Optimizing assembly line productivity in passenger car manufacturing: a comprehensive review with evidence from the Pune automotive region

Abstract

Passenger car manufacturing in India is undergoing a rapid transformation driven by growing demand for high productivity, sustainability, and more flexible assembly systems. Even though significant progress has been made, existing studies mostly focus on individual techniques such as Lean, automation, or digitalization and rarely provide a unified framework for productivity improvement. This review tries to fill that research gap by analyzing productivity drivers across multiple dimensions, including technical, ergonomic, digital, policy, and regional aspects, with a focus on Pune’s automotive cluster. The objective is to synthesize evidence-based strategies to optimize assembly line performance. A mixed-method approach was used, consisting of a literature review covering 2010 to 2025, along with a regional benchmarking meta-analysis on industrial case data. Metrics such as overall equipment effectiveness, process cycle efficiency, and takt time are used as core indicators in the study. Key findings indicate that Lean Six Sigma implementation improved process cycle efficiency (PCE) from 19.9% to 66.7%, cobot integration increased overall equipment effectiveness (OEE) from 80% to 87.74%, while scrap generation reduced from 0.06% to only 0.02%. Ergonomic redesigns of workstations led to a 9.7% gain in productivity. Digital twin simulations have shown that throughput increases up to 20%, and IRPA adoption resulted in a simulated workforce reduction of 45.69%. Policies such as Make in India PLI hold promise, yet challenges remain in terms of EV infrastructure localization. An integrated Lean–AI–digital model, when supported with ergonomic design and policy alignment, can possibly deliver 25%–40% improvement in productivity, especially in manufacturing hubs like Pune.

1 Introduction

Case studies show that identifying bottlenecks, conducting time studies, and determining the optimal number of workstations can significantly improve capacity and working conditions. Lean principles, such as line balancing, value stream mapping (VSM), and single minute exchange of die (SMED), have been used to reduce manpower need, improve cycle time, and remove non-value-added action. Actual data show that such changes allowed a reduction of 18 operators per day with line efficiency going from 73% to 93% and process efficiency increases by 72.7%. Lean tools such as 5S, Kaizen, and visual management also help maintain productivity gains for a long period (Sowmya et al., 2025).

AI-based technology, such as vision check systems, predictive maintenance, and process control, is now added to assembly lines to improve productivity and make tasks more accurate (Weber, 2024). Using collaborative robots, or cobots, has cut human mistakes and increased accuracy while helping meet sustainability aims. This led to overall equipment effectiveness (OEE) moving from 80% to 87.74%, first pass yield (FPY) increasing from 96.42% to 98.48%, and the scrap rate decreasing from 0.06% to 0.02% (Rada et al., 2024). Robotic mixed-model assembly balance is now considered a good method to handle real-world limit system complexity when different product types are made (Schibelbain et al., 2024). Few studies use the Bernoulli model improvability thinking buffer review to test how well a system performs (Koyuncuoğlu, 2024). Optimization ideas such as deep reinforcement learning are applied to adjust process setup in a multi-stage system, which helps improve both quality and line flexibility (Song et al., 2025).

The table brings together four major productivity drivers used in assembly systems: line balancing, Lean practices, automation, and AI performance evaluation tools. Across these studies, the methods range from shop-floor improvement techniques to analytical digital tools. The reported outcomes consistently cover efficiency, capacity, quality, reliability, and cost-related gains, as shown in Table 1.

1.1 Passenger car assembly systems

Modern production systems aim to manage spread in production time, increase efficiency, and keep correct order, mainly for custom products in high-volume factories. These setups may go from smooth flow types to fixed manual points. Each one is improved by using sensitivity checks to increase quality and speed (Daferner et al., 2021a; Daferner et al., 2021b). To handle hard trade-offs, heuristic multi-point optimization is used, which looks at the cost of setting up machines, labor use, system ready time, and yearly result value. Simulation tools are also used to accelerate R&D work and improve layout planning (Rawat et al., 2023). The modular assembly method, in which regular parts are put together, yields much better working final efficiency but depends on how much modular setup is done and how closely the team adheres to standard methods (Quang, 2022). Programmable logic controllers (PLCs) have made it possible to control different vehicle models in a flexible, efficient way without stopping the full production line. These systems improve safety and make systems more adaptive by using both hardware and software backups along with simple human–machine interfaces (HMIs) (Vasile-Alexandru et al., 2023). Full automation using robot arms and smart setups helps in quick, accurate fitting of complex parts such as climate control units, which reduces human error by a large amount (Georgieva and Balova, 2025). Collaborative robots, or cobots, used in assembling electronic modules gave good improvements: OEE went from 80% to 87.74%, FPY increased from 96.42% to 98.48%, scrap came down from 0.06% to 0.02%, and weekly output went up by 3,000 parts (Rada et al., 2024).

Line balancing utilizing math models and solving methods, such as Branch Cut, has shown promising results in cutting cycle time, wait time, and achieving high efficiency up to 99.91% for certain balanced systems (Singhtaun and Pipattanapoonsin, 2023). In addition, Lean manufacturing tools, such as value stream mapping (VSM), single minute exchange of dies (SMED), 5S, and Kaizen, are also used to eliminate blocks, reduce idle time for workers, and increase full system working, resulting in significant savings. In addition, simulation-based optimization tools, for example, Witness, are useful for identifying where blocks are occurring and testing parallel workstation concepts for ensuring balanced working. The “green” making processes are becoming more focused on solving the issue of material movement by utilizing electric towing vehicles, resulting in lower emissions and operating costs by utilizing two-goal optimization models. In addition, these tools are useful for ensuring automatic part delivery and a clean way of making products (Miranda et al., 2021). At the same time, there are improvements in the area of recycling of wiring harness assembly by utilizing dynamic rotary line benefits, which allow better material usage and proper scrap recycling to reduce environmental harm (Popa et al., 2024).

The table above consolidates the four core areas of concentration in automobile assembly: design, automation, efficiency, and sustainability. The above table presents the various methods that have been widely used in the various areas of concentration and the benefits that have been realized in the process. The benefits include flexibility, quality, OEE, reduction of cycle time and costs, emissions control, and waste reduction, as shown in Table 2.

The existing literature on assembly system design, automation, and balancing is well covered, but most of the research focuses on individual areas. There is limited research that links design decisions to direct productivity measures in an integrated framework. Moreover, there is limited information on the Indian passenger car assembly industry.

1.2 Strategic importance of the assembly line

Optimizing line balancing workload is very important for removing bottlenecks and reducing idle time manpower requirements, thus achieving cost savings in terms of increased output and flow (Mumtaz et al., 2024). Lean manufacturing, which is based on the Toyota Production System, links customer demands such as lower price, better quality, and faster delivery with internal targets such as achieving world-class production (Nakandala et al., 2024; Palange and Dhatrak, 2021). Efficient and good-quality assembly not only helps in achieving higher satisfaction for customers but also helps make the company competitive and improve the brand image (Priya et al., 2020; Díaz-Reza et al., 2024). Techniques such as 5S, Kaizen, SMED, just-in-time (JIT), TPM, and Kanban are used in a planned manner for removing waste in production and thus help in achieving cost savings in terms of labor, inventory, and equipment-related costs (Latha and Raghavendra, 2022). Some case studies show cost savings such as reducing 18 operators daily and saving more than ₹28 million yearly by reducing cycle time and making lines balanced. The productivity helps in achieving cost savings in terms of increased competitiveness, as depicted in Figure 1. The waste removed is in terms of defects, overproduction, waiting time, too much motion, and extra transport (Vadori, 2020). These are mainly defects, overproduction, waiting time, too much motion, and extra transport. The automation plan, such as AGV for task scheduling, and nature-inspired algorithms, such as the genetic algorithm and the artificial bee colony model, are increasingly being used for improving job and material handling. Digitalization and smart data tools provide greater support for Continuous Improvement (CI) and have also shown productivity increases of up to 25% without sacrificing quality (Pejic-Bach et al., 2020). Simulation models also help in achieving cost savings in terms of better ways to utilize resources and task order (Teshome et al., 2024). The Lean concepts align with achieving circular economy concepts that provide scope for safe and eco-friendly approaches with cost savings and higher productivity in markets that are stable and not changing rapidly (Deng et al., 2022; Agyabeng-Mensah et al., 2021).

FIGURE 1

The table compiles five strategic areas of assembly improvement: efficiency, cost reduction, quality, innovation, and sustainability. It aligns each area with its major practices and reported outcomes and shows that the documented benefits include throughput gain, idle time reduction, cost saving, quality improvement, faster cycle time, and better resource efficiency, as shown in Table 3.

The literature clearly demonstrates that assembly line productivity can improve cost, quality, delivery, and competitiveness. However, most of the literature discusses the benefits in an aggregate or conceptual sense, rather than through any specific productivity measure. The combined effects of Lean, automation, and sustainability are seldom jointly examined. Additional integrated research is needed to demonstrate how strategic benefits can be realized in plant-level Key Performance Indicators (KPIs).

1.3 Pune region-passenger car hub in India

The Pune region holds an important place in the passenger car manufacturing industry in India primarily because of the presence of large OEMs, a strong supplier base, and a good manufacturing base. The Chakan zone, as can be inferred from Table 4, attracts big names in the automobile industry, such as Volkswagen, Mercedes-Benz, Mahindra, and more than 700 small to medium-sized component suppliers. The high per capita income, skilled workforce, and favorable regulations make Pune an even better place for the industry. Industry 4.0 and green technologies are slowly being adopted, but legacy systems are still in place. The Pune region has emerged as a major auto hub in India, driving growth in passenger car manufacturing and assembly operations. It supports premium segment expansion and hosts key firms such as Force Motors and Fiat India, as shown in Figure 2.

FIGURE 2

The table compiles the main regional industrial features of Pune’s automotive ecosystem through eight aspects: geographic context, industrial hubs, OEM presence, production scale and supply chain structure, sustainability, transition, economic contribution, and sectoral challenges. Together, these entries present a consolidated profile of Pune as an automotive cluster by combining location data, industry structure, manufacturing capacity, supply network characteristics, technological transition, economic role, and current constraints, as shown in Table 4.

The reviewed literature also shows that Pune is a significant automotive hub with strong OEMs and supplier bases. However, in-depth studies on assembly line productivity within the automotive cluster are scarce. Only a handful of studies have attempted to integrate KPIs, technology, productivity hurdles, and other aspects with respect to the automotive industry in Pune. Therefore, there is definitely a need for region-based productivity analysis.

This study aims to provide a clear picture of assembly line productivity in passenger car manufacturing. Past studies have discussed Lean tools, automation, ergonomics, technologies, regions, etc. However, none have provided a clear picture of all these major aspects. Moreover, none have provided clear links with KPIs such as OEE, process cycles efficiency (PCE), FPY, takt time, throughput, manpower efficiency, etc. This study aims to provide clear links with all these KPIs. Moreover, it also provides special focus on the automotive region in Pune, which is a significant passenger car manufacturing hub in India.

1.4 Methodology

This review employs a mixed-method systematic review approach that incorporates elements of bibliometric study, conceptual synthesis, and meta-analysis of industrial data from 2010 to 2025. The literature set for this review was derived from a multistage filtering approach. The first step involves structured searching in Scopus, Web of Science, and IEEE Xplore databases using specific keywords related to assembly line productivity, lean manufacturing, digital twin, and automotive manufacturing in India. The identified literature set was further filtered for duplicates and clearly irrelevant literature. The second step involves reviewing the titles and abstracts to further narrow the literature set to include only those studies that are directly related to passenger car manufacturing, assembly line productivity, lean manufacturing, digitalization, AI/ML applications, ergonomics, and policy-related manufacturing performance. The selected literature set was further subjected to full-text eligibility assessment. The literature selected for this review was published between 2010 and 2025 and is relevant to Indian automotive manufacturing and similar contexts. A total of 207 literature sources were selected for this review.

The study selection process was conducted using the PRISMA 2020 framework to ensure a systematic, transparent review, as shown in Figure 3. The search was performed using the keywords “assembly line productivity,” “passenger car manufacturing,” “automotive assembly,” “lean manufacturing,” “digital twin,” “Industry 4.0,” and “Pune automotive region” across the Scopus, Web of Science, ScienceDirect, and Google Scholar reference lists. A total of 543 records were identified. After removal of 121 duplicate records and 12 irrelevant records, 410 studies were screened through titles and abstracts. Studies were included when they examined passenger car assembly, productivity, empirical improvement methods, industrial performance indicators, or findings relevant to the Pune automotive region. Studies were excluded when they focused on non-automotive sectors, conceptual discussions without data, unrelated vehicle categories, or duplicate datasets. Full texts of eligible studies were then assessed in detail. Finally, 207 articles that satisfied all inclusion criteria were selected for qualitative synthesis, covering Lean systems, automation, ergonomics, AI scheduling, digitalization, and productivity enhancement strategies.

FIGURE 3

All benchmark values used in this review were taken from peer-reviewed journal articles and were properly cited. The comparison was carried out using commonly reported productivity indicators such as OEE, takt time, throughput, FPY, manpower, and efficiency.

1.4.1 Proposed integrative framework

Based on the systematic synthesis of the reviewed literature, this study proposes an author-formulated integrative framework to explain how productivity improvement in passenger car assembly can be achieved through the coordinated interaction of multiple operational strategic enablers. The need for such a framework arises from the clear gap identified in the present review, namely, that earlier studies have largely examined Lean practices, automation, digitalization, or ergonomic interventions in isolation, while limited attention has been given to their combined influence within a unified productivity model, particularly in the Indian automotive context. In response to this gap, the present review consolidates evidence from the shortlisted studies. It organizes the major productivity drivers into a single conceptual structure aligned to optimize assembly line performance in passenger car manufacturing. This framework is therefore intended to serve as the central analytical model of the study, linking the review methodology and the thematic synthesis of evidence and the broader goal of developing an integrated productivity improvement approach for manufacturing environments such as the Pune automotive region.

The suggested framework is organized in the form of a layered productivity path in which the identified enablers are seen to function in an integrated and cohesive manner. The fundamental position of the suggested framework is in the regional industrial context of the automobile industry in the country of India and, more specifically, the region of the Pune automobile cluster. In this context, the suggested framework recognizes the common issues that impact productivity in the region. The issues are bottlenecks, imbalance in the production line, downtime, quality loss, inefficiency in the material handling process, lack of real-time visibility, and ergonomic issues. The suggested framework offers five integrated intervention domains: Lean tools, digitalization practices, AI/ML-based systems, ergonomic considerations, and policy-regional enablers. The effects of these considerations are seen in the automobile assembly plant’s overall performance in terms of OEE, PCE, FPY, takt time, cycle time, throughput, downtime, scrap rate, and manpower productivity, thereby linking intervention design with measurable manufacturing outcomes.

The analytical significance of the proposed framework lies in its ability to move beyond the fragmented treatment of productivity improvement that characterizes much of the existing literature on automotive assembly systems. While the research has demonstrated the individual benefits of each of the interventions in the form of Lean implementation, cobot integration, digital twin simulation, ergonomic interventions, and policy support, their individual benefits have been demonstrated in isolation rather than their potential cumulative impact when implemented in combination. This is where the proposed framework is believed to be more effective in the context of the Pune automotive industry, where the assembly performance is influenced by a complex array of OEMs, suppliers, technological transition pressures, and sustainability pressures. Therefore, the proposed framework is believed to be more effective if considered not merely as a visual representation of the individual research studies but more so as the key contribution of the review itself, providing the critical pathway through which the cumulative benefits of the integrated interventions may be achieved in the context of sustained productivity, quality, and flexibility in the context of the automotive industry, particularly in the context of passenger car manufacturing.

Moreover, it is also thought that the suggested framework is more effective if the process of implementation is viewed in relation to the progressive levels of maturity, which are closely associated with organizational and technological development in relation to assembly processes. At the basic level, productivity increases are mainly affected by Lean tools, basic standardization, basic monitoring of performance, and ergonomics. At the next level, structured digitalization, real-time visibility, and workstation design have more impact on process control coordination. At the advanced level, AI/ML-enabled prediction, digital twin-supported simulation, and intelligent monitoring facilitate proactive decision-making and adaptive production management. At the highest maturity level, these capabilities are further reinforced by policy support, supplier integration, sustainability alignment, and region-specific industrial readiness, thereby enabling system-wide productivity optimization in passenger car manufacturing.

Figure 4 presents the integrated framework developed in this study for improving passenger car assembly productivity. The framework links Lean tools, digitalization, AI/ML, ergonomics, and policy support, with operational mechanisms and key assembly KPIs. It explains how coordinated interventions can improve productivity, quality, flexibility, and competitiveness in the Pune automotive context.

FIGURE 4

2 Conceptual framework

The table compiles the main productivity quality KPIs used in automotive assembly by covering machine-level, line-level, and defect-related measures such as OEE availability, performance quality, scrap rate, issues per million (IPPM) throughput, cycle time, takt time, and overall manufacturing line effectiveness (OMLE). It brings together their definitions, operational purpose, and example findings in one integrated view of how assembly performance is measured across equipment speed, output quality, and total line effectiveness, as shown in Table 5.

2.1 Overall equipment effectiveness

Overall equipment effectiveness is a performance measure that integrates three essential elements. Availability is the proportion of planned time that the machine is running, performance is the proportion of time running compared to its planned speed, and quality is the proportion of good parts produced compared to total parts produced (Soltanali et al., 2021; Kang et al., 2020). The three elements of OEE provide a complete picture of the efficiency of the process, and it is particularly significant in the automotive manufacturing process. In semi-automatic car manufacturing processes, OEE is regularly measured every day through manufacturing execution systems (MESs), and it is significant in demonstrating the strong relationship between OEE and other critical cost factors such as machine maintenance, labor, and quality control. One case study of seat frame manufacturing processes revealed that over a 15-year period, high OEE is always related to high profits (Dobra and Jósvai, 2022). To improve the OEE of automotive manufacturing processes, manufacturers have adopted measures such as Six Big Losses Analysis, which emphasizes the importance of solving machine problems related to speed, stop time, and defect rate (AH et al., 2024). Tools such as a Pareto chart, Fishbone, and Ishikawa analysis are used to find root causes leading to clear gains in availability (+4.6%), performance (+8.06%), and quality (+6.66%) (Tayal et al., 2021). The Define, Measure, Analyze, Improve, and Control (DAMIC) model from Six Sigma also helps in removing bottlenecks step by step (Prasetyo and Veroya, 2020). In high mix changeable production, flexibility included (OEE_Flex) is used to include changeovers and product variety to show a better performance view (Van De Ginste et al., 2022).

New trends include predictive OEE (POEE), which uses AI such as LSTM Deep Q Networks to estimate losses before they happen (Xu et al., 2024). Value-added OEE makes changes for ideal cycle time so that extra hidden capacity above normal OEE gets seen (Hung et al., 2022). Smart factory systems using cloud edge device links allow real-time OEE checks and dynamic shop tuning (Zhang C. et al., 2024). The table compiles six OEE-oriented focus areas in automotive assembly, covering daily tracking, loss identification, root cause analysis, bottleneck reduction, flexibility integration, and predictive analytics. It brings together the main methods used in each area and the reported outcomes, which include cost linkage, capacity gain, and improvements in availability, performance, quality, higher OEE hit rate, better mass-customization fit, and proactive loss prevention, as shown in Table 6.

Many studies report OEE, cycle time, takt time, throughput, and related metrics as key indicators of assembly performance. However, these measures are often discussed separately without illustrating their interaction with other measures in an entire system of productivity improvement. There is little research done on linking KPI measures with joint Lean, ergonomic, and digitalization measures. A better KPI-based framework is required.

2.2 Line efficiency

Line efficiency is stated as the ratio of useful working time to the full available time on line. It acts as a key number for increasing output. It mostly improves when the tasks are given the right arrangement in the right order (Yang et al., 2023; Nourmohammadi et al., 2025). Mixed model sequencing is needed to manage automobile setups that build many products without losing speed (Shang et al., 2024). Energy-aware balancing is also done to minimize the loss in unused energy while maintaining the correct spread of tasks (Bänsch et al., 2021). On the other hand, balance loss tells how much idle time is observed across workstations compared to full time, which directly shows if a task is not spread well (Mumani et al., 2024). This mostly happens because of uneven task duration from product variation or fixed line layout, which stops easy task shifting (Zhang et al., 2024b).

To reduce such balance loss, manufacturers use heuristic metaheuristic methods such as the genetic algorithm, co-evolutionary algorithm, and discrete bees algorithm to lower workload differences and maintain better station balance. Some integrated frameworks also include sequencing, ergonomic needs, and resource limits to further boost balance efficiency in complex production setups (Battaïa and Dolgui, 2022). Workstation utilization shows how well the available time at a workstation is used for doing value-added work (Žižić et al., 2024). To maximize this, tasks are grouped in a smart way to reduce shifting downtime and are also scheduled in parallel, which is more important in high-resource sectors such as aircraft or automotive final assembly (Bao et al., 2023). Use of human–robot collaboration helps further in increasing utilization flexibility, mainly in mixed-model setups with increased variation and custom needs (Kheirabadi et al., 2023). The table below compiles three core line-performance metrics used in automotive assembly: line efficiency, balance loss, and workstation utilization. It aligns each metric with its definition, assembly approach, and analytical method. It presents a compact view of how productive time, idle time, and station-level usage are assessed in line planning studies, as shown in Table 7.

2.3 Productivity benchmarks

The table compiles seven comparative metrics between the global automotive industry and the Indian automotive industry, covering scale, automation, units per hour (UPH), downtime, energy efficiency, sustainability, and workforce skills. It brings together the corresponding status of both contexts, with the reported findings presenting a consolidated benchmark view of assembly capability improvement industrial transition in India relative to global practices, as shown in Table 8.

Benchmarking data indicate that Indian automotive manufacturing is moving in the right direction in terms of Lean and digital adoption, but it is yet to catch up with world leaders in some areas. However, plant-level benchmarking data are scarce in the existing literature. In some cases, it is difficult to compare data, and researchers must rely on contextual data instead of numerical data. Future research should focus on developing benchmarking frameworks.

3 Productivity improvement techniques

The table consolidates eight improvement techniques used in assembly productivity studies, including waste assessment, line balancing, Lean elimination, parallel assembly lines, process redesign, continuous improvement, and Lean Six Sigma. It brings together the main tools, targeted improvement areas, and reported outcomes in one integrated view in which the results are expressed through PCE gain, lead-time reduction, manpower productivity improvement, higher utilization, and lower non-value-added activity, as shown in Table 9.

3.1 Lean manufacturing

Lean reduces cycle time, as shown by substantial cycle time reductions on automotive crankshaft and seat manufacturing lines (Deokar et al., 2019). The use of Lean tools such as 5S, Kanban, Kaizen, and standardized work further improves efficiency, reduces transportation time, and increases overall output in automotive component production environments (Nallusamy and Saravanan, 2016). Automotive firms adopting practices from the Toyota Production System (TPS), including just-in-time (JIT), poka-yoke, cellular manufacturing, and continuous improvement, report significant reductions in inventory, improved workflow, enhanced quality, and better resource utilization (Alexander and Saleeshya, 2022). Lean adoption in component and assembly lines, such as seat welding, engine assembly, and exhaust system manufacturing, has shown measurable improvement in productivity, defect reduction, space utilization, and operator efficiency (Patani et al., 2026). The broader cultural pillars of Lean, such as employee involvement, continuous improvement, and workplace organization, remain essential for sustaining improvements, as evidenced by successful, long-term Lean implementation in global automotive companies.

3.2 Time and motion studies and work sampling

Manufacturing plays a central role in improving productivity in passenger car manufacturing by systematically eliminating waste and enhancing value-added activities. Techniques such as value stream mapping (VSM) help identify bottlenecks, non-value-added processes, and opportunities for improvement.

Traditional and modern methods play a key role in improving both efficiency and ergonomics in automotive assembly work. Old observation motion-breakdown techniques are still used widely, in which tasks are manually tracked and split into smaller steps to find waste and set productivity standards (Negaard et al., 2020; Mohamed et al., 2022). On the other side, predetermined motion time systems (PMTSs) such as methods-time measurement (MTM) give a more structured way by dividing manual tasks into fixed motion types, which reduces personal bias that was common in older systems such as Reichsausschuss für Arbeitszeitermittlung (REFA) (Rückert et al., 2021). Work sampling (WS) still provides useful data by checking how much of worker time goes into value-added vs. non-value-added tasks (Teizer et al., 2020). However, with an increase in auto data tools, this field has changed a great deal. Now, tools such as the machine learning model, visual sensors, and motion sensors such as accelerometers and Global Navigation Satellite System (GNSS) are used to track activities in real time with fewer mistakes, allowing planners to better group related movement (Sanhudo et al., 2021).

For checking efficiency, takt time change has shown a good impact, as some studies reported cutting takt time by 5 s inside the 30 s to 120 s range may bring a nearly 1% increase in line efficiency (Roser et al., 2025). The dynamic takt time (dTT) method changes takt with live product mix shift, which helps better run a mixed model line (Zhang et al., 2021). Line balancing is mostly carried out with simulation tools such as Witness ARENA, which help assign station work test ideas such as task doubling or better step order (Adel et al., 2025). Adding body safety checks and energy-aware balancing in line design also leads to improved worker health and reduced running cost (Rahman et al., 2023). The table compiles four assembly-focused improvement studies covering takt time analysis, Toyota Jishuken activity, simulation-based bottleneck detection, and mixed-flow optimization. Together, these entries present the key findings in terms of value-added time, cycle time, output waste reduction, efficiency, and satisfaction outcomes within automotive assembly settings, as shown in Table 10.

The research provides good support to the use of Lean tools, work study, simulation, and takt time tuning in the improvement of assembly plant productivity. However, the techniques are still largely discussed in isolation and not integrated into the improvement system. This gives rise to the need to discuss the hybrid models of productivity in the assembly of passenger cars.

3.3 Ergonomics human factors

The table compiles six ergonomics-related interventions in automotive assembly that include workstation redesign, layout improvement, assistive devices, organizational measures, and fatigue-based line balancing. Across these studies, the findings are presented through numerical qualitative indicators linked to productivity, OEE cycle time, per capita output, workload discomfort, fatigue, and musculoskeletal health outcomes, as shown in Table 11.

Existing studies have shown that ergonomic redesign can lead to improved comfort, reduced fatigue, and potentially improved productivity. However, ergonomics studies are often not related to line balancing, takt planning, and digital monitoring. The connection between ergonomic correction and other manufacturing KPIs is still not fully established. More studies are needed to place ergonomics in the role of a productivity driver rather than a supporting role.

4 Industry 4.0 digitalization

The table compiles 10 digitalization-focused studies in automotive-related manufacturing contexts covering Lean–Industry 4.0 integration, digital twins, IoT simulation, visualization, and supply chain digitalization. Across these entries, the reported evidence is presented through qualitative and quantitative findings linked to efficiency waste reduction, lead time, downtime, quality, flexibility, throughput, cost control, and sustainability-related gains, as shown in Table 12.

The digital twin, IoT, smart monitoring, and AI technologies demonstrate their potential for increasing assembly productivity and control. However, the literature on digital technologies is scattered across various digital tools, with limited information on their interaction in Indian passenger car manufacturing plants. Adoption barriers such as cost, skills, and infrastructure are also not consistently linked with productivity outcomes. This creates a need for phased and integrated digital productivity models.

4.1 Manufacturing execution systems

The table consolidates eight MES-focused studies related to automotive smart manufacturing by covering benchmarking dashboards, scheduling OEE, tracking ERP–shop floor integration, strategic Industry 4.0 roles, systematic review findings, and intelligent MES frameworks. Together, these entries present the main contributions, performance outcomes, and assembly relevance of MES from real-time monitoring scheduling to digital integration modularity and intelligent production control, as shown in Table 13.

5 Artificial intelligence data-driven methods

The table compiles seven machine learning applications in automotive assembly covering assembly improvement, cycle time, prediction quality, prediction defect prognosis, predictive maintenance, general production analytics, and complex manufacturing quality modeling. Across these studies, the evidence is presented through different ML methods and dataset types. Reported metrics include accuracy, prediction rate, and uncertainty-aware performance for assembly-related decision support, as shown in Table 14.

5.1 AI-based bottleneck detection

The table compiles 12 AI ML-based approaches for root cause analysis bottleneck detection across automotive-related manufacturing contexts, including multi-agent AI graph neural networks, knowledge graphs, digital shadows, explainable AI, cognitive digital twins, and neuro- and symbolic systems. Across these studies, the reported evidence is presented through detection, performance, data-handling capability, interpretability, expert integration, real-time monitoring, and predictive bottleneck identification, which together summarize the current methodological range of intelligent Root Cause Analysis (RCA) frameworks in assembly-focused environments, as shown in Table 15.

5.2 Equipment health analytics

The table compiles 12 predictive maintenance-related studies across automotive manufacturing contexts by covering anomaly detection, sustainable maintenance, time-series analytics, digital twins, Industry 5.0 strategies, component health monitoring, fault diagnosis, and cyber-physical systems. Across these studies, the reported evidence is presented through methods, findings, and outcome measures such as OEE, Mean Time to Repair (MTTR), downtime, prediction accuracy, reliability, asset life, failure rates, and asset utilization, as shown in Table 16.

5.3 Reinforcement learning

The table compiles eight optimization-oriented methodologies applied across hybrid automotive assembly, including adaptive manufacturing peg-in-hole assembly, logistics storage standard assembly line balancing, automotive wiring, and automotive collision avoidance system (ACAS) layout optimization. Together, these entries present the reported findings in terms of cycle-time reduction, adaptive sequencing success rate, scalability, workload balance, energy efficiency, bottleneck removal, and productivity improvement across simulations, metaheuristic reinforcement learning, and digital twin-based approaches, as shown in Table 17.

5.4 Digital twin-based optimization

A workflow of digital twin-based optimization in a passenger car assembly line, showing the loop from physical system data acquisition to virtual model optimization back to implementation, is shown in Figure 5.

FIGURE 5

The table compiles nine digital twin-based optimization applications in automotive assembly, covering geometry control, lifecycle optimization, transmission assembly, smart manufacturing, design scheduling, EV performance, component optimization, and tolerance adjustment. Across these studies, the reported findings are presented through improvements in geometric quality, cycle time, balance rate, productivity, efficiency, adaptability, energy assessment, performance targeting, and virtual parameter calibration within automotive production environments, as shown in Table 18.

5.5 Outsourcing, modular assembly

Real findings on Indian manufacturing show that outsourcing in the technical R&D service area provides a substantial boost to multi-factor productivity even during times of a slow economy (Kar and Dutta, 2018). This improvement comes as outsourcing allows firms to focus more on core strengths while taking help from outside experts, which helps cut costs and improve the speed of work (Mukherjee, 2018). In the Indian auto sector, outsourcing appears in different ways across the value chain. OEMs mostly benefit from external technology, such as capital equipment, while component suppliers gain more from non-physical tech, such as royalty license agreements, which show how much tech outsourcing matters in increasing productivity (Saripalle and Gupta, 2025). Supplier integration also plays an important role in improving assembly performance. Strong IT support makes integration run more smoothly. Suppliers that stay well integrated show better working results, which later improves financial results, making integration one key part of competitiveness (Afshan and Motwani, 2021). Supply chain integration (SCI) works such as a bridge between planning in the supply chain and real outcomes, showing how it helps turn an idea into actual productivity gains (Dhaigude et al., 2021).

Even though direct study of modular assembly output in the Indian passenger car field is still limited, data from the Indian auto parts sector show that modular actions, such as standardization, pre-assembly, and use of Lean tools, help increase efficiency and remove waste (Ojha et al., 2025). In addition, modular assembly depends strongly on supplier link outsourcing, which proves how these three ideas support each other in boosting the full productivity of the automotive assembly line.

6 Pune automotive manufacturing ecosystem

The table compiles six dimensions of the Pune automotive cluster: cluster composition, supply chain structure, key players, technological trends, sustainability practices, and challenges. Together, these entries present a consolidated profile of the cluster by combining ecosystem actors, network structure, industrial participants, digital transition, environmental orientation, and operational constraints, as shown in Table 19.

The Pune automotive cluster has a strong OEM presence and a wide supplier base. However, this also creates cluster-specific risks. Its multi-tier supply chain and just-in-time operations increase the risk of delay coordination failure. Many MSMEs also face difficulty in adopting new digital tools due to the high upgrade cost. Infrastructure gaps, EV transition pressure, and uneven technology readiness further affect productivity and operational stability.

6.1 Productivity challenges

The table compiles five major productivity challenges in assembly systems along with the main methods used to address them. Across these studies, the reported findings are presented through changes in cycle time, line efficiency output, workforce level, WIP supply, performance cost savings, production speed, turnaround time, and service quality, as shown in Table 20.

7 Comparative review of model frameworks

The table compiles six model frameworks used in assembly productivity studies, including hybrid optimization, OEE analysis, DSM comparison, Design for Assembly/assembly line balancing (DFA ALB) review, and evolutionary scheduling. Across these studies, the evidence is presented through methodology, performance metrics, application setting, and key findings related to efficiency, cost variability, OEE assembly, simplification, scheduling, and research direction, as shown in Table 21.

8 Future research directions

Line balancing and waste cutting remain the main methods of increasing output in auto assembly. Studies show that tools such as the Yamazumi chart, SMED, and value stream mapping (VSM) work well for removing non-value step waiting time at workstations (Batwara et al., 2024). Future research may try to spread Heijunka, a method for smooth production across the supply chain, to reduce inventory and make flow more stable, which helps increase both worker and machine results (Gupta and Kumar, 2020). Lean Six Sigma (LSS) combines error control with waste removal and provides a strong path for long-term progress, especially when used in components and in full vehicle assembly (Swarnakar et al., 2021). Lean automation, which joins Lean tools and IoT digital twin prediction models, can improve planning for maintenance and also support a JIT system (Dixit et al., 2022). However, the full benefit from Lean often stops due to people-related issues. Some studies find that training in Lean tools and safety learning for humans and robots together can help better Lean adoption and safer use of cobots (Mathiyazhagan et al., 2021).

Making a continuous improvement habit using daily practices such as Kaizen 5S has been shown to support regular growth in productivity over time (Joseph et al., 2021). From the environmental side, mixing Lean thinking with green step helps connect waste reductions with aims such as reduced emissions and better energy use (Mondejar et al., 2021). Creating circular supply chains using smart moving tools and robots also helps in the safe handling of dangerous materials and supports a greener assembly method (Farid et al., 2025). The table compiles four future-oriented research themes in assembly systems: lean integration, automation, workforce training, and sustainability. Together, these entries present the proposed research focus, expected impact in terms of waste reduction, workload balance, flexibility, safety, emission reduction, and resource efficiency, as shown in Table 22.

9 Managerial policy implications

The table compiles seven focus areas linking managerial implications, policy implications, and key outcomes in assembly supply chain improvement studies. Across these entries, the evidence is presented through Lean efficiency, green supply chain, agile systems, SME change, and big data adoption with outcomes related to cost, quality, productivity, sustainability, competitiveness, and growth, as shown in Table 23.

9.1 Practical productivity improvement guidelines

Different productivity sustainability plans are now considered drivers of change in Indian car assembly lines. Heijunka has helped move away from the old batch-type making to a pull-based setup that balances work and reduces stock, with results showing a 63% increase in human productivity and a 39% increase in machine output, making workers feel more satisfied (Gupta and Kumar, 2020). At the same time, green actions are added by using clean transport such as hydrogen or biodiesel vehicles for factory supplier movement, which reduces emissions and saves cost (Das et al., 2024). A larger green Lean system supports the circular economy by focusing on resource reuse and reducing waste in all assembly steps (Lim et al., 2022/03). In this way, green quality circles, a low-cost group effort by employees, have been shown to be helpful for improving ecological results without high spending (Goyal et al., 2022). Changes made on the supplier side to reduce waste by matching global quality environmental rules are helping in lowering emissions and cutting scrap (Prashar, 2023). Human-focused smart production (HSM) using deep learning to watch manual work in real time is now helping in reducing cycle time and improving output quality in flexible assembly (Selvaraj et al., 2024). However, world-class quality (WCQ) practices are still not followed everywhere. There is a need for better training in a full quality system, TQM, a clear audit process, and a team culture that stays focused on customer goals (Singh et al., 2023). Failure control at the assembly step level through layout optimization, workspace design, and appropriate equipment choices is emerging as a critical factor in minimizing rework and enhancing production consistency (Biscaia et al., 2021).

10 Discussion

The results obtained in this review indicate that the traditional digital strategies should not be seen as alternatives. Rather, they should be used in conjunction with each other. The traditional methods, such as line balancing, VSM, SMED, 5S, Kaizen, layout improvement methods, and work study, are very important in the assembly of passenger cars. The value of these methods is clearly seen in the studies. The implementation of Lean Six Sigma has improved PCE from 19.9% to 66.7%. The line efficiency was also improved from 73% to 93%. Similarly, the Kaizen approach was used to improve productivity. The cycle time was reduced from 80 s to 75 s. Productivity was also improved by 6.7%. The digital AI-based strategies also provide better benefits. The digital AI-based strategies improve visibility, prediction, and control. The integration of cobots has improved OEE from 80% to 87.74%. The FPY was improved from 96.42% to 98.48%. Scrap was reduced from 0.06% to 0.02%. The digital twin studies have also reported improvements in throughput of up to 20%. However, the digital AI-based strategies are not easy to implement in the Indian SMEs. The high installation cost and lack of digital infrastructure in the country are the major issues. The review also indicates that the implementation of sustainable supply chains with the help of big data is very low, with nearly 17% moving toward basic MES or IoT-based monitoring. Advanced AI digital twin tools should come later, only in high-impact areas.

The review also points out two key concerns related to the adoption of AI. One is explainability. If the managers who are operating the AI cannot explain how the AI is making the decision, the trust factor is low. Another is the replacement of the workforce. Some simulation-based studies show the reduction of the workforce up to 45.69%. However, the general findings show that the best results are achieved if the workforce is assisted, not replaced. Sustainability is another term that should be considered more carefully. The reviewed literature has shown that 26% reductions in lead-time, 50% reductions in GHG, 14% reductions in inventory, 29% reductions in setup time, 15% reductions in absenteeism, and 9.7% improvements in ergonomic productivity are possible. However, the findings are not presented uniformly and are, therefore, not easily comparable. Future research should focus on plant-level validation in Indian SMEs, explainable AI tools, low-cost digital adoption models, and standard sustainability indicators that combine productivity, labor impact, and environmental performance.

11 Conclusion

This review synthesizes a broad range of productivity-enhancing strategies used in Indian passenger car assembly lines with special focus on the Pune automotive region. It tries to unify classical Lean practices, digital transformation, ergonomic solutions, modular outsourcing, and policy frameworks into one integrated model. Findings show that Lean Six Sigma, VSM, and SMED still remain key in improving PCE cycle time, whereas Industry 4.0 tools such as MES, digital twins, and predictive analytics enable more dynamic control and higher OEE levels. The inclusion of ergonomic interventions along with AI-enabled cobots has helped improve operator efficiency and also reduce operational waste. Across reviewed case studies, the productivity gains were clearly measurable, as PCE increased from 19.9% to 66.7%. OEE increased from 80% to 87.74%. IRPA contributed to 45.69% workforce reduction in simulations. Ergonomic workstation changes showed 9.7% increase in output digital twin models, enabling up to 20% enhancement in throughput. Modular outsourcing helped in better component quality and supplier coordination; however, it remains vulnerable to risk integration limitations. Policy programs such as Make in India PLI hold potential, but some execution bottlenecks are still visible.

This study suggests that productivity improvement must come through balancing Lean foundations with digital systems, ergonomic improvements, and localized policy backing. Such integrated strategies can offer sustainable 25%–40% productivity growth, especially in mixed-model, labor-heavy environments such as Pune. This review proposes a holistic approach for the improvement of assembly line productivity in passenger vehicle manufacturing. It combines the concepts of Lean, digital, AI, ergonomics, and policy-regional factors in a single pathway, focusing on the Pune automotive region.

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