Prioritizing critical success factors for implementation of green-lean product development in industry 4.0 era

Developing new methods, forms and mechanisms to create and master new competitive products is an important means to establish the competitive position of enterprises [4, 30]. Krishnan and Ulrich [31] defined PD as “the process of transforming a market opportunity and a set of hypotheses about product technology into a marketable product”. Earlier research investigated the approaches for accelerating the development process [32, 33] and the best practices for PD [34, 35]. Furthermore, academics have examined the impact of consumer [36] and supplier participation [37] on the new product development (NPD). As the lean concept has achieved remarkable results in various fields, the academic community has gradually begun to incorporate lean concepts and thinking into the PDP in pursuit of a more efficient development model [38]. Initial scholarly investigations into LPD primarily focused on elucidating its roots and tenets. Liker and Morgan [39] systematically analyzed Toyota’s PD system, distilling 13 management principles that form the conceptual foundation of LPD strategy. Later research has expanded the investigation of LPD into practical applications, with particular emphasis on implementation frameworks [7, 40, 41] and critical enabling factors [8, 42].

Over the past decade, energy consumption, environmental pollution and sustainable development have aroused wide concern and heated discussion [43, 44]. Significantly, research shows that decisions made during PD are key to addressing environmental issues, as up to 80% of environmental impacts are determined at this stage [5]. Based on the compatibility of green and lean concepts, a growing consensus suggests that environmental pollution associated with industrial development should be addressed in conjunction with LPD, thus contributing to GLPD [18]. Mirroring the evolution of LPD scholarship, GLPD research has undergone a progression from theoretical conceptualization to empirical research. Johansson and Sundin [45] theoretically compared the concepts of LPD and GPD, identified a clear synergy between them, and perceived the merging of the two concepts as a valuable prospective research proposition. Several scholars have performed comprehensive investigations into the practices, drivers, barriers, and success factors associated with GLPD through literature review methodologies [19, 46].

As a new manufacturing paradigm characterized by the interactive integration of emerging technologies, I4.0 offers opportunities for enhancing NPD processes and process innovation [47]. Previous scholars have attempted to identify the opportunities and challenges brought by the I4.0 concept to PD [48], and to develop conceptual frameworks [11, 49], implementation paths [50], and smart virtual product development (SVPD) systems [51]. Moreover, several researchers examine the possibility of applying I4.0 technologies in PDPs [29]. I4.0 concepts and technologies have certainly provided a boost to enterprises, but also bring greater feasibility for organizations to implement GLPD.

Despite considerable scholarly investigation of LPD, significant implementation challenges persist in organizational practice [7]. This implementation gap has motivated researchers to examine the critical enablers and success factors that facilitate effective LPD adoption. The extant research focuses on identifying success factors of specific previous literature, such as frameworks [52, 53]; of a specific context in the manufacturing sector, such as aerospace [54] and automobile industry [55]; or specific cases, such as customized products [56]. Notably, the success factors of LPD, including customer value orientation, chief engineers, cross-functional teams, standardization, and knowledge sharing, have been unanimously acknowledged by scholars [57, 58].

Empirical evidence consistently indicates limited systematic adoption of GPD across industrial sectors [59]. This limited adoption stems from insufficient understanding of both the enablers and obstacles influencing GPD implementation [60]. As a consequence, scholarly attention has increasingly shifted toward investigating success factors for GPD, with the aim of establishing evidence-based guidelines for organizational adoption. Contemporary research has systematically examined and discussed its drivers through three primary lenses: design for environment (DfE) or eco-design [61, 62], PD [9, 10], and product innovation [63]. The successful implementation of GPD is enhanced through collaborative contributions from supply chain partners, including technological innovations from suppliers and environmental consumer preferences from customers [64]. Consequently, scholars have deliberately underscored the pivotal role of stakeholder engagement in relation to GPD. Such as Lee [65] analyzed how green supply chain integration influences PDPs and innovation outcomes.

As research on LPD and GPD has matured, researchers have begun exploring the success factors that can contribute to the effective implementation of GLPD. Through a systematic review, Oliveira et al. [20] proposed 16 driving factors for lean and green NPD operations, including continuous improvement, definition of value and value stream, cross-project knowledge transfer, life cycle assessment, supplier integration, etc., and constructed an evaluation system for lean and green practices based on these elements. Similarly, Oliveira et al. [18] investigated the maturity of lean-green approaches in PDPs of Brazilian and Japanese SMEs with reference to 18 lean-green enablers. While existing research has preliminarily identified drivers for integrating lean and green principles in PD, these factors remain broad and untested, and the mechanisms governing their interplay are not well understood. According to Paneerselvam et al. [66], the identification of CSFs pertinent to specific technologies or methodologies furnishes organizations with targeted insights, thereby enabling strategic resource prioritization. Thus, there is a pressing need to investigate the CSFs of GLPD and their interactions to establish a robust foundation for its implementation in manufacturing firms.

As mentioned earlier, I4.0 technologies facilitate the strategic alignment of economic performance with environmental sustainability goals within organizations. A growing body of academic literature is now examining the synergistic integration of I4.0, lean, and green paradigms into PDPs. I4.0 technologies are seen by some scholars as key enablers of mass customization, as they enhance design flexibility and meet diverse customer needs, aligning with the principles of lean thinking [67]. In addition, researchers express significant concern regarding the implementation of intelligent technology in prolonging product lifespans, a critical approach in achieving a circular economy model [68]. Beyond theoretical research on the synergies between certain I4.0 technologies and lean or green paradigms, scholars have also used empirical analysis to demonstrate their compatibility [69, 70]. A significant gap in the current literature is the absence of a systematic model detailing the role of I4.0 technologies in enabling lean-green integration within PD.

The inability to effectively implement GLPD is an important factor impacting the survival and long-term development of global manufacturing enterprises. In light of the multifaceted demands of diverse stakeholders and the imperative to establish a distinctive competitive advantage, manufacturing organizations should consider integrating GLPD as a fundamental component of their developmental strategy. The literature review reveals a research gap regarding the determinants of effective GLPD implementation within I4.0-enabled digital transformation contexts. Thus, it is imperative to identify the CSFs that facilitate the effective implementation of GLPD in the I4.0 era and establish the interrelationships among these factors.

ISM is a recognized methodology used to identify and synthesize interrelationships among specific factors that define a complex problem or issue [112]. This technique systematically organizes a set of directly and indirectly related elements into a comprehensive structural model, thereby elucidating the architecture of complex problems through a well-defined representational pattern [113]. The basic idea of this technology is an approach that transforms complex issues into a clear, hierarchical framework by systematically harnessing expert judgment. The ISM methodology facilitates the imposition of order and directionality onto the complex interrelationships among system elements. The ISM methodology consists of the following steps [114]:

The MICMAC method is a system of matrix multiplications for structural analysis, the objective of which is to classify variables hierarchically by examining the propagation of impacts through reaction paths and feedback loops [115]. The MICMAC technique categorizes critical factors into four distinct clusters based on the driving power and dependence power of each factor, thereby determining their relative influence within a system. The driving power of a given variable represents the aggregate number of factors it influences, whereas its dependence power denotes the total number of factors by which it is influenced [116]. Utilizing the MICMAC method, all factors can be categorized into four distinct groups:

ISM methodology employs expert judgment, informed by management techniques such as brainstorming and nominal group processes, to derive contextual relationships among variables. Thus, in order to identify the contextual relationships among CSFs for GLPD in the I4.0 era, a panel of seven experts with industrial and academic backgrounds was consulted in this research. For the analysis of CSFs, a “leads to” contextual relationship was adopted, denoting a unidirectional influence where one variable directly affects another. This relationship served as the foundation for constructing the structural model.

The contextual relationship of each CSF must be carefully considered, and the presence and direction of relationships between any factor pair (i and j) should be systematically examined. Four distinct symbols are employed to denote the directionality of contextual relationships among CSFs.

V: CSF i will help achieves CSF j;

A: CSF j will help achieves CSF i;

X: CSFs i and j will help achieve each other; and.

O: CSFs i and j are unrelated.

SSIM is developed based on contextual relationships between the CSFs. Table 4 clarifies the application of symbols V, A, X, and O when creating SSIM. As illustrated in Table 4, CSF4 (senior management commitment) facilitates the implementation of most other factors, while no factor directly promotes to CSF4.

SSIM is the foundation for the development of the reachability matrix. In this phase, the SSIM is translated into an initial reachability matrix (IM) by converting the symbolic entries of each cell into binary digits (1 or 0). Regulations for this conversion are as follows [114]:

In accordance with these rules, the IM is presented in Table 5. The IM developed in this step solely illustrates the direct relationships among the factors and does not reflect any indirect relationships [116]. A fundamental premise of the analysis is transitivity, meaning that if a relationship exists from A to B and from B to C, then an indirect relationship from A to C is inferred. Consequently, power iteration analysis must be applied to the IM to obtain the final reachability matrix (FM) which helps to show both direct and indirect relationships. The FM is derived by calculating the IM, i.e. FM = IMⁿ = IMⁿ⁺¹, as shown in Table 6.

The FM has been analyzed to identify the reachability and antecedent set for each CSF. The CSF is assigned the highest level in the ISM hierarchy when its reachability set and intersection set are identical. It is clear from Table 7 that ‘modular production systems (CSF11)’ and ‘life cycle assessment (CSF17)’ are identified as level Ⅰ (i.e., the top level). The iterations proceed until all factors are assigned hierarchical levels, as detailed in Table 7. The identified levels help in constructing the directed graph and the final ISM model.

The structural model is generated from the FM, and the resulting graph is referred to as a digraph. Following the removal of transitive links and the substitution of node identifiers with corresponding CSFs, the ISM framework depicted in Fig. 2 is derived. The CSFs for implementing GLPD in the era of I4.0 are composed of nine levels, and there are influence relationships among these factors. The causes of the previous level are represented by the downward levels in the ISM hierarchy diagram. As can be seen from Fig. 2, ‘senior management commitment (CSF1)’ emerges as the most important success factor for GLPD implementation within the manufacturing enterprise, as it serves as the foundation of the ISM model.

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The CSFs for GLPD are classified according to the DEP and DRP values for each factor in Table 6. As illustrated in Fig. 3, the 19 CSFs are grouped into four categories. The four quadrants are representative of the following factors: autonomous factors, dependent factors, linkage factors, and driving factors respectively. For instance, CSF 1 (multi-stakeholder value orientation) with a dependence power of 9 and a driving power of 18 is situated at coordinates (9, 18) on the plane and is classified as a driving factor.

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In this study, ‘multi-stakeholder value orientation (CSF1)’, ‘environmentally sustainable strategic vision (CSF2)’, ‘senior management commitment (CSF4)’, and ‘lean product development training (CSF14’) are identified as the primary drivers for the successful implementation of GLPD. These factors exhibit the greatest capacity to influence other variables while remaining minimally susceptible to external influences.

This study identifies and validates 19 CSFs essential for manufacturing enterprises to effectively implement GLPD within I4.0 environments. Utilizing ISM methodology, we developed a nine-tier hierarchical model (see Fig. 2) to systematically analyze the interrelationships among these critical factors. The graphical representation of these relationships revealed multiple complex interdependencies among the CSFs. These interdependencies underscore the importance of strategic factor prioritization when implementing GLPD in organizational contexts.

As demonstrated in Fig. 2, CSF4 (senior management commitment) is positioned at the base of the ISM model of GLPD. Consistent with our findings, similar studies have confirmed that CSF4 constitute are the most important factors for effective GLPD implementation [17]. According to Gatell and Avella [122], the lean and green initiatives are enterprise-wide cultural changes. Therefore, senior management must be proactive and take the lead in accepting change, actively learning and adapting to novel management models. The impact of CSF4 on CSF1 (multi-stakeholder value orientation), CSF2 (environmentally sustainable strategic vision), and CSF7 (green supply chain integration) has been corroborated by prevailing research [123]. For example, the top management, as primary organizational decision-makers, demonstrate substantive commitment to CSF1 and convince all company’s employees about its high priority. This commitment prompts actions to be taken, such as fully understanding the value demands of different stakeholders. This initiative further facilitates the comprehension of staff regarding the benefits associated with sustainable strategies on the one hand, and enhances information exchange among green supply chain partners on the other. CSF1 and CSF7 have a direct impact on CSF18 (big data). Given the dynamic evolution of stakeholder value demands, real-time data from diverse sources must be collected and analyzed to ensure PD teams operate with the most current information. This relationship finds empirical support in the work of Nguyen et al. [124], whose research demonstrates that CSF1 significantly facilitate organizational adoption of CSF18. Organizational implementation of CSF7 presents challenges such as supplier selection dilemmas and conflicting stakeholder objectives [125]. The application of CSF18 serves to mitigate these integration complexities and associated uncertainties. In addition, CSF2 positively influences CSF14 (lean product development training). Knowledge gaps pertaining to the implementation of novel technologies, lean methodologies, and sustainable management practices can be mitigated through CSF14 [126]. Therefore, CSF14 is essential for ensuring that each department can effectively attain its specific environmental objectives. Positioned at lower levels within the ISM hierarchy, these CSFs demonstrate significant systemic influence, warranting prioritized consideration in implementation strategies.

CSF15 (chief engineer system) occupies a central position within the ISM model, serving as a pivotal nexus that connects the strategic and practical dimensions. CSF15 exhibit a positive relationship with both CSF8 (standardization of the development process) and CSF13 (cross-functional team collaboration). To effectively translate defined value into customer-aligned product specifications, the chief engineer facilitates CSF13 through structured coordination mechanisms. As Laurent and Leicht [127] demonstrated, leaders who possess both interdisciplinary competence and facilitation skills are particularly effective in fostering cross-functional collaboration to achieve shared value-creation objectives. Furthermore, with the support of the chief engineer, the specific steps and technical documents of the PDP belonging to different departments are systematically recorded and updated, which also promotes the CSF8. A key finding emerging from the expert panel discussions was the robust interdependence between CSF7 and CSF5 (investment in green innovative technologies). Existing research has extensively demonstrated that CSF7 enhances the greening of both upstream and downstream processes in PD by improving information processing capabilities [128]. Nonetheless, the findings of the research further suggest that such collaboration assists corporations in comprehending consumers’ propensity to pay for the green attributes of products, thus fostering joint decision-making regarding CSF5. Strategic information exchange with suppliers regarding objectives, responsibilities, and strategies allows for the assimilation of critical technologies and reveals priority areas requiring enhanced investment. The preceding analysis discloses that while CSF15 does not occupy the foundational level in the ISM hierarchical structure, its synergistic interactions with multiple factors necessitate prioritized attention from decision-makers.

The MICMAC analysis (see Fig. 3) identifies CSF1, CSF2, CSF4 and CSF14 (lean product development training) as the driving factors for the implementation of GLPD. These factors are critical to the effective implementation of GLPD, as they influence the entire system and have a significant impact on various other elements with minimal dependencies. Consequently, CSFs classified as driving factors should receive maximal prioritization in GLPD implementation planning and resource allocation. Interestingly, this finding underscores the widespread impact of CSF14 on GLPD strategy. As highlighted by Dinis-Carvalho [129], training serves as a crucial mechanism for equipping managers and employees with the knowledge necessary to comprehend and apply lean principles, as well as sustainable improvement methodologies within the organization. While CSF3 (continuous improvement culture), CSF6 (knowledge management and sharing), CSF7, CSF8, CSF9 (involvement of external stakeholders), CSF12 (real-time capability), CSF13, and CSF18 are all classified as linkage factors due to their significant capacity to interact with other factors. Given their dual characteristics of strong driving power and high dependence, these factors warrant particular managerial consideration. Besides, emerging high-impact factors, specifically CSF2, CSF5, CSF6, and CSF7, further demonstrate that sustainability knowledge and technology are core elements for improving environmental performance. These trends also indicate that the GLPD strategy recognizes upstream and downstream suppliers and consumers as vital channels for green knowledge acquisition, a key distinction from traditional PD models. Access to comprehensive information regarding environmental issues enables employees to improve their understanding of ecosystems, thereby fostering a shared responsibility for sustainable development [130]. This environmental and social responsibility is one of the primary drivers of green behavior and sustainable practices. The deployment of eco-friendly technologies and the transformation of operational design directly enhance environmental performance by reducing the consumption of natural resources and the generation of waste and pollutants [131]. Although the dependent factors (such as modular design (CSF10) and product platform (CSF16)) are intricately linked to other components of the strategy, managers need not allocate substantial resources for their implementation. Interventions targeting the driving and linkage factors facilitate the desired outcomes in dependent factors by altering the dynamics of the system. Table 8 presents the decision matrix derived from the findings of the MICMAC analysis, which aids in establishing the priority ranking.

Overall, the research models demonstrate that all 19 CSFs play a significant role in promoting GLPD implementation in the I4.0 era. This conclusion is supported by the resource-based view, a theory suggesting that firms achieve sustainable advantage by leveraging internal resources and engaging with the external environment [132]. Internally, organizations enable GLPD by strategically reallocating and combining resources. Externally, they must adapt to increasing environmental influence by strengthening stakeholder collaboration and integrating external resources for effective strategy execution. Notably, the four I4.0-related CSFs enable the PDP to achieve simultaneous rapid modeling, data-driven decision-making, real-time monitoring, and flexible production. By facilitating the identification of optimal decisions among a range of alternatives, these powerful capabilities allow for the concurrent maximization of both economic and environmental outcomes in PD.

Integrating lean, green, and I4.0 paradigms is a key catalyst for the digital and sustainable transformation of manufacturing. The use of real-time internet of things (IoT) data enables organizations to accurately identify waste and precisely quantify environmental impact, which facilitates highly refined operational management [133]. Furthermore, managers can leverage big data analysis results to predict potential design, production, and environmental performance issues, allowing them to implement proactive preventive measures. Similarly, rapid modeling and simulation technologies enhance design efficiency, reduce material waste and production errors, and enable dynamic product optimization throughout the design process. Simulation models, such as digital twins, by creating a precise digital replica of a physical product, enable organizations to enhance product lifecycle management through accurate prediction and continuous monitoring [134]. Systemically, the integration of these three paradigms links formerly independent PDPs, enhancing sustainability across the comprehensive value chain.

The objective of in-depth exploration of the CSFs of GLPD is to seek development and competitive advantages for the enterprise through this PD model. Therefore, the formulation of effective strategies and measures is crucial. The successful implementation of GLPD is contingent upon the coordinated collaboration of all organizational members. Accordingly, the study proposes a set of measures to make full use of various factors from three different levels: managers, supervisors/chief engineer and general employees.

As the highest decision-makers of the enterprise, managers’ comprehension and attitude towards GLPD fundamentally determine the success or failure of this strategy. First, it is imperative that managers reach a consensus on the necessity of implementing GLPD and agree to integrate it into their long-term strategy. On this basis, further establish corporate values with a core focus on value orientation and environmental sustainability. Specifically, the creation and utilization of a mission statement can facilitate the establishment of a shared value system. The clear internal communication of environmental goals, along with their associated organizational risks and benefits, constitutes an effective management approach [60]. The promotion of cooperation with supply chain partners and the implementation of environmentally friendly initiatives needs to be facilitated through the signing of cooperation agreements and the formulation of internal environmental policies. These documents and regulations can serve as specific constraints on employee codes of conduct.

The supervisor mainly plays a supervisory role in the implementation of GLPD strategy. Assigning a seasoned chief engineer to the PD team is a crucial assurance for the effective implementation of the strategy formulated by the top management. In today’s rapidly evolving market, customer value demands are changing dynamically. Thus, the chief engineer must proactively monitor these shifts using big data technology to ensure that product attributes and performance align with diverse needs. The chief engineer also serves as the pivotal person in control of the progress of projects. Therefore, it is essential to establish real-time monitoring for both the development and production systems, enabling them to define and flexibly adjust project milestones as needed [84]. Furthermore, a dedicated lean PD group needs to be established to provide training and guidance to team members, thereby enhancing their capacity to identify value and waste in the PDP.

General employees are the executors of GLPD strategies. A distinguishing feature of PD is the flow of knowledge, which differs from product manufacturing. To enable the conversion of tacit knowledge into explicit knowledge, organizations should establish a network-integrated database accessible to all employees. This database has been developed to promote mutual understanding of each other’s work and responsibilities among team members from different departments. Moreover, it facilitates the sharing of knowledge and experience from different projects [6]. Under the coordination of the chief engineer, the standardization of the GLPD project is to be undertaken, along with the specification of performance and environmental indicators. Finally, the use of novel technologies and tools must be promoted. For example, the organization might host educational sessions focused on emerging technologies and tools, confer certificates upon employees who demonstrate proficiency in these areas, and allocate supplementary financial resources to initiatives that utilize these novel tools.

This research advances theoretical understanding through several key contributions. Firstly, this research establishes the theoretical viability of synthesizing lean, green, and I4.0 paradigms in PD systems. Through comprehensive literature analysis and expert consultations, this research identifies 19 CSFs for implementing integrated GLPD enabled by I4.0 technologies, thereby validating the compatibility of these three paradigms. Secondly, this study elucidates how manufacturing enterprises can strategically leverage these success factors to facilitate GLPD implementation. Utilizing ISM method and MICMAC analysis, this research developed a hierarchical model, with its theoretical foundations grounded in resource-based view and its practical relevance confirmed through expert verification. Manufacturing enterprises can employ this model as a strategic roadmap for systematically implementing integrated lean, green, and digital PD approaches. Thirdly, the findings enable senior executives and service providers to formulate robust SPD strategies that accelerate organizational transition toward I5.0. I5.0, characterized by human-centric operations, environmental sustainability, and operational resilience, aligns with and extends the principles of GLPD enabled by I4.0 technologies.

While existing studies have identified enabling factors for GLPD, the interrelationships among these factors remain unexplored, and the catalytic role of I4.0 technologies in facilitating this strategy has yet to be comprehensively examined. This research achieves the relevant objectives of identifying and prioritizing CSFs using ISM method, and bridges the gap between lean, green, and digital transformation strategies in PD. The findings deliver practical insights for manufacturing firms navigating the transition to digital and sustainable PD, while also offering strategic guidance for I5.0 readiness.

This study yields significant practical implications for manufacturing enterprises implementing GLPD strategy to address evolving sustainability demands from both regulatory and consumer stakeholders. According to the ISM-based hierarchical structure, it is essential to prioritize addressing the underlying CSFs, as they serve as the driving forces for all subsequent factors. According to the findings of the ISM approach, it is only when top management acknowledges the significance and necessity of GLPD that this strategy can be consistently promoted over the long term, thereby fundamentally transforming the PD model. Organization managers can effectively achieve this strategic adjustment related to organizational culture by publicly issuing mission statements, sustainability objectives, and environmental policies. Furthermore, when factors exhibiting dynamic correlations within the model are identified, senior management can facilitate project progression and achieve desired outcomes by leveraging the interrelationships and directional influences among these factors. For example, the MICMAC analysis highlight CSF14 as a driving factor with substantial influence within the system. The identification of environmentally impactful waste or the application of tools such as life cycle assessment, modular design, and big data analytics is fundamentally interconnected with comprehensive skills training. Hence, organizational leaders should consider either forming dedicated lean training units or engaging external lean specialists to conduct systematic workforce development programs, thereby enhancing comprehension and implementation of GLPD.