Value Engineering in the Era of Industry 4.0: From Gap Analysis to Research Methodologies and Strategic Framework

Value Engineering (VE) is a systematic, function-driven methodology designed to maximize value by achieving the desired performance at the lowest total cost. It emerged in the 1940s at General Electric, where Lawrence D. Miles developed what was initially called value analysis in response to material shortages during World War II. His approach aimed to identify alternative solutions that could deliver required functions at reduced costs without compromising quality, performance, or reliability. Over the decades, VE evolved into a comprehensive, interdisciplinary practice applied across a wide range of sectors, including manufacturing, construction, healthcare, transportation, and public infrastructure. According to SAVE International, VE is defined as “the systematic application of recognized techniques by a multidisciplinary team to identify the functions of a product or service, establish a worth for those functions, and generate alternatives that achieve the necessary functions at the lowest total cost of ownership”. This definition underscores VE’s core principles: function-based reasoning, stakeholder collaboration, and lifecycle cost optimization [1,2,3].

Value Engineering (VE) is based on the principle that value equals function over cost, emphasizing that value can be increased by improving functionality, reducing cost, or both. Unlike conventional cost-cutting approaches, VE preserves essential features, safety, and user satisfaction by focusing on holistic value optimization. VE has evolved beyond its traditional applications in manufacturing and construction to encompass product development, supply chain management, and service industries, reflecting its adaptability to complex, multidisciplinary challenges. As shown in Figure 1, the Value Engineering Cycle comprises three interconnected phases: Value Discovery, Value Realization, and Value Optimization. Value Discovery identifies improvement opportunities through systematic analysis of stakeholder needs, functional requirements, and cost-performance trade-offs. Value Realization translates these insights into feasible, cost-effective solutions aligned with project objectives and technical constraints. Value Optimization ensures sustained value through continuous evaluation, lifecycle feedback, and adaptive refinement, enabling organizations to respond to evolving requirements and technological advancements. VE is executed through a structured Job Plan comprising six phases: Information, Function Analysis, Creative, Evaluation, Development, and Presentation. This approach allows multidisciplinary teams to generate innovative, cost-efficient alternatives, integrating analytical rigor with creative problem-solving to enhance design quality, reduce waste, and maximize value across the product or service lifecycle [1,4,5]. By integrating technical analysis, stakeholder engagement, and strategic decision-making, VE provides a robust framework for achieving operational excellence, innovation, and sustainable competitiveness in both traditional and modern industrial contexts.

VE has proven to be a strategic tool across industries. In manufacturing, it contributes to leaner product designs, material savings, and faster time-to-market. In construction and infrastructure, VE supports better constructability, improved scheduling, and long-term cost savings. Across public and private sectors, it facilitates early design interventions, interdisciplinary decision-making, and continuous improvement. Its structured, repeatable methodology makes VE particularly valuable in complex projects where performance, cost, and stakeholder alignment must be carefully balanced [6,7].

However, as industrial ecosystems rapidly digitize, traditional VE practices are increasingly misaligned with the realities of Industry 4.0. Conventional VE remains largely analog, sequential, and document-based—limiting its ability to respond to the interconnected, data-intensive, and real-time demands of smart manufacturing and digital service systems. VE is still frequently deployed as a one-time, event-driven initiative rather than as a dynamic, embedded process. Moreover, it operates independently of advanced modeling tools, enterprise platforms, and live data environments, which restricts its capacity to evaluate complex trade-offs, integrate customer feedback, or optimize lifecycle performance in real-time. Critically, VE remains underutilized in its engagement with enabling technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, Cyber-Physical Systems (CPS), and Big Data analytics—technologies that could significantly enhance function modeling, scenario simulation, and predictive optimization.

These limitations underscore the urgent need to transition from conventional VE toward a more integrated, intelligent, and adaptive methodology. This shift marks the emergence of VE 4.0—a digitally empowered paradigm that fuses classical function-based logic with the transformative potential of Industry 4.0 technologies. VE 4.0 facilitates real-time, data-driven, and collaborative value creation across the product–process–service continuum. It repositions VE from a cost-reduction tool to a strategic system for continuous value optimization aligned with customer expectations, sustainability goals, and business agility. By integrating digital capabilities, VE 4.0 enables dynamic function modeling, stakeholder co-creation, and informed decision-making within cyber-physical environments.

In this context, the present study explores the evolution of VE and its redefinition in light of digital transformation imperatives. It examines how VE can be reimagined to meet the performance, resilience, and sustainability challenges of Industry 4.0. The paper is structured as follows: Section 2 reviews the theoretical foundations and historical development of VE, highlighting its emerging transformation. Section 3 presents a critical analysis of current limitations in VE practice and identifies barriers to VE 4.0 adoption. Section 4 proposes a strategic framework for VE 4.0 that integrates function-based thinking with digital technologies and Lean Six Sigma principles. Finally, Section 5 offers concluding insights and outlines a research agenda to advance VE 4.0 as a foundational methodology for digital value management in future-ready industrial systems.

This section examines the theoretical foundations of Value Engineering (VE), its applications across diverse sectors, and the emerging evolution toward VE 4.0 within the Industry 4.0 paradigm. A systematic literature review (SLR) was conducted to analyze the development, conceptual underpinnings, and practical applications of Value Engineering. Insights were synthesized across disciplines and industries, highlighting key methodologies, emerging trends, and technological enablers shaping the field. Peer-reviewed articles published between 2015 and 2025 were systematically retrieved from Scopus, Web of Science, and ScienceDirect using targeted search terms, including Value Engineering (VE), Value Optimization, Industry 4.0, VE 4.0, Digital Engineering, Function-Oriented Design, Lifecycle Cost Optimization, Lean Six Sigma (LSS), and DMAIC. The selection adhered to rigorous inclusion and exclusion criteria, prioritizing empirical studies, conceptual frameworks, and applied research relevant to Industry 4.0 contexts. The analysis identified critical research gaps, including limited empirical validation, underexplored areas, and insufficient alignment with contemporary challenges such as digitalization, sustainability, and smart manufacturing ecosystems. By integrating these insights, the study provides a comprehensive understanding of the field. It establishes a foundation for a forward-looking framework that bridges theory and practice, advancing innovation, operational resilience, and sustainable competitiveness in Industry 4.0 ecosystems.

Value Engineering (VE) is a systematic and function-oriented methodology that aims to maximize value by optimizing the relationship between performance and cost. Originally conceived in the 1940s at General Electric by Lawrence D. Miles as “value analysis”, VE emerged in response to wartime material shortages and rising production costs. It focused on identifying cost-effective alternatives that maintained or enhanced functionality. The formalization of VE through the founding of the Society of American Value Engineers (now SAVE International) in 1958 standardized its practices and enabled its global adoption across industries such as manufacturing, construction, aerospace, and infrastructure. At its core, VE is grounded in the classic value equation (Value = Function/Cost), which emphasizes enhancing value either by improving function, reducing cost, or both, without sacrificing quality, reliability, or stakeholder satisfaction [1,7,8].

Unnecessary project costs frequently arise from functional inefficiencies, fragmented decision-making, and misaligned stakeholder priorities. A team-based, problem-solving approach has proven effective in overcoming these challenges by fostering collaboration, cross-disciplinary insight, and structured analysis [9]. VE embodies this approach by forming cohesive, multidisciplinary teams dedicated to achieving clearly defined objectives. At the core of VE is the Job Plan, a systematic and phased methodology that guides the team in analyzing functions, exploring alternatives, and optimizing value. This structured process improves decision quality and ensures efficient resource allocation by delivering required functions at the lowest feasible lifecycle cost. Simultaneously, VE integrates the owner’s value considerations—such as aesthetics, environmental performance, safety, flexibility, reliability, and schedule—ensuring that technical solutions are aligned with broader project goals and stakeholder expectations [8].

VE operates through a disciplined, phased process known as the Job Plan, typically comprising eight stages: Preparation, Information, Function Analysis, Creative, Evaluation, Development, Presentation, Implementation, and Follow-up (Janani, 2018). This structured methodology enables multidisciplinary teams to systematically dissect products, processes, or systems into their basic and secondary functions and develop innovative, cost-effective alternatives. Key tools supporting this process include brainstorming, cause-and-effect matrices, and the Function Analysis System Technique (FAST). FAST diagrams are particularly valuable, as they graphically represent logical relationships among functions using “How” and “Why” logic, thereby enhancing problem clarity, encouraging multifunctional design thinking, and identifying value gaps [10,11].

VE has been widely applied across diverse sectors. In the construction industry, it is instrumental in enhancing constructability, reducing lifecycle costs, and aligning design decisions with project objectives. In manufacturing, VE supports lean product design, material efficiency, and design-to-cost strategies. In civil engineering, VE is especially effective when conducted during early project phases, where functional decisions have significant cost and performance implications. Late-stage VE application often results in increased costs, delays, or suboptimal solutions [1,11]. Beyond these sectors, VE has been successfully implemented in the automotive industry [12], IT and software engineering [13], and large-scale industrial systems [1]. Numerous empirical studies affirm VE’s impact on improving functional outcomes, enhancing project coordination, and achieving measurable cost savings [14,15,16,17,18,19].

Despite its effectiveness, VE is often misinterpreted as a cost-reduction tool and conflated with Value Management (VM). While VM encompasses broader project values and stakeholder perspectives, VE remains narrowly focused on functional optimization through structured analysis and team-based creativity [1,2]. Traditional VE, however, has significant limitations in today’s increasingly digital and dynamic industrial environments. It remains largely sequential, analog, and documentation-heavy, limiting its responsiveness to real-time data and its integration within agile development or smart production environments. VE processes are rarely embedded in digital enterprise systems or continuously informed by evolving operational, environmental, and customer data, thereby reducing their strategic relevance in cyber-physical contexts.

Industry 4.0 introduces a suite of transformative technologies—Artificial Intelligence (AI), Building Information Modeling (BIM), the Internet of Things (IoT), Digital Twins, Cyber-Physical Systems (CPS), and Big Data analytics—that offer powerful opportunities to enhance VE. These technologies facilitate real-time sensing, predictive analytics, dynamic simulation, and cross-functional integration. For example, BIM allows early-stage modeling of function-cost-performance trade-offs; AI supports automated function recognition, optimization algorithms, and intelligent decision-making; and Digital Twins provide real-time synchronization between digital and physical systems, enabling predictive and lifecycle-aware value assessments. IoT platforms and big data environments further support continuous feedback loops and context-aware decision-making, expanding VE’s capacity to adapt to real-time changes in stakeholder requirements, operational constraints, or environmental conditions.

In light of these developments, the concept of VE 4.0 has emerged as a digitally empowered evolution of the classical VE framework. VE 4.0 integrates function-based thinking with real-time data intelligence, automation, and cyber-physical connectivity to enable continuous, agile, and sustainable value creation. Rather than remaining a discrete, event-driven intervention, VE 4.0 becomes an embedded, dynamic process that is responsive to changing contexts and complex trade-offs. This transformation repositions VE as a strategic methodology aligned with the goals of digital transformation, operational excellence, and systems innovation. As industrial ecosystems move toward smart, connected, and human-centered paradigms, VE 4.0 offers a foundational approach to optimize value holistically—across the entire lifecycle of products, processes, and services.

The evolution of industrial revolutions reflects humanity’s progressive integration of technology, intelligence, and sustainability into production systems, demonstrating a continuous drive toward efficiency, innovation, and societal value. As shown in Figure 2, this transformation begins with Industry 1.0, characterized by mechanization through steam engines and basic labor-saving tools, and progresses to Industry 2.0, which introduced mass production, assembly lines, and electrical power. Industry 3.0 brought automation, digital computing, and programmable logic controllers, enhancing productivity and reducing human intervention in repetitive tasks. Industry 4.0 represents the convergence of cyber-physical systems, the Internet of Things (IoT), big data analytics, and real-time intelligence, enabling interconnected, adaptive, and self-optimizing industrial ecosystems. Emerging paradigms such as Industry 5.0 and the conceptual framework of Industry 6.0 mark a pivotal shift—moving beyond digital efficiency toward human-centric, ethically governed, and regenerative industrial ecosystems. These next-generation paradigms emphasize human-machine collaboration, resilience, social value creation, and environmental sustainability, integrating technological advancement with societal and planetary well-being [20,21,22]. Overall, the evolution of industrial revolutions illustrates a trajectory toward increasingly intelligent, integrated, and sustainable production paradigms, providing a conceptual foundation for frameworks such as Value Engineering 4.0, which aligns value optimization with the ethical, regenerative, and digital dimensions of modern industry.

Industry 4.0 marks a paradigm shift in industrial systems, driven by the integration of digital technologies with physical operations to create intelligent, self-organizing, and interconnected production environments. First introduced at the Hannover Messe in 2011 and formally defined in 2013, Industry 4.0 harnesses technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), cloud computing, and big data analytics to enable real-time monitoring, predictive decision-making, and adaptive control [23,24]. These capabilities redefine conventional manufacturing by embedding intelligence across the value chain.

Industry 4.0 is enabled by a powerful convergence of advanced technologies that integrate physical and digital systems, reshaping traditional manufacturing into intelligent, connected, and adaptive environments. As illustrated in Figure 3 and supported by recent studies, the core technologies underpinning this transformation include the Internet of Things (IoT), which facilitates real-time connectivity and data exchange among devices, systems, and machines, and smart sensors, which enable continuous monitoring and data acquisition. Advanced robotics enhances precision, flexibility, and automation in production processes, while artificial intelligence (AI) supports intelligent decision-making and process optimization through data-driven learning. Cyber-Physical Systems (CPS) bridge the physical and virtual worlds by integrating digital models with physical assets for real-time control and coordination. Immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) enhance design, training, and maintenance applications. Cloud computing provides scalable infrastructure for data storage and processing, complemented by machine learning (ML) for predictive analytics and system adaptability. Digital twin technology enables dynamic simulation and lifecycle optimization through virtual representations of physical systems. Additive manufacturing (3D printing) allows for rapid prototyping, customization, and efficient material use. Big data analytics transforms vast, heterogeneous datasets into actionable insights, while cybersecurity ensures the protection and integrity of interconnected systems. Blockchain technology offers secure, transparent, and decentralized data management, while location detection technologies, such as GPS and RFID, enhance traceability and operational visibility. Together, these technologies form the technological backbone of Industry 4.0, enabling smart, resilient, and sustainable industrial systems [25,26,27,28].

Industry 4.0 moves beyond centralized control and isolated systems by promoting distributed intelligence, machine-to-machine (M2M) communication, and seamless integration across organizational levels. Smart manufacturing environments foster dynamic interaction among connected assets, enabling mass customization, reduced lead times, and accelerated innovation [29,30]. Vertical, horizontal, and end-to-end integration ensures that design, production, logistics, and customer systems operate in harmony, enhancing agility and responsiveness.

Global adoption of Industry 4.0 is expanding rapidly, with market size projected to grow from $66.7 billion in 2016 to over $227 billion by 2025 [31]. Advanced economies are driving adoption through national strategies such as Germany’s Industrie 4.0, China’s Made in China 2025, and Japan’s Society 5.0 [20,32]. However, developing countries face significant challenges—ranging from infrastructural deficiencies to workforce gaps—which hinder the full-scale integration of Industry 4.0 technologies [33,34].

Beyond performance optimization, Industry 4.0 plays a critical role in advancing sustainability. Economically, it enhances asset utilization and resource efficiency. Environmentally, it supports circular production, waste minimization, and energy efficiency. Socially, it improves workplace safety, fosters digital upskilling, and promotes inclusive design [35]. Increasingly, sustainability is embedded as a guiding principle in Industry 4.0 implementation strategies [35].

Core enabling technologies—including digital twins, edge computing, IoT, and cloud platforms—facilitate real-time simulation, optimization, and predictive analytics [36]. These tools allow organizations to anticipate disruptions, enhance product quality, and improve resilience. Nonetheless, implementation barriers persist, such as cybersecurity vulnerabilities, interoperability limitations, and organizational resistance. Effective deployment requires not only technological readiness but also cultural transformation, leadership alignment, and change management [37].

While Industry 4.0 emphasizes automation, interconnectivity, and efficiency, Industry 5.0 introduces a complementary focus on human-centricity, ethical innovation, and sustainability. This next paradigm seeks to harmonize technological advancement with human values, autonomy, and inclusion, promoting deeper collaboration between people and machines [21]. It redefines industrial success by embedding circularity, resilience, and societal well-being into future production systems.

Industry 4.0 technologies have significantly impacted key industrial functions. In supply chains, IoT and big data enhance visibility, real-time tracking, and predictive logistics [38]. In lean manufacturing, advanced analytics support waste elimination and just-in-time production [39]. AI-driven predictive maintenance minimizes downtime and extends asset lifecycles [40]. Smart products communicate usage data; smart machines adapt in real-time; and augmented operators, supported by AR and wearables, optimize performance. These developments enable high-mix, low-volume production and reinforce systemic agility [41].

Recent literature highlights both the transformative potential and emerging risks of Industry 4.0. Choudhary and Nandy (2024) [42] identified sixteen sustainability risks, extending the conventional triple bottom line to include organizational and systemic dimensions. Barata and Kayser (2023) [21] traced the evolution of Industry 5.0 literature, emphasizing circularity, ethical AI, and human-machine collaboration. Rijwani et al. (2025) [43] reviewed enabling technologies—blockchain, cobots, 6G, edge computing—highlighting their benefits and integration challenges. Gomaa (2024) [22] proposed a strategic framework combining AI, digital twins, and collaborative robotics to drive agile, resilient, and human-centered smart manufacturing.

The convergence of Industry 4.0 technologies with Value Engineering (VE) offers a compelling opportunity to reconfigure VE as a dynamic, digitally enabled methodology. Traditionally reliant on static analysis, VE can be enhanced through integration with real-time data, digital twins, and AI to support continuous value optimization across the product and process lifecycle. This synergy enables predictive function modeling, automated trade-off analysis, and lifecycle-based decision support, aligning engineering innovation with strategic, economic, and sustainability imperatives. As such, VE 4.0 represents an evolutionary leap—embedding intelligent systems into value optimization and transforming VE into a core enabler of smart, adaptive, and value-centric manufacturing ecosystems [44,45,46,47,48,49].

As Value Engineering (VE) integrates with Industry 4.0 technologies, several critical challenges must be addressed to unlock its full transformative potential. While tools such as Artificial Intelligence (AI), the Internet of Things (IoT), and digital twins offer new capabilities for enhancing VE, significant gaps remain in both theoretical development and practical application. Bridging these gaps is essential to advancing VE 4.0 as a dynamic, intelligent, and collaborative methodology tailored for smart manufacturing and digital engineering contexts. Table 1 identifies key deficiencies and emerging priorities across four thematic areas that require focused research and strategic innovation [50,51,52,53,54,55].

In conclusion, addressing these interconnected gaps is vital for repositioning VE as a digitally enabled, human-centered, and sustainability-oriented methodology. VE 4.0 has the potential to play a central role in the design and management of smart, adaptive, and resilient industrial systems. Advancing this transformation will require collaborative research, empirical validation, intelligent tools, and ethically grounded frameworks that align VE with the broader vision of Industry 4.0.

VE 4.0 signifies a strategic advancement of classical value engineering by integrating Industry 4.0 technologies to enable intelligent, real-time, and lifecycle-oriented value creation. As a function-driven, data-intelligent, and human-centric methodology, VE 4.0 supports continuous value optimization across products, processes, and services. The proposed methodology is structured around six interrelated components that collectively form a comprehensive, adaptive, and future-oriented framework.

In summary, VE 4.0 redefines value engineering as a digitally empowered, ethically governed, and sustainability-aligned methodology. Uniting classical function analysis with advanced technologies and human-centered principles, it enables organizations to achieve continuous innovation, strategic agility, and long-term value. VE 4.0 thus emerges as a vital platform for optimizing performance, fostering resilience, and supporting smart, sustainable transformation in the Industry 4.0 era and beyond.

VE 4.0 marks a strategic departure from traditional cost-focused methodologies, evolving into a digitally integrated, ethically governed, and systemically aligned framework for value creation. Grounded in five interdependent principles, VE 4.0 aligns advanced technologies with human insight and organizational capability to support intelligent, sustainable, and inclusive value optimization. These principles, summarized in Table 2, form the foundation for modern VE practices in Industry 4.0 environments:

Together, these principles constitute a holistic framework that unites digital technologies, human expertise, and organizational systems—positioning VE 4.0 as a transformative enabler of smart, sustainable, and resilient value engineering in the digital era.

VE process is undergoing a fundamental transformation driven by Industry 4.0 technologies, shifting from traditional, static methods to dynamic, data-enabled, and lifecycle-integrated systems. These advancements reshape VE into a continuous, collaborative, and real-time value optimization practice. Table 3 outlines a framework mapping key Industry 4.0 technologies across five strategic domains that collectively enhance the VE process.

Together, these technologies transform VE into an agile, intelligent, and lifecycle-driven discipline. By embedding digital tools across all VE phases, organizations can achieve enhanced innovation, operational excellence, and competitive advantage within Industry 4.0 ecosystems.

The traditional VE Job Plan offers a structured pathway for value optimization, but the demands of today’s digitally driven industrial landscape require its evolution. VE 4.0 transforms this framework by embedding Industry 4.0 technologies, creating a smarter, more agile, and data-driven methodology that enhances precision, collaboration, and real-time decision-making across the entire product and service lifecycle. Table 4 details how advanced digital technologies integrate across each phase of the VE Job Plan, elevating traditional practices into a cohesive cyber-physical process.

Collectively, these enhancements transform the VE Job Plan into a dynamic, proactive, and cyber-physical process that supports continuous, intelligent value creation. By embedding connectivity, intelligence, and responsiveness at every step, VE 4.0 aligns fully with Industry 4.0 principles, enabling smarter, faster, and more sustainable value engineering.

To fully unlock the potential of VE in the digital era, a strategic framework is required—one that fuses classical VE principles with the transformative power of Industry 4.0 technologies. Value Engineering 4.0 (VE 4.0) represents this integration, redefining VE as a function-driven, data-intelligent, and sustainability-oriented methodology for real-time value optimization across the product–process–service lifecycle. As illustrated in Table 5, the VE 4.0 framework is structured around five interrelated pillars: digital integration, intelligent function analysis, lifecycle value orientation, human-centric collaboration, and organizational enablement.

To operationalize this framework, a phased implementation roadmap is proposed. The assessment phase evaluates current VE practices, digital infrastructure, and readiness levels. During the planning phase, organizations articulate a VE 4.0 vision, align it with strategic objectives, and map required technologies and skills. The pilot phase introduces VE 4.0 tools—such as AI-enabled function modeling and digital twin platforms—in selected projects to validate benefits and refine methods. Successful pilots lead to the scaling phase, where VE 4.0 is institutionalized across the enterprise, integrated into digital systems, and monitored through performance metrics. The continuous improvement phase sustains innovation and adaptability through feedback loops, strategic partnerships, and learning ecosystems.

Ultimately, this strategic framework positions VE 4.0 as a forward-looking methodology that moves beyond cost reduction to enable smart, resilient, and sustainable value creation. By combining classical VE logic with digital technologies, lifecycle thinking, and ethical collaboration, VE 4.0 emerges as a critical enabler of transformation in Industry 4.0 and beyond.

In the era of Industry 4.0, the traditional Lean Six Sigma DMAIC (Define–Measure–Analyze–Improve–Control) framework must evolve to meet the demands of complex, data-intensive, and interconnected industrial systems. VE 4.0 addresses this need by reimagining DMAIC as a digitally empowered, function-driven, and lifecycle-oriented methodology for continuous value optimization. As outlined in Table 6, the enhanced DMAIC cycle integrates advanced technologies—such as the Internet of Things (IoT), artificial intelligence (AI), digital twins, edge computing, simulation tools, and collaborative digital platforms—into every phase. This transformation enables precise problem definition, real-time performance monitoring, predictive analytics, and sustainable value realization across the full product–process–service lifecycle.

In conclusion, the enhanced DMAIC methodology within VE 4.0 transitions value engineering from a linear, project-specific exercise into a continuous, digital, and systemic optimization model. By embedding intelligence, agility, and connectivity into every phase, it empowers organizations to respond dynamically to change, maximize lifecycle value, and drive strategic innovation in line with the principles of Industry 4.0.

Implementing VE 4.0 in contemporary industrial settings offers transformative potential but also introduces multifaceted challenges. While VE 4.0 leverages advanced Industry 4.0 technologies to enable real-time, lifecycle-oriented value optimization, its successful adoption demands overcoming significant technical, organizational, and cultural hurdles. As summarized in Table 7, addressing these challenges through well-defined strategic enablers is critical to unlocking the full benefits of VE 4.0 and ensuring its sustainable integration across the enterprise.

In summary, the successful implementation of VE 4.0 requires a balanced strategy that addresses leadership alignment, workforce readiness, digital infrastructure, and responsible data management. The strategic enablers outlined in Table 7 serve as a roadmap for overcoming these challenges and embedding VE 4.0 as a foundational pillar in smart, sustainable, and human-centric industrial transformation.

This study presents Value Engineering 4.0 (VE 4.0) as a strategic evolution of classical value engineering, redefined through the lens of Industry 4.0. While traditional VE focused on optimizing the function-to-cost ratio, VE 4.0 expands its scope to enable intelligent, real-time, and lifecycle-oriented value creation. By integrating core VE principles with advanced technologies—such as Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, Cyber-Physical Systems (CPS), and Big Data analytics—VE 4.0 enables adaptive, data-driven, and collaborative approaches to value optimization across complex industrial ecosystems. This approach moves beyond static analysis toward predictive and prescriptive decision-making, allowing organizations to anticipate inefficiencies, optimize costs, and continuously improve performance. Furthermore, VE 4.0 incorporates sustainability metrics, circular economy principles, and human-centric considerations, ensuring that value creation aligns with economic, environmental, and social objectives.

The study also addresses key limitations of conventional VE practices, including fragmented digital integration, reactive implementation, and limited support for continuous decision-making. To overcome these gaps, a comprehensive VE 4.0 framework is proposed, comprising six components: (1) foundational principles emphasizing digital readiness, sustainability, human-centric collaboration, and organizational adaptability; (2) digital transformation of VE processes to support predictive and connected decision-making; (3) enhanced VE Job Plan leveraging tools such as NLP, AR/VR, and blockchain for improved analytical precision, speed, and lifecycle alignment; (4) phased implementation roadmap covering assessment, planning, piloting, scaling, and continuous improvement; (5) enhanced Lean Six Sigma DMAIC framework embedded with smart technologies for real-time optimization; and (6) enablers and mitigation strategies addressing leadership, digital competencies, infrastructure, and cybersecurity challenges.

By reconceptualizing VE as a digitally empowered, ethically governed, and sustainability-aligned methodology, VE 4.0 emerges as a key enabler of innovation, strategic agility, and long-term value creation in Industry 4.0 contexts. The framework not only extends the function-oriented nature of VE but also aligns it with the demands of dynamic, data-intensive, and interconnected industrial environments, offering both a practical roadmap and a foundation for future research.

Theoretical Implications: This study expands the conceptual scope of VE by embedding it in the digital transformation paradigm. It presents a hybrid model that integrates function-based reasoning with AI, real-time data analytics, and collaborative platforms—offering a foundation for future academic exploration in systems thinking and intelligent design optimization.

Practical Implications: The VE 4.0 framework provides actionable strategies for engineers, designers, and transformation leaders. It supports the use of digital twins, AI tools, and immersive technologies to enhance the accuracy, speed, and relevance of value-driven decisions across complex product and service environments.

Managerial Implications: For business leaders and decision-makers, VE 4.0 serves as a strategic tool for aligning engineering processes with enterprise-wide goals such as innovation, sustainability, and operational excellence. The roadmap and KPI frameworks help organizations evaluate readiness, foster cross-functional collaboration, and ensure scalable VE 4.0 deployment.

Study Limitations: As a conceptual study, this work lacks empirical validation and sector-specific case applications. Differences in digital maturity, infrastructure, and organizational culture may affect the adaptability of the framework across industries.

Future Work: Future research should focus on validating the VE 4.0 framework through empirical case studies, simulation modeling, and longitudinal analysis across sectors. The development of maturity models, training programs, and standardized toolkits will be essential for practical implementation. Moreover, the convergence of VE 4.0 with emerging Industry 5.0 paradigms—such as ethical AI, human-machine symbiosis, and regenerative systems—offers fertile ground for further inquiry and innovation.

While preparing this work, the author used ChatGPT to improve the writing quality of certain paragraphs. He confirms that no generative artificial intelligence (GenAI) was used in creating the content of this manuscript.

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Data supporting this study are included 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.