Identification and Prioritization of Critical Success Factors of a Lean Six Sigma–Industry 4.0 Integrated Framework for Sustainable Manufacturing Using TOPSIS

Abstract

The relationship between Lean Six Sigma, Industry 4.0 and sustainable manufacturing has been evaluated only to a limited extent within this domain of the published literature. A DMAIC-DMADV-based framework along with a phase-by-phase implementation path is proposed in this study to integrate Lean Six Sigma and Industry 4.0 technologies for achieving sustainable manufacturing. The paper also focused on identifying and prioritizing the critical success factors for the implementation of the proposed framework. The critical success factors identified through a literature review are ranked using the multi-decision criteria technique TOPSIS, with input from selected experts across various manufacturing companies. The results highlight that the most important enablers set clear sustainability goals, regularly monitor progress and have a skilled workforce. The findings provide actionable guidance for practitioners, and the study contributes to the existing body of knowledge by offering a comprehensive methodology to integrate Lean Six Sigma and Industry 4.0 for sustainable manufacturing. Further research must focus on the validation of the framework in diverse industrial settings and refining the sustainability assessment model to enhance its adaptability.

1. Introduction

Due to rising environmental concerns and regulatory pressures, sustainable manufacturing is considered a vital priority for industries worldwide [1]. Sustainable manufacturing based on the triple bottom line (TBL) balances economic performance, environmental protection and social equity [2]. This approach has encouraged manufacturers to adopt strategies that improve their process efficiency while reducing waste and minimizing environmental impacts. Through sustainable manufacturing, industries can achieve profitability, promote social well-being and significantly reduce their ecological footprint [3].

Lean Six Sigma is a widely applied methodology for process improvement that minimizes variability and reduces defects [4]. Lean principles ensure value creation and efficiency in the manufacturing process through the elimination of waste and streamlining of workflows [5]. Six Sigma applies statistical tools to identify and reduce variability, ensuring high-quality and consistent production output [6]. The combination of lean principles and Six Sigma tools can contribute to achieving sustainability goals through efficient energy utilization, process optimization, manufacturing high-quality products and reducing environmental impacts [7].

Industry 4.0 refers to the fourth Industrial Revolution that brought a technological revolution to the manufacturing sector through the introduction of advanced technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), smart sensors, cyber–physical systems, big data analytics, etc., for facilitating interconnected intelligent systems [8]. Manufacturers can obtain real-time data using smart sensors and the Internet of Things to generate predictive insights with machine learning algorithms [9]. The available data can be shared between departments seamlessly to coordinate the production process efficiently with the support of cyber–physical systems and cloud computing technologies [10]. Big data analytics can further optimize operations to uncover the patterns and trends that hinder or support improvements to the process [11].

Integrating Lean Six Sigma and Industry 4.0 holds great potential to facilitate sustainable manufacturing practices [12]. Lean Six Sigma provides structured methodologies, such as DMAIC (define, measure, analyze, improve and control) and DMADV (define, measure, analyze, design and verify), which support systematic continuous process improvement by identifying the major inefficiencies and optimization of workflows [13]. Industry 4.0 introduces advanced real-time, data-driven technologies, including smart sensors, machine learning and the Internet of Things, to enable automation and predictive insights [14]. The integration of these paradigms creates a powerful approach, whereby Lean Six Sigma offers a systematic framework for continuous improvement, while Industry 4.0 accelerates the implementation of solutions with advanced analytics and real-time monitoring [15]. For example, the use of predictive maintenance tools supported by the IoT and big data analytics within a Lean Six Sigma framework reduces unplanned downtime and optimizes the utilization of resources [16]. Similarly, digital twin simulations combined with DMAIC’s improve phase enable the virtual testing of proposed changes, ensuring sustainable outcomes with minimal risks [17]. However, this approach is still a developing area of research, with limited studies exploring how these paradigms complement each other to achieve environmental, economic and social sustainability goals [18]. Further research is needed to address the gaps and validation of integrated frameworks through practical applications across diverse industrial contexts [19].

In this context, this study highlights the mapping and verification of the influence of Lean Six Sigma’s and Industry 4.0’s impacts on sustainable manufacturing. This study proposes an integrated Lean Six Sigma–Industry 4.0 framework for sustainable manufacturing, and the critical success factors for implementing the framework are identified through a comprehensive literature review. The critical success factors are ranked based on their significance using the multi-criteria decision-making technique, TOPSIS. This prioritization provides actionable insights to industry practitioners, supporting them to focus on the most important and impactful decisions for the effective implementation of the framework. The work is presented as follows: Section 2 provides a comprehensive literature review related to the influence of Lean Six Sigma and Industry 4.0 over sustainable manufacturing, followed by Section 3 which outlines the research methodology. The proposed framework is presented in Section 4 and the prioritization of critical success factors in the framework’s implementation is detailed in Section 5. Section 6 discusses the results, research implications and limitations. The study draws conclusions in Section 7, summarizing key findings and providing future research directions.

2. Literature Review

The literature review focused on studies related to the influence of Lean Six Sigma and Industry 4.0 on sustainability and identifying the scope of integration between them. Existing frameworks are also reviewed to highlight research gaps and areas for improvement.

The main aspect of Lean Six Sigma’s contribution towards sustainability is the minimization of the consumption of resources and energy [20]. This is achieved by removing non-value-adding activities from the manufacturing process. Applying the Lean Six Sigma strategy, not only to the production side but also to the supply chain and logistics, also leads to optimum resource utilization [21]. The defect and variability reduction from Six Sigma ensures fewer products are reworked, further conserving resources and energy. These benefits not only enhance operational efficiency but also lower expenses and carbon footprints. Using value stream mapping (VSM), industries can map the current state of processes to streamline workflows and identify bottlenecks for improved throughput, which contributes to sustainable efforts [22]. The main advantage of lean Six Sigma in sustainability is its profound impact on product quality and that it meets the needs of customers for a higher satisfaction level [23]. This will support process reliability improvements and a significant reduction in product recall. Case studies demonstrate its positive influence on sustainability in various industries and sectors [24,25,26].

The primary contribution of Industry 4.0 towards sustainable manufacturing is its ability to enhance resource efficiency through real-time usage monitoring using the Internet of Things and smart sensors [27]. Energy consumption can be tracked using smart sensors and adjusted dynamically to reduce wastage. Studies show that Industry 4.0 technologies can facilitate automation and avoid human errors to reduce costs and enhance operational efficiency [28,29,30]. One of the key areas in which Industry 4.0 is applied is predictive maintenance, as the traditional approach relies on reactive methods that have higher costs, more downtime and wasted resources. The studies show that IoT, big data analytics, machine learning and artificial intelligence can predict failures in advance, allowing the industries to perform only necessary maintenance [31,32,33]. Industry 4.0 facilitates the circular economy through additive manufacturing, such as 3D printing and blockchains, which help to produce products that can be repaired, refurbished and recycled [34]. The product lifecycle can be optimized using digital twin simulations, ensuring the products are repaired, refurbished and recycled [35]. Not only in the production part, but also in the supply chain, artificial intelligence applications are used for route planning and optimization for facilitating sustainable supply chains [36].

Integrating Lean Six Sigma and Industry 4.0 can provide an integrated framework that carries the strengths of both paradigms: combining the structure of problem-solving methodologies with advanced real-time technologies [37,38]. Lean Six Sigma, through DMAIC and DMADV methodologies, offers robust statistical tools for continuous process improvement, while Industry 4.0 provides real-time data-driven technologies such as machine learning, IoT and big data analytics, which enhance the accuracy, speed and scalability of efforts for improvements [39]. For example, predictive maintenance that leverages IoT and machine learning can be integrated with Six Sigma’s defect-reduction goal, which can reduce variability and minimize downtime along with resource wastage [40]. The control phase in the Lean Six Sigma approach can be significantly enhanced by Industry 4.0 tools, enabling continuous feedback loops that ensure process efficiency over time [41]. The key performance indicators such as defect rates and energy consumption can be tracked continuously using smart sensors and can trigger corrective actions as needed based on big data analytics [42]. The integration also extends to the optimization of workflows, in which digital twin simulations can model the proposed processes identified in the improve phase of Lean Six Sigma. This will allow industry practitioners to test and refine solutions in a virtual environment before applying them in the real world to reduce the risk of process changes [43]. The outcomes of the integration between Lean Six Sigma and Industry 4.0 are illustrated in Table 1.

Table 1. Integrated outcomes of Lean Six Sigma and Industry 4.0.

Lean Six Sigma Methodology	Industry 4.0 Technology	Integrated Outcome
DMAIC phase: Analyze	Machine Learning, Big Data Analytics	The enhancement of root cause analysis with pattern detection and predictive insights.
DMAIC phase: Improve	Digital Twins	Reducing risk through virtual testing of changes in the process.
DMAIC phase: Control	IoT, Smart Sensors	Continuous monitoring of KPIs to ensure sustained improvement through automated feedback loops.
DMADV phase: Design	Additive Manufacturing	Sustainable product design using simulations and iterative improvements.
Six Sigma Defect Reduction	Predictive Maintenance	Reduced variability and downtime through real-time failure prediction and monitoring of equipment.

The limitation of Lean Six Sigma, specifically, its reliance on statistical analysis tools and manual data collection, can be overcome by adapting Industry 4.0 technologies [44]. Thus, both paradigms can complement each other to improve efficiency, customer satisfaction and sustainability. DMAIC (define, measure, analyze, improve and control) can provide a structured road map and framework for implementing Industry 4.0 technologies [45]. There are various studies in which researchers have attempted to create frameworks for combining Lean Six Sigma and Industry 4.0 [46,47,48,49,50,51]. Most frameworks remain conceptual as they are derived from literature reviews, and more studies are needed to validate them through real-world applications. DMAIC (define, measure, analyze, improve and control) and DMADV (define, measure, analyze, design and verify) phases are adapted for the framework structure and guidance to incorporate Industry 4.0 tools into each stage [52]. A significant research gap exists in developing a comprehensive framework combining Lean Six Sigma and Industry 4.0 for sustainable manufacturing. Also, limited studies for the combined application of paradigms address the social, economic and environmental aspects of sustainable manufacturing. Critical success factors must be identified and ranked for the effective implementation of such an integrated framework. Targeted studies and the application of multi-criteria decision-making (MCDM) tools can address the research gaps.

3. Research Methodology

The study’s main objective is to identify and prioritize the critical success factors of a Lean Six Sigma–Industry 4.0 integrated framework for sustainable manufacturing using TOPSIS. The methodology starts with framework conceptualization based on the insights from the literature review and the framework structure designed with DMAIC and DMADV principles. DMAIC (define, measure, analyze, improve and control) is a structured technique used in Lean Six Sigma that focuses on continuous improvement that starts with defining the problem, measuring the current performance, analyzing root causes, implementing the proposed solutions or improvements and ensuring the improvements are sustained over time [48]. At the same time, another Six Sigma methodology, DMADV (define, measure, analyze, design and verify), is applied during a new process or new product design stage. It involves defining the requirements, measuring key performance indicators, analyzing the alternatives, designing the process or product and verifying that the design meets the needs or requirements [53]. The main reason for selecting DMAIC and DMADV is that they provide a well-structured, systematic method of achieving continuous improvements that lead to the achievement of sustainability goals. The data-driven approach of DMAIC allows for target problem solving, and DMADV ensures that new designs or processes meet performance expectations and are optimized for sustainability from the beginning stage. The methodology continues to identify critical success factors of the proposed framework’s implementation using a literature review and it prioritizes the critical success factors using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) [54]. TOPSIS is a multi-criteria decision-making method that systematically ranks the critical success factors by measuring their closeness to the ideal point and distance from the negative ideal [55]. The highest priority factor will be the distance, which is the shortest from the positive ideal and the longest from the negative ideal. It has the advantage of representing the rationale of human choice in a logical way and demonstrates the scalar value that accounts for the best and worst alternatives [56]. In this study, a total of 25 experts were initially consulted, representing five different manufacturing companies. To streamline the data representation and maintain clarity, we aggregated the individual inputs from each company into a consolidated score (one per company) rather than displaying all 25 individual responses. This approach allowed us to reflect the group consensus with each organization, as the experts engaged in discussions and iteration reviews to arrive at a shared perspective. Although each company’s consolidated score appears as a single dataset in the decision matrix, it is underpinned by multiple expert inputs from diverse functional areas.

4. DMAIC–DMADV-Based Framework for Integration

The proposed framework is conceptualized by incorporating the Lean Six Sigma methodologies DMAIC and DMADV, as well as Industry 4.0 technologies, as illustrated in Figure 1.

Figure 1. Lean Six-Sigma Industry 4.0 framework for sustainable manufacturing.

In the define phase, the sustainable goals will be established by identifying existing problems, such as high amounts of waste, or sustainability challenges, such as energy inefficiencies. Also, the goals for new processes or product designs will align with these sustainability goals. To gather the baseline data and information on the needs of stakeholders, Industry 4.0 technologies such as big data analytics and IoT can be used. Data collection in the measure phase can be carried out with the support of Industry 4.0 tools such as IoT, smart sensors and cloud computing. The collected data contain the defect rate, energy consumption rate, baseline requirements, etc., which undergo a deep analysis during the analyze phase. Machine learning, simulation tools and advanced data analysis tools can be used to identify the root causes of inefficiencies and predict the behavior of new systems under development. The improve phase makes incremental improvements or changes to existing processes to remove the inefficiencies. Also, the product or process design needs to be finalized with sustainability in mind in the design phase, and Industry 4.0 technology like digital twins can be used to simulate and improve the process efficiency. The improvement needs to be maintained over time during the control stage through real-time feedback using Industry 4.0 technologies. Similarly, the designed product or process needs to be validated in the verify phase to ensure that it meets the sustainable targets or requirements. The findings from the control/verify phase feedback to earlier phases using feedback loops to adapt to changing market demands, regulatory requirements and technological advancements. The triple bottom line elements such as the environmental, economic and social impact of the framework’s adaptation can be assessed using the sustainability assessment model listed in Table 2.

Table 2. Sustainability assessment model of Lean Six Sigma–Industry 4.0 framework.

Sustainability Dimension	KPIs	Assessment Criteria	Industry 4.0 Technology
Environmental	Waste reduction rate Carbon emission rate Energy consumption	Reduction in energy usage (in kWh), CO2 emissions (tons) and waste generation (kg)	Sensors and IoT
Economic	Resource utilization Productivity Cost saving	Reduction in production costs (in a given currency), increase in production output and resource utilization efficiency (%)	Big data analytics, cloud computing Machine learning
Social	Job satisfaction Accident rates Community impact	Reduction in workplace incidents, improvement in employee satisfaction surveys (%) and positive feedback from stakeholders.	Big data analytics, cloud computing
Operational	Machine uptime Defect rates Process cycle time	Reduction in cycle time (hours), defect rates (%) machine downtime (hours).	Predictive maintenance, AI, machine learning.

4.1. Implementation Path for the Integrated Lean-Six Sigma–Industry 4.0 Framework

The proposed Lean Six Sigma–Industry 4.0 framework needs to be operationalized effectively through a structured, phase-by-phase implementation path. In the define phase, the main objective is to pinpoint key sustainability or operational challenges and establish the baseline performance metrics. This needs to be led by the Lean Six Sigma team collaborating with top management to set clear goals. The IT and Engineering teams facilitate the deployment of IoT sensors and data acquisition tools on targeted production lines, ensuring the collection of real-time data right from the outset. Cross-functional stakeholders, including those involved in production, the supply chain, quality control and IT, are engaged so that they are aligned in terms of the scope and anticipated outcome. There exists a shared understanding of the performance targets and long-term sustainability objectives. The framework then proceeds to the measure phase to measure the current state of operations to form a robust baseline. The deployed IoT sensors capture key parameters such as defect counts, process speeds and energy consumption at regular intervals and provide an accurate picture of the current performance. This forms a large dataset, and these datasets are then stored securely in cloud solutions, in which they can be easily accessed and analyzed by multiple stakeholders. To make sense of these data, Lean Six Sigma practitioners adopt tools such as SIPOC diagrams or value stream mapping, which are enriched with real-time insights that provide clear information regarding the key inefficiencies in cycle times, lead times or overall resource usage. Through this way of translating the raw sensor data into meaningful metrics, a clear and quantifiable picture of how each stage of the process is performing and where there are gaps can be obtained. The analyze phase uncovers the root cause of identified inefficiencies and validates potential improvement opportunities. In this stage, traditional Lean Six Sigma statistical methods such as Pareto analysis and control charts are integrated with advanced AI or machine learning algorithms to detect patterns within the data that point to recurring defects, unanticipated energy waste or any subtle correlations in the production environment. To further assist in visualizations of these insights, real-time dashboards will be implemented that enable the production and quality engineers to spot the abnormal trends immediately. Once the critical issues are well understood, the improve or design phase focuses on devising and implementing the solutions that address the identified root causes. A cross-functional team will work on the range of potential improvements such as alternative material choices, reconfigured workflows or machine settings. Digital twin simulations support this phase, which allows the data science and engineering teams to model the proposed solutions in a virtual environment, predicting outcomes without risking the current real-world production settings. A pilot test also validates the feasibility and effectiveness of the interventions. The process loops back to further refinements if there are discrepancies between the expected and actual outcomes, and it will be guided by additional analytics from machine learning models. After successful piloting and refinement, the control or verify phase ensures that the improvements are consistently delivering the intended benefits. The key performance indicators such as the defect rates and power consumption are constantly tracked using automated IoT-based monitoring systems and trigger the team with alerts whenever critical thresholds are crossed. To sustain the improvement, the organization formalizes new SOPs (standard operating procedures) to embed the improved process into routine operations. The data generated throughout this stage are then relayed back to previous phases for ongoing calibration, laying the groundwork for continuous improvement. Periodic technology updates and audits serve as checkpoints to monitor the performance of IoT sensors, the tuning of machine learning Models and adherence to SOPs. This will also ensure that all elements remain aligned with evolving production volumes, production mixes or sustainability goals. The entire implementation pathway is a cyclic process in which data-driven decisions, iterative enhancements and stakeholder collaboration converge to solidify the Lean Six Sigma–Industry 4.0 framework as a dynamic, organization-wide force for operational excellence and sustainability. Figure 2 illustrates the flow of activities, people and technologies involved in the framework’s implementation and Figure 3 shows the business process model of framework.

Figure 2. Implementation path for the integrated Lean Six Sigma–Industry 4.0 framework.

Figure 3. Business process model of framework.

4.2. Integration of Internal and External Stakeholders in the Framework

The proposed framework integrates external stakeholders, including customers, suppliers and logistic operators, ensuring alignment across the entire supply chain to extend its impact beyond the internal processes. This integration fosters collaboration, optimizes resource utilization and improves customer satisfaction. The development of IoT-enabled products and sensors embedded in smart products plays a critical role in capturing real-time feedback on product quality, enabling manufacturers to monitor customer satisfaction and address the areas of improvement proactively. This will also facilitate the early detection of defects, enabling proactive resolutions that enhance the loyalty of customers, reduce product returns and improve their reputation. The strengthening of collaborations with suppliers through real-time data sharing and predictive analysis ensures the availability of resources and minimizes waste. The inventory levels can be tracked using IoT systems and communicate replenishment needs in advance directly to suppliers. Meanwhile, predictive analysis forecasts the demand trends, allowing suppliers to optimize production schedules and their inventory. This avoids material shortages, reduces overproduction and improves the efficiency of the supply chain. Logistic operators also benefit from the framework with AI-based logistics systems that optimize the routes of delivery to reduce transportation costs and emissions. The traffic patterns and weather conditions can be analyzed using machine learning algorithms to determine the most efficient routes. The vehicles equipped with IOT devices provide real-time updates to logistic teams and this will reduce the transportation costs, delivery times and environmental impact. Thus, by leveraging Industry 4.0 technologies, manufacturers can seamlessly connect with stakeholders across the value chain, ensuring a smooth flow of information and collaboration. The integration of internal and external elements across DMAIC–DMADV phases is listed in Table 3.

Table 3. Integration of internal and external elements across DMAIC–DMADV phases.

DMAIC–DMADV Phase	Internal Structure	External Relations
Define	Inefficiencies identification using IoT and big data analytics	Alignment of sustainability goals with suppliers.
Measure	Collection of process data using smart sensors and cloud	Collecting real-time customer feedback on quality.
Analyze	Application of machine learning to detect the patterns.	Optimization of logistics with traffic and weather data.
Improve/Design	Test solutions with digital twin	Collaboration with suppliers on material efficiency.
Control/Verify	Monitor and track KPIs with IoT dashboards	Track customer satisfaction and supplier performance.

5. Prioritization of Critical Success Factors

The critical success factors for implementing the proposed Lean Six Sigma–Industry 4.0 framework for sustainable manufacturing are identified through a detailed literature review and listed in Table 4. The role of critical success factors in framework implementation is presented in Table 5. To prioritize the factors, the TOPSIS method is employed, which systematically ranks the critical success factors by their relative importance. This ranking helps industry practitioners to focus their efforts on the most important success factors, ensuring an efficient and effective approach to the implantation of the proposed framework. The TOPSIS methodology evaluates the distance of each factor from the ideal and non-ideal solutions, enabling objective decision making and actionable guidance.

Table 4. Critical success factor for Lean Six Sigma–Industry 4.0 framework’s implementation.

Code	Critical Success Factor	Description	Reference
PI	Process integration	Seamless coordination of Lean Six Sigma, Industry 4.0 and sustainability	[57]
TR	Technology readiness	Capability to adapt advanced technologies	[58]
SG	Clear sustainability goals	Well-defined sustainability targets	[59]
LC	Leadership commitment	Support from leaders to achieve the sustainability goals	[60]
DI	Data infrastructure	Advanced systems for real-time data collection	[61]
SW	Skilled workforce	Skilled workforce trained in Lean Six Sigma, Industry 4.0 and sustainability	[62]
CR	Cost and resource management	Utilization and optimization of resources and investments	[63]
SC	Stakeholder collaboration	Involvement of internal and external stakeholders	[64]
CM	Continuous monitoring	Real-time tracking and improvements through feedback loops	[65]
AC	Acceptance of changes	Foster the acceptance of changes	[66]

Table 5. Role of critical success factors in DMAIC and DMADV phases.

Critical Success Factor	DMAIC–DMADV Phases	Contribution
Process integration	Define Measure	Ensures alignment between goals of Lan Six Sigma and Industry 4.0 during definition of problem and collection of data.
Technology readiness	Measure Analyze	Facilitates the use of advanced technologies like IoT and machine learning for data-driven decision making.
Clear sustainability goals	Define Control	Establishes sustainability targets and ensures they are monitored continuously.
Leadership commitment	Define Improve	Provides strategic direction and resources for framework’s adoption and continuous improvement initiatives.
Data infrastructure	Measure Analyze Control	Enables data-driven insights and feedback loops.
Skilled workforce	Improve Verify	Ensures competency of workforce in implementing and sustaining the improvement.
Cost and resource management	Define Control	Focuses on budget allocation and optimization of resource utilization.
Stakeholder collaboration	Define Improve	Promotes alignment of framework objectives with stakeholder expectations.
Continuous monitoring	Control Verify	Enables real-time monitoring of improvements.
Acceptance of changes	Define Improve	Drives acceptance of changes by fostering a culture of innovation.

The identified critical success factors were further validated through expert consultations involving 25 industry experts in the field of Lean Six Sigma, Industry 4.0 and sustainable manufacturing across five manufacturing industries. The TOPSIS method was then applied to prioritize or rank the critical success factors based on their significance, using a ten-point scale, with one for the least important and ten for very important. The profiles of the companies and experts are given in Table 6. Experts with more than 3 years of experience in Industry 4.0, Lean Six Sigma and production are involved in the decision-making process.

Table 6. Profile of responding companies and experts.

Code	Industry Sector	Methodologies/Technologies Implemented	Number of Experts
M1	Composite products manufacturing	Six Sigma, cloud computing	5
M2	Cable support manufacturing	Sustainable Development Goals, ISO standards	5
M3	Ceramic products manufacturing	Various Industry 4.0 technologies	5
M4	Electrical products manufacturing	Lean Six Sigma and TQM	5
M5	Food products manufacturer	Sustainable practices and quality control	5

TOPSIS focused on the distance of each alternative from the positive ideal and the distance of each alternative from the negative ideal [67]. In other words, the best alternative should have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. The decision matrix is shown in Table 7.

Table 7. Decision matrix.

	M1	M2	M3	M4	M5
Process integration (PI)	6	5.4	6	6.4	5.2
Technology readiness (TR)	6.4	7.6	7.4	7	7.4
Clear sustainability goals (SG)	8.4	8.2	7.8	8.6	7.4
Leadership commitment (LC)	7.8	7.6	7	6.2	8
Data infrastructure (DI)	6.8	4	8.6	7.6	6.6
Skilled workforce (SW)	8	7.8	8.2	5.8	8.4
Cost and resource management (CR)	6	6.6	5.8	6.4	6.8
Stakeholder collaboration (SC)	6.6	5	5.4	5	6.2
Continuous monitoring (CM)	6	8.4	7.2	8	8.8
Acceptance of changes (AC)	6.2	7	7	3	7.2

The steps for calculating TOPSIS [68] start with the normalization of the decision matrix using Equation (1), and the normalized matrix is shown in Table 8.

$$r_{ij}\left(\mathrm{x}\right)={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^m{x}_{ij}^2}}} i=1,\dots , m ;j=1, \dots , n$$

(1)

Table 8. Normalized matrix.

	M1	M2	M3	M4	M5
Process integration (PI)	0.276	0.247	0.267	0.308	0.226
Technology readiness (TR)	0.294	0.348	0.329	0.337	0.322
Clear sustainability goals (SG)	0.386	0.375	0.347	0.413	0.322
Leadership commitment (LC)	0.359	0.348	0.311	0.298	0.348
Data infrastructure (DI)	0.313	0.183	0.383	0.365	0.287
Skilled workforce (SW)	0.368	0.357	0.365	0.279	0.365
Cost and resource management (CR)	0.276	0.302	0.258	0.308	0.296
Stakeholder collaboration (SC)	0.304	0.229	0.24	0.24	0.27
Continuous monitoring (CM)	0.276	0.385	0.32	0.385	0.383
Acceptance of changes (AC)	0.285	0.321	0.311	0.144	0.313

According to Equation (2), the normalized matrix is multiplied by the weight of the criteria, and the weighted normalized decision matrix is shown in Table 9.

$$v_{ij}\left(\mathrm{x}\right)=w_j{r}_{ij}\left(x\right) i=1,\dots , m ;j=1, \dots , n$$

(2)

Table 9. Weighted normalized decision matrix.

	M1	M2	M3	M4	M5
Process integration (PI)	0.055	0.049	0.053	0.062	0.045
Technology readiness (TR)	0.059	0.07	0.066	0.067	0.064
Clear sustainability goals (SG)	0.077	0.075	0.069	0.083	0.064
Leadership commitment (LC)	0.072	0.07	0.062	0.06	0.07
Data infrastructure (DI)	0.063	0.037	0.077	0.073	0.057
Skilled workforce (SW)	0.074	0.071	0.073	0.056	0.073
Cost and resource management (CR)	0.055	0.06	0.052	0.062	0.059
Stakeholder collaboration (SC)	0.061	0.046	0.048	0.048	0.054
Continuous monitoring (CM)	0.055	0.077	0.064	0.077	0.077
Acceptance of changes (AC)	0.057	0.064	0.062	0.029	0.063

The degree of the distance of each alternative from the positive and negative ideals is determined according to Equations (3)–(6), and Table 10 shows both the positive and negative ideal values.

$$A^{+}=\left(v_1^{+},v_2^{+}, \dots , v_n^{+}\right)$$

(3)

$$A^{-}=(v_1^{-},v_2^{-}, \dots , v_n^{-+})$$

(4)

Table 10. Positive and negative ideal values.

	Positive Ideal	Negative Ideal
M1	0.077	0.055
M2	0.077	0.037
M3	0.077	0.048
M4	0.083	0.029
M5	0.077	0.045

So that

$$v_j^{+}=\left\{\left(max{ v}_{ij}\left(x\right)\right| jϵj_1) , \left(min{ v}_{ij}\left(x\right)\right| jϵj_2)\right\} i=1,\dots , m$$

(5)

$$v_j^{-}=\left\{\left(min v_{ij}\left(x\right)\right| jϵj_1) , \left(max{ v}_{ij}\left(x\right)\right| jϵj_2)\right\} i=1,\dots , m$$

(6)

where j₁ and j₂ denote the negative and positive criteria, respectively.

The distances between each alternative and the positive and negative ideal solutions are obtained using Equations (7) and (8), and the results listed in Table 11.

$$d_i^{+}=\sqrt{{\sum }_{j=1}^n{[v_{ij}\left(x\right)-v_j^{+}\left(x\right)]}^2} , i=1, \dots , m$$

(7)

$$d_i^{-}=\sqrt{{\sum }_{j=1}^n{[v_{ij}\left(x\right)-v_j^{-}\left(x\right)]}^2} , i=1, \dots , m$$

(8)

Table 11. Distance to positive and negative ideal points.

	Distance to Positive Ideal	Distance to Negative Ideal
Process integration (PI)	0.057	0.036
Technology readiness (TR)	0.03	0.057
Clear sustainability goals (SG)	0.014	0.075
Leadership commitment (LC)	0.029	0.056
Data infrastructure (DI)	0.048	0.054
Skilled workforce (SW)	0.028	0.061
Cost and resource management (CR)	0.046	0.043
Stakeholder collaboration (SC)	0.061	0.024
Continuous monitoring (CM)	0.026	0.072
Acceptance of changes (AC)	0.062	0.036

The relative closeness degree (C_i) of each alternative to the ideal solution is obtained by Equation (9). The alternative has a shorter distance from the positive ideal solution and a longer distance from the negative ideal solution if the relative closeness degree has a value near one [69].

$${\mathrm{C}}_i={\displaystyle \frac{d_i^{-}}{(d_i^{+}+d_i^{-})}} , i=1, \dots , m$$

(9)

Table 12 shows the relative closeness degree of each alternative to the ideal solution and its ranking. Figure 4 shows C_i values indicating the priority rankings of the critical success factors.

Table 12. C_i values and rankings.

	Ci	Rank
Process integration (PI)	0.385	8
Technology readiness (TR)	0.657	4
Clear sustainability goals (SG)	0.841	1
Leadership commitment (LC)	0.654	5
Data infrastructure (DI)	0.532	6
Skilled workforce (SW)	0.683	3
Cost and resource management (CR)	0.482	7
Stakeholder collaboration (SC)	0.278	10
Continuous monitoring (CM)	0.734	2
Acceptance of changes (AC)	0.364	9

Figure 4. Relative closeness degree (C_i) indicating the priority rankings of the critical success factors.

6. Discussion and Implications

The results from the TOPSIS analysis revealed that setting and following clear sustainability goals emerged as the highest-priority critical success factor. Without a clear direction, efforts towards sustainability are not practical. This aligns with the existing studies, as it underscores the importance of goal setting to focus on resource utilization, progress monitoring and the adjustment of strategies. The second-highest-ranked success factor for the framework’s effective implementation is the continuous monitoring of sustainability metrics and process efficiencies. Proactive decision making can be achieved through continuous monitoring and ensures sustainability practices are optimized over time. The critical role of a skilled workforce is listed as the third priority, followed by technology readiness and leadership commitment. While technology plays a critical role, a skilled and trained workforce is essential for data interpretation, the management of advancement systems and the application of relevant technology to develop innovative solutions to sustainability challenges. Continuous professional development of the workforce in emerging Industry 4.0 technologies and Lean Six Sigma methodologies are also crucial for maintaining workforce competency. The readiness of the organization to implement Industry 4.0 technologies and Lean Six Sigma strategies significantly influences its ability to achieve sustainability goals. At the same time, leadership is crucial for fostering a culture of innovation and sustainability, as well as driving change across the organization. The lower-priority factors, such as process integration, cost–resource management, the acceptance of changes and stakeholder collaboration, also need to be considered while setting plans for the framework’s implementation.

The findings provide a roadmap for the industries that implement sustainable manufacturing practices. The manufacturing industries need to prioritize establishing clear sustainability goals during the strategic planning stage. The goals should align with quality and global sustainability standards to ensure relevance and effectiveness. Adopting relevant Industry 4.0 technologies to facilitate continuous monitoring, real-time tracking and process optimization stays a prime focus. These efforts will not be effective unless their workforce is trained to handle the complexities of Industry 4.0 advanced technologies. Industries should evaluate their readiness in terms of technology and resources and address the gaps to ensure the smooth transition to sustainable and process improvement practices. Leadership teams must commit to generating policies, allocating budgets and fostering a culture that prioritizes long-term environmental and social benefits over short-term financial gains. In addition to the enablers, organizations may face practical challenges in framework implementation such as the need for high levels of investment, resistance to change and data management complexity.

The results also establish a strong link to existing studies and address the gaps identified in the prior research. The combination of Lean Six Sigma process capabilities with Industry 4.0 technologies is supported by various studies [70,71], and the insights from this work state that such integration accelerates process improvement and sustainability outcomes. Also, the inclusion of data infrastructure as a critical success factor is aligned with recent studies [72] which highlight the need for robust data systems to handle the data complexity in the process. While the final TOPSIS rankings are based on these five aggregated datasets, they effectively capture the viewpoints of 25 experts. Each company’s rating is the product of multiple expert deliberations, thereby reflecting a collective judgement rather than a single individual’s opinion.

Despite its insights and contributions, this study possesses some limitations. A key limitation of our study is the relatively small sample size of experts, which may not achieve broad statistical power in a classical sense. Although the consensus-based approach helps in capturing the diverse perspectives of each organization, the findings are not necessarily generalizable to all manufacturing contexts. Further studies should consider enlarging the participant pool across a wide range of industries, potentially employing advanced statistical methods such as confirmatory factor analysis or structural equational modelling to strengthen the robustness of the results. The study focused on critical success factors without exploring their interdependence, and the study does not account for variability across geographical contexts. Another limitation is that the framework’s practical implementation challenges are underexplored, leaving room for further research in this area.

7. Conclusions

This study proposed the Lean Six Sigma–Industry 4.0 framework for facilitating sustainable manufacturing and prioritizing the key success factors that influence the framework’s implementation.

• The proposed framework integrates the structured problem-solving approach with advanced technologies to align operational efficiency with environmental and economic sustainability.
• Using TOPSIS, setting clear sustainability goals is identified as the most significant success factor for the framework’s effective implementation. Continuous monitoring and having a skilled workforce ranked as the second and the third critical success factors, highlighting their central role in sustainable manufacturing;
• Ranking critical success factors can provide practical guidance and a road map for manufacturing industries to deploy sustainable manufacturing practices;
• Future research studies can focus on studying the interrelationships among critical success factors and incorporating advanced decision-making models that can improve the robustness and reliability of prioritization results;
• The validation of the framework and sustainability assessment model using real case studies in manufacturing industries should also be considered in the future scope of research;
• The proposed framework outlines key components and implementation steps for integrating Lean Six Sigma and Industry 4.0, providing a structured approach for manufacturers to enhance sustainability and operational efficiency. By detailing the critical success factors and their prioritization using TOPSIS, this study offers practical guidance for decision makers to focus on key enablers during the implementation process;
• This work contributes to the ongoing efforts in sustainable manufacturing by presenting actionable insights and laying the groundwork for further research and validation in diverse industrial contexts;
• This study establishes guidance for integrating Lean Six Sigma and Industry 4.0 to achieve sustainable manufacturing and a holistic approach that ensures the manufacturing process evolves toward sustainability while maintaining competitiveness in a rapidly changing industrial landscape.