Smart Supply Chain Management Integrating Lean Six Sigma and Industry 4.0–5.0: Framework, Applications, and Challenges for Sustainability and Resilience

Modern supply chains are increasingly complex, global, and digitally interconnected, spanning multiple tiers of suppliers, manufacturers, distributors, and customers. This complexity exposes them to demand volatility, supply disruptions, quality inconsistencies, inventory inefficiencies, and growing ESG pressures [1,2,3]. Rapid technological advancements, globalization, and evolving customer expectations further intensify these challenges, requiring supply chains to become more agile, resilient, adaptive, and sustainable. Traditional strategies focused solely on cost reduction or process efficiency are no longer sufficient. To remain competitive, organizations must adopt integrated approaches that combine operational excellence, advanced digital technologies, and human-centric practices, enabling intelligent and future-ready supply chains [4,5,6].

Despite significant advancements in supply chain management, critical challenges persist in integrating process improvement methodologies, digital technologies, and human-centric practices. Existing approaches remain fragmented, resulting in siloed implementations, limited interoperability, and weak alignment between operational excellence and digital transformation. These gaps lead to fragmented digital adoption, constrained human–AI collaboration, and insufficient integration of sustainability and ESG objectives. Consequently, supply chains struggle to achieve efficiency, resilience, adaptability, and social responsibility simultaneously. A unified and structured framework integrating Lean Six Sigma (LSS), Industry 4.0 (I4.0), and Industry 5.0 (I5.0) is therefore required to enable next-generation Smart Supply Chain Management (SSCM).

Effective Supply Chain Management (SCM) depends on structured frameworks that align operational activities with strategic objectives. Core functions include demand and supply planning, production and operations, logistics, digitalization, human capital, risk and compliance, sustainability, performance measurement, finance, service delivery, and organizational agility [7]. These functions drive measurable outcomes such as inventory optimization, process efficiency, regulatory compliance, technology adoption, collaboration, sustainability, and market responsiveness. Table 1 presents a comprehensive SCM framework linking functions to actionable objectives, supporting continuous improvement, digital transformation, and the development of smart, resilient, and sustainable supply chains [6,8].

Lean Six Sigma (LSS) integrates Lean’s waste-elimination principles with Six Sigma’s defect-reduction focus, enabling organizations to improve efficiency, reduce lead times, minimize errors, and lower costs [5,9]. Beyond operational gains, LSS enhances delivery reliability, scalability, supplier collaboration, and sustainable resource utilization. Tools such as DMAIC, Value Stream Mapping, and root-cause analysis provide structured approaches for process improvement and strategic alignment. Table 2 presents a 13-cluster LSS-driven SCM framework, mapping tools to applications and demonstrating how LSS supports efficiency, quality, resilience, responsiveness, and sustainability [5,7,10,11].

Industry 4.0 (I4.0) and Industry 5.0 (I5.0) technologies are transforming supply chains by embedding intelligence, connectivity, automation, and human-centric innovation. I4.0 technologies—including IoT, AI, machine learning, big data analytics, digital twins, cloud computing, and robotics—enable real-time monitoring, predictive analytics, autonomous decision-making, and improved operational efficiency [2,4,12].

I5.0 extends this paradigm by emphasizing human creativity, ethical governance, and resilience, leveraging collaborative robots (Cobots), AR/VR, human digital twins, blockchain, and emerging technologies such as quantum computing and brain–computer interfaces [6,13]. Table 3 outlines 15 technology groups, linking technologies to supply chain capabilities and applications, showing how I4.0 drives automation and intelligence while I5.0 enables human-centric and sustainable practices [4,6,14,15,16].

Integrating LSS with I4.0–5.0 provides a holistic approach to Smart Supply Chain Management (SSCM). LSS ensures structured process optimization and performance measurement; I4.0 enables real-time visibility, predictive analytics, and automation; and I5.0 fosters human-centric collaboration and ethical governance [1,4,6]. This integration enables supply chains that are efficient, resilient, sustainable, and adaptive, bridging the performance-driven focus of I4.0 with the human-centered vision of I5.0 [17,18,19].

Figure 1 illustrates the phased evolution of smart supply chain capabilities from Phase 1 (2023–2025) to Phase 5 (2029–2030), integrating LSS, I4.0, I5.0, sustainability, and resilience. Key milestones include process optimization, IoT-enabled visibility, predictive analytics, digital twins, collaborative robots, AR/VR engagement, ethical decision-making, and circular economy integration. The roadmap highlights temporal progression and interdependencies, demonstrating how integrated adoption addresses digital fragmentation, limited human–AI collaboration, operational inefficiencies, and sustainability challenges, while guiding the transformation toward intelligent and socially responsible supply chains.

Despite extensive research, critical technical bottlenecks persist in integrating and operationalizing Lean Six Sigma (LSS), Industry 4.0 (I4.0), and Industry 5.0 (I5.0) within supply chain systems. Existing studies highlight fragmented adoption, weak alignment between process improvement and digital transformation, limited human–AI collaboration, insufficient ESG integration, and a lack of comprehensive frameworks for adaptive and resilient operations.

This study addresses these gaps by proposing a Smart Supply Chain Management (SSCM) framework that integrates LSS with I4.0–5.0 technologies. The framework combines process excellence, digital intelligence, and human-centric innovation to deliver a structured roadmap for operational improvement, predictive decision-making, and sustainable performance. It bridges the performance-driven paradigm of I4.0 with the human-centered vision of I5.0, offering both theoretical and practical contributions.

The paper is organized as follows: Section 2 reviews Lean Six Sigma (LSS), Industry 4.0–5.0 technologies, and sustainable supply chain management (SCM); Section 3 identifies research gaps and technical bottlenecks; Section 4 develops the SSCM framework; Section 5 provides a discussion of the findings; and Section 6 concludes with key insights and recommendations.

Effective Supply Chain Management (SCM) is critical for manufacturing competitiveness, enabling organizations to deliver high-quality products efficiently, reliably, and cost-effectively. In today’s volatile and globalized markets, continuous optimization and strategic alignment of supply chain processes are essential for sustaining competitive advantage. Flexible, responsive, and cost-efficient supply chains reduce operational costs, enhance customer satisfaction, and improve long-term organizational performance [1,4,20,21].

This study reviews literature from 2011 to 2025, indexed in Scopus, Web of Science, and ScienceDirect, focusing on the integration of Lean Six Sigma (LSS) methodologies with Industry 4.0 and 5.0 technologies to enhance supply chain performance, resilience, and sustainability. Modern supply chains are complex, globally dispersed, and digitally interconnected, facing challenges such as demand volatility, operational disruptions, geopolitical risks, and rising environmental and social pressures. Traditional cost- and efficiency-focused approaches are increasingly insufficient, driving the adoption of Smart Supply Chain Management (SSCM), which integrates process excellence, digital intelligence, and sustainability-oriented practices.

SSCM is redefining traditional supply chains through Industry 4.0 technologies, including artificial intelligence (AI), the Internet of Things (IoT), robotics, big data analytics, cloud computing, and digital twins [22,23]. These technologies enable real-time monitoring, seamless data exchange, and predictive analytics, providing end-to-end visibility, operational transparency, and enhanced responsiveness across manufacturing, logistics, and distribution networks. By embedding these digital capabilities, SSCM supports distributed production, mass customization, and customer-centric value creation, allowing organizations to respond rapidly to dynamic market demands and shifting consumer preferences [24].

Through intelligent automation and advanced analytics, SSCM enhances operational efficiency, reduces unplanned downtime, ensures consistent product quality, and optimizes resource utilization. Decision-making is strengthened by predictive analytics, cyber-physical systems, and digital twins, facilitating proactive planning, risk mitigation, and continuous performance improvement [25,26,27]. These capabilities enable manufacturers to simulate scenarios, anticipate disruptions, and implement agile responses without interrupting ongoing operations, thereby improving flexibility and resilience.

Furthermore, SSCM drives strategic cost reduction, waste minimization, and faster production cycles through flexible, reconfigurable, and synchronized operations [28,29,30,31,32]. Beyond operational and financial benefits, SSCM supports sustainability and long-term resilience by optimizing energy and resource usage, reducing environmental impacts, and promoting circular supply chain practices. By bridging the efficiency-driven paradigm of Industry 4.0 with the human-centric, resilient, and sustainability-focused vision of Industry 5.0, SSCM fosters adaptive, agile, and globally competitive supply chains that thrive in complex, dynamic, and uncertain global environments [4].

Lean Six Sigma (LSS) is a cornerstone methodology for enhancing SCM, providing a structured approach to reduce waste, minimize variability, and improve operational performance. By integrating Lean principles—focused on process efficiency and elimination of non-value-added activities—with Six Sigma methodologies—emphasizing defect reduction, statistical rigor, and process reliability—LSS enables holistic optimization of supply chain performance. Its implementation consistently improves lead times, inventory management, product quality, and responsiveness [33].

When aligned with strategic planning, LSS connects operational improvements with long-term organizational goals such as profitability, quality, sustainability, and customer satisfaction. This alignment enhances agility, risk management, and cross-functional collaboration, enabling supply chains to adapt dynamically to market volatility and global disruptions [34]. Incorporating sustainability into LSS initiatives allows firms to optimize energy use, reduce carbon emissions, and implement circular economy practices while maintaining operational efficiency.

Empirical and bibliometric studies underscore LSS’s growing significance in SCM. Gera et al. [35] synthesized empirical studies highlighting the impact of lean practices, quality management, green supply chain initiatives, innovation, and Industry 4.0 technologies on operational, environmental, and economic performance. Mahdikhani [5] identified key trends and research gaps, particularly in integrating sustainability and digitalization into LSS-driven supply chains. Barbosa et al. [1] proposed frameworks for Green LSS adoption and highlighted challenges such as cultural resistance and technology integration.

Integration of LSS with digital technologies further strengthens its strategic value. AI, IoT, predictive analytics, and automation enhance LSS by enabling real-time monitoring and data-driven optimization [36,37]. Lean Six Sigma combined with Industry 4.0—often referred to as LSS 4.0—has been shown to reduce waste, defects, and lead times while improving resilience and flexibility [38]. Overall, LSS has evolved from a process improvement methodology into a strategic enabler of supply chain excellence.

Industry 4.0 has reshaped SCM by enabling digitally integrated, automated, and real-time decision-making systems. Through cyber-physical systems and intelligent automation, traditional supply chains are transformed into adaptive, interconnected networks capable of responding dynamically to disruptions and demand fluctuations. Core technologies such as AI, IoT, digital twins, big data analytics, cloud computing, and robotics enhance visibility, agility, and operational responsiveness [4,39].

IoT and cyber-physical systems enable continuous data collection and decentralized decision-making, improving traceability, predictive maintenance, and operational monitoring across supply chain networks [40,41]. Blockchain integration further strengthens transparency, traceability, and trust by enabling secure and immutable data sharing [42]. AI, big data analytics, and digital twins enhance predictive and prescriptive decision-making, supporting demand forecasting, risk analysis, and scenario planning [43,44,45].

Digital twins provide virtual representations of supply chain systems for simulation, optimization, and disruption forecasting. Cloud and edge computing enable scalable real-time data processing, while robotics and collaborative robots improve flexibility and human–machine interaction [46]. Integrating Lean principles with Industry 4.0 technologies (Lean 4.0/LSS 4.0) enhances operational efficiency through real-time optimization and automation [38].

Industry 5.0 builds on this foundation by introducing a human-centric paradigm that emphasizes creativity, ethics, and sustainability. Unlike Industry 4.0’s automation focus, Industry 5.0 integrates human intelligence with advanced technologies to co-create value in socio-technical systems [47,48]. It prioritizes workforce empowerment, ethical governance, and resilience, ensuring that digital transformation aligns with societal and environmental goals.

Empirical studies confirm that Industry 5.0 technologies improve supply chain efficiency, visibility, responsiveness, and ESG performance [49,50]. Supply chain integration plays a critical role in translating technological adoption into performance outcomes by aligning material, financial, and informational flows [51,52,53]. However, challenges such as high costs, cybersecurity risks, and workforce skill gaps remain significant barriers to implementation [54,55,56].

The convergence of Lean Six Sigma (LSS), Industry 4.0 (I4.0), and Industry 5.0 (I5.0) offers substantial potential to enhance supply chain efficiency, resilience, and sustainability. However, organizations continue to face technological, operational, organizational, and socio-environmental challenges, revealing critical research gaps that must be addressed to develop fully integrated Smart Supply Chain Management (SSCM) frameworks capable of delivering operational excellence, agility, sustainability, and human-centric value.

While LSS remains a cornerstone for operational efficiency and quality improvement, its application in modern, multi-tier supply chains faces notable constraints:

I4.0 technologies—including AI, IoT, digital twins, cloud computing, and robotics—enable real-time, data-driven supply chain management, yet adoption faces several hurdles:

I5.0 emphasizes human-centricity, ethical governance, and sustainability, introducing additional implementation challenges:

Despite significant advances, substantial research gaps remain that constrain the creation of fully integrated, resilient, and sustainable SSCM frameworks. Table 4 summarizes the key challenges, research gaps, and strategic implications, grouped into four dimensions: Technology & Digitalization, Human & Organizational Factors, Sustainability & Governance, and Performance Measurement & Sectoral Coverage [2,72,73,74,75,76,77,78,79,80].

In summary, addressing these gaps is critical for developing holistic, adaptive, human-centric, and sustainable SSCM frameworks. Future research should focus on creating empirically validated models that integrate operational excellence, digital technologies, human creativity, ethical governance, and sustainability, while also conducting longitudinal, cross-sectoral, and multi-geographical studies to assess long-term impacts. Additionally, practical implementation challenges—such as digital integration, workforce readiness, ESG alignment, and Supply Chain Integration (SCI) adoption—must be addressed. By combining LSS with I4.0/I5.0 technologies and sustainability-driven human-centric practices, SSCM frameworks can bridge the efficiency-oriented focus of Industry 4.0 with the resilient, ethical, and sustainable vision of Industry 5.0, enabling adaptive, future-ready, and globally competitive supply chains.

The Integrated Smart Supply Chain Management (SSCM) Framework provides a comprehensive and actionable roadmap for modern supply chains by systematically integrating core SCM functions, Lean Six Sigma (LSS) methodologies, and Industry 4.0–5.0 technologies. By combining operational excellence, advanced digital intelligence, and human-centered practices, the framework transforms strategic objectives into measurable outcomes and practical applications, enabling organizations to achieve efficiency, agility, resilience, and sustainability across complex and dynamic supply chain networks.

Figure 2 illustrates the SSCM ecosystem, highlighting the synergy among LSS, Industry 4.0 (I4.0), and Industry 5.0 (I5.0). LSS provides the operational foundation, driving process optimization, quality improvement, and waste reduction. I4.0 technologies—including AI, IoT, big data analytics, digital twins, cloud computing, and robotics—enable real-time monitoring, predictive insights, and adaptive decision-making. I5.0 complements these capabilities with human-centered, ethical, and collaborative practices, fostering supply chains that are not only efficient and resilient but also socially responsible. Cross-cutting enablers such as supply chain integration, institutional support, workforce capability, and cybersecurity ensure that operational and technological initiatives are strategically aligned, promoting continuous improvement and innovation.

Modern supply chains face increasing complexity, including demand volatility, supply disruptions, operational inefficiencies, intricate logistics networks, regulatory pressures, and rising sustainability expectations. Traditional approaches focused solely on cost reduction or standardization are no longer sufficient. The SSCM framework addresses these challenges by integrating Lean-driven process optimization, predictive automation, real-time analytics, and human-centered innovation. Table 5 maps twenty-two critical supply chain functions—including demand and supply planning, inventory and warehouse management, sourcing, production, logistics, product lifecycle management, customer service, reverse logistics, workforce collaboration, risk and resilience management, compliance, sustainability, finance, and performance measurement—to corresponding LSS tools, enabling technologies, and operational applications. LSS methodologies—DMAIC, Kaizen, FMEA, Value Stream Mapping, SIPOC, and Voice of the Customer (VOC)—support continuous improvement, quality enhancement, and waste reduction, ensuring alignment with organizational strategy and long-term operational goals.

The framework harnesses Industry 4.0 and 5.0 technologies to enable transformative supply chain capabilities. I4.0 tools—including AI, machine learning, big data analytics, digital twins, IoT, cloud/edge computing, autonomous robotics, drones, and additive manufacturing—provide operational visibility, predictive insights, automation, and advanced decision support. I5.0 innovations—including collaborative robots (Cobots), AR/VR, human digital twins, emotional AI, and brain–computer interfaces—introduce human-centered intelligence, enhancing workforce engagement, collaboration, creativity, and ergonomic safety. Integrating I4.0 and I5.0 enables data-driven decision-making, predictive maintenance, intelligent exception handling, and scenario-based planning, ensuring supply chains are technologically advanced, human-centered, and socially responsible.

The SSCM framework delivers multi-dimensional benefits. Operational efficiency is improved through streamlined processes and optimized production, inventory, and logistics management. Customer-centricity is strengthened via accurate demand fulfillment, predictive services, and responsive operations. Agility allows rapid adaptation to market fluctuations and disruptions, while sustainability and ESG compliance are advanced through green logistics, circular operations, and ethical sourcing. Risk management and governance are reinforced through proactive disruption anticipation, cyber-risk mitigation, and regulatory compliance. Human–AI collaboration enhances workforce productivity, decision-making, and ergonomic safety, bridging technological advancement with human-centered innovation. Strategically, the framework provides a roadmap for designing future-ready supply chains that can dynamically respond to technological, market, and societal pressures while fostering continuous improvement and sustainable innovation.

The DMAIC framework—Define, Measure, Analyze, Improve, Control—offers a structured, data-driven methodology for process optimization, quality enhancement, and waste reduction. Applied to SSCM, DMAIC enables adaptive, resilient, and sustainable supply chains by systematically aligning operational processes with digital technologies, human-centered practices, and ESG objectives. Table 6 demonstrates how each DMAIC phase integrates with I4.0 technologies, I5.0 human-centered innovations, and sustainability principles, providing a practical roadmap for implementing structured improvements across all supply chain functions.

In the Define phase, organizations clarify supply chain objectives, identify critical processes, and align initiatives with ESG and circular economy principles. Digital twins and process-mapping platforms provide end-to-end visibility, supporting scenario modeling, risk assessment, and informed strategic decision-making. Human-centered practices emphasize cross-functional collaboration, ethical governance, and stakeholder engagement, establishing a foundation for resilient, sustainable operations. The Measure phase captures operational, environmental, and human-centered performance data to establish a baseline. IoT sensors, AI analytics, and cloud platforms deliver real-time insights, while workforce engagement ensures accurate interpretation. Key metrics—including energy consumption, emissions, resource utilization, and workforce performance—enable informed, data-driven decisions.

During the Analyze phase, predictive analytics, simulations, and collaborative problem-solving uncover root causes of inefficiencies, quality deviations, and sustainability gaps. The Improve phase implements targeted interventions—including AI-driven optimization, automation, robotic collaboration, and human–machine interaction—to enhance efficiency, quality, and sustainability while fostering creativity, ethical decision-making, and ergonomic safety. The Control phase sustains improvements through continuous monitoring, dashboards, predictive KPIs, ESG-aligned performance tracking, and workforce training, embedding long-term operational resilience and sustainability.

By integrating DMAIC into SSCM, organizations create a holistic, adaptive framework where operational excellence, predictive intelligence, human creativity, and sustainability converge. Each DMAIC phase aligns with I4.0/5.0 technologies, human-centered practices, and circular economy principles, transforming supply chains into intelligent, resilient, and socially responsible systems. Predictive analytics, digital twins, and collaborative robotics optimize planning, inventory, sourcing, production, logistics, and customer service, ensuring competitiveness and ESG alignment.

Figure 3 illustrates the integration of the Lean Six Sigma (LSS) DMAIC framework within Smart Supply Chain Management (SSCM), highlighting how each phase—Define, Measure, Analyze, Improve, and Control—is enabled by Industry 4.0/5.0 technologies and sustainability principles. Define sets objectives, ESG targets, and digital twin models; Measure captures real-time operational, environmental, and workforce data; Analyze applies root cause analysis and predictive analytics; Improve implements AI-driven optimization, automation, Cobots, AR/VR, and Lean tools; and control ensures continuous monitoring, ESG compliance, and risk management. Together, these elements create a data-driven, adaptive, resilient, and socially responsible supply chain capable of addressing complex operational, environmental, and societal challenges.

In conclusion, DMAIC-driven SSCM establishes a future-ready, integrated framework that unites structured process improvement, advanced digital technologies, and human-centered sustainability. By linking each DMAIC phase with Industry 4.0/5.0 capabilities and ESG objectives, organizations can develop supply chains that are efficient, adaptive, resilient, and socially responsible, bridging operational excellence with ethical and sustainable practices. This approach enhances process efficiency, enables predictive decision-making, fosters human-centered innovation, and provides a strategic roadmap for next-generation supply chain management.

This study presents an integrated Smart Supply Chain Management (SSCM) framework that systematically combines Lean Six Sigma (LSS), Industry 4.0 (I4.0) digital technologies, and Industry 5.0 (I5.0) human-centric innovations. The framework addresses operational, technological, organizational, and socio-environmental challenges by providing a structured methodology for achieving efficiency, resilience, agility, and sustainability. Unlike existing models, it operationalizes interactions across layers, showing how process optimization, predictive intelligence, and human-centered practices work together to generate actionable outcomes.

Theoretical Significance: The SSCM framework contributes to supply chain theory by formalizing a multi-paradigm integration. LSS provides the operational foundation, driving process optimization, waste reduction, and quality improvement. I4.0 technologies—including AI, IoT, digital twins, cloud computing, and robotics—enable predictive analytics, real-time monitoring, and adaptive decision-making. I5.0 innovations, such as Cobots, AR/VR, human digital twins, and emotional AI, facilitate human-centered creativity, ethical governance, and collaborative decision-making. By embedding resilience, ESG principles, and circular economy practices alongside operational excellence, the framework extends digital supply chain literature beyond efficiency and automation, incorporating human-centric and sustainable dimensions.

Operationalization and Layer Integration: The framework provides practical guidance for implementation. LSS tools identify inefficiencies and quality gaps, I4.0 technologies provide predictive insights and automation, and I5.0 practices enhance workforce engagement, creativity, and ethical decision-making. ESG and circular economy objectives are integrated into processes to ensure alignment with organizational strategy and societal expectations. Operationalization occurs through continuous feedback loops: predictive analytics from I4.0 inform LSS-driven improvements, human-centered practices validate decisions, and real-time monitoring sustains adaptive performance. This transforms the SSCM framework from a conceptual model into an actionable roadmap for integrated, resilient, and socially responsible supply chains.

Strategic Benefits: Organizations adopting this framework can enhance agility and resilience through scenario planning, cross-functional coordination, and predictive insights, enabling rapid adaptation to disruptions. Competitive advantage is strengthened by combining operational excellence, predictive intelligence, and human-centric innovation, improving efficiency, service quality, and sustainability performance. The framework formalizes inter-layer interactions: the process layer (LSS) standardizes operations and identifies improvements, the technology layer (I4.0) provides real-time insights and automation, and the human-centric layer (I5.0) guides ethical decision-making, creativity, and collaboration. Together, these layers create continuous feedback loops connecting operational, strategic, and sustainability objectives, enabling adaptive and resilient supply chains.

DMAIC Implementation in SSCM: The DMAIC methodology operationalizes the framework. In the Define phase, organizations clarify objectives, identify critical processes, and align initiatives with ESG and circular economy principles. Measure captures operational, environmental, and human-centered performance using IoT, AI analytics, and workforce engagement. Analyze applies predictive analytics, simulations, and collaborative problem-solving to uncover inefficiencies and sustainability gaps. Improve implements AI-driven optimization, automation, Cobots, AR/VR, and Lean tools, while Control sustains improvements via real-time dashboards, predictive KPIs, ESG tracking, and workforce development. This structured approach transforms supply chains into intelligent, resilient, and socially responsible systems.

Limitations and Future Directions: While the framework offers theoretical and practical contributions, empirical validation is needed to assess scalability and effectiveness across industries. Longitudinal studies are required to evaluate sustained impacts on performance, resilience, and sustainability. Implementation challenges—including workforce readiness, multi-tier coordination, and human–digital integration—must be addressed. Ethical governance, AI accountability, and transparency mechanisms also need further development to ensure responsible adoption.

In conclusion, the proposed SSCM framework formalizes the integration of process optimization, predictive intelligence, and human-centric practices, offering both theoretical and practical guidance for next-generation supply chains. By linking operational excellence with ethical and sustainable practices, it enables organizations can develop adaptive, resilient, and globally competitive supply chains that are future-ready and socially responsible.

This study presents an integrated Smart Supply Chain Management (SSCM) framework that aligns Lean Six Sigma (LSS) methodologies with Industry 4.0 (I4.0) digital technologies and Industry 5.0 (I5.0) human-centric innovations, enabling supply chains that are intelligent, adaptive, resilient, and sustainable. The framework translates strategic objectives into actionable mechanisms and measurable outcomes, linking process excellence, digital intelligence, and human-centered value creation across complex supply networks. LSS provides the structured foundation for process optimization, quality improvement, and waste reduction. I4.0 technologies—including AI, IoT, digital twins, robotics, and cloud computing—enable real-time visibility, predictive analytics, and intelligent decision-making. I5.0 innovations, such as collaborative robots, AR/VR, human digital twins, and emotional AI, embed ethical, inclusive, and collaborative principles, enhancing workforce engagement, creativity, safety, and ergonomic performance. Cross-cutting enablers—supply chain integration, workforce development, institutional support, and cybersecurity—ensure alignment, scalability, and continuous improvement.

The framework operationalizes the DMAIC (Define–Measure–Analyze–Improve–Control) methodology to integrate process improvement with digital technologies, human-centric practices, and ESG objectives. In Define, critical processes and improvement opportunities are identified and aligned with sustainability targets. Measure captures operational, environmental, and workforce data through IoT, AI, and cloud platforms. Analyze applies predictive analytics and collaborative problem-solving to uncover inefficiencies and sustainability gaps. Improve implements AI-assisted optimization, automation, and human–machine collaboration. Control sustains improvements via predictive KPIs, ESG-aligned monitoring, and workforce training. This approach clarifies how LSS, I4.0, and I5.0 layers interact, operationalizing a multi-layered strategy that connects process excellence, digital intelligence, and human-centered value creation.

Theoretical Contributions: This study contributes to supply chain theory by conceptualizing a unified, multi-paradigm framework integrating LSS, I4.0, and I5.0. It extends operational excellence and digital supply chain literature by embedding human-centricity, ethical governance, resilience, and sustainability within a structured methodology, providing a foundation for empirical validation and formal theoretical development.

Practical Implications: For practitioners, the framework provides a structured roadmap for designing and implementing smart, resilient, and sustainable supply chains. It supports process optimization, predictive decision-making, quality improvement, waste reduction, and ESG alignment, translating strategic objectives into operational results.

Managerial Implications: Managers can use the framework to guide technology adoption, organizational transformation, and workforce empowerment. It enhances human–AI collaboration, strengthens risk and resilience management, and institutionalizes sustainability in multi-tier supply chains.

Study Limitations: The study is conceptual, based on literature and secondary data. Empirical validation is needed to assess effectiveness, adaptability, and performance impacts across industries, organizational sizes, and geographical regions.

Future Research Directions: Future research should empirically test and refine the framework through case studies, surveys, experiments, and longitudinal studies. Key areas include developing quantitative metrics for I5.0 adoption, exploring advanced human–AI collaboration, and assessing long-term impacts on sustainability, resilience, and operational performance. Further studies should examine applicability in emerging economies, high-risk or regulated industries, and culturally diverse supply chains. Advancing these directions will strengthen theoretical generalization and provide evidence-based guidance for intelligent, ethical, resilient, and sustainable supply chains in dynamic environments.

The author acknowledges that ChatGPT (OpenAI) was used exclusively for language editing and stylistic refinement of the author’s text, including improvements to clarity, grammar, and academic tone. The tool was not used to generate original scholarly content, data, analyses, or references. The author has carefully reviewed and verified the final manuscript and accept full responsibility for its content.

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All data supporting this study are contained within the article.

This research received no external funding.

The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.