An Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Study

Abstract

The implementation of lean manufacturing to increase productivity often neglects the impact on the environment and the well-being of employees. This can result in negative consequences such as environmental harm and poor employee satisfaction. To address this issue, an integrated ergo-green-lean conceptual model was developed in the literature. However, no case study has been conducted to support this model. Therefore, this research aims to investigate the practical outcomes of implementing the integrated framework in an automobile parts industry. Key performance indicators (KPIs) were identified, including ergonomic risk score, job satisfaction, carbon footprint emission both from direct energy consumption and material wastage, cycle time, lead time, die setup time, and rejection rate. Various assessment techniques were employed, such as the rapid entire body assessment (REBA) with the Standard Nordic Questionnaire (SNQ), job stress survey, carbon footprint analysis (CFA), and value stream mapping (VSM) to evaluate the KPIs at the pre- and post-intervention phases. The results demonstrate significant improvements in job satisfaction (49%), improved REBA score of 10 postures with very high risk numbers by 100%, a 30.3% and 19.2% decrease in carbon emissions from energy consumption and material wastage, respectively, a 45% decrease in rejection rate at the customer end, a 32.5% decrease in in-house rejection rate, a 15.5% decrease in cycle time, a 34.9% decrease in lead time, and a 21% decrease in die setup time. A Python regression model utilizing sklearn, pandas, and numpy was created to assess the relationship between process improvement and the chosen KPIs.

1. Introduction

The changing business landscape has prompted companies to revamp their approach to accommodate a highly interconnected global economy that is intertwined with a collaborative ecosystem. Additionally, the emergence of Industry 4.0 has driven the above-cited changes, and the effects of this revolution have permeated every aspect of social, economic, and political life. Thus, to remain competitive, enhance production efficacy, and lower operational expenses, organizations must adopt creative strategies [1,2,3].

Among these strategies, lean manufacturing (LM), originating from the Toyota Production System, is a widely recognized approach for enhancing an organization’s operational performance by identifying waste and optimizing resource utilization. Key features of lean thinking include just-in-time (JIT) practices, waste reduction, process improvement strategies, defect-free production, and work standardization [4]. Numerous manufacturing processes, spanning from the automotive sector to the service industry, incorporate lean thinking principles into their production strategies to enhance productivity and the quality of their products by reducing costs. Value stream mapping (VSM) is an important lean tool that is utilized to identify wasteful and non-value-added tasks and to prioritize future process improvements [5,6]. The VSM tool captures information related to time, labor force, transportation, and other relevant data, thus, enabling the identification of the desired current state process, the desired future state process, and the associated implementation plans required for future process improvement activities [7].

In response to mounting concerns about environmental impacts and growing customer demand for sustainable products and services, companies have adapted their operational strategies to conform to environmental regulations [8]. The concept of “green” is regarded as a philosophy and operational approach that promotes ecological efficiency, reduces the adverse environmental impact of products or services, promotes sustainable alternatives, and enhances financial performance in terms of finished products, waste, and carbon emissions [9,10].

Over the course of time, the importance of human factors and ergonomics has increasingly become a critical aspect in optimizing organizational performance. They focus on prioritizing the physical health and psychological well-being of employees and have been shown to have a significant impact on business productivity [11]. The applied science of ergonomics is concerned with understanding the importance of workstations, work process design, and their effects on worker safety and health. Implementing ergonomics in practical workplace design provides several benefits, including reducing injuries and absenteeism rates, and improving productivity, quality, and reliability. The rapid entire body assessment (REBA) is the most used tool developed to meet the need for a practitioner’s field tool, specifically designed to address unpredictable working postures found in industries such as automobile and service industries [12,13]. To leverage the benefits of creative strategies, organizations should explore diverse avenues, and one practical approach is to seamlessly integrate lean, green, and ergonomics into their operations. The literature provided in subsequent sections serves as evidence of endeavors made to integrate creative strategies in different ways across various industries.

1.1. Lean Green

Lean manufacturing is the most reflective paradigm as it considers both organizational and sustainable development goals. Efficiency and profit reflect organizational goals [9], whereas customer satisfaction, quality, and responsiveness belong to eco-friendly objectives [14]. Keeping them in mind, companies have been forced to provide environmentally friendly products and services [15]. Lean and green are both thought to be proportionate to one another because they both aim to reduce “waste” [16], but each synergy targets a different kind of waste [17].”Green approach” is a theoretical framework that focuses on environmental depletion caused by manufacturing. It emphasizes environmental waste reduction due to inefficient resource usage, pollution, non-decomposable waste, greenhouse gas emissions, and acidification [18,19]. Organizations use lean manufacturing in green practices to overcome their non-lean objectives. Due to the commitment to LM, there’s a natural tendency toward green practices. Research shows that the application of an integrated lean and green framework can provide more efficient results [20,21,22,23,24,25,26].

Schmitt et al. [20] conducted an empirical analysis of 17,000 US companies and concluded that ISO 9000-certified companies are more likely to implement ISO 14000. Baumer-Cardoso et al. [17] demonstrated the importance of integration between lean and green by developing a discrete event simulation model (DES) for the job shop of Brazilian manufacturing organizations. The author proposed the slogan “Lean is Green” through this simulation-based case study. Lean aspires to increase production efficiency through waste elimination as well as green elimination waste [27,28].

1.2. Lean Ergonomics

Ergonomics is the study of workstation and process design which affects employee health and safety [29,30]. Ergonomic workplaces and job activities boost reliability and provide quality output while reducing absenteeism and work injuries [31]. Brito et al. [32] concluded that ergonomics and production requirements critically influence the lean planning process. The authors further added that the effectiveness of the lean approach is significantly impacted by the integration of ergonomic concepts into the workflow for plastic packaging companies by employing a workstation assessment tool (ErgoSafe CI).

Jarebrant et al. [33] developed Ergo-VSM, a technique that aims to improve ergonomics while concurrently monitoring data on production performance for Swedish manufacturing companies. With the use of ergonomic and lean workspace design guidelines, this evaluation approach allows for the examination of each demand. Brown et al. [34] used the sustainable VSM tool for designing assembly line workstations in three different US industries. The assessment tool was tested by implementing a car assembly line to improve “waste reduction” and “inventory and material logistics.” Test results were used to make necessary changes in operational zones, workstation layouts, storage areas, and component feed systems. Seppälä and Klemola [35] conducted a study that analyzed the level of adoption of lean production and related technologies in four manufacturing companies in Finland. The research explored how various occupational groups perceived their organization and work after the implementation of lean production principles. Additionally, it investigated the factors within the organization and change processes that influenced employees’ positive or negative perceptions of production, job satisfaction, and stress levels. Similarly, Botti et al. [29,36] developed the integrated lean-ergo framework and implement it in manufacturing assembly lines.

1.3. Green Ergonomics

Thatcher and Yeow introduced the term “Green Ergonomics”, which states: (1) conserve, preserve, and restore nature; (2) eco-system for the well-being of human systems. Human systems and natural systems interact bidirectionally [37]. The authors proposed a top-down and bottom-up paradigm of the parent, sibling, and child systems, and it is referred to as a sustainable system. Ergonomics steps up from work–life balance to social sustainability from the micro level (individual) to the macro level (ecology). Thatcher and Milner [38] presented three case studies of green buildings and concluded that 50% ventilation is enough for green building solutions, but green ergonomics can only be fully obtained by designing workstations, workspaces, and equipment ergonomically.

1.4. Ergonomics Green Lean

Sarbat [39] and Mehmood [40] developed an integrated ergo-green-lean framework that aims to optimize operations, improve productivity, and reduce waste while considering employee well-being and environmental impact. This integrated framework offers a holistic approach to organizational performance, with potential correlations yet to be explored.

The above literature review indicates that the integration of any two paradigms (lean green; lean ergonomics; green ergonomics) yields breakthrough results for various industries, whereas an integrated lean, green, and ergonomics framework has not been extensively researched in the literature. While a conceptual model has been developed to address these three domains, no practical results have been recorded because the model has not been implemented. Therefore, the aim of this study is to validate the integrated model proposed by the authors [40] by implementing it as a case study in an automotive parts manufacturing unit and analyzing the practical results by comparing the situation pre- and post-intervention. The study identified several key performance indicators (KPIs) to assess the effectiveness of the framework, including ergonomic risk score, job satisfaction, carbon footprint emissions from energy consumption and material wastage, cycle time, lead time, die setup time, and rejection rate. To evaluate these KPIs before and after the intervention, various assessment techniques were utilized, such as the rapid entire body assessment (REBA) combined with the Standard Nordic Questionnaire (SNQ) for ergonomic evaluation, job stress surveys for measuring job satisfaction, carbon footprint analysis (CFA) for assessing environmental impact, and value stream mapping (VSM) for analyzing process efficiency. This case study contributed to the literature by presenting a Python regression model using the libraries sklearn, pandas, and numpy, allowing for the investigation of the correlation between process improvement and the selected KPIs.

2. Materials and Methods

An integrated ergonomics, green, lean framework developed by Mehmood [40] was adopted for this case study. Figure 1 illustrates the integrated ergo-green-lean model developed by Mehmood in 2021.

According to the authors, the incorporation of ergonomics and environmental sustainability into lean production systems is crucial in order to enhance productivity, reduce resource consumption, minimize expenses, achieve optimal quality, and ensure customer satisfaction. Integrating these aspects is essential for improving the competitiveness of organizations. However, if implemented separately, they may lead to various incompatibilities. Moreover, this unified approach places emphasis on the organization’s commitment to efficient resource utilization and sustainability across economic, environmental, and social aspects. It establishes a connection between lean practices, environmental consciousness, and ergonomic considerations. Additionally, since the key performance indicators (KPIs) of lean, green, and ergonomic principles are aligned with ISO 9001, ISO 14001, ISO 45001, and SA 8000 standards, this integration can facilitate the implementation of a comprehensive management system. A well-developed framework for effectively managing various stakeholders, such as top management, functional managers, employees, customers, suppliers, regulatory bodies, and the community, can be achieved through integrated management systems. By adopting an integrated approach, organizations can better understand the impact of multiple paradigms on their performance and devise optimal strategies for establishing and implementing secure, sustainable, and efficient practices.

A stepwise procedure was developed for its implementation in an automotive parts industry. The selection of industry was made according to the 2 criteria indicated in the published literature, i.e., positive management style [41,42] and workforce size [43]. The selected industry has a positive management mindset with a workforce size of 138 employees (including workers and management personnel). The industry was supplying critical parts to all automotive original equipment manufacturers (OEMs) in Pakistan. However, only 1 part (crossmember front cowl) was targeted during this research, which was the most problematic part in terms of rejection both in-house and at the customer end. The failure in functionality of the part can cause a major accident. Four different process shops, i.e., die storage shop, hydraulic press shop, assembly shop, and final inspection were mainly involved in the selected part manufacturing.

The first step of the methodology comprised of formulation of the quality circles in each shop which consists of 7 workers, 1 supervisor, and a manager, led by the researcher. The semi-structured interviews (SSIs) were then conducted for each quality circle to identify the challenges in the 3 targeted areas (lean, green, and ergonomics). For the analysis of interviews, the Braun and Clarke [44] method was used for thematic analysis which summarized the major issues faced at each level in the form of themes. After identifying the hot areas related to the 3 selected paradigms, the second step involved selecting the key performance indicators (KPIs). The following 3 steps were employed for setting up these KPIs:

• KPIs used in the literature: detailed research was conducted to review the KPIs used in the literature of any 2 integrated paradigms [17,19,26,29,33,38,40,45,46,47,48,49,50,51].
• Industry management: The industry’s top management was involved in setting up the KPIs that were feasible for implementation and could be achieved in a limited timeframe.
• Area experts: experts were involved who were already working in these 3 paradigms and picked up the specific areas that can have a positive impact on the selected integrated approach that will ultimately improve performance.

The analytical hierarchy process (AHP) was then utilized to prioritize the key performance indicators (KPIs). This tool is effective for selecting optimal alternatives and planning interventions [52,53]. The AHP approach involved gathering responses from 9 individuals (each quality circle) to categorize the KPIs in order of importance. Pairwise comparisons were conducted using Saaty’s scale, which enabled the ranking of the KPIs in a descending order of significance. Ultimately, 8 KPIs were chosen for the integrated ergo-green-lean approach, and they are listed in

Table 1. List of KPIs for integrated ergo-green-lean approach.

Paradigm/KPI	Ergonomics	Green	Lean
	Ergonomics risk assessment (ERA) score Job satisfaction	Carbon emission by direct energy consumption Carbon emission by material wastage	Cycle time Die setup time Lead time Part rejection

.

For the assessment of selected KPIs during the third step, the research techniques for each paradigm were carefully selected after the extensive literature review. Considering ergonomics, a rapid entire body assessment (REBA), Standard Nordic Questionnaire (SNQ), and job stress survey were selected for the current situation analysis of the workers. For green, a carbon footprint analysis (CFA) tool was incorporated as the methodology. For Lean, VSM was selected to visualize the waste in terms of time. The current state of VSM illustrates all the value-added and non-value-added activities’ time, while the future VSM is proposed by eliminating all the non-value-added times [5,6]. The KPIs were assessed based on the current scenario of the industry, and after the assessment of KPIs, the quality circles were developed in each shop which consisted of a manager, supervisor, and workers. Each quality circle consisted of 6 workers, 1 supervisor, and a manager, led by the researcher. Root cause analysis (RCA) was performed using an Ishikawa diagram with the help of quality circles. All stakeholders and quality circles were involved in the discussion which helped to explain all the problems and their potential causes. The fourth step involved in the development of the intervention plan and the Kaizen approach was utilized for designing the interventions. For this reason, all the problems and their underlying causes were carefully examined, taking into account all the study objectives. The following 3 conditions were considered during the formulation of the plan:

• Interventions must be practical/feasible for the stakeholders.
• Capital investment should not be required in any intervention.
• All interventions should be time bounded.

During the fourth stage, all the selected interventions were implemented with the involvement of quality circles. Each member of the quality circle was responsible for their respective area of expertise. A weekly status meeting was held to review the progress of the interventions and to gather feedback on their effectiveness. Post-intervention results were measured for each selected KPI after each month of implementation of the intervention plan until the 4th month. For comparison’s sake, between the pre- and post-intervention phases, the results after the 4th month of implementation are discussed in the subsequent section. In the final stage, a regression model using Python libraries (sklearn, pandas, and numpy) was developed to determine the correlation between process improvement and the selected KPIs. The Google colaboratory integrated development environment was used to run the Python code. Figure 2 shows the flow diagram of the complete methodology employed during this case study.

3. Results and Discussion

3.1. Identification of Problem Areas

Semi-structured interviews were conducted from each quality circle to identify the major problems faced at each level in the industry and results are presented in

Table 2. Identified issues faced by the industry using the SSIs.

Issue	Top Management	Middle Management	Workers
Energy consumption	✓	✓
Renewable energy	✓	✓
Production capacity	✓	✓	✓
Cycle time	✓	✓	✓
Die changeover	✓	✓	✓
Material rejection	✓	✓
Musculoskeletal problems			✓
Job satisfaction		✓	✓
Absenteeism	✓	✓
Turnover rate	✓	✓
Job hazards		✓	✓
Worker adaptability	✓	✓

.

After the identification of issues through SSIs, thematic analysis [44] was used which resulted in the form of word cloud. Based on this analysis, KPIs were selected targeting all problematic areas of the three paradigms (See Table 1). AHP was then used to rank the KPIs. A total of nine participants (members of the quality circle) from the selected industry were chosen for pair-wise comparison utilizing Saaty’s fundamental scale and results were computed using an Excel template [54]. All KPIs are arranged in descending order according to the weights that resulted from AHP and are shown in Figure 3.

3.2. Assessment of KPIs at Pre-Intervention Phase

After setting up the KPIs, their current states were assessed for the selected industry by employing various techniques for the ergonomics, green, and lean paradigms at the pre-intervention stage.

3.2.1. Ergonomics Paradigm

Two key performance indicators (KPIs) were selected for the ergonomics paradigm: the ergonomic risk assessment (ERA) score and job satisfaction. The rapid entire body assessment (REBA) technique was chosen for the ergonomic risk assessment. Before performing REBA, the widely used self-administered Standard Nordic Questionnaire (SNQ) was employed to assess the physical activity levels and musculoskeletal problems experienced by individual workers due to work-related postures. The questionnaire consisted of questions regarding pain, aches, or discomfort in various body parts over the last 12 months in general and the recent week in particular. Figure 4 depicts the survey results graphically, highlighting the major musculoskeletal problems faced by workers.

Following the results of the SNQ survey, a posture from each operation was selected for REBA that was causing major issues during the manufacturing of the selected part. An example posture is shown in Figure 5a, where the worker is involved in loading the part from the palette and placing it on the floor. The figure illustrates that the neck is in a 29.7° flexion position, the trunk is bent, the lower limb is twisted, and both legs are asymmetrical. There is flexion in the lower limb as the worker is loading the part in a position where the weight is dominant on the left lower limb. The wrist is in a 15°+ position with an extension in the upper limb. All postures were assessed using REBA scores, as presented in Figure 5b, and a summary of all the REBA evaluation scores is provided in

Table 3. REBA evaluation scores for each operation during pre-intervention phase.

Process No.	Process Name	Posture	REBA Score
Process No. 1	Shearing	Loading of the part	7
		Processing	8
		Offloading of the part	10
Process No. 2	Blanking	Loading of the part	12
		Processing	6
		Offloading of the part	10
Process No. 3	Draw 1	Loading of the part	11
		Processing	9
		Offloading of the part	8
Process No. 4	Draw 2	Loading of the part	12
		Processing	8
		Offloading of the part	10
Process No. 5	Trimming	Loading of the part	13
		Processing	7
		Offloading of the part	11
Process No. 6	Trim/piercing	Loading of the part	10
		Processing	7
		Offloading of the part	11
Process No. 7	Nut welding	Loading of the part	11
		Processing	6
		Offloading of the part	8
Process No. 8	Nut welding	Loading of the part	12
		Processing	4
		Offloading of the part	5
Process No. 9	Assembly	Loading of the part	11
		Processing	5
		Offloading of the part	5

.

Table 3 reveals that eight processes (process no. 1 to process no. 9) and nine postures during loading or offloading of the part scored a “very high” REBA score. In addition, all nine processes had nine postures with “high” REBA scores. To evaluate job satisfaction, psychological, physiological, and environmental ergonomic techniques were employed. The job stress survey was conducted to assess the psychological comfort of the workers at the workplace, and the obtained data were analyzed using SPSS 2022 to determine the reliability of the questionnaire. The obtained Cronbach’s alpha value of 0.830 indicated the reliability of the questionnaire. The results of the survey showed that 64% of respondents reported very high job stress levels, 4% reported high stress levels, 25% reported moderate stress levels, and only 7% reported low stress levels.

For environmental ergonomics, illuminance at every shop was measured through the lux meter (TES-1334A, TES Electrical Electronic Corp., Taipei, Taiwan). The illuminance level at every shop with the standard value (1926.56(a), OSHA [55]) is shown in

Table 4. Illuminance level at pre-intervention phase.

Sr. No	Shop	Standard Value (lux)	Actual Value (lux)
1	Press shop	300	178
2	Die repairing shop		134
3	Spot welding shop		128
4	Quality inspection area	500	173

.

3.2.2. Green Paradigm

To assess the pre-intervention state of the KPIs in the green paradigm, the carbon footprints for electricity and material wastage due to rejection were determined. The electricity consumption per day was calculated by taking into account the power of each piece of electric equipment in the shop and multiplying it by the number of working hours per day. The carbon emissions per year were then calculated by multiplying the carbon emission value with the power generation against one kWh. The calculations of carbon footprints for each shop are presented in detail in

Table 5. Carbon footprints for electricity consumption at various shops at pre-intervention phase.

Shop	No. of Equipment	Power (Watt)	Consumption/Day (kWh)	Consumption/Annum (kWh)	Carbon Emission/Annum 1 (kg-CO2/kWh)
Die Shop	8	50	3.2	960	338.89
Press Shop	11	500	44	13,200	4660
	10	50	4	1200	423.6
Spot Shop	6	200	9.6	2880	1016.6
	5	50	2	600	211.8
Quality Inspection	1	200	1.6	480	169.4
	1	50	0.4	120	42.36

¹ Number of working hours = eight/day. Number of working days = 300/annum. Carbon emissions in the country by power generation = 0.353 kg-CO₂/kWh. Total carbon emissions = 6862.6 kg-CO₂.

.

In addition to electricity consumption, the carbon footprint due to rejected parts was also calculated as part of the pre-intervention state assessment of the environmental ergonomics paradigm. The data from rejected parts at both the customer end and in-house over a period of eight months were taken into account for the calculation.

Carbon footprint for steel per kg = 1.4 kg-CO2

Sheet weight =3.01 kg

Rejection in-house = 325 pcs/8 months

Rejection at the customer end = 217 pcs/8 months

Total material waste = 542 × 3.01 = 1631.4 kg

Total CF = 1631.4 × 1.4 = 2283.3 kg-CO2

3.2.3. Lean Paradigm

To implement lean, the current state of the manufacturing process was developed using value stream mapping. The process begins with receiving orders from customers through email, which is forwarded to the supply chain department for arranging raw materials. The materials are received from the supplier in the form of sheets and stored in the storage area. The production planning and control department shares the production plan with the production in-charge and the storage area. The manufacturing process is a “push” process, where the material is transferred from the storage area to the shearing area for cutting into blank sizes (3.01 kg/blank) and then transferred to the press shop for press operations. The entire batch of 3000 parts is then transferred to the assembly shop via a lifter, followed by nut welding at two stations through spot welding machines. Finally, child parts are assembled through welding guns.

To develop the current state map, the cycle time for each process was observed three times using a stopwatch, and the mean value was used for analysis. The process time was noted to identify non-value-added time in each process. The total cycle time was calculated by adding the cycle time of each process. The inventory before each process was noted and the waiting time was calculated using the customer demand and takt time. The die setup time was observed to be relatively high as workers were readjusting die height by hit and trial method. The lead time was calculated by adding all the waiting times and plotted on the current state map as shown in Figure 6.

3.3. Root Cause Analysis

After the careful examination of the current state of all three paradigms, the following major issues were identified and listed in

Table 6. Major issues related to quality and supply.

Sr. No.	Domain	Major Issue
1	Quality	High rejection rate on the customer end and in-house. Fitment issues on the customer end.
2		Part not according to the standard specification.
3	Supply	High lead time from supplier to customer.

.

A quality circle was utilized to identify the recurring issues related to quality and supply by conducting a root cause analysis with the help of an Ishikawa diagram and 5Why techniques. In the Ishikawa diagram for quality, the team members presented various reasons for rejection, and root causes were categorized into the following: man, machine, material, and method. The analysis revealed that the material was up to the specifications. In the method category, the major cause of rejections was the non-calibrated checking fixtures, which were unable to verify the shape and drawing specifications of the part. Additionally, the sample size for inspection was inadequate, with only 10–5 pcs/lot inspected. The workers were found to not be following the standard operating procedures (SOPs) correctly in the man category, whereas in the machine category, the press parallelism was off, leading to defects in the manufactured parts. Figure 7 depicts the Ishikawa diagram for quality-related issues.

The Ishikawa diagram shows that the absence of a formalized plan, including maintenance, inspection, and SoPs, resulted in quality defects. In terms of supply, the lead time for delivering products to customers was longer than desired, which was impacting the supply efficiency of the industry. The team conducted a root cause analysis for the supply-related issues similar to the approach used for quality issues and identified that repeated changeovers and long die setting time were the main causes of delayed supplies, as shown in Figure 8. These causes were also attributed to a lack of formalized plans and poor ergonomics design.

3.4. Proposed Intervention Plan

Following the root cause analysis, various alternatives were proposed for interventions with the quality circle. After brainstorming sessions and the multiple meetings with the quality circle and top management, the most feasible solutions were chosen. The Kaizen approach was then implemented for continuous process improvement. The suggested improvements for each targeted paradigm, namely ergo-green-lean, along with their respective performance objectives, are summarized in

Table 7. Intervention plan for improvements of KPIs of integrated ergo-green-lean paradigm.

Sr. no.	KPIs	Suggested Intervention	Performance Objective
Ergonomics paradigm
1	Job satisfaction	Improvement in workplace design. Provision of benches and tables for material/parts. Design the standard illuminance level at various shops.	Improved work design. Improved worker posture.
2	ERA score
Green paradigm
3	Carbon emissions by direct energy consumption	Improved illuminance using natural light in workplace during the working hours to reduce the power consumption.	To reduce carbon emissions produced from direct use of electricity.
5	Carbon emissions by material wastage (due to rejection)	Employee training regarding the SoPs and maintenance plan.	To reduce carbon emissions produced from excessive material processing.
Lean paradigm
6	Part rejection	Calibration of inspection equipment.	To reduce rejection.
7	Lead time	Development of preventive maintenance plan.	Minimize machine downtime and breakdown maintenance.
8	Die setup time	Die marking to reduce die setup time.	To minimize die setup time.
9	Cycle time	Employee training regarding the SoPs and maintenance plan.	To improve the cycle time.

.

3.5. Implementation of the Proposed Intervention Plan

An intervention plan was put into action to enhance the chosen KPIs regarding the ergo-green-lean approach within the given time limit. The specifics of each intervention are outlined in

Table 8. Implementation details of the intervention plan.

Suggested Intervention	Implementation
Ergonomics paradigm
Workplace design improvement. Benches and tables for materials/parts. Standard illuminance level.	Worker bent to pick up the parts from the trolley (before)	Bins were provided on tables for the parts (after)
	Worker bent to pick up the parts from the floor (before)	Tables are provided for the parts (after)
	Spot shop roof (before)	Transparent sheets installed on spot shop roof for improved illuminance (after)
Green paradigm
Improved illuminance using natural light in workplace during the working hours. Employee training.	Press shop wall (before)	Press shop steel sheets replaced with transparent sheets to bring natural light into workplace (after)
	On-Job worker training	Supervisor training
Lean paradigm
Calibration of inspection equipment. Development of preventive maintenance plan. Die marking to reduce die setup time. Employee training.	Calibration procedure was prepared. Responsible person was designated. Labels were placed on all inspection equipment with calibration date. Preventive maintenance plan for all the production machinery was prepared. Die height was marked using a simple procedure. Smaller batch sizes were suggested to reduce the waiting time of the in-process parts. Technical training was conducted on shop floors related to part/process knowledge, identification and solution of the problems, spot machine operation, and daily machine maintenance.

.

3.6. Assessment of KPIs at Post-Intervention Phase

Following the implementation of the intervention plan, the KPIs related to the ergo-green-lean approach were reassessed in the post-intervention phase using the same techniques as before, after a time span of six months.

3.6.1. Ergonomics Paradigm

After the implementation of the intervention plan, the REBA scores were recalculated for each process, and the improved results were recorded in

Table 9. REBA score during post-intervention phase.

Process No.	Process Name	Posture	REBA Score
Process No. 1	Shearing	Loading of the part	7
		Processing	8
		Offloading of the part	10
Process No. 2	Blanking	Loading of the part	8
		Processing	6
		Offloading of the part	8
Process No. 3	Draw 1	Loading of the part	8
		Processing	7
		Offloading of the part	7
Process No. 4	Draw 2	Loading of the part	6
		Processing	8
		Offloading of the part	8
Process No. 5	Trimming	Loading of the part	10
		Processing	7
		Offloading of the part	10
Process No. 6	Trim/piercing	Loading of the part	10
		Processing	7
		Offloading of the part	10
Process No. 7	Nut welding	Loading of the part	7
		Processing	6
		Offloading of the part	7
Process No. 8	Nut welding	Loading of the part	5
		Processing	3
		Offloading of the part	5
Process No. 9	Assembly	Loading of the part	10
		Processing	5
		Offloading of the part	5

. The table shows that only one process (process no. 8) was reduced to a “low-risk level.” This improvement was attributed to the use of trolleys and work benches during the implementation phase, which resulted in a lower REBA score and reduced risk level. However, it was also observed that the REBA score was not improved as expected due to some work design improvements that required financial capital to change the machinery, making it difficult to implement.

Following the interventions, a post-intervention survey was conducted to evaluate the job stress level among workers using the same questionnaire. The results show that 56% of the respondents reported low stress levels, while 40% reported moderate stress levels and only 4% reported high stress levels. The measurement of illuminance levels in each shop was also conducted and the results are presented in

Table 10. Illuminance level at various shops in post-intervention phase.

Sr. No	Shop	Standard Value	Actual Value
1	Press shop	300	997
2	Die repairing Shop		1783
3	Spot welding shop		786
4	Quality inspection area	500	953

. It is important to note that the implementation of certain work design improvements, such as the use of trolleys and workbenches, may have contributed to the reduction in job stress levels among the workers.

3.6.2. Green Paradigm

In the post-intervention phase, the carbon footprint calculation was conducted to assess the impact of interventions on electricity consumption and material wastage. The use of sunlight for illumination in the workplace resulted in a reduction in the number of bulbs used for lighting, which subsequently reduced power usage for lighting in all four shops. The results of carbon emissions after interventions are summarized in

Table 11. CF calculation at post-intervention phase.

Shop	No. of Equipment	Power (Watt)	Consumption/Day (kWh)	Consumption/Annum (kWh)	Carbon Emission/Annum (kg-CO2/kWh) 1
Die shop	2	50	0.8	240	84.72
Press shop	9	500	36	10,800	3812.4
	8	50	3.2	960	338.9
Spot shop	1	200	1.6	480	169.4
	4	50	1.6	480	169.4
Quality inspection	1	200	1.6	480	169.4
	1	50	0.4	120	42.36

¹ Number of working hours = 8/day. Number of working days = 300/annum. Carbon emissions in the country by power generation = 0.353 kg-CO₂/kWh.

.

After intervention, four months of data were considered for the calculation of CF for material wastage.

Carbon footprint for steel per kg = 1.4 kg-CO2

Sheet weight =3.01 kg

Rejection in-house = 126 pcs/4 months

Rejection at the customer end = 93 pcs/4 months

Total material waste = 219 × 3.01 = 659.19 kg/ 4 months

Total CF = 659.19 1.4 = 922.8 kg-CO2

3.6.3. Lean Paradigm

Following the interventions, future state mapping was conducted to assess the improvement in KPIs. Figure 9 illustrates the value stream mapping of the modified process, which showed significant improvements. The use of small batch size resulted in reduced waiting time, and the introduction of lifted trolleys improved work design, leading to a reduction in cycle time. Overall, the interventions led to a more efficient and effective process.

3.7. Comparison of Pre-Intervention Phase and Post-Intervention Phase Results

To summarize the case study, a comparative analysis between pre- and post-intervention phases for the selected KPIs of the integrated ergo-green-lean framework is provided in this section.

3.7.1. Ergonomics Paradigm

Table 12. Comparison of REBA score for pre- and post-intervention phases.

REBA Score	REBA Risk Level	Pre-Intervention Phase	Post-Intervention Phase
2–3	Low	0	1
4–7	Medium	10	14
8–10	High	7	12
11–15	Very high	10	0

summarizes the REBA scores before and after interventions were implemented. Before work design improvement, the REBA assessment revealed that a considerable number of postures were classified as very high-risk (10), high risk (7), or medium risk (10), with no postures being in the low-risk category. This indicated an increased likelihood of musculoskeletal injury among workers. However, after the intervention, the number of postures classified as very high-risk was reduced to zero, with these postures being shifted to the high, medium, and low-risk categories. Specifically, there were 12 postures in the high-risk category, 14 postures in the medium-risk category, and 1 posture in the low-risk category. It was not possible to shift most of the postures to the medium or low-risk category due to limitations in the design improvement, such as the inability to lift machines. Overall, the REBA assessment results indicate that the work design improvement had a positive impact.

Figure 10a,b presents a comparison of the job stress survey results before and after the implementation of training and work design improvement. The findings indicate that these interventions were successful in reducing the job stress levels experienced by employees. This reduction in job stress can lead to various benefits, such as improved well-being, increased job satisfaction, and enhanced job performance, which can be advantageous for both employees and the organization.

Table 13. Comparison of illuminance level for pre- and post-intervention phases.

Sr. no.	Shop	Standard Value	Actual Value (Before)	Actual Value (After)
1	Press shop	300	178	997
2	Die repairing shop		134	1783
3	Spot welding shop		128	786
4	Quality inspection area	500	173	953

provides a comparison of illuminance levels in four shops before and after the implementation of interventions. The results indicate a substantial improvement in illuminance levels across all four shops. This improvement can lead to better working conditions, increased safety, and reduced strain on workers’ eyes, resulting in improved efficiency and reduced errors.

3.7.2. Green Paradigm

Table 14. Comparison of carbon footprint values for pre- and post-intervention phases.

KPIs	Carbon Emissions (kg-CO2/kWh)		Improvement (%)
	Pre-Intervention Phase	Post-Intervention Phase
Direct electricity consumption	6223.287	4336.343	30.3%
Material wastage	2068.381	1669.22	19.2%

presents a comparison of the carbon footprints (CF) before and after interventions in terms of electricity consumption and material wastage. The results indicate a reduction in carbon emissions for both electricity consumption (30.3%) and material wastage (19.2%) after the interventions were implemented. The reduction in electricity consumption was achieved by partially shifting to the use of sunlight for illuminance, which decreased the overall energy usage and subsequently the carbon emissions. The reduction in the CF of material wastage suggests that process improvements led to a decrease in production rejection, resulting in a reduction in the CF value. Overall, the comparison of the carbon footprints before and after interventions indicates a positive impact on the environment.

3.7.3. Lean Paradigm

Table 15. Comparison of VSM for pre- and post-intervention phases.

Item	Pre-Intervention Phase	Post-Intervention Phase	Improvement
Total cycle time (sec)	180	152	15.5%
Lead time (day)	18.19	11.85	34.9%
Die setup time (sec)	33,900	26760	21%
Rejection at customer end (PPM)	847	465	45%
In-house rejection (PPM)	46.4	31.5	32.5%

presents a comparison of selected key performance indicators (KPIs) before and after interventions. The percentage improvements in the KPIs demonstrate that the interventions led to significant improvements in all of the metrics. The improvements in total cycle time, lead time, total changeover time, rejection at the customer end, and in-house rejections suggest that the production process has become more efficient, with fewer defects and improved customer satisfaction. These positive changes can lead to increased profitability, reduced costs, and improved competitiveness for the organization.

Figure 11 illustrates the holistic view of comparisons between pre- and post-intervention results.

3.8. Regression Modeling

To predict the correlation of all selected KPIs with the process improvement for the integrated ergo-green-lean paradigm, a Python regression model is utilized. The mathematical representation of the multi-linear regression model can be described by Equation (1).

y = bo + b1 × 1 + b2 × 2………+ bkx

(1)

It considers ERA score, job satisfaction, carbon emissions by direct electricity consumption and material wastage, cycle time, die setup time, lead time, and part rejection as process variables as per Table 1. Using the available input and output data, the program utilizes Python libraries (sklearn, pandas, and numpy) to predict process improvement. The regression model was developed and trained within the Google colaboratory integrated development environment [56]. Although the model was trained on a four-month dataset, incorporating more data into the database could enhance its predictive capabilities. Equation (2) represents the regression model specifically designed to forecast process improvement.

	78.49726722 + (−1.31493524 × 104 × ERA score) + (1.22162162 × 103 × job satisfaction) +
Process improvement =	(6.62302581 × 105 × carbon emission by direct electricity consumption) +
(%)	(−1.81089695 × 103 × carbon emission by material wastage) + (−1.57869883 × 104 ×	(2)
	cycle time) + (−1.47734080 × 104 × die setup time) + (3.44858961 × 105 × lead time) +
	(−5.74650191 × 103 × part rejection )

4. Discussion

The primary objective of this study is to examine the real-world effects of implementing an integrated framework that combines lean manufacturing, environmental considerations, and ergonomics in the context of the automobile parts manufacturing industry. To evaluate the outcomes, the study utilizes various assessment techniques, including the rapid entire body assessment (REBA) [35] in conjunction with the Standard Nordic Questionnaire (SNQ) to assess ergonomic risks, a job stress survey to measure employee job satisfaction, carbon footprint analysis (CFA) to evaluate environmental impacts [17], and value stream mapping (VSM) to analyze operational efficiency [26,34]. These assessment methods are applied both before and after the intervention phases to gauge the effectiveness of the integrated approach.

The research findings demonstrate a favorable relationship between ergonomics, green practices, and lean principles concerning performance indicators. Firstly, the study indicates a significant increase in job satisfaction (see Figure 10), indicating that the integrated framework positively influenced the well-being of employees. Furthermore, there was a notable decrease in the REBA score (refer to Table 14), implying an enhancement in ergonomic conditions, improved lighting levels, and a reduction in physical stress experienced by the workers. Consequently, this led to a substantial reduction of 30.3% in carbon emissions, showcasing a positive environmental outcome. This decline in carbon footprint underscores the effectiveness of the go-green aspect of the framework in promoting sustainability. The study also yielded positive outcomes in terms of operational efficiency. It revealed a noteworthy 15.5% reduction in cycle time, indicating improved speed and efficiency in production processes. Moreover, there was a significant decrease of 34.9% in lead time, suggesting enhanced overall process flow and reduced waiting times. Additionally, the study observed a 21% decrease in die setup time, indicating improved operational effectiveness and minimized downtime. Furthermore, the rejection rates, both at the customer end and in-house, exhibited substantial improvements. The rejection rate at the customer end decreased by 45%, signifying enhanced product quality and increased customer satisfaction. Additionally, there was a 32.5% decrease in the in-house rejection rate, indicating improved internal quality control measures and reduced waste. These findings highlight the positive impact of the integrated framework on operational efficiency and quality management.

The findings of this study are consistent with the existing literature [29,32,33,35,56]. Brito et al. [33] emphasized the significance of integrating ergonomic considerations within the lean manufacturing journey. They concluded that ergonomic risks and lean wastes can have reciprocal effects on each other, underscoring the interconnectedness of workplace ergonomics and lean principles. Similarly, Brown et al. [34] proposed the concept of ErgoVSM, which incorporates ergonomics into waste reduction efforts and facilitates transformative processes. Their suggestion emphasized the importance of including ergonomic interventions alongside waste reduction strategies. These literature references support the notion that the integration of ergonomics and lean practices is crucial for achieving optimal outcomes. Similarly, Ng et al. [27] conducted a study that demonstrated the potential for improving carbon-value efficiency. They reported that by enhancing production lead time by 64.7% and reducing the carbon footprint by 29.9%, carbon-value efficiency could be improved by 36.3%. The case study showcased how the proposed methodology effectively addresses the challenge of integrating and implementing lean and green practices, leading to favorable outcomes. This research highlights the positive impact of adopting the proposed approach in achieving beneficial results.

The findings of the present case study provide evidence that the integrated ergo-green-lean conceptual model is successful in generating favorable results in terms of employee satisfaction, environmental impact, and operational efficiency. The study emphasizes the significance of taking a holistic approach and integrating these factors into lean manufacturing practices to achieve comprehensive enhancements. These findings underscore the importance of considering the interconnectedness of these factors in order to achieve overall improvements in organizational performance. One of the key novelties of this case study lies in the development of a regression model to investigate the correlation between process improvement and selected key performance indicators (KPIs) encompassing all three paradigms. The developed multi-linear regression model, upon maturity, can be a powerful tool for practitioners to accurately forecast process improvements influenced by all three paradigms.

5. Conclusions

During this case study, a conceptual model of integrated ergo-green-lean has been validated for an automotive part manufacturing industry. Ergonomic risk score, job satisfaction, energy consumption, material wastage, cycle time, die setup time, lead time, and rejection rate were selected as KPIs for the integrated ergo-green-lean approach. The rapid entire body assessment (REBA) and job stress survey were the selected techniques for ergonomics; carbon footprint analysis (CFA) was considered for green; and a value stream mapping (VSM) tool was used for the lean paradigm. Kaizen was utilized to develop the intervention plan. After the comprehensive analysis of results obtained from the implementation of the model and comparison of pre- and post-intervention states, the following conclusions were drawn:

• Job stress survey results show that the work design improvements were helpful to reduce the physical and the psychological stress on the employees after the intervention plan as 56% of the employees were recorded in the low-stress zone compared to the pre-intervention results of 25%.
• The adopted techniques for all the three paradigms have shown significant improvements in selected KPIs, such as modified work design (ergonomics paradigm) leading to a reduction in cycle time by 15.5%; the inspection equipment calibration plan decreased the rejection rate and carbon emissions 19.2%; use of daylight not only improved the illuminance level in the workplace but also decreased the carbon footprint by 30.3% of that produced from electricity consumption (green paradigm); and die markings and smaller lot sizes reduced the lead time by 34.9% and lowered the die setup time by 21% (lean paradigm), hence validating the appropriateness of the adopted methodology. The results are aligned with the published research [27,33,35,36].
• The study revealed a substantial decrease of 45% in the rejection rate at the customer end, indicating a significant improvement in product quality and a higher level of customer satisfaction. Furthermore, there was a notable reduction of 32.5% in the in-house rejection rate, highlighting the implementation of enhanced internal quality control measures and a reduction in waste. These findings demonstrate the positive impact of the integrated framework on both external and internal quality performance and aligned with the literature [32].
• The developed multi-linear regression model can provide valuable insights and predictions, empowering practitioners to make informed decisions and drive effective process optimization.
• The study also reveals that the implementation of quality circles and employee training proved to be highly effective in increasing employee participation and obtaining valuable suggestions for improvement, hence validating the literature findings [5,6].

This case study has validated the use of the integrated ergo-green-lean framework, but it is limited to an automotive part manufacturing sector. The time span for the post-intervention state measurement is another limitation of the study. For the future recommendation, research can be conducted by developing multiple case studies in various automotive sectors to generalize the implication of the proposed integrated model as well as the regression model. Such studies could analyze the results from the perspective of sustainability’s triple-bottom-line approach [57,58,59]. Additionally, the effectiveness of the integrated model could be enhanced by implementing it during the part development phase.