An integrated VSM 4.0 and interval-valued q-Rung orthopair fuzzy approach to sustainable process development in the apparel manufacturing industry

The AMI is a dynamic and multilayered sector with heavily integrated upstream and downstream SC operations, for which SPD is pivotal to ensuring long-term economic viability. That means an investment in adopting SMPs emerging from the growing need to avoid escalating manufacturing costs, consumer demand, and increased foreign competition (Al Breiki and Nobanee, 2019). Industry 4.0 technologies, including robotics, blockchain, IoT, and big data analytics, are proving to be a cornerstone for the AMI cost reduction, efficiency enhancement, and increased transparency as it undergoes transformation initiated by the next generation of cutting-edge technologies. These technologies improve economic SPD by minimizing the waste of resources and creating optimum decision-making for advanced SC operations (Akram et al., 2022). The value chain of apparel manufacturing consists of many steps, including procurement, interpretation of design, pattern making, cutting, sewing, finishing, and packaging, which are highly interdependent and susceptible to inefficiencies. These inefficiencies like over-processing, over-inventory and transport delay, are lean wastes that inhibit economic sustainability (Yeen Gavin Lai et al., 2019). Lean manufacturing provides a strategic remedy through waste elimination and value addition to address these persistent challenges. Implementing a fundamental lean tool, VSM allows transparency in the processes where inefficiency occurs and permanent improvements based on focused intervention points (Vasconcelos Ferreira Lobo et al., 2020). VSM 4.0 is a modern form of VSM that integrates AI, IoT, and big data analytics to deliver data-informed decision-making and enhance operational performance (Pal and Yasar, 2020). Integrating VSM 4.0 with the AMI production process can improve the SPD, decrease lead time, and enhance economic viability by promoting global competitiveness.

The imperative for responsive and sustainable production has positioned VSM as a cornerstone methodology for continuous improvement and operational excellence across various industrial sectors. The AMI utilizes VSM in manufacturing processes to reduce lead time and improve SC’s transparency and efficiency through operational streamlining (Hussain and Figueiredo, 2023). Seth et al. (2020) demonstrated that the utilization of VSM in the cutting section of an apparel factory led to a reduction in lead times by 60%, lessening cycle times by 20.8%, and decreasing NVA activities by 69.89%. Moreover, Porras et al. (2023) found that VSM enhanced the AMI production process flow more effectively when implemented with other tools like the Yamazumi chart. In the same vein, recent advancements have led to the implementation of VSM 4.0 across various industries, Mariappan et al. (2023) developed an Intelligent VSM (IVSM) model that combined Industry 4.0 applications with lean applications to study real-time manufacturing set-up, which decreased manufacturing lead time in the electronics component industry from 35.5 to 24 days and improved PCE by 0.9%. Additionally, Bega et al. (2023) demonstrated that VSM 4.0 enhanced data specification, communication protocol, and processing to enable smooth interaction between software and domain engineers. Extending the application of VSM in sustainable manufacturing, Phuong and Guidat (2018) applied sustainable VSM (SVSM) in the AMI to discuss the impact of RFID, big data and ergonomic equipment for sustainability in apparel production. However, there is an iota of research carried out to date about implementing technology-driven lean tools in the AMI to develop the sustainability of production processes. As a result, the industry is lagging in achieving SDGs for economic growth, innovation, and responsible production. Furthermore, there is no direct evidence of implementing VSM 4.0 for SPD in the AMI accounting for the total production process. Furthermore, from the economic and process sustainability perspective, no study has been conducted concerning how VSM 4.0 can enhance them in the AMI accounting emerging economy perspective. Therefore, no comprehensive study concerning the application of VSM 4.0 in the production process of the apparel sector creates a vital knowledge gap and retards efforts toward promoting sustainability in production process innovation.

Therefore, the following essential RQs are the focus of this study to bridge these abovementioned research gaps:

To address these RQs, an initial current-state map was developed using Microsoft Visio to determine process inefficiencies. A list of solution approaches to address SPD was then developed and evaluated using the IVQROF-DEMATEL approach. This more recent MCDM method has improved uncertainty handling and interdependency modeling over traditional methods like AHP and DEMATEL and, therefore, supports effective evaluation and ranking of the strategies.

This research proposes a novel framework through the integration of VSM 4.0 and the IVQROF-DEMATEL approach to generate an intelligent and data-driven solution to facilitate sustainable production within the AMI. Unlike other traditional frameworks, this novel framework integrates real-time data analysis and expert-driven solutions. This framework does not simply identify and build a roadmap of inefficiencies that occur throughout the whole production chain; it also ranks the most strategically appropriate interventions that will directly impact the reduction of NVA activities, enhancement of OEE, and optimization of resource use. The IVQROF-DEMATEL combination permits a rigorous approach to uncertainty and subjective judgments with greater decision-making accuracy. This research, therefore, contributes to the literature by unveiling an empirically validated, digitally infused framework for achieving economic sustainability and operational excellence in the AMI. The results also provide valuable insights to industry practitioners in emerging economies to improve production competitiveness and sustainability performances.

The rest of the paper is organized as follows: In Section 2, the application of the VSM lean tool in various sectors of the textile industry and the interaction between VSM and Industry 4.0 technologies will be discussed. The research methodology followed in the research is explained in Section 3. Section 4 describes the case study and results. Section 5 presents the discussion and implications of the research. Section 6 provides the conclusion finally.

Numerous methods for enhancing the sustainability of the AMI’s production process have been developed to date. To review the current literature, keywords such as “VSM 4.0,” “VSM in the AMI,” “Utilizing MCDM techniques to evaluate AMI sustainable development” and “Industry 4.0 in the textile industry” were used to identify relevant Scopus-indexed articles from 2019 to 2024. Following the procedure mentioned above and the snowball technique, relevant articles were identified exploiting various lean tools and MCDM techniques in the AMI. For example, to improve PCE in the apparel section, an integrated framework of VSM and OEE was applied in the production process for assembling men’s trousers and achieved a rise in PCE from 0.9% to 1.5% (Kansul and Karabay, 2019). Aziz et al. (2024) successfully increased OEE by 14% in the RMG sector of Bangladesh by reducing downtime and quality losses using DMADV-integrated VSM. Alper et al. (2024) also optimized SC with the help of VSM and Kaizen, contributing to its long-term sustainability. Accordingly, Gowtham et al. (2023) enhanced efficiency in order picking by re-designing inventory, while Khairul Akter et al. (2022) realized considerable material wastage that can be reused using the concept of CE based on VSM. Coronel-Vasquez et al. (2022) applied Lean Warehouse and JIT methods, which resulted in lead time and cost reductions in purchase orders. Similarly, Barzola-Cisneros et al. (2021) utilized VSM-5S to enhance productivity by 59%, while Mohan Prasad et al. (2020) applied Kanban-5S in lean manufacturing. Finally, Demirci and Gündüz (2020) validated the method of time measurement path to sustainability, and Andrade et al. (2020) presented an 84% revenue growth with Kanban-VSM-5S.

Considering the sustainable development of the AMI, Wang et al. (2019) explored the application of the FAHP to optimize the garment industry’s supplier evaluation and selection process considering social, environmental, and economic sustainability. Moreover, Debnath et al. (2023) employed the DEMATEL technique to adopt GSCM in the Bangladeshi AMI and uncovered that the two most important success criteria for GSCM implementation were “demand from buyers” and “economic and tax benefits”. Additionally, Debnath et al. (2024) explored the biggest obstacles to implementing sustainable production practices in the AMI from the perspective of an emerging economy and identified that the biggest obstacles were “slow return on investment” and “lack of proper waste management systems.” Similarly, Hossain et al. (2024) used the Bayes theorem and Best-Worst Method to determine the obstacles to industrial symbiosis implementation in the Bangladeshi AMI and identified that the biggest obstacle was “lack of infrastructure” and “technical readiness”. Besides, Raut et al. (2019) applied interpretive structural modeling to determine the interrelation between the most influential obstacles in the sustainable development of textile and apparel SCs and concluded that the two biggest obstacles were poor infrastructure and poor government policies. Xu et al. (2024) also implemented the DEMATEL-based ANP approach according to customer preference and deciding factors in purchasing sustainable apparel, and they found sustainable material to be the deciding factor for customers.

VSM 4.0 is an evolutionary adaptation of traditional VSM, which integrates lean manufacturing methodologies with Industry 4.0 technology, including automation, big data analytics, and IoT. The synergy enhances the process using real-time data for optimization, eliminates waste, optimizes workflows, and prevents mistakes, aside from predictive maintenance (Ramadan et al., 2020). The convergence of lean thinking and Industry 4.0 also increases the transparency of material and information flow, which facilitates better decision-making, stakeholder communication, and continuous improvement towards increased operational efficiency and customer satisfaction (Wang et al., 2022). In this context, Ferreira et al. (2022) developed an integrated agent-based modeling and simulation to assist small and medium-sized enterprises to comprehend Industry 4.0 application scenario changes regarding supplies, machinery, work processes, and information flows. Similarly, de Mattos Nascimento et al. (2022) emphasized the integration of LM with Industry 4.0 technologies to promote CE in the recycling industry. Besides, utilizing the IIoT technology, Nounou et al. (2022) applied a smart VSM 4.0 to the manufacturing sector in order to enhance the flow of materials and information and enable real-time self-decision-making during production processes. Additionally, Tripathi et al. (2021) developed an agile system for the machine manufacturing industry employing a methodology in conjunction with VSM and Industry 4.0 to maintain productivity improvements whereas leveraging IoT, Balaji et al. (2020) explained how VSM 4.0 enhanced the manufacturing industry decision-making process. Kihel et al. (2022) also developed a VSM 4.0-based approach for the ongoing enhancement of the downstream SC distribution procedure in the automotive wiring industry.

The literature review in Sections 2.1 and 2.2 has unveiled a research gap regarding the implementation of VSM 4.0 for SPD in the AMI. This study aims to contribute to this area by engaging the unexplored methodology of VSM 4.0 integrated with the IVQROF-DEMATEL approach for SPD in the AMI accounting total production process and emerging economy perspective by identifying and mitigating NVA activities responsible for impeding productivity within the AMI.

Industry 4.0 technologies, including IoT, robotics, AI, big data, blockchain, and cloud computing, possess the revolutionary potential for lean-sustainable manufacturing systems (Bai et al., 2020). Their implementation in AMI enables real-time tracing, predictive analytics, enhanced material traceability, and intelligent automation—enabling the basis for detecting and eliminating NVA activities (Ahmad et al., 2020). At first, this study developed 18 preliminary strategies to mitigate NVA activities by embracing Industry 4.0 technologies in conjunction with lean tools and VSM. From these, 15 strategies were finalized after expert validation (explained in Sction 3.1), focusing on enhancing traceability, reducing lead time, improving decisions, and maximizing process flow. The finalized strategies are presented in Table 1. The detail description of the finalized 15 strategies is provided in the Table A2 in the supplementary file.

Quantitative and qualitative methods were employed to address the research questions (RQs), as depicted in Figure 1. A quantitative method was used to develop and analyze the production process’s current state and future state map. In parallel, qualitative methods were employed to identify and prioritize advanced technologies for incorporation into the future state map. To begin with, a thorough literature review was conducted concurrently with direct observations of the apparel production process. Field data were collected through standardized data sheets and formal questionnaires (provided in Table A1 of Section 1 in the “Supplementary” file). Once a large amount of information had been obtained and NVA activities hindering SPD had been determined, the current state map was developed based on the relevant process parameters. This mapping produced eighteen initial strategic solutions directed toward eliminating NVA activities. A two-stage qualitative validation and prioritization was subsequently undertaken. During stage one, domain experts reviewed and validated these initial strategies (Table A1, supplementary file). Thirty DEs were emailed, Google Forms, and phoned to conduct interviews, of which twenty-two responded, providing a response rate of 73.33%. Experts were asked to approve, include, or exclude strategies based on their relevance. Three strategies, including robotics for quality monitoring and automation, AR for virtual prototyping, and AI for predictive analysis and SC optimization, were excluded due to the low frequency of approval. The finalized list of strategies is presented in Table 1 for further analysis.

The finalized strategies were ranked at the second stage based on the IVQROF-DEMATEL method. Sixteen seasoned DEs (over five years of experience in the industry) out of the initial pool were identified using purposive sampling. This method intentionally selects respondents based on experience, expertise, or strategic importance (Campbell et al., 2020). These DEs provided their views in semi-structured interviews and questionnaires, evaluating the causal relations among strategies based on the QROF linguistic scale (Table C1 of Section 3 in the supplementary file). Subsequently, the IVQROF-DEMATEL method was employed to rank the strategy, and these strategies were utilized in the future state map based on their prominence ranking and current state-identified problems.

A case study of the future state map was conducted to finalize the quantitative analysis. All process parameters were redone to represent the impact of the implemented strategies. A comparison of future and current state led to specific recommendations for improving performance. Profiles of participating DEs are described in Table A3, Section 1 of the supplementary file.

By digitizing the value stream, VSM 4.0 is an advanced methodology to enhance process transparency, responsiveness, and efficiency. Unlike the traditional VSM, which is static and manual, VSM 4.0 integrates Industry 4.0 technologies to address complex manufacturing environments via real-time information, agility, and automation (de Mattos Nascimento et al., 2022). The implementation begins by selecting a target production line and developing the current state map. The KPIs are calculated to identify inefficiencies. A future state map is then developed based on digital and lean strategies to eliminate the identified waste. The following KPIs are remeasured in the future state to quantify improvements (Kundgol et al., 2019):

The IVQROF-DEMATEL methodology combines the DEMATEL technique with IVQROFS to address complex decision problems with high levels of uncertainty and interdependence (Naz et al., 2024). Compared to Type-1, Intuitionistic, and Pythagorean fuzzy sets, IVQROFS have a broader and more flexible framework to capture expert judgment under uncertainty (Habib et al., 2019). This method directly applies to decision-making in dynamic and fuzzy environments such as SCM and sustainability assessment (Luqman and Shahzadi, 2023). IVQROF-DEMATEL procedure has the following steps:

• Step-1: Strategy Identification

Strategies were established via semi-structured interviews. Deductive reasoning was applied to move from broad observations to specific conclusions, and an iterative improvement process was utilized to ensure that the strategies addressed the root issues.

• Step 2: Scale Translation into IVQROF

The expert linguistic scale was converted into equivalent IVQROF values using a pre-set scale (Table C1, Section 3 in the supplementary file).

• Step 3: IVQROF Value Aggregation

Expert responses were aggregated into a unified DRM using weighted average aggregation by Eq. (8). Table C2 in the supplementary file provides the aggregated matrix.

where x_ij indicates the influence of factor F_i on F_j.

• Step 4: Defuzzification

The IVQROF values were summed up and transformed into crisp values using the scoring and accuracy functions by using Eq. (9) (Liu et al., 2019). The resulting DRM is presented in Table C3 in the supplementary file.

For an IVQROFN $p~=[μP~L,μP~U],[υP~L,υP~U]$ , the accuracy function H $(P~)$ and the scoring function s $(P~)$ among $P~$ are calculated as follows:

• Step 5: Normalization

Each element of the DRM was normalized by dividing each value by the row and column maximum. The normalized DRM is presented in Table C4 in the supplementary file.

• Step 6: Construction of the TRM

The TRM is obtained by applying Eqs. (10) and (11). The TRM is provided in Table C5 of the supplementary file.

where G is the normalized DRM and I is the identity matrix. The elements t_ij of matrix T represent the total influence of factor i on factor j.

• Step 7: Determination of Prominence ranking

Prominence ranking was determined using Eqs. (12) and (13):

where D_i + R_j indicates the prominence ranking.

This study employed an integrated VSM 4.0 and IVQROF-DEMATEL method in the AMI to facilitate SPD by analyzing the entire production process. This study investigated the scenario before and after the implementation of VSM 4.0 in an “X” apparel factory in Dhaka, Bangladesh, an ISO-certified company. Though the product mix ranged from T-shirts and polo shirts to sweaters, this study focused on a basic T-shirt to conquer the RQs. The current and future state map of the apparel manufacturing process is developed in this section.

Figure 2 illustrates the current state map of the total apparel manufacturing process, and a detailed calculation is provided in Table B1 of Section 2 in the supplementary file.

Figure 2 illustrates the current state map of the total apparel manufacturing processes. Table 2 demonstrates the significant causes of NVA and other production process inefficiency that have been unveiled through a direct examination of processes.

The various NVA activities recognized as a result of current state mapping of the whole apparel manufacturing process were carefully investigated. In order to minimize the NVA time, the identified strategies in Table 1 were prioritized and ranked through the IVQROF-DEMATEL methodology according to the knowledge gained from industry experts. Table 3 illustrates the prominence ranking of the VSM 4.0 strategies to tackle NVA activities at different stages of production.

An integrated VSM 4.0 and IVQROF-DEMATEL technique was utilized in this study to facilitate SPD in the total apparel manufacturing process. In the first phase, the existing workflow of the apparel manufacturing process was analyzed, and the NVA activities were identified. After identifying the NVA activities, the solution strategies were developed against the NVA activities to enhance production efficiency. Industry experts further validated these strategies, and the prominence ranking of these strategies was identified through the IVQROF-DEMATEL method. In the second phase, the strategies were implemented in different sections of the apparel manufacturing process based on their prominence ranking and current state-identified problem, and the VSM 4.0 future state map was developed. Finally, in the third phase, all data from the total apparel manufacturing process were collected and centrally managed by ERP software. Figure 3 depicts the implementation framework of the integrated VSM 4.0 and IVQROF-DEMATEL method.

After implementing the strategies based on their prominence ranking and current state-identified problem, the KPIs were calculated again. After that, a future state map was developed, improving on data in the “X” apparel factory. The future state map of the total apparel manufacturing process is illustrated in Figure 4, and Table D1 in the supplementary file provides a comprehensive calculation.

In Figure 5, some KPIs are compared after the implementation of integrated VSM 4.0 and the IVQROF-DEMATEL method. The improvements and efficiency in the total apparel manufacturing process are also calculated. The detailed calculation of the improvements is provided in Table D2 of Section 4 in the supplementary file.

This study presents a novel integration of VSM 4.0 and the IVQROF-DEMATEL method to enable SPD in the AMI. Apart from evaluating the applicability of tools in Industry 4.0, this synergy enables visualization and prioritization of lean practices required for optimizing operational and economic sustainability. By examining real-time operational inefficiencies and rationally correlating them with innovative lean solutions, the model facilitates a robust decision-support framework for improving SPD in emerging economies.

Current state analysis validated the sewing department’s maximum NVA time of 25 min. This was mitigated in the future state map by implementing the “SMED for minimizing changeover time (S12)” method, which ranked second on the prominence ranking. With the optimization of internal and external procedures such as button application and care label processing, productivity significantly improved. By integrating a real-time monitoring system, machine efficiency increased by minimizing downtime. A sensor-based warning system also reduced internal and external changeover times, resulting in a 48% reduction in NVA time, a 5% improvement in OEE, and an 11.8% improvement in efficiency, which contributes to sustainable operations within the sewing department.

Similarly, the finishing and packaging department had the second-highest NVA time (20 min), which was diminished through the implementation of “RFID for real-time tracking of products (S1)”. This strategy enhanced workflow visibility, reduced WIP inventory, and maximized flow from cutting to packaging. Subsequently, OEE was enhanced by 5% and efficiency by 4.2%, which significantly improved resource utilization and traceability.

To decrease the third-highest NVA duration of 15 min in fabric testing before the cutting process, “QR code scanning for product tracking and resource optimization (S2)” was implemented, which ranked first in the prominence ranking. It facilitated precise traceability of trim and fabric rolls, eliminating misidentification related to fabric. The intervention trimmed 32% from cycle time, reduced waste, and enhanced production planning and inventory accuracy, generating substantial process sustainability benefits.

In the cutting stage, material loss and idle time were reduced in the “JIT for coordinating production process and reducing inventory loss (S4)” strategy, which was ranked third in prominence ranking. It reduced fabric spreading NVA time by 53.33% by strategically locating inventory near the workstation. Additionally, “Streamlining the cutting process through auto-cutting (S6),” being the fifth in prominence ranking, enhanced cutting precision, reduced 80% of NVA time, 30% of cycle time, and boosted OEE by 29%. Another strategy, “Improving workplace organization and safety by streamlining 5S (S15),” ranked sixth which reduced NVA time in numbering and bundling process by 66.67%, cycle time by 21.43%, and increased OEE by 1%, resulting in total section efficiency gain of 10.7%.

The second-lowest NVA time of the inventory process (5 min) was reduced by “Enabling centralized database management by ERP (S8),” which ranked seven in prominence ranking. It reduced the cycle time by 20% by data integrity, faster communication between departments, and lower risk of stockouts and overstocking. All these process improvements further enhanced SPD through increased responsiveness and shorter production delays.

The findings of this investigation differ from previous studies in this field due to the unique application of the integrated IVQROF-DEMATEL and VSM 4.0 methodology for SPD in the examined AMI. Chan and Tay (2018) implemented Kanban in printing factory to boost 10–30% productivity. Moreover, Ratnayake et al. (2021) discovered a 22% increase in the AMI productivity through implementing Kanban. Furthermore, Cáceres-Mauricio et al. (2023) employed 5 S, JIDOKA, and Total productivity maintenance, and integrated with the Kotter model to increase total inventory precision of 95% and reduce defective products 2%. Consequently, as opposed to previous findings, “QR code scanning for product tracking and resource optimization (S2)”, “SMED for reducing changeover time (S12)” were identified the most important strategies for SPD in the AMI. Moreover, after implementation of these strategies based on their prominence ranking and current state-identified problems in the future state for the total apparel manufacturing process, some significant improvements were obtained, including total manpower, total lead time, and total NVA time reduced by 13.73%, 17.31%, 23.05% while OEE and efficiency were improved 5% and 8.87% respectively. Finally, the first application of an integrated VSM 4.0 and IVQROF-DEMATEL approach for SPD in the AMI makes this research particularly unique.

Integrating VSM 4.0 with IVQROF-DEMATEL provides a significant theoretical advance over conventional VSM and qualitative assessments. Conventional tools can only visualize processes statically, while the hybrid model supports dynamic analysis of complex cause-effect relationships among SPD drivers in an uncertain setting. It provides higher analytical precision in measuring the influence of Industry 4.0 technologies on economic sustainability, enabling localized interventions and generalized conclusions for AMI transformation in emerging economies.

From a practical viewpoint, this study provides a viable solution for lean implementation in the AMI. Practices such as the QR code scanning function allow for easy product traceability and resource planning, reducing inventory discrepancies and lead time by a significant margin. SMED enables enhanced flexibility and reduced unproductive time in the sewing operation, enabling more responsive production. JIT enables lean demand-based inventory management, while RFID enables enhanced end-to-end SC visibility. ERP integration eases decision-making, planning validity, and information transparency. These interventions, in combination, achieve enormous productivity improvement, waste reduction, and process enhancement, which are major drivers of economic sustainability.

The integrated model takes the economic sustainability of AMI operations to an entirely new level. For example, QR code scanning reduces stockouts and inventory discrepancies, while SMED improves machine utilization and reduces labor costs. RFID allows forward-looking SC management, and ERP enables accurate forecasting and resource planning. Such behavior, in conjunction, creates downward costs and upward efficiencies, instilling long-term profitability and competitiveness.

Furthermore, the implementation of this novel approach achieves multiple SDGs specifically. More specifically, SDG-12 (Responsible Consumption and Production) is achieved by S2 and S6 strategies by reducing the wastage of fabric and managing stocks. SDG-8 (Decent Work and Economic Growth) is enabled by S12 and S15 strategies by simplifying the working environment and maintaining safety. SDG-9 (Industry, Innovation, and Infrastructure) is enabled by S4, S8, and S1 strategies through innovative manufacturing practices, real-time transparency, and robust infrastructure. The abolishment of NVA operations, enhanced coordination, and process structuring to support sustainability requirements all enhance resilience and long-term value addition in the AMI.

This study presents an innovative technological framework that integrates VSM 4.0 with the IVQROF-DEMATEL method to facilitate SPD in the AMI of Bangladesh. As the AMI is a strategic sector for GDP and employment, this combination is imperative to ensure operation effectiveness and financial sustainability. The integrated VSM 4.0 and IVQROF-DEMATEL framework enable the acquisition of real-time data, performance measurement, and decision-making to provide greater manufacturing transparency, in addition to improving resource utilization across the SC. Although the high initial capital outlay is a drawback, the enhanced throughput, shorter lead times, and optimized workflow this integration achieves shortens the return on investment period. Aside from these, incorporating lean practices and employee training in NVA activity elimination makes Bangladesh AMI more competitive globally.

The initial implementation of VSM 4.0 commenced with a detailed observation of the current production status and identifying inefficiencies from raw material inventory to dispatch. The next step was devising improvement strategies, which were verified through the IVQROF-DEMATEL approach. QR code scanning for product tracking and resource optimization (S2) and SMED to reduce changeover time (S12) were identified as the essential strategies for SPD in the AMI. Afterward, the strategies were implemented in the future state based on their prominence ranking and current state-identified problems. After implementing these strategies, a significant improvement was observed, including a reduction in total manpower by 13.73%, lead time by 17.31%, and NVA time by 23.05%, with enhanced OEE and efficiency of 5 and 8.87%, respectively. Additionally, the implementation of VSM 4.0 is consistent with SDGs – specifically SDG-8 (Decent Work and Economic Growth), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-12 (Responsible Consumption and Production), which demonstrates a commitment to long-term resilience and sustainability of the AMI.

Implementing VSM 4.0 on the basic T-shirt manufacturing process has a promising effect on increasing economic sustainability, but there are still several research gaps. The most critical gap is the implementation duration, which was just three months and restricts the ability to determine if it can bring long-term economic improvements and efficiency gains. The period should extend to understand the trend of VSM 4.0 in reducing operational costs and improving financial outcomes over time. Although there were notable reductions in lead time, manpower, and NVA activities, further research has explored how these improvements can be fully maintained in the long run without constant time adjustment.

Future work should focus on extended implementation durations for longitudinal effects and ensuring evidence of sustainability for performance improvement. Increased scope across several product lines and configurations in manufacturing will facilitate the assessment of the scalability and flexibility of VSM 4.0 in the AMI. Besides, integrating VSM 4.0 with simulation and advanced MCDM techniques, such as fuzzy AHP-TOPSIS or fuzzy ANP-VIKOR, would further enhance predictive ability and encourage real-time process optimization. These advancements will enable the upgradation of data-driven, cost-effective, and globally competitive garment production systems in the new world.

The supplementary material for this article can be found online.