of to recognize vulnerable factors of supply chain procedures and also supply possibilities for enhancement (Lee & Song, 2023 ; Kabir, Khan & Kabir, 2024 , Yu & Sunlight, 2024). This not just raises functional performance, however additionally sustains sustainability objectives as well as reduces ecological effects (Sawik, 2025 ; Lampon & Rivo-Lopez, 2021). Assessing and also analyzing such information provides a basis for sustaining firms critical choices and also constructing an extra lasting, effective plus versatile supply chain. Financial data is obtained from both internal corporate sources and macroeconomic data sources to support the strategic decisions of enterprises. Data such as labour costs, equipment costs, investment costs, transportation and freight costs from internal sources provide an important basis for cost optimization and budget management by analyzing the company's operational cost structure (Tsai & Urmetzer, 2023; Longauer, Hauck & Vasvari, 2023; Charpin, 2021). Gross margin data are used to assess the sustainability of existing business models by analyzing profitability (Ahmed et al., 2023 ; Yu & Kim, 2018 ). Data on GDP, industrial production and business activity obtained from macroeconomic data sources play a critical role in understanding the general outlook of economic conditions and demand forecasts (Akundi et al., 2022 ; Gray et al., 2017 ; Yu & Kim, 2018 ). For example, GDP growth is an indicator for assessing market expansion potential, while industrial production data provides valuable information for monitoring demand trends by sector (Andres et al., 2024 ; Longauer, Hauck & Vasvari, 2023). Analyzing this data set provides important contributions to companies in terms of cost efficiency, strategically direct investments and creating flexible responses to market dynamics. Thus, companies can increase both their financial and operational sustainability. Data on the political and legal situation supports firms strategic planning and operational decisions. Information on tax deductions helps enterprises enhance their cost advantages and utilize their available financial resources more effectively (Freund et al., 2024 ; Tsai and Urmetzer, 2023). Incentives given to reshoring contribute to improving the supply chain resilience of enterprises and increasing their resilience to global risks by increasing local production activities (Ali et al., 2022 ; Akundi et al., 2022 ; Andres et al., 2024 ). Local labour support helps enterprises strengthen regional employment opportunities and expand the talent pool for enterprises, while at the same time, it facilitates the achievement of their social responsibility goals (Charpin, 2021; Iakovou et al., 2024 ). This data set contributes to enterprises gaining competitive advantage and achieving their long-term sustainability goals by evaluating the economic and social effects of legal regulations and political support. By evaluating political and legal incentives, enterprises can take more strategic positions in both the local and global markets. Technology data is a critical resource to support firms digital transformation processes and provide competitive advantage. Equipment as well as facilities information play a crucial duty in identifying efficiency enhancements as well as financial investment requires by analyzing the ability of the existing technical framework (Freund et al., 2024 ; Chidozie et al. 2024 ; Kertens et al., 2024). Making use of AI-based modern technologies raises performance by boosting automation procedures while the combination of IoT tools uses substantial advantages in supply chain administration with real-time information collection plus evaluation (Lampon & Rivo-Lopez, 2021; Ahmed et al., 2023 ; Andres et al., 2024 ). The degree of fostering of big data analytics devices sustains data-driven decision-making procedures making it possible for a lot more foreseeable as well as adaptable organization designs (Dossou Mozos & Pawlewski, 2024). Applications such as ERP SCM as well as CRM raise functional performance by assisting in procedure combination, while cyber protection software supplies safety as a vital part in securing digital settings (Dabo & Hosseinian-Far, 2023 ; Hussien et al., 2023 ). Additionally, worker innovation skills information is important consider the success of electronic improvement jobs by raising the efficient use digital devices and also business adjustment (Kabir Khan & Kabir, 2024; Leng et al., 2024 ). This information established supplies a critical benefit to services in regards to maximizing modern technology framework, boosting organization procedures and also adjusting to the demands of the digital age. Potential risks are critical elements that require a thorough analysis for businesses to develop strategic planning and resilience strategies. Geopolitical risks can disrupt supply chains and increase costs due to factors such as wars, regional tensions, trade wars and tariffs; therefore, analyses of regional stability and building alternative supply chain scenarios offer important contributions (Gray et al., 2017 ; Tsai & Urmetzer, 2023). Economic risks are related to the uncertainty in the returns on investments and behavior of financial markets, analysis of which delivers information necessary for financial predictions and a company’s liquidity (Longauer, Hauck & Vasvari, 2023; Sawik, 2025 ). Operational risk factors are related to the local sourcing of critical components and experts, skilled labor shortage and modernization of the production lines, analysis of which gives direction for strategic improvements in processes and technology investments (Chidozie et al., 2024 ; Kertens et al., 2024; Giammetti et al., 2021). Environmental and sustainability risks arise from natural disasters, global crises such as pandemics, and production and waste management, allowing businesses to seize opportunities to strengthen their sustainability policies and reduce their environmental impact (Sharma et al., 2024; Yu & Kim, 2018 ; Charpin, 2021). Each of these risk categories helps businesses optimize their risk management strategies, increase operational resilience and support long-term sustainability goals. An effective risk analysis enables a better identification of not only threats but also opportunities. When the data are analyzed in terms of usability, there are differences between the sources. Some data can be measured in real time, while others are obtained weekly, quarterly or annually. In this context, AI-supported data preprocessing models are used to clean, process and make sense of large data sets. These models generally utilize machine learning, deep learning and statistical techniques. It has been proven in some studies that the related techniques can be used in the data preprocessing phase (Giammetti et al., 2021; Zhen & Yao, 2024 ; Andres et al., 2024 ; Kabir, Khan & Kabir, 2024 , Akundi et al., 2022 , Chidozie et al., 2024 ). In this context, Principal Component Analysis (PCA) for dimension reduction and categorical data processing, Recurrent Neural Networks (RNN) to detect anomalous data and complete missing data play a role in improving the efficiency and interpretability of the model. The pre-processing approach structured in this way ensures that the model uses detailed and up to date data. 6.2 Situation analysis Situation analysis is the basis for defining an existing problem, evaluating the performance of existing systems or processes, and developing strategies to guide future decisions (Tsai & Urmetzer, 2023; Longauer, Hauck & Vasvari, 2023). The relevant analysis includes the assessment of critical factors such as production capacity, demand forecasts, financial performance, supply chain structure, political and legal situation and potential risks. There are various methods used to analyze the situation in the reviewed studies (Giammetti et al., 2021; Lo, 2023 ; Dossou, Mozos & Pawlewski, 2024). In this context, the current operational status of the company is examined. Determine which production processes are suitable for reshoring. Supply chain risks are analyzed and all data are classified (Giammetti et al., 2021; Dossou, Mozos & Pawlewski, 2024; Hussien et al., 2023 ). Support Vector Machine (SVM) method is used to classify and priorities the data. The Long Short-Term Memory (LSTM) method is used at the end of this phase to predict and analyze data trends and to present the current situation. As a result of the analysis, the existing operational structure, risk profile and modernization requirements of the company are clearly determined. 6.3 Scenario development and simulation Following the outputs obtained in the situation analysis, the scenario development phase starts with optimized and classified data. Scenario development plays a crucial role in understanding the impact of different conditions and future uncertainties and testing possible strategies. The scenarios developed at this stage are simulated and provide important insights for companies, supply chain stakeholders and policy makers. 6.3.1 Defining scenario types Scenario development is a systematic process used to predict future events, manage risks and make strategic decisions. Scenario analysis helps to evaluate different alternatives in decision-making processes involving uncertainties. In this context, multiple forecasting options are created based on predefined assumptions about the variables. These scenarios fall into three categories: Optimization scenarios, risk-based scenarios and technology adaptation scenarios. With optimization scenarios, the lowest cost or the most efficient production strategies are determined (Yu & Kim, 2018 ; Yu & Sun, 2024 ; Dossou, Mozos & Pawlewski, 2024). With risk-based scenarios, stress tests are conducted based on production outages, logistics disruptions and other uncertainties (Giammetti et al., 2021; Andres et al., 2024 ). With technology adaptation scenarios, Industry 5.0 components such as digital twins, automation and AI integration are tested (Zhen & Yao, 2024 ; Kabir, Khan & Kabir, 2024 ; Andres et al., 2024 ). By modelling different uncertainty and risk situations with these scenarios, a roadmap is created to evaluate how the systems perform under different conditions and to make better decisions. 6.3.2 Simulation The simulation phase is an important modelling process for modelling, analyzing and optimizing supply chain processes and is used to predict possible future outcomes. In particular regarding reshoring, supply chain optimization and Industry 5.0 transformation, simulation of the supply chain allows to simulate different scenarios, measure performance and mitigate risks. Many articles adopt the simulation phase to express different methodologies for decision making, optimization, process analysis and risk assessment (Kabir, Khan & Kabir, 2024 ; Andres et al., 2024 ; Akundi et al., 2022 ; Zhen & Yao, 2024 ). In this research, various AI and statistical simulation techniques are used for modelling the scenarios created: Discrete Event Simulation (DES) allows the analysis of discrete events such as production lines, inventory management and logistics processes (Dossou, Mozos & Pawlewski, 2024; Leng et al., 2024 ; Yu & Sun, 2024 ). Each event is assumed to have a different effect on the system and the relationships between events are modelled. It is suitable for use in determining the impact of disruptions in production processes, optimizing routes, determining criteria for the efficient use of the workforce. Monte Carlo simulation is a very useful method to study uncertainties and predict estimated outputs based on different probabilities (Kabir, Khan & Kabir, 2024 ; Akundi et al., 2022 , Yu & Sun 2024 ). In particular, it allows the analyses of different scenarios’ impact on business performance, and also their long-term impact of these scenarios, it helps to identify the lowest-risk and most fruitful scenarios. In this context, various risk factors can be simulated and potential losses can be determined. The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model can effectively forecast demand and analyze supply chain fluctuations (Dossou, Mozos & Pawlewski, 2024; Lo, 2023 ; Ahmed et al., 2023 ). ARIMAX analyses not only historical data but also exogenous variables such as economic indicators, seasonality and political factors. The model is used to predict delays in the supply chain and to determine how to optimize production volumes according to customer demand. When used together, these models enable the establishment of a holistic system in supply chain management that is data-driven, minimizes risks and predicts the future. 6.4 Risk management and resilience strategy development Risk management and the development of sustainable strategies is a critical stage to ensure supply chain continuity, minimize operational risks and support long-term sustainable growth. Identifying and managing potential risk factors, especially in reshoring decisions in supply chains and Industry 5.0 optimization processes, can help companies gain competitive advantage. Risk administration is an important procedure to decrease unpredictabilities, determine possible hazards as well as reduce possible losses (Zhen & Yao 2024 ; Akundi et al., 2022 ). This procedure is vital in numerous markets such as financing, supply chain, production, logistics, power wellness plus ecological administration. Lasting methods are strategies as well as techniques established to ensure long-lasting success by considering ecological, financial as well as social elements (Andres et al., 2024 ; Chidozie et al., 2024 ). AI as well as crossbreed evaluation techniques raise the performance of lasting methods by making danger administration procedures much more vibrant, data-driven together with enhanced (Kabir, Khan & Kabir 2024 ). In this context, using the data obtained from the simulation phase, the identification and management of operational risks, financial risk management and economic sustainability, environmental risk mitigation and sustainability planning, issues affecting supply chain resilience and risks to the proper optimization of Industry 5.0 technologies are analyzed. Using the Random Forest (RF) method (Akundi et al., 2022 ; Dossou, Mozos & Pawlewski, 2024), the risk in each category is scored. Sustainable strategies are developed for each risk heading by prioritizing and ranking according to risk scoring. 6.5 Integration and optimization Industry 5.0 technologies In the light of the strategies created, modernization, integration and optimization of Industry 5.0 technologies are carried out for the effective operation of the process. Industry 5. 0 describes a production along with supply chain change that boosts human-machine communication plus consists of sophisticated modern technologies such as AI, digital twins, blockchain IoT, big data analytics as well as increased truth (Dabo & Hosseinian-Far 2023 ; Leng et al., 2024 ). Nonetheless, on the occasion that the business cannot gain from these modern technologies completely, it is required to remove the insufficiencies and identify the determine the correct integration actions. The success of Industry 5.0 depends on healthy integration between technology, people and processes (Nicoletti & Apolloni, 2024). At this stage, it should be assessed which Industry 5.0 components are missing and how to integrate them in line with the outputs obtained from the analyses, scenario development, risk management and simulations carried out in the previous stages. The evidence from the previous stages shows us to what extent the following premises have been realized: Digital transformation infrastructure deficiencies, Automation and robotic integration deficiencies Data management and AI utilization deficiencies, Adequacy of man-machine co-operation, Security of the supply chain, Sustainability and compliance with green technologies. Industry 5.0 implementation and optimization strategy should be implemented and strategic action plans should be implemented in order to eliminate the identified deficiencies. 6.5.1 Strengthening digital transformation infrastructure The current structure of the company may not be sufficiently adapted to digital transformation technologies such as IoT, cloud computing and big data analytics. In this context, the use of IoT sensors should be enabled in all processes and real-time data collection should be ensured. On the other hand, by creating digital twin models, production processes should be modelled in digital environment in real time and optimization should be made with virtual simulations (Kabir, Khan & Kabir, 2024 ; Yu & Sun, 2024 ). If in-house data centers are insufficient, cloud-based solutions should be used. AI-supported data management systems should be deployed for big data analyses. In order to ensure the security of the relevant data, data security should be ensured by using blockchain-based security solutions (Andres et al., 2024 ). 6.5.2 Automation and robotic integration Existing automation systems of the company may not have the flexibility and adaptation Industry 5.0 brings. In this manner, reinforcement learning methods should be applied for adapting the robots to the processes (Zhen & Yao, 2024 ). Workforce optimization should be ensured by using intelligent robot systems in production lines and AI-supported quality control systems should be activated. 6.5.3 Realization of AI supported optimization and data management The company may be lacking in collecting and analyzing data effectively. In this context, machine learning and deep learning techniques should be used to analyze large data sets in a better way. On the other hand, AI-supported maintenance systems should be installed, especially to predict failures and problems that may occur. It is important to use advanced forecasting methods such as ARIMAX and LSTM in inventory management and logistics processes. In addition, the use of Convolutional Neural Network (CNN) based image processing systems to detect anomalies in production lines provides important insights (Zhen & Yao, 2024 ). 6.5.4 Strengthening human-machine cooperation Company employees may not be fully integrated with automation systems. In this context, co-operative robot systems that will enable industrial robots and workers to work together should be put into operation (Kabir, Khan & Kabir, 2024 ). AI-based detection systems should be developed so that robots can work safely with humans in production processes. On the other hand, it is also important to train employees and improve their digital capabilities. Employees should be given augmented reality and virtual reality training, and by creating simulation supported training programs, digital skills should be improved. 6.5.5 Ensuring supply chain security and transparency with blockchain If there is insufficient data security and traceability across the company's supply chain, smart contracts driven by blockchain should be triggered. Blockchain technology should be used to secure the sharing of required data among suppliers and logistics companies. 6.5.6 Sustainability and compliance with green technologies There may be inefficient process management in carbon emission, energy efficiency and recycling processes in the supply chain processes of the company. IoT-based energy management systems should be used to increase energy efficiency, and deep learning-based prediction models should be created to optimize energy consumption within the factory. In addition, blockchain-based monitoring systems should be used to increase sustainability in material utilization. Following the outputs obtained, it is very important to integrate Industry 5.0 technologies to eliminate company-specific deficiencies. In this process, which technologies will be prioritized should be determined through company-specific analyses and the integration process should be carried out in stages. Solutions such as digital twins, AI-assisted optimization, blockchain-based supply chain management and the use of collaborative robots (cobot) accelerate the digital transformation of the company and enable it to gain long-term competitive advantage. Once this optimization process is completed, continuous improvement and adaptation mechanisms can be put in place to ensure full integration into Industry 5.0. With the completion of these stages, key performance indicators are established and the effectiveness of the established system is ensured. 6.6 Performance monitoring and adaptation The performance monitoring and adaptation phase is the process of evaluating and improving the effectiveness of processes optimized with Industry 5.0 technologies and adapting to changing conditions. Continuous monitoring and adaptation is necessary to verify that the risk management, simulation, sustainable strategy development, optimization and integration processes carried out in the previous phases are working properly. This stage also provides feedback on the first three stages of the model and includes updating these stages in line with the outputs obtained. Therefore, decision-making processes are made dynamic based on data, and businesses provide continuous improvement in the Industry 5.0 transformation process. 6.6.1 Main objectives The main objectives of the performance monitoring and adaptation phase are as follows: To continuously monitor the performance of the system with real-time data analysis, Identify anomalies, bottlenecks and opportunities for improvement, Continuously update and optimize the models created in the previous phases, Providing feedback on risk management, simulation and optimization processes, Develop adaptation strategies to enhance human-machine interaction and sustainability. Performance monitoring and adaptation processes supported by Industry 5.0 components include automatic learning systems and real-time feedback mechanisms, enabling businesses to make quick decisions. Performance monitoring involves continuously monitoring the state of the system by collecting real-time data through sensors, big data analytics, AI and IoT-enabled systems. Within the relevant framework, the key performance indicators are continuously analyzed and the performance of the system is assessed for compliance with the objectives. Based on the data obtained here, adaptation mechanisms are developed and processes are optimized. AI-supported deep reinforcement learning models enable systems to learn on their own and optimize performance. With twin models, the real world and the virtual environment are integrated and performance scenarios are continuously updated. These stages range from automatic maintenance planning with the detection of anomalies in machine maintenance processes to adaptation to changes in smart energy management systems. The data obtained from the performance monitoring phase also feeds the previous four phases and enables the continuous improvement of the system. Optimization algorithms are updated with real-time performance data and processes are constantly made more efficient. simulation models are validated with real world data and new scenarios are created. The workload of the Industry 5.0 technologies used in processes is optimized. In addition, new risk factors are identified by analyzing performance data and risk management strategies are updated. With feedback mechanisms, companies create an ecosystem that supports continuous improvement processes by adapting quickly to dynamic changes in the system. In this way, long-term competitiveness increases and operational excellence of the company is ensured. 7 Conclusion and Discussion The main objective of this study is to propose an innovative model that integrates Industry 5.0 principles and enables effective implementation of reshoring strategies in supply chains. Industry 5.0 can help with this by using advanced automation, AI to help with decision-making, human-machine collaboration and sustainable production models. This allows companies to fix problems in global supply chains, be more efficient and support local production ecosystems. The model we're proposing is Industry 5.0-oriented and AI-based, and it'll be a big help to academics, businesses, supply chain partners, technology providers and policymakers, in terms of strategy, operations and technology. 7.1 Contribution to literature The study makes considerable contributions to the existing literature on supply chains, Industry 5.0 and also reshoring approaches. Based upon the previous research study performed up until now reshoring is normally researched in the context of manufacturing expense, profession national politics as well as logistics procedures (Zhen & Yao, 2024 ; Akundi et al., 2022 ). However, there are very limited academic studies on the effects of Industry 5.0 technologies on reshoring decision-making processes. In addition, our study reveals how AI-powered optimization, IoT-based performance monitoring, digital twins and blockchain technologies can be integrated into reshoring processes. In this context, reshoring processes are optimized with a holistic approach to the relevant field. Reshoring strategies have become increasingly significant in crisis situations that affect the world globally, such as pandemics and wars, and where geopolitical risks come to the fore. Our model makes extensive use of Industry 5.0 equipped tools that enhances the sustainability and productivity of any manufacturing and supply decision making processes, especially reshoring decisions. The system proposed in the study aims to create data-driven, flexible and dynamic decision-making mechanisms in supply chains by integrating risk management, simulation and optimization processes with real-time performance monitoring. 7.2 Contribution to firms For firms, this model helps achieve competitive advantage by increasing operational efficiency. Digital twin and AI-supported simulations optimize production processes in real time. With machine learning supported algorithms, failure rates in production lines can be reduced and inventory costs can be significantly reduced by increasing efficiency in inventory management. Full transparency is ensured in all process from supplier to customer by using blockchain and IoT technologies. On the other hand, with smart energy monitoring systems incorporated to decrease carbon impact, transport procedures can be enhanced with environment-friendly logistics applications. Staff member efficiency and human-robot communication can be boosted thanks to cobots together with AI-enabled automation systems. Many thanks to all these, firms can enhance supply chain adaptability make the most of functional efficiency enhance risk administration as well as assist in reshoring decision-making procedures. 7.3 Supply chain stakeholders and technology providers The model developed in the study helps all stakeholders in the supply chain ecosystem to improve their processes by providing a data-driven, integrated and robust structure. More reliable and transparent trade processes are provided for suppliers. For logistics providers, more efficient transport processes are created with dynamic route optimizations and predictive analysis systems. On the other hand, our model opens new business areas for companies providing IoT, AI, blockchain and automation systems and helps technology providers to implement Industry 5.0 solutions more effectively. For example, companies developing AI-based production management systems or smart logistics solutions can integrate into large-scale projects by producing software and hardware in accordance with this framework and receive support when creating decision-making platforms. 7.4 Policy makers Our model provides a strategic roadmap for public institutions and policy makers that increases economic sustainability and promotes local production. In this context, the design can assist governments methods to boost regional manufacturing as well as assistance state-sponsored motivation programs, AI and digital transformation financial investments. On the various other hand making use of Industry 5.0 modern technologies can be raised in accordance with eco-friendly economic climate targets. Green production incentives and regulations to reduce carbon emissions can be based on our model. Policies can be developed for the training and upskilling of employees in digital transformation processes. 7.5 Usability of the model in different sectors The model we developed through our research can be applied in various sectors such as health, energy, automotive, retail, agriculture and logistics, especially in manufacturing sectors. Medication monitoring as well as supply procedures can be enhanced by utilizing digital twins along with blockchain innovations in clinical supply chains. In the automotive industry efficiency can be enhanced with wise manufacturing facilities along with AI-supported manufacturing systems. Machine learning supported demand forecasting and inventory management can be provided in the retail sector in the field of energy, renewable energy optimization can be achieved through smart grid management. 7.6 Future research fields In future studies, strengthening the sectoral adaptability of this model and eliminating technological constraints are among the issues to be emphasized. On the other hand, studies that will address human-machine collaboration in a more holistic framework and make sustainability one of the focal points of the studies are also needed. In particular, cross-sectoral impacts of Industry 5.0 technologies, how to optimize the balance between automation and labour more efficiently are among the important topics for future research. Combining the model developed within the scope of the research with different disciplines and supporting it with more advanced simulations, case studies and field experiments can provide a more effective application of the model. 7.7 Model constraints The model developed within the scope of the research has some limitations in terms of applicability despite the new understandings discussed and developed. Firstly, the fact that IoT and blockchain technologies are not widespread enough in some regions or countries may make it difficult to implement the model. Secondly, cyber safety and security threats related to blockchain systems and also IoT tools must likewise be thought about. Ultimately the training demands of workers that will certainly utilize AI-based innovations incorporated with Market 5. 0 elements can posture a considerable obstacle for firms. 7.8 Conclusion Reshoring decisions are shaped by supply chain risks, operational costs, sustainability requirements and the advantages offered by Industry 5.0 technologies. The developed AI-based and Industry 5.0-supported model can significantly affect the decision-making processes of companies by enabling them to make their reshoring decisions more data-driven, flexible and sustainable. Our model supports firms reshoring decisions in several ways. Firstly, simulating production processes through digital twins increases the accuracy of reshoring decisions. Machine learning algorithms can predict how production capacity will be managed after reshoring and enable secure data sharing throughout the supply chain, making logistics processes more predictable. Automated production lines and robot-assisted processes can offset local labour costs in the reshoring process. Our model provides an important framework for reshoring decisions and supply chain management by evaluating all the opportunities offered by AI and Industry 5.0. With its scientific contributions to literature, operational benefits for firms, innovation opportunities for technology providers and guidance for policy makers, the model is a multi-faceted transformation tool. Declarations Conflict of interest The author have no relevant financial or nonfinancial interests to disclose. Funding The author declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution M.M.A. contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.M.A. The manuscript was written by M.M.A. References Ahmed, T., Karmaker, C. L., Nasir, S. B., Moktadir, M. A., & Paul, S. K. 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A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing. Advanced Engineering Informatics, 56 , 101998. https://doi.org/10.1016/j.aei.2023.101998 Longauer, D., Hauck, Z., & Vasvári, T. (2023). Make-or-buy strategies in a multi-stage manufacturing process and the role of learning effect in relocation decisions. Computers & Industrial Engineering, 180 , 109259. https://doi.org/10.1016/j.cie.2023.109259 Marrone, P. V., Mathias, F. R., Bernardo, W. M., Orlandini, M. F., Serafim, M. C. A., Scoton, M. L. R. P. D., ... & Dias, E. M. (2023). Decision Criteria for Partial Nationalization of Pharmaceutical Supply Chain: A Scoping Review. Economies , 11 (1), 25. https://doi.org/10.3390/economies11010025 Nicoletti, B., & Appolloni, A. (2024). Green Logistics 5.0: a review of sustainability-oriented innovation with foundation models in logistics. European Journal of Innovation Management , 27 (9), 542-561. https://doi.org/10.1108/EJIM-07-2024-0787 Sawik, T. (2023). Reshore or not reshore: a stochastic programming approach to supply chain optimization. Omega, 118 , 102863. https://doi.org/10.1016/j.omega.2023.102863 Sawik, T. (2025). Economically viable reshoring of supply chains under ripple effect. Omega, 131 , 103228. https://doi.org/10.1016/j.omega.2024.103228 Sharma, M., Sehrawat, R., Luthra, S., Daim, T., & Bakry, D. (2022). Moving towards industry 5.0 in the pharmaceutical manufacturing sector: Challenges and solutions for Germany. IEEE Transactions on Engineering Management , 71 , 13757-13774. doi: 10.1109/TEM.2022.3143466 Tsai, T. Y., & Urmetzer, F. (2024). A decisional framework for manufacturing relocation: Consolidating and expanding the reshoring debate. International Journal of Management Reviews, 26 (2), 254-284. https://doi.org/10.1111/ijmr.12352 Yu, H., & Sun, X. (2024). Uncertain remanufacturing reverse logistics network design in industry 5.0: Opportunities and challenges of digitalization. Engineering Applications of Artificial Intelligence, 133 , 108578. https://doi.org/10.1016/j.engappai.2024.108578 Yu, U. J., & Kim, J. H. (2018). Financial productivity issues of offshore and “Made-in-USA” through reshoring. Journal of Fashion Marketing and Management: An International Journal, 22 (3), 317-334. https://doi.org/10.1108/JFMM-12-2017-0136 Zhen, Z., & Yao, Y. (2024). The confluence of digital twin and blockchain technologies in Industry 5.0: Transforming supply chain management for innovation and sustainability. Journal of the Knowledge Economy , 1-27. https://doi.org/10.1007/s13132-024-02151-0 Additional Declarations No competing interests reported. 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Supply chain disruptions and geopolitical tensions, especially with the COVID-19 pandemic, have forced companies to create more resilient and flexible structures (Lee et al., 2022). In this context, reshoring, which means moving the parts of production activities previously carried out in offshore locations to home countries or nearby geographies, has become a strategic choice for companies (Tsai \u0026amp; Urmetzer, 2023).\u003c/p\u003e \u003cp\u003eThe main challenges encountered in reshoring processes can be listed as high costs, low flexibility, supply chain interruptions and long delivery times (Longauer, Hauck \u0026amp; Vasvari, 2023; Charpin, 2021). Industry 5.0 offers various innovative approaches to overcome these problems. Industry 5.0 has emerged as a new industrial paradigm that goes beyond the digitalization and automation provided by Industry 4.0 and puts the human factor at the center (Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and aims to increase supply chain resilience and sustainability by combining human creativity and problem-solving capabilities with advanced technologies (Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the literature, the potential of advanced technologies brought by Industry 5.0 in supply chain management is emphasized.\u003c/p\u003e \u003cp\u003eDigital twin technologies have shown every phase of manufacturing and can thus help with early-stage problem identification (Marrone et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sawik, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, documented are cases of artificial intelligence-based analytical systems supporting decision optimization. Blockchain technology simultaneously improves supply chain transparency and traceability while IoT devices help to enable continuous data collecting, thereby enhancing operational efficiency (Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These developments will enable businesses to apply a more efficient reshoring plan with a competitive edge as well.\u003c/p\u003e \u003cp\u003eThe main objective of this study is to propose an innovative model that integrates Industry 5.0 principles and enables effective implementation of reshoring strategies in supply chains. Within the scope of the research, firstly, a literature search was conducted in the Web of Science database, and it was seen that there was no study on \u0026lsquo;reshoring and industry 5.0 optimization in supply chains\u0026rsquo;. In this framework, the phenomena of \u0026lsquo;reshoring optimization in supply chains\u0026rsquo; and \u0026lsquo;industry 5.0 optimization in supply chains\u0026rsquo; have been examined and analyzed separately, and a theoretical framework has been created based on this information. In the following, the main building blocks of the proposed model are identified and the implementation steps of the model are explained. This method is suitable for making companies more competitive by increasing the efficiency of the entire supply chain network. Therefore, in this context, the research is an important and valuable study in that the phenomenon of reshoring and Industry 5.0 optimization in supply chains are examined together.\u003c/p\u003e \u003cp\u003eThe rest of the paper is organized as follows: In the next section, the existing literature is reviewed in detail and the salient areas are highlighted. In Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the relationship between reshoring and Industry 5.0 phenomena and supply chain is expressed. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e explains the methodology of the research and elaborates the conceptual framework. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e5\u003c/span\u003e describes the development of the model in full detail, and the requirements of the proposed artificial intelligence-oriented model are presented. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e6\u003c/span\u003e contains the general conclusions, contributions, limitations and future research directions of the model.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cp\u003eThe literature on reshoring optimization in supply chains shows that various academic studies have been conducted on production, reshoring and restructuring of supply chains, especially in recent years. One study by Gray et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigates why US Small and Medium-sized Enterprises (SMEs) relocate their production activities back into high-cost countries. The study analyses decision making in case studies and system dynamics modelling of four SMEs and points out that the lessons learnt from the previous offshoring decisions are an important factor. The decision depends on a mix of factors, such as high freight costs, quality problems and the rising need for flexibility. Exploring the rebirth of the Belt and Road Initiative in the global supply chain under the post-COVID-19 era, Lee \u0026amp; Song (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) bring nine research agendas for improving port efficiency and logistics networks, such as port governance improvement, green transport corridors establishment, and hub of logistics distribution.\u003c/p\u003e \u003cp\u003eTsai \u0026amp; Urmetzer (2023) provides a systematic literature review on production location change, presenting a three-stage framework for the decision-making process and highlighting critical gaps for future research. With a three-stage decision framework, gaps in the understanding of regional impacts were expressed. It emphasized that institutional learning and innovation capacity are important determinants. Freund et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyzed the US effort to reshape its trade policies with China and analyzed the changes in supply chains caused by this process. The research found that US tariffs reduced imports from China and increased imports from nearby geographical regions, but there was no evidence of significant reshoring. It was stated that imports from neighboring countries increased and this situation created new loyalties.\u003c/p\u003e \u003cp\u003eLampon \u0026amp; Rivo-Lopez (2021) examined the impact of technology intensity on turnaround decisions in the European manufacturing sector and found that turnaround decisions are made with cost-oriented strategies in low-tech industries and with innovation and proximity-oriented strategies in high-tech industries. Strategic commitment and quality improvement motivations come to the fore in reshoring decisions. Similarly, a study by Bettiol et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified the strategic alternatives of Italian manufacturing firms after offshoring and suggested that these strategies could be supported by a framework. Researchers identified six ways that improve long-term resilience and are cost-effective. These strategies include product innovation and selective sourcing.\u003c/p\u003e \u003cp\u003eAnother study by Ali et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposes an integrated mineral supply agreement to bridge the infrastructure gap for decarbonization. This agreement will be able to reduce costs while increasing supply chain resilience. Sawik (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) developed a scenario-based stochastic model to optimize reshoring decisions under supply chain disruptions and showed that these decisions provide cost reduction and flexibility. Implementing reshoring methods can improve resilience, but they require considerable backing from the government. According to the findings of a study conducted by Yu \u0026amp; Kim (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on the fashion business, reshoring is more lucrative in cases when there is uncertainty regarding demand. The study analyzes the financial efficiency of offshore and reshoring scenarios. In addition, Marrone et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted a review that provided decision criteria for partial nationalization of pharmaceutical supply chains. More specifically, they emphasized the fact that this strategy has the potential to decrease supply risks.\u003c/p\u003e \u003cp\u003eLongauer, Hauck \u0026amp; Vasvari (2023) examined making or buying strategies in multi-stage manufacturing processes and the role of the learning effect on reshoring decisions. Using dynamic optimization models, this study has shown that reshoring decisions are effective, especially when cross-stage learning improves efficiency. The stochastic optimization model of Sawik (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) uses a scenario-based approach for economic reshoring under random influences. Gur \u0026amp; Dılek (2023) conduct at literature review and case study focused on China's strategic rise in the international system by analyzing various impacts faced regionally and globally. Park et al. (2021) conducted a literature review and case study to examine innovation, current trends and impact of current trends on agriculture and food sector. Charpin (2021) examined the strategies used to understand and manage the effects of political risk in global supply chain management.\u003c/p\u003e \u003cp\u003eFinally, Giammetti et al. (2021) examined the economic effects of regulations and deglobalization processes in European countries and conducted scenario analyses with input-output modelling. The results show that some regions will emerge from this process as winners and others as losers.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Industry 5.0 optimization in supply chains\u003c/h2\u003e \u003cp\u003eIn the literature on supply chain optimization in the context of Industry 5.0, numerous ways for rearranging production and supply chains, as well as the ramifications of these techniques in diverse industrial settings, have been studied in recent years. These techniques have been discussed in different industrial settings.\u003c/p\u003e \u003cp\u003eAkundi et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have identified the current research trends in human-machine interaction in Industry 5.0. They assert that these trends are centered on topics such as sustainability, artificial intelligence (AI), and digital transformation. Kabir, Khan, and Kabir (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) modeled the hierarchical structure of supply chain 5.0 capabilities and examined the interrelations between these capabilities. Expert opinions were collected using the Delphi method and the order of priority of these competencies was determined. Zhen \u0026amp; Yao (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the integration of digital twin and blockchain technologies into supply chain management within the scope of Industry 5.0 and revealed that these technologies provide transparency and operational efficiency. Iakovou et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the key issues and potential solutions in the realm of solar recycling via the lens of next-generation reverse logistics networks. A case study was carried out by the authors to prove the model's usefulness.\u003c/p\u003e \u003cp\u003eLi (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) assessed the strategies in the US-Mexico border region that are designed to enhance local production within the context of Industry 5.0. She underscored the importance of infrastructure development and supplier selection in the success of these strategies. Technological adaptation is of paramount importance during this process. Andres et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyze the practical advantages of supporting technologies for Logistics 5.0.\u003c/p\u003e \u003cp\u003eSharma et al. (2024) employed a comprehensive methodology integrating AHP, ELECTRE, and DEMATEL to identify, rank, and devise feasible solutions for addressing the challenges associated with the adoption of Industry 5.0. Dossou, Mozos, and Pawlewski (2024) simultaneously sought to develop a tool for evaluating supply chain performance regarding sustainability, digitization, and optimization. The researchers delineated three primary axes: sustainable transformation, digital transformation, optimum transformation, and established performance metrics for each axis. The document outlines a framework for a performance assessment tool designed to assess and enhance supply chain performance.\u003c/p\u003e \u003cp\u003eIn their study, Ahmed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) utilized a synthesis of Pareto analysis and the Bayesian Best-Worst Method to evaluate the impact of Industry 5.0 requirements, informed by artificial intelligence, on enhancing supply chain flexibility within the garment and footwear sectors in Bangladesh in the post-pandemic era. Strategic insights were obtained with the purpose of improving supply chain resilience. These insights were obtained by determining the relative relevance of AI-based Industry 5.0 requirements and constructing a hierarchical ranking of those requirements. In their study, In the realm of AI-based fault diagnostics, Leng et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) underlined the need of using machine learning and artificial intelligence algorithms. They also performed comparative study of many algorithms. Dabo \u0026amp; Hosseinian-Far (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated the significance of Industry 5.0 in the shift to the circular economy and the need of reverse logistics and to maximize reverse logistics operations by combining binary logistic regression with decision tree approaches.\u003c/p\u003e \u003cp\u003eNicoletti \u0026amp; Apolloni (2024) emphasized the effects of basic models on green logistics. Kertens et al. (2024) conducted a performance evaluation of Internet of Things (IoT) service providers in the context of Industry 5.0 through case studies. Yu \u0026amp; Sun (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) aimed to design a sustainable reverse logistics network under uncertainty conditions by developing a multi-objective model that balances economic, environmental and social objectives and compared various models. Lo (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) utilized multi-criteria decision-making methods to create a data-driven and non-human factor-dependent decision support system, with the objective of improving supplier evaluation processes.\u003c/p\u003e \u003cp\u003eColimbi et al. (2024) compared various methods for anomaly detection in e-industrial applications and examined the potential of large language models. Chidozie et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) evaluated the impacts of Industry 4.0 and 5.0 on supply chain sustainability and the role of digital transformation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The role of AI based approaches\u003c/h2\u003e \u003cp\u003eA review of the literature on reshoring, supply chain and optimization show that there is very little focus on AI-based applications and approaches, which is the most important supporter of Industry 5.0. In most of the studies, theoretical analysis, case studies and traditional modelling methods are at the forefront. Sawik\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) study employed stochastic mixed integer programming methods to analyze and mitigate risks in reshoring decisions, with the ultimate aim of optimizing cost.\u003c/p\u003e \u003cp\u003eIn contrast, an assessment of studies on supply chain and Industry 5.0 optimization challenges suggests a frequent use of AI-based methodologies within the research scope. In pioneering research, Sharma et al. (2024) used the Deep Q-Learning algorithm to predict state-action values, whereas Leng et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used the Random Forest (RF) algorithm to make solid predictions by rigorous examination of data feature connections. SVM makes it simpler to locate hyperplanes that, when used properly, might properly determine information borders. Dabo \u0026amp; Hosseinian-Far (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) perform view evaluation plus comprehensive textual evaluations utilizing all-natural language handling strategies.\u003c/p\u003e \u003cp\u003eThrough their research, Ahmed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored the application of mixed artificial intelligence systems in conjunction with the Bayesian Best-Worst Method. These systems are very efficient in integrating symbolic logic and neural networks to arrive at proper solutions in complex decision-making situations. Hussien et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used deep learning and alternate direction algorithms for the analysis of feedback mistakes and delays. In their paper, Zhen \u0026amp; Yao (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used genetic algorithms and particle swarm optimization (PSO) to solve engineering optimization problems.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Reshoring and supply chain relations","content":"\u003cp\u003eReshoring is the movement of supply chains and manufacturing from previously relocated overseas sites to one near the company's headquarters or local markets. This idea is usually supported in response to events including supply chain interruptions, fast changing consumer expectations and growing logistics costs (Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tsai \u0026amp; Urmetzer, 2023). Reshoring strategy is clearly one of the key choices businesses have to keep their competitive edge and boost their operational resilience. In this regard, several elements have been successful in implementing reshoring plans. These elements are investigated in the literature under four key headings: cost, risk management, strategic advantages and political and legal elements (Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Longauer, Hauck \u0026amp; Vasvari, 2023).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cost factors\u003c/h2\u003e \u003cp\u003eWhile cost benefits are a major determinant of offshore plans, reshoring decisions are driven by growing labor wages in the nations of manufacture and more logistics expenses brought on by distance. Reshoring has becoming increasingly appealing as labor prices rise, particularly in traditional offshore nations like China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Supply chain risks\u003c/h2\u003e \u003cp\u003eGlobal crises, such as the COVID-19 pandemic, have led to disruptions in supply chains and caused companies to review their resilience strategies. Factors such as geopolitical risks, natural disasters and trade wars have accelerated the adoption of reshoring strategies (Lampon \u0026amp; Rivo-Lopez, 2021; Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Strategic and competitive advantages\u003c/h2\u003e \u003cp\u003eThe return to local production allows companies to respond faster to customer demands, improve product quality and provide a higher level of service (Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Especially fast delivery times and flexible production capabilities are among the factors that strengthen the competitive advantage of reshoring strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Political and legal factors\u003c/h2\u003e \u003cp\u003eGovernment incentives, tax advantages and sustainability requirements are other important factors influencing reshoring decisions. Policies favoring local production encourage companies to adopt such strategies and facilitate decision-making (Freund et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsai \u0026amp; Urmetzer, 2023).\u003c/p\u003e \u003cp\u003eThe effects of reshoring practices on supply chains are discussed, particularly in the context of supply chain structure, costs, risk management and service level. First of all, reshoring allows supply chains to become shorter and more localized. This reduces logistics costs and complexity in the supply chain (Lampon \u0026amp; Rivo-Lopez 2021; Longauer, Hauck \u0026amp; Vasvari, 2023). Secondly, the establishment of local production facilities can lead to an increase in fixed costs, while shorter logistics chains can lead to savings in transport and storage costs. Moreover, by lowering inventory costs, faster response to consumer needs helps to improve financial performance (Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other side, a change from global to local supply chains could assist lower geopolitical uncertainty and global trade hazards. Local manufacture also helps to strengthen supply networks (Tsai \u0026amp; Urmetzer, 2023; Sawik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By reducing quality control procedures, reshoring finally helps to guarantee more constant product quality. Furthermore, faster responses to customer needs help to raise the degree of service (Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Industry 5.0 and supply chain relations","content":"\u003cp\u003eThe concept of Industry 5.0 signifies a novel industrial revolution, wherein technological innovations are amalgamated with human values and sustainability objectives (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This paradigm is predicated on a model of collaboration between human beings and robotic technologies. Key components of Industry 5.0 include AI, the IoT, big data analytics, additive manufacturing, blockchain, digital twins and metaverse supply chain technologies (Hussien et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu \u0026amp; Sun, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernandez-Miguel et al., 2024).\u003c/p\u003e \u003cp\u003eThe objective of Industry 5.0 is to optimize the utilization of resources, reduce waste, and promote sustainable production and consumption patterns by leveraging circular economy principles (Hussien et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is anticipated that the related elements will improve supply chain management systems in terms of efficiency, flexibility, and transparency.\u003c/p\u003e \u003cp\u003eOn the other hand, supply chains often have complex structures and require the efficient integration of different stages (Nicoletti \u0026amp; Apolloni, 2024). Industry 5.0 presents effective solutions for alleviating this issue. First of all, IoT devices make it possible to monitor all assets in the supply chain with sensors. This can help make better decisions in areas such as inventory management, shipment tracking and machine maintenance (Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Big data analytics analyses real-time and historical data to identify trends, predict demand and optimize processes. Additive manufacturing creates customized components on demand, reducing waste and shortening supply chains (Fernandez-Miguel et al., 2024). Blockchain technology offers transparency and traceability along the supply chain. The use of this technology ensures trust in data from logistics to production (Dossou, Mozos \u0026amp; Pawlewski, 2024). Digital twins are digital replicas of physical systems that allow to simulate and optimize supply chain\u0026rsquo;s operations. Digital twins are virtual models of physical systems that enable the simulation and optimization of supply chain operations. The metaverse supply chain creates immersive virtual environments for visualizing and collaborating on supply chain processes (Chidozie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fernandez-Miguel et al., 2024). In addition, Industry 5.0 is focused on reducing environmental impact and increasing sustainability. Renewable energy sources and the use of technology to reduce carbon emissions support environmental responsibility in supply chains (Colimbi et al., 2024).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Supply chain optimization with Industry 5.0\u003c/h2\u003e \u003cp\u003eThe optimization procedure in the supply chain covers price decrease, optimizing making use of readily available sources plus raising shipment rate (Leng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Industry 5. 0 offers a range of advancements that add boosting the performance of the supply chain procedure. IoT-based sensing units as well as instantaneous information analytics enable business to anticipate the need as well as get used to supply plus result appropriately. This is vital for both setting you back conserving as well as enhancing client contentments (Dabo \u0026amp; Hosseinian-Far, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AI powered projecting designs can determine future need cycles coupled with possible traffic jams. This is an essential procedure for source preparation as well as stock monitoring (Lo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AR innovation can lessen mistake prices by executing real-time advice for staff members. VR can be used for critical preparation by restoring supply chain situations (Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndustry 5.0 is not only a technology transformation for supply chains, but also a step towards a humane and sustainable future. In this new era, companies can create more flexible, efficient and responsible supply chains by adopting technological innovations (Kertens et al., 2024; Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Research methodology and conceptional framework","content":"\u003cp\u003eThis section outlines the methodological approach, detailing the conceptual framework developed to integrate AI and Industry 5.0 principles, enabling the effective implementation of reshoring decisions in supply chains. The methodology integrates situation analysis, scenario development, simulation phase, industry 5.0 integration and performance evaluation and monitoring phase to improve the accuracy of reshoring decisions. The framework proposed in this study is structured to enable effective reshoring decisions in supply chains and provides important outputs for firms, supply chain stakeholders and policy makers.\u003c/p\u003e \u003cp\u003eWithin the scope of the research, Web of Science database was searched with the keywords \u0026lsquo;supply chain\u0026rsquo;, \u0026lsquo;reshoring\u0026rsquo;, \u0026lsquo;industry 5.0\u0026rsquo; and \u0026lsquo;optimization\u0026rsquo;, but no study was found. Upon this, the keywords \u0026lsquo;supply chain\u0026rsquo;, reshoring\u0026rsquo; and \u0026lsquo;optimization\u0026rsquo; were used and 18 articles were obtained. The analysis methods, variables and factors used in the related studies were used as a guide on how to achieve optimization. Secondly, the keywords \u0026lsquo;supply chain\u0026rsquo;, industry 5.0\u0026rsquo; and \u0026lsquo;optimization\u0026rsquo; were searched. The evidence from these research and Industry 5.0 optimization in supply chains and the methods used are expressed in descriptive form. Thus, the ways in which Industry 5.0 components can be integrated with reshoring decisions have been determined and a guideline for the creation of the model has been established. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the conceptual framework of the research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis framework provides a structured overview of how to integrate the components of reshoring and industry 5.0 optimization in supply chains.\u003c/p\u003e"},{"header":"6 Model development","content":"\u003cp\u003eThe AI-driven analysis and decision framework implemented in the study is designed to evaluate options for the effective implementation of reshoring operations in supply chains, to use various data sources in an integrated manner, to provide Industry 5.0 optimization and to assist decision-making processes. Within the scope of the study, firstly, the relevant literature has been analyzed in detail and the methods and variables/factors used in the studies have been expressed in an explanatory manner. From this point of view, it has been determined which factors should be taken into consideration by firms when making reshoring decisions. The methods and variables used in the studies examined within the scope of designing the model are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethods and Variables/Factors Used in the Analyzed Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors \u0026amp; Date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariables \u0026amp; Factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase studies, system dynamics modeling, and heuristic decision-making analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecision type: Reshoring or offshore\u003c/p\u003e \u003cp\u003eDrivers: Labor costs, total cost of ownership, and transportation cost\u003c/p\u003e \u003cp\u003eDecision environment: Level of uncertainty and executive experience\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee \u0026amp; Song, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpacts of Covid 19: Stalled projects and logistics chain disruptions\u003c/p\u003e \u003cp\u003eLogistics performance: Port efficiency and green logistics corridors\u003c/p\u003e \u003cp\u003ePolitical Risk: China-US trade dispute, regional tensions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTsai \u0026amp; Urmetzer, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystematic literature review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReasons for relocation: Cost advantage, flexibility and sustainability\u003c/p\u003e \u003cp\u003eInternal factors: Strategic firm resources and dynamic capabilities\u003c/p\u003e \u003cp\u003eExternal factors: Political risks, market proximity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreund et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifference-in-differences econometric approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImport share: Change in import share by country\u003c/p\u003e \u003cp\u003eStrategic sectors: High technology products\u003c/p\u003e \u003cp\u003eTrade policy variables: Tariffs and trade disputes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLampon \u0026amp; Rivo-Lopez, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconometric analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndogenous factors: Innovation capacity, technological level of production processes\u003c/p\u003e \u003cp\u003eExternal factors: Logistics and labour costs, security of supply\u003c/p\u003e \u003cp\u003eTypes of strategy: Cost-oriented and innovation-oriented strategy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBettiol et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiple case study analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecision-making factors: Drivers, inhibitors and facilitators\u003c/p\u003e \u003cp\u003eImplementation process: Scope, timing and management\u003c/p\u003e \u003cp\u003ePerformance outcomes: Operational efficiency, customer satisfaction and cost reduction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConceptual research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupply chain factors: Security of raw material supply and processing capacity\u003c/p\u003e \u003cp\u003eExternal factors: Geopolitical risks and environmental regulations\u003c/p\u003e \u003cp\u003eCarbon reduction strategies: Recycling, energy efficiency and localization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSawik, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStochastic mixed integer programming and scenario-based optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRipple effect: Disruptions spread throughout the supply chain\u003c/p\u003e \u003cp\u003eReshoring costs: Capital expenditure, operating costs\u003c/p\u003e \u003cp\u003eService level: Average and worst-case performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer simulations comparing financial metrics under different sourcing scenarios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResource selection: Offshore and reshore scenarios.\u003c/p\u003e \u003cp\u003ePerformance metrics: Gross margin, stock turn ratio and service level\u003c/p\u003e \u003cp\u003eTypes of error: Volume and variety error\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarrone et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScoping review of academic literature and thematic analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypes of criteria: Local production capacity, logistics costs\u003c/p\u003e \u003cp\u003eRisk management: Inventory build-up and diversification strategies\u003c/p\u003e \u003cp\u003eStakeholder views: Industry representatives and public authority\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongauer, Hauck \u0026amp; Vasvari, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic optimization modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction stages: Learning potential and process complexity at different stages\u003c/p\u003e \u003cp\u003eCost elements: Labour costs, equipment costs, production error rates\u003c/p\u003e \u003cp\u003eLearning effect: Cost-reducing learning rate depending on the production volume at each stage.\u003c/p\u003e \u003cp\u003eTypes of strategic decisions: Partial or full internalization, outsourcing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSawik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStochastic mixed integer programming models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReshoring decisions: Localization of main suppliers, replenishment portfolios from reserve suppliers.\u003c/p\u003e \u003cp\u003ePerformance metrics: Total cost, service level, business viability and resilience\u003c/p\u003e \u003cp\u003ePolicy factors: Government incentives (subsidies for capital expenditure), local labour subsidies\u003c/p\u003e \u003cp\u003eRisk metrics: Scenario-based worst-case performance, supply chain disruptions due to ripple effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIakovou et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSituation analysis and Resource-Task-Network based model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse logistics network performance: Recycling rate and logistics costs\u003c/p\u003e \u003cp\u003ePolicy factors: Regulatory gaps and optimization of recycling infrastructure\u003c/p\u003e \u003cp\u003eSustainability: Resource utilization efficiency and waste minimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction capacity. Local production potential and existing infrastructure\u003c/p\u003e \u003cp\u003eSupply chain security: Logistics network structure and critical material supply\u003c/p\u003e \u003cp\u003ePolicy factors: Incentives and regional co-operation strategies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiammetti et al., 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInput-output analysis, scenario analysis and hypothetical inference method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction network structure: Local and international intermediate goods flows\u003c/p\u003e \u003cp\u003eRegional shortages: Direct and indirect effects of production disruptions\u003c/p\u003e \u003cp\u003eTypes of scenarios: Complete disengagement from globalization, refocusing on Europe and a return to past production patterns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharpin, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypes of nationalism: Economic nationalism and national hostility\u003c/p\u003e \u003cp\u003eRisk factors: Trade wars government interventions and local requirements\u003c/p\u003e \u003cp\u003eSupply chains: Import bans, tariffs and costs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGur \u0026amp; Dilek, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review and case studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEconomic size, diplomatic relations, security strategies and levels of regional co-operation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark et al. 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review and case studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction quantities, quality parameters and economic effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review and text mining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain themes related to Industry 5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelphi, ISM-DEMATEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupply chain 5.0 capabilities: personalization, real-time data, cloud supply chain, connectivity, transparency, agility, flexibility, sustainability and proactive approach\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase study and mediation analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable supply chain management, digital transformation applications\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig data analysis, sustainability, decision-making processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSharma et al. 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHP, ELECTRE and DEMATEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCritical barriers and solutions for Industry 5.0 adoption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDossou, Mozos \u0026amp; Pawlewski, 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-based methods and optimization algorithms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable transformation: Economic, social, environmental and circular economy\u003c/p\u003e \u003cp\u003eDigital transformation: Creating a digital twin of the company's supply chain\u003c/p\u003e \u003cp\u003eOptimal transformation: Improving the internal organization of the supply chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAhmed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBayesian Best-Worst Method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-based requirements of Industry 5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDabo \u0026amp; Hosseinian-Far, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary logistic regression and decision trees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactors affecting reverse logistics networks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicoletti \u0026amp; Apolloni, 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasic models (FMs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent status and expectations of Logistics 5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKertens et al. 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHedonic double boundary estimation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInputs: Price and training cost\u003c/p\u003e \u003cp\u003eOutputs: After-sales service, reliability, storage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu \u0026amp; Sun, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzzy Mixed-Integer Linear Programming, Monte Carlo simulation and Discrete event simulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycling logistics network design, demand and capacity requirements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVC-DRSA, CRITIC and CTOPSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupplier evaluation criteria: Sustainability, digital transformation, real-time data sharing and organizational culture transformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColimbi et al. 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge language models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisual and language models for anomaly detection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChidozie et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConceptual review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactors affecting supply chain performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHussien et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeep learning and alternate direction algorithms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeedback errors and delays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiterature review and machine learning techniques (LSTM-RNN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClosed-loop supply chain management and remanufacturing processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Data collection and assessment\u003c/h2\u003e \u003cp\u003eIn order to make effective reshoring decisions in supply chains, it is necessary to use various data sources that include production and demand, economic, political, technological and potential risk factors. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the primary data sources, categorized as production and demand data, supply chain data, financial data, technological data, political and legal situation and potential risks. Each category brings together a large data set of different factors, providing key insights for effective reshoring decisions. Production and demand data in this context are of critical importance for understanding the basic resources and potential of the companies. The obtained data regarding supply chain operations provide an important overview in terms of the efficiency, quality and safety of the operations in progress, and a future course can be drawn with economic analysis to be made with financial data. Technological data, especially when evaluated within the framework of Industry 5.0 and AI-based transformation process, clearly reveals the current situation of companies and provides guidance on the issues that need to be developed. Political and legal situation data provide a framework for the regulatory implications of reshoring, while potential risks help to clearly account for uncertainties in decision-making.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Sources and Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypes of Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasurement \u0026amp; Frequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eManufacturing \u0026amp; Demanding Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal corporate source data, market research, public reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal production capacity, local labour capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly logs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduction lines data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduction error rate, performance of production lines, inventory level, production capacity per labour force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReal time/daily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSales and CRM data, market research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent demand, potential demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual logs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSupply Chain Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSupply chain operations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuality of logistics infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual updates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLead times and security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReal time/daily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLogistics chain disruptions, supply chain dependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePort/station activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFinancial Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInternal corporate source data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabour costs, equipment costs,\u003c/p\u003e \u003cp\u003einvestment costs, transport and freight costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal cost of ownership, gross margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual logs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacroeconomic variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP, industrial production, business activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual logs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTechnology Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInventory department reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComputers, servers, IoT devices, network equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIT department reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-based technology and application usage, integration of IoT devices, adoption level of big data analytics tools, cyber security applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReal time/daily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIT department reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERP, SCM, CRM applications data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman resources department reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployees competence in the use of technology (training, technology skills)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuarterly/annual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical and Legal Situation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal government regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTax reductions and reshoring incentives, local labour support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReal time/weekly\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePotential Risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeopolitical risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWars, regional tensions, tariffs, trade wars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolitical risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTariffs and trade wars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental and sustainability risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural disasters, fire, flood, earthquake, pandemic, production and waste management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly/quarterly reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncertainty of return on investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly/quarterly reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal source of critical components, availability of skilled labour, modernization of production lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeekly/monthly reports\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOptimizing reshoring operations in supply chains with Industry 5.0 and AI-based methods and building a decision-making model is based on data collection and pre-processing. In this research, data sources are divided into six categories: production and demand data, supply chain data, financial data, technological data, political and legal situations and potential risks. Each type of data processed is key to making the right decision.\u003c/p\u003e \u003cp\u003eManufacturing and demand data plays a critical role in improving a company's operational efficiency and responding more effectively to market needs. Indicators such as local and offshore production capacity, production capacity per workforce and local workforce capacity allow optimizing production processes (Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sharma et al., 2024). While in determining defect rates in production processes may identify opportunities to eliminate waste in operational improvements and balance supply chain operational costs (Sawik, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nicoletti \u0026amp; Apolloni, 2024). Demand data generated on currently and potentially saleable products from sales and CRM systems helps in forecasting and thus provide an understanding of the expectations of customers to optimize production planning (Marrone et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further, demand fluctuations can be predicted more accurately when combined with inputs from market research and both local and offshore production strategies can be suitably shaped (Chidozie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Analyzing the operational data is crucial for achieving the advancement of resource efficiency, adaptable production processes, and flexibility in conditions to accommodate market flexibility (Hussien et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite escalating competition for the organization, consumer happiness yields greater profitability.\u003c/p\u003e \u003cp\u003eSupply chain information is a vital resource of evaluation, consisting of logistics facilities top quality, preparation, safety and security, logistics chain disturbances, port/station effectiveness plus supply chain dependency. In-depth evaluation of this information is essential in boosting the performance of the supply chain along with avoiding prospective disturbances. For example, the quality of logistics infrastructure can help reduce costs and increase customer satisfaction by shortening shipment times (Lee \u0026amp; Song, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lead times and safety data build resilience and flexibility in the supply chain by enabling better management of risks (Dabo \u0026amp; Hosseinian-Far, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Giammetti et al., 2021).\u003c/p\u003e \u003cp\u003eAdditionally, information such as logistics chain disturbances plus port/station task can be made use of to recognize vulnerable factors of supply chain procedures and also supply possibilities for enhancement (Lee \u0026amp; Song, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Yu \u0026amp; Sunlight, 2024). This not just raises functional performance, however additionally sustains sustainability objectives as well as reduces ecological effects (Sawik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lampon \u0026amp; Rivo-Lopez, 2021). Assessing and also analyzing such information provides a basis for sustaining firms critical choices and also constructing an extra lasting, effective plus versatile supply chain.\u003c/p\u003e \u003cp\u003eFinancial data is obtained from both internal corporate sources and macroeconomic data sources to support the strategic decisions of enterprises. Data such as labour costs, equipment costs, investment costs, transportation and freight costs from internal sources provide an important basis for cost optimization and budget management by analyzing the company's operational cost structure (Tsai \u0026amp; Urmetzer, 2023; Longauer, Hauck \u0026amp; Vasvari, 2023; Charpin, 2021). Gross margin data are used to assess the sustainability of existing business models by analyzing profitability (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Data on GDP, industrial production and business activity obtained from macroeconomic data sources play a critical role in understanding the general outlook of economic conditions and demand forecasts (Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, GDP growth is an indicator for assessing market expansion potential, while industrial production data provides valuable information for monitoring demand trends by sector (Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Longauer, Hauck \u0026amp; Vasvari, 2023). Analyzing this data set provides important contributions to companies in terms of cost efficiency, strategically direct investments and creating flexible responses to market dynamics. Thus, companies can increase both their financial and operational sustainability.\u003c/p\u003e \u003cp\u003eData on the political and legal situation supports firms strategic planning and operational decisions. Information on tax deductions helps enterprises enhance their cost advantages and utilize their available financial resources more effectively (Freund et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsai and Urmetzer, 2023). Incentives given to reshoring contribute to improving the supply chain resilience of enterprises and increasing their resilience to global risks by increasing local production activities (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Local labour support helps enterprises strengthen regional employment opportunities and expand the talent pool for enterprises, while at the same time, it facilitates the achievement of their social responsibility goals (Charpin, 2021; Iakovou et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This data set contributes to enterprises gaining competitive advantage and achieving their long-term sustainability goals by evaluating the economic and social effects of legal regulations and political support. By evaluating political and legal incentives, enterprises can take more strategic positions in both the local and global markets.\u003c/p\u003e \u003cp\u003eTechnology data is a critical resource to support firms digital transformation processes and provide competitive advantage. Equipment as well as facilities information play a crucial duty in identifying efficiency enhancements as well as financial investment requires by analyzing the ability of the existing technical framework (Freund et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chidozie et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kertens et al., 2024). Making use of AI-based modern technologies raises performance by boosting automation procedures while the combination of IoT tools uses substantial advantages in supply chain administration with real-time information collection plus evaluation (Lampon \u0026amp; Rivo-Lopez, 2021; Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The degree of fostering of big data analytics devices sustains data-driven decision-making procedures making it possible for a lot more foreseeable as well as adaptable organization designs (Dossou Mozos \u0026amp; Pawlewski, 2024). Applications such as ERP SCM as well as CRM raise functional performance by assisting in procedure combination, while cyber protection software supplies safety as a vital part in securing digital settings (Dabo \u0026amp; Hosseinian-Far, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hussien et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, worker innovation skills information is important consider the success of electronic improvement jobs by raising the efficient use digital devices and also business adjustment (Kabir Khan \u0026amp; Kabir, 2024; Leng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This information established supplies a critical benefit to services in regards to maximizing modern technology framework, boosting organization procedures and also adjusting to the demands of the digital age.\u003c/p\u003e \u003cp\u003ePotential risks are critical elements that require a thorough analysis for businesses to develop strategic planning and resilience strategies. Geopolitical risks can disrupt supply chains and increase costs due to factors such as wars, regional tensions, trade wars and tariffs; therefore, analyses of regional stability and building alternative supply chain scenarios offer important contributions (Gray et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tsai \u0026amp; Urmetzer, 2023). Economic risks are related to the uncertainty in the returns on investments and behavior of financial markets, analysis of which delivers information necessary for financial predictions and a company\u0026rsquo;s liquidity (Longauer, Hauck \u0026amp; Vasvari, 2023; Sawik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Operational risk factors are related to the local sourcing of critical components and experts, skilled labor shortage and modernization of the production lines, analysis of which gives direction for strategic improvements in processes and technology investments (Chidozie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kertens et al., 2024; Giammetti et al., 2021). Environmental and sustainability risks arise from natural disasters, global crises such as pandemics, and production and waste management, allowing businesses to seize opportunities to strengthen their sustainability policies and reduce their environmental impact (Sharma et al., 2024; Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Charpin, 2021). Each of these risk categories helps businesses optimize their risk management strategies, increase operational resilience and support long-term sustainability goals. An effective risk analysis enables a better identification of not only threats but also opportunities.\u003c/p\u003e \u003cp\u003eWhen the data are analyzed in terms of usability, there are differences between the sources. Some data can be measured in real time, while others are obtained weekly, quarterly or annually. In this context, AI-supported data preprocessing models are used to clean, process and make sense of large data sets. These models generally utilize machine learning, deep learning and statistical techniques. It has been proven in some studies that the related techniques can be used in the data preprocessing phase (Giammetti et al., 2021; Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Chidozie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, Principal Component Analysis (PCA) for dimension reduction and categorical data processing, Recurrent Neural Networks (RNN) to detect anomalous data and complete missing data play a role in improving the efficiency and interpretability of the model. The pre-processing approach structured in this way ensures that the model uses detailed and up to date data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Situation analysis\u003c/h2\u003e \u003cp\u003eSituation analysis is the basis for defining an existing problem, evaluating the performance of existing systems or processes, and developing strategies to guide future decisions (Tsai \u0026amp; Urmetzer, 2023; Longauer, Hauck \u0026amp; Vasvari, 2023). The relevant analysis includes the assessment of critical factors such as production capacity, demand forecasts, financial performance, supply chain structure, political and legal situation and potential risks. There are various methods used to analyze the situation in the reviewed studies (Giammetti et al., 2021; Lo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dossou, Mozos \u0026amp; Pawlewski, 2024).\u003c/p\u003e \u003cp\u003eIn this context, the current operational status of the company is examined. Determine which production processes are suitable for reshoring. Supply chain risks are analyzed and all data are classified (Giammetti et al., 2021; Dossou, Mozos \u0026amp; Pawlewski, 2024; Hussien et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Support Vector Machine (SVM) method is used to classify and priorities the data. The Long Short-Term Memory (LSTM) method is used at the end of this phase to predict and analyze data trends and to present the current situation. As a result of the analysis, the existing operational structure, risk profile and modernization requirements of the company are clearly determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Scenario development and simulation\u003c/h2\u003e \u003cp\u003eFollowing the outputs obtained in the situation analysis, the scenario development phase starts with optimized and classified data. Scenario development plays a crucial role in understanding the impact of different conditions and future uncertainties and testing possible strategies. The scenarios developed at this stage are simulated and provide important insights for companies, supply chain stakeholders and policy makers.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e6.3.1 Defining scenario types\u003c/h2\u003e \u003cp\u003eScenario development is a systematic process used to predict future events, manage risks and make strategic decisions. Scenario analysis helps to evaluate different alternatives in decision-making processes involving uncertainties. In this context, multiple forecasting options are created based on predefined assumptions about the variables. These scenarios fall into three categories: Optimization scenarios, risk-based scenarios and technology adaptation scenarios.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWith optimization scenarios, the lowest cost or the most efficient production strategies are determined (Yu \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yu \u0026amp; Sun, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dossou, Mozos \u0026amp; Pawlewski, 2024).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWith risk-based scenarios, stress tests are conducted based on production outages, logistics disruptions and other uncertainties (Giammetti et al., 2021; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWith technology adaptation scenarios, Industry 5.0 components such as digital twins, automation and AI integration are tested (Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBy modelling different uncertainty and risk situations with these scenarios, a roadmap is created to evaluate how the systems perform under different conditions and to make better decisions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e6.3.2 Simulation\u003c/h2\u003e \u003cp\u003eThe simulation phase is an important modelling process for modelling, analyzing and optimizing supply chain processes and is used to predict possible future outcomes. In particular regarding reshoring, supply chain optimization and Industry 5.0 transformation, simulation of the supply chain allows to simulate different scenarios, measure performance and mitigate risks. Many articles adopt the simulation phase to express different methodologies for decision making, optimization, process analysis and risk assessment (Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this research, various AI and statistical simulation techniques are used for modelling the scenarios created:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDiscrete Event Simulation (DES) allows the analysis of discrete events such as production lines, inventory management and logistics processes (Dossou, Mozos \u0026amp; Pawlewski, 2024; Leng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu \u0026amp; Sun, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Each event is assumed to have a different effect on the system and the relationships between events are modelled. It is suitable for use in determining the impact of disruptions in production processes, optimizing routes, determining criteria for the efficient use of the workforce.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMonte Carlo simulation is a very useful method to study uncertainties and predict estimated outputs based on different probabilities (Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yu \u0026amp; Sun \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, it allows the analyses of different scenarios\u0026rsquo; impact on business performance, and also their long-term impact of these scenarios, it helps to identify the lowest-risk and most fruitful scenarios. In this context, various risk factors can be simulated and potential losses can be determined.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model can effectively forecast demand and analyze supply chain fluctuations (Dossou, Mozos \u0026amp; Pawlewski, 2024; Lo, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ARIMAX analyses not only historical data but also exogenous variables such as economic indicators, seasonality and political factors. The model is used to predict delays in the supply chain and to determine how to optimize production volumes according to customer demand.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWhen used together, these models enable the establishment of a holistic system in supply chain management that is data-driven, minimizes risks and predicts the future.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Risk management and resilience strategy development\u003c/h2\u003e \u003cp\u003eRisk management and the development of sustainable strategies is a critical stage to ensure supply chain continuity, minimize operational risks and support long-term sustainable growth. Identifying and managing potential risk factors, especially in reshoring decisions in supply chains and Industry 5.0 optimization processes, can help companies gain competitive advantage.\u003c/p\u003e \u003cp\u003eRisk administration is an important procedure to decrease unpredictabilities, determine possible hazards as well as reduce possible losses (Zhen \u0026amp; Yao \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This procedure is vital in numerous markets such as financing, supply chain, production, logistics, power wellness plus ecological administration. Lasting methods are strategies as well as techniques established to ensure long-lasting success by considering ecological, financial as well as social elements (Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chidozie et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI as well as crossbreed evaluation techniques raise the performance of lasting methods by making danger administration procedures much more vibrant, data-driven together with enhanced (Kabir, Khan \u0026amp; Kabir \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, using the data obtained from the simulation phase, the identification and management of operational risks, financial risk management and economic sustainability, environmental risk mitigation and sustainability planning, issues affecting supply chain resilience and risks to the proper optimization of Industry 5.0 technologies are analyzed. Using the Random Forest (RF) method (Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dossou, Mozos \u0026amp; Pawlewski, 2024), the risk in each category is scored. Sustainable strategies are developed for each risk heading by prioritizing and ranking according to risk scoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Integration and optimization Industry 5.0 technologies\u003c/h2\u003e \u003cp\u003eIn the light of the strategies created, modernization, integration and optimization of Industry 5.0 technologies are carried out for the effective operation of the process. Industry 5. 0 describes a production along with supply chain change that boosts human-machine communication plus consists of sophisticated modern technologies such as AI, digital twins, blockchain IoT, big data analytics as well as increased truth (Dabo \u0026amp; Hosseinian-Far \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nonetheless, on the occasion that the business cannot gain from these modern technologies completely, it is required to remove the insufficiencies and identify the determine the correct integration actions.\u003c/p\u003e \u003cp\u003eThe success of Industry 5.0 depends on healthy integration between technology, people and processes (Nicoletti \u0026amp; Apolloni, 2024). At this stage, it should be assessed which Industry 5.0 components are missing and how to integrate them in line with the outputs obtained from the analyses, scenario development, risk management and simulations carried out in the previous stages. The evidence from the previous stages shows us to what extent the following premises have been realized:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDigital transformation infrastructure deficiencies,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAutomation and robotic integration deficiencies\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eData management and AI utilization deficiencies,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdequacy of man-machine co-operation,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecurity of the supply chain,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustainability and compliance with green technologies.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIndustry 5.0 implementation and optimization strategy should be implemented and strategic action plans should be implemented in order to eliminate the identified deficiencies.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e6.5.1 Strengthening digital transformation infrastructure\u003c/h2\u003e \u003cp\u003eThe current structure of the company may not be sufficiently adapted to digital transformation technologies such as IoT, cloud computing and big data analytics. In this context, the use of IoT sensors should be enabled in all processes and real-time data collection should be ensured. On the other hand, by creating digital twin models, production processes should be modelled in digital environment in real time and optimization should be made with virtual simulations (Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu \u0026amp; Sun, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf in-house data centers are insufficient, cloud-based solutions should be used. AI-supported data management systems should be deployed for big data analyses. In order to ensure the security of the relevant data, data security should be ensured by using blockchain-based security solutions (Andres et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e6.5.2 Automation and robotic integration\u003c/h2\u003e \u003cp\u003eExisting automation systems of the company may not have the flexibility and adaptation Industry 5.0 brings. In this manner, reinforcement learning methods should be applied for adapting the robots to the processes (Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Workforce optimization should be ensured by using intelligent robot systems in production lines and AI-supported quality control systems should be activated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e6.5.3 Realization of AI supported optimization and data management\u003c/h2\u003e \u003cp\u003eThe company may be lacking in collecting and analyzing data effectively. In this context, machine learning and deep learning techniques should be used to analyze large data sets in a better way. On the other hand, AI-supported maintenance systems should be installed, especially to predict failures and problems that may occur. It is important to use advanced forecasting methods such as ARIMAX and LSTM in inventory management and logistics processes. In addition, the use of Convolutional Neural Network (CNN) based image processing systems to detect anomalies in production lines provides important insights (Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e6.5.4 Strengthening human-machine cooperation\u003c/h2\u003e \u003cp\u003eCompany employees may not be fully integrated with automation systems. In this context, co-operative robot systems that will enable industrial robots and workers to work together should be put into operation (Kabir, Khan \u0026amp; Kabir, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI-based detection systems should be developed so that robots can work safely with humans in production processes.\u003c/p\u003e \u003cp\u003eOn the other hand, it is also important to train employees and improve their digital capabilities. Employees should be given augmented reality and virtual reality training, and by creating simulation supported training programs, digital skills should be improved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e6.5.5 Ensuring supply chain security and transparency with blockchain\u003c/h2\u003e \u003cp\u003eIf there is insufficient data security and traceability across the company's supply chain, smart contracts driven by blockchain should be triggered. Blockchain technology should be used to secure the sharing of required data among suppliers and logistics companies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e6.5.6 Sustainability and compliance with green technologies\u003c/h2\u003e \u003cp\u003eThere may be inefficient process management in carbon emission, energy efficiency and recycling processes in the supply chain processes of the company. IoT-based energy management systems should be used to increase energy efficiency, and deep learning-based prediction models should be created to optimize energy consumption within the factory. In addition, blockchain-based monitoring systems should be used to increase sustainability in material utilization.\u003c/p\u003e \u003cp\u003eFollowing the outputs obtained, it is very important to integrate Industry 5.0 technologies to eliminate company-specific deficiencies. In this process, which technologies will be prioritized should be determined through company-specific analyses and the integration process should be carried out in stages. Solutions such as digital twins, AI-assisted optimization, blockchain-based supply chain management and the use of collaborative robots (cobot) accelerate the digital transformation of the company and enable it to gain long-term competitive advantage. Once this optimization process is completed, continuous improvement and adaptation mechanisms can be put in place to ensure full integration into Industry 5.0. With the completion of these stages, key performance indicators are established and the effectiveness of the established system is ensured.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.6 Performance monitoring and adaptation\u003c/h2\u003e \u003cp\u003eThe performance monitoring and adaptation phase is the process of evaluating and improving the effectiveness of processes optimized with Industry 5.0 technologies and adapting to changing conditions. Continuous monitoring and adaptation is necessary to verify that the risk management, simulation, sustainable strategy development, optimization and integration processes carried out in the previous phases are working properly.\u003c/p\u003e \u003cp\u003eThis stage also provides feedback on the first three stages of the model and includes updating these stages in line with the outputs obtained. Therefore, decision-making processes are made dynamic based on data, and businesses provide continuous improvement in the Industry 5.0 transformation process.\u003c/p\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e6.6.1 Main objectives\u003c/h2\u003e \u003cp\u003eThe main objectives of the performance monitoring and adaptation phase are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo continuously monitor the performance of the system with real-time data analysis,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIdentify anomalies, bottlenecks and opportunities for improvement,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eContinuously update and optimize the models created in the previous phases,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProviding feedback on risk management, simulation and optimization processes,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDevelop adaptation strategies to enhance human-machine interaction and sustainability.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003ePerformance monitoring and adaptation processes supported by Industry 5.0 components include automatic learning systems and real-time feedback mechanisms, enabling businesses to make quick decisions. Performance monitoring involves continuously monitoring the state of the system by collecting real-time data through sensors, big data analytics, AI and IoT-enabled systems. Within the relevant framework, the key performance indicators are continuously analyzed and the performance of the system is assessed for compliance with the objectives. Based on the data obtained here, adaptation mechanisms are developed and processes are optimized. AI-supported deep reinforcement learning models enable systems to learn on their own and optimize performance. With twin models, the real world and the virtual environment are integrated and performance scenarios are continuously updated. These stages range from automatic maintenance planning with the detection of anomalies in machine maintenance processes to adaptation to changes in smart energy management systems.\u003c/p\u003e \u003cp\u003eThe data obtained from the performance monitoring phase also feeds the previous four phases and enables the continuous improvement of the system. Optimization algorithms are updated with real-time performance data and processes are constantly made more efficient. simulation models are validated with real world data and new scenarios are created. The workload of the Industry 5.0 technologies used in processes is optimized. In addition, new risk factors are identified by analyzing performance data and risk management strategies are updated. With feedback mechanisms, companies create an ecosystem that supports continuous improvement processes by adapting quickly to dynamic changes in the system. In this way, long-term competitiveness increases and operational excellence of the company is ensured.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"7 Conclusion and Discussion","content":"\u003cp\u003eThe main objective of this study is to propose an innovative model that integrates Industry 5.0 principles and enables effective implementation of reshoring strategies in supply chains. Industry 5.0 can help with this by using advanced automation, AI to help with decision-making, human-machine collaboration and sustainable production models. This allows companies to fix problems in global supply chains, be more efficient and support local production ecosystems. The model we're proposing is Industry 5.0-oriented and AI-based, and it'll be a big help to academics, businesses, supply chain partners, technology providers and policymakers, in terms of strategy, operations and technology.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Contribution to literature\u003c/h2\u003e \u003cp\u003eThe study makes considerable contributions to the existing literature on supply chains, Industry 5.0 and also reshoring approaches. Based upon the previous research study performed up until now reshoring is normally researched in the context of manufacturing expense, profession national politics as well as logistics procedures (Zhen \u0026amp; Yao, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akundi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there are very limited academic studies on the effects of Industry 5.0 technologies on reshoring decision-making processes. In addition, our study reveals how AI-powered optimization, IoT-based performance monitoring, digital twins and blockchain technologies can be integrated into reshoring processes. In this context, reshoring processes are optimized with a holistic approach to the relevant field.\u003c/p\u003e \u003cp\u003eReshoring strategies have become increasingly significant in crisis situations that affect the world globally, such as pandemics and wars, and where geopolitical risks come to the fore. Our model makes extensive use of Industry 5.0 equipped tools that enhances the sustainability and productivity of any manufacturing and supply decision making processes, especially reshoring decisions. The system proposed in the study aims to create data-driven, flexible and dynamic decision-making mechanisms in supply chains by integrating risk management, simulation and optimization processes with real-time performance monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Contribution to firms\u003c/h2\u003e \u003cp\u003eFor firms, this model helps achieve competitive advantage by increasing operational efficiency. Digital twin and AI-supported simulations optimize production processes in real time. With machine learning supported algorithms, failure rates in production lines can be reduced and inventory costs can be significantly reduced by increasing efficiency in inventory management. Full transparency is ensured in all process from supplier to customer by using blockchain and IoT technologies.\u003c/p\u003e \u003cp\u003eOn the other hand, with smart energy monitoring systems incorporated to decrease carbon impact, transport procedures can be enhanced with environment-friendly logistics applications. Staff member efficiency and human-robot communication can be boosted thanks to cobots together with AI-enabled automation systems. Many thanks to all these, firms can enhance supply chain adaptability make the most of functional efficiency enhance risk administration as well as assist in reshoring decision-making procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Supply chain stakeholders and technology providers\u003c/h2\u003e \u003cp\u003eThe model developed in the study helps all stakeholders in the supply chain ecosystem to improve their processes by providing a data-driven, integrated and robust structure. More reliable and transparent trade processes are provided for suppliers. For logistics providers, more efficient transport processes are created with dynamic route optimizations and predictive analysis systems.\u003c/p\u003e \u003cp\u003eOn the other hand, our model opens new business areas for companies providing IoT, AI, blockchain and automation systems and helps technology providers to implement Industry 5.0 solutions more effectively. For example, companies developing AI-based production management systems or smart logistics solutions can integrate into large-scale projects by producing software and hardware in accordance with this framework and receive support when creating decision-making platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Policy makers\u003c/h2\u003e \u003cp\u003eOur model provides a strategic roadmap for public institutions and policy makers that increases economic sustainability and promotes local production. In this context, the design can assist governments methods to boost regional manufacturing as well as assistance state-sponsored motivation programs, AI and digital transformation financial investments. On the various other hand making use of Industry 5.0 modern technologies can be raised in accordance with eco-friendly economic climate targets. Green production incentives and regulations to reduce carbon emissions can be based on our model. Policies can be developed for the training and upskilling of employees in digital transformation processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e7.5 Usability of the model in different sectors\u003c/h2\u003e \u003cp\u003eThe model we developed through our research can be applied in various sectors such as health, energy, automotive, retail, agriculture and logistics, especially in manufacturing sectors. Medication monitoring as well as supply procedures can be enhanced by utilizing digital twins along with blockchain innovations in clinical supply chains. In the automotive industry efficiency can be enhanced with wise manufacturing facilities along with AI-supported manufacturing systems. Machine learning supported demand forecasting and inventory management can be provided in the retail sector in the field of energy, renewable energy optimization can be achieved through smart grid management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e7.6 Future research fields\u003c/h2\u003e \u003cp\u003eIn future studies, strengthening the sectoral adaptability of this model and eliminating technological constraints are among the issues to be emphasized. On the other hand, studies that will address human-machine collaboration in a more holistic framework and make sustainability one of the focal points of the studies are also needed. In particular, cross-sectoral impacts of Industry 5.0 technologies, how to optimize the balance between automation and labour more efficiently are among the important topics for future research. Combining the model developed within the scope of the research with different disciplines and supporting it with more advanced simulations, case studies and field experiments can provide a more effective application of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e7.7 Model constraints\u003c/h2\u003e \u003cp\u003eThe model developed within the scope of the research has some limitations in terms of applicability despite the new understandings discussed and developed. Firstly, the fact that IoT and blockchain technologies are not widespread enough in some regions or countries may make it difficult to implement the model. Secondly, cyber safety and security threats related to blockchain systems and also IoT tools must likewise be thought about. Ultimately the training demands of workers that will certainly utilize AI-based innovations incorporated with Market 5. 0 elements can posture a considerable obstacle for firms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e7.8 Conclusion\u003c/h2\u003e \u003cp\u003eReshoring decisions are shaped by supply chain risks, operational costs, sustainability requirements and the advantages offered by Industry 5.0 technologies. The developed AI-based and Industry 5.0-supported model can significantly affect the decision-making processes of companies by enabling them to make their reshoring decisions more data-driven, flexible and sustainable. Our model supports firms reshoring decisions in several ways. Firstly, simulating production processes through digital twins increases the accuracy of reshoring decisions. Machine learning algorithms can predict how production capacity will be managed after reshoring and enable secure data sharing throughout the supply chain, making logistics processes more predictable. Automated production lines and robot-assisted processes can offset local labour costs in the reshoring process.\u003c/p\u003e \u003cp\u003eOur model provides an important framework for reshoring decisions and supply chain management by evaluating all the opportunities offered by AI and Industry 5.0. With its scientific contributions to literature, operational benefits for firms, innovation opportunities for technology providers and guidance for policy makers, the model is a multi-faceted transformation tool.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe author have no relevant financial or nonfinancial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.M.A. contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.M.A. The manuscript was written by M.M.A.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, T., Karmaker, C. L., Nasir, S. 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The confluence of digital twin and blockchain technologies in Industry 5.0: Transforming supply chain management for innovation and sustainability. \u003cem\u003eJournal of the Knowledge Economy\u003c/em\u003e, 1-27. https://doi.org/10.1007/s13132-024-02151-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"supply chain, reshoring, artificial intelligence, optimization, industry 5.0, sustainability","lastPublishedDoi":"10.21203/rs.3.rs-6252236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal supply chains face increasingly uncertain phenomena and reshoring decisions have become a strategic necessity. This paper presents an artificial intelligence-based decision support model for optimizing reshoring processes in connection with sustainability and Industry 5.0 principles. The developed model supports multidimensional decision-making processes in supply chain management by using big data analytics, machine learning and optimization techniques. The proposed framework evaluates critical factors such as lead time, cost, operational risks, environmental impact, and resilience in an integrated approach. Combining different data sources, the model allows decision makers to determine the most appropriate reshoring strategies by conducting dynamic scenario analyses. This approach, which adopts the human-machine collaboration approach of Industry 5.0, not only increases economic and operational efficiency, but also contributes to the principles of sustainable production and supply management. With the model developed in the study, it is aimed to make significant contributions to academic literature and industrial applications by presenting a new perspective on supply chain management and optimization of reshoring decisions.\u003c/p\u003e","manuscriptTitle":"Reshoring Decisions in Supply Chains and Industry 5.0 Optimization: AI Based Sustainable Decision Support Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 12:57:32","doi":"10.21203/rs.3.rs-6252236/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf77e467-936f-4017-b91e-93e6911efb17","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-15T02:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 12:57:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6252236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252236","identity":"rs-6252236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}