An empirical assessment of assembly line productivity constraints in automotive manufacturing systems using statistical analysis

Abstract

Introduction:

Assembly line productivity is a critical performance factor in passenger car manufacturing because it directly affects output stability, production cost and delivery efficiency. Existing studies discuss machine, manpower, supply chain and quality issues separately, but limited empirical work examines these barriers together in passenger car manufacturing units of the Pune region. To address this gap, the present study provides an integrated empirical assessment of productivity constraints by combining barrier ranking, interrelationship analysis and factor-based classification within a single quantitative framework. This study aims to identify, evaluate and classify the major barriers affecting assembly line productivity using quantitative industry responses.

Methods:

A structured questionnaire was used to collect responses from 535 respondents associated with passenger car manufacturing operations. The data were analyzed using frequency analysis, descriptive statistics, correlation analysis and exploratory factor analysis.

Results:

The results showed that lack of modern tools or outdated machinery was the most critical barrier with 97.0% total agreement and the highest mean score of 4.60. Inadequate maintenance followed with 95.5% agreement and a mean score of 4.54. Frequent product defects recorded 93.7% agreement and a mean score of 4.42. Equipment breakdowns recorded 89.3% agreement while customer complaints due to defects recorded 85.3% agreement. Correlation analysis showed strong association between absenteeism or operator delays and poor worker coordination with r = 0.685. Factor analysis extracted four major productivity dimensions with a KMO value of 0.863 and 58.223% total variance explained.

Discussion and Conclusion:

The main contribution of the study is the identification of statistical associations and shared dimensions among technical, workforce, supply-logistics and quality-related barriers. The study concludes that productivity improvement needs integrated action through technology upgradation, preventive maintenance, defect control, logistics improvement and workforce coordination. Beyond the Pune automotive cluster, the findings provide useful guidance for passenger-car assembly units and similar manufacturing systems in emerging industrial regions where perceived productivity constraints are associated with machine, workforce, material-flow and quality-control limitations. The study is limited to questionnaire-based responses from selected units and therefore the results should be generalized cautiously without plant-level longitudinal validation. Future work may apply regression, SEM or machine-learning models for predictive validation.

1 Introduction

Productivity of assembly lines within the automotive industry in India has changed due to the adoption of lean manufacturing practices, industry structural changes and technological developments. Traditionally, automobile manufacturing industries in India have used manual and semi-automatic processes thus, productivity has depended on changes in the manufacturing industry and adoption of technology, mainly in the early 2000s. The adoption of lean manufacturing practices is one of the major factors behind productivity improvements because researchers have found evidence on the wide use of lean manufacturing systems LMS and other process improvement practices in the auto component companies and OEMs to increase efficiency, eliminate wastes and compete internationally (Prabhushankar et al., 2015). Productivity strategies are still based on the Toyota Production System, which stresses just in time production and respect for the human system to minimize wastes (Sugimori et al., 1977). Productivity is enhanced by lean manufacturing techniques such as line balancing, ergonomic improvement and elimination of non-value adding activities, such as the application of Yamazumi chart, Kaizen, 5S, single minute exchange of die (SMED) and value stream mapping VSM (Sowmya et al., 2025; Gambhire et al., 2024).

Simulation based techniques have also found application in the analysis of potential disruptions, layout testing and evaluation of balance decisions without pausing actual production operations as indicated in the case study from some of the Indian auto manufacturing companies (Padhi et al., 2013). In the context of industries, the factors that impact competitiveness and productivity include skills among the workforce, performance of the supply chain, regulatory requirements and the extent of adaptability among auto manufacturers to changing demands and product varieties (Joshi et al., 2013). The emergence of mixed model assembly lines in the automobile manufacturing sector is indicative of the rising product variety. Research shows that higher product variety is detrimental to productivity due to lack of proper planning and management (Asadi et al., 2019). The advent of new technologies such as digital twin technologies, industry 4.0 technologies and artificial intelligence based production processes has led to the reengineering of productivity in automobile assembly line (Sharma et al., 2022). While there have been notable advancements, challenges continue to exist in balancing human elements with other parameters (Papanagnou, 2019).

The Table 1 synthesizes major productivity constraints affecting automotive assembly lines. Key issues include line imbalance, non-value-added activities, ergonomic strain, bottleneck processes, fatigue, worker variability, material delays, cycle time variation, workstation reliability, excess motion, product variety, weak planning, human reliability, absenteeism, limited lean adoption and poor buffer management. The evidence provided by the literature review shows that machine-related variables, workforce variables and process variables all contribute significantly towards productivity outcomes in the Indian automotive industry. Machine-related variables include effectiveness, capital productivity, maintenance and automation. Workforce variables include skills, gender diversity, motivation, operator variables and human factors. Process variables are connected with the implementation of lean concepts, process quality, scheduling and waste elimination. Research conducted on the improvement in OEE in India and other automotive manufacturers illustrates that productivity can be achieved through TPM, lean concepts and scheduling optimization (Gupta and Vardhan, 2016; Sharma, 2019). Variables in the workforce, including gender diversity, have positively contributed to productivity growth, including total factor productivity TFP (Sofi et al., 2025), Other workforce variables like motivation, skills and teamwork are found to contribute positively towards productivity (Mohanty et al., 2017; Rengamani, 2019). In terms of process variables, research conducted in implementing lean six sigma, materials management and non-value-added activities has led to significant increases in productivity such as a 23% increase in production in core shop (Janarthanam and Rao, 2024; Swarnakar et al., 2021).

The productivity within the manufacturing sector of Indian automobiles is largely influenced by the interaction between machines, manpower and process aspects. The machine-related factors have been shown to largely contribute to the level of productivity where studies indicate that increase in productivity can be achieved through improvements in capital productivity while labour productivity in some automobile components has no significance due to efficiency improvements in the past (Chaudhuri et al., 2010). The impact of machine maintenance and equipment effectiveness is even more important, whereby TPM and OEE oriented strategies contribute to improvements in terms of availability, equipment effectiveness and quality, whereby improvements in OEE range between 39% and 71%, resulting into production increases (Fattah et al., 2017). Other manpower factors that are known to positively impact the level of productivity include diversity whereby increased gender diversity improves the productivity of labour by 2.9% and total factor productivity TFP by 2.7% in Indian manufacturing (Sofi et al., 2025). Factors relating to manpower such as lack of motivation, high absenteeism, inadequate documents and low skill levels have also been noted as some of the factors contributing to idling hours in addition to production delays, thus proving the significant contribution of this factor (Mohanty et al., 2017; Rengamani, 2019). In relation to process-related productivity influences, it is important to consider aspects like lean production, six sigma and elimination of waste. These aspects have been found to increase productivity through elimination of non-value-added tasks by 65.56% and reduction in process time by 61.03%. As a result, productivity is increased by up to 23% (Janarthanam and Rao, 2024; Swarnakar et al., 2021). Lean production enhances process quality and inventory control as well as productivity, thereby increasing overall productivity (Bubber et al., 2025). The literature points to a synergistic relationship where machine efficiency, a skilled and engaged workforce and streamlined processes combine to elevate productivity in India’s automotive manufacturing ecosystem (Shree and Raman, 2016).

The Table 2 synthesizes machine, workforce and process factors influencing manufacturing productivity. Machine factors include capital productivity, TPM, OEE and downtime. Workforce factors cover diversity, skills, absenteeism, motivation and teamwork. Process factors include lean, Six Sigma, layout, planning, simulations, digital twins and scheduling optimisation for improved productivity outcomes. It is through statistical analysis that one can be able to identify productivity barriers facing Indian automotive manufacturing, because it enables the researcher to quantitatively determine and rank productivity barriers according to their significance. The various barriers to productivity faced by Indian automotive manufacturing can be analyzed using a combination of statistical techniques such as importance indexing, Cronbach’s alpha, exploratory factor analysis EFA, confirmatory factor analysis CFA and multi criteria decision making MCDM. In particular, Lean Six Sigma LSS barrier identification in Indian automotive component manufacturing involves the use of statistical instruments such as the Importance Index and CIMTC to assess managerial survey responses, backed up by high internal consistency Cronbach’s alpha = 0.971 to ascertain reliability of productivity barriers identified as LSSB three and LSSB 4, as factors that impede LSS (Rathi et al., 2022). Empirical studies that use Cronbach’s alpha to ensure validity of data gathered, coupled with EFA, in classifying the LCO barriers faced by the Indian automobiles. This assists in knowing which LCO barriers are more important than others for Indian automobile firms (Kumar et al., 2023).

Further analysis in manufacturing in general has indicated how statistical instruments help categorize barriers and rank barriers under different dimensions such as regulatory constraints, organizational capability issues and external environment problems, which are some of the main sources of difficulty for Indian manufacturers in maintaining their productivity levels Statistical instruments such as stochastic frontier analysis SFA can help decompose the growth in productivity into its various components including technical changes and efficiency changes, which will aid in understanding whether Indian manufacturers’ barriers to productivity arise from inefficiencies, technological limitations and poor management of resources (Rawat and Sharma, 2021). Through providing an objective measure, substantiating expert opinion and allowing prioritization, statistical analysis forms the basis of evidence-based decision-making that is required to address the obstacles to productivity in the Indian automotive manufacturing industry.

Existing studies on automotive assembly line productivity mainly discuss equipment, manpower, supply chain and quality issues separately. Limited empirical work has quantified these barriers together in passenger car manufacturing units of the Pune region. There is also a lack of factor-based classification that explains how these barriers form broader productivity loss dimensions. This study aims to identify and evaluate the major barriers affecting assembly line productivity in passenger car manufacturing units of the Pune region. It uses questionnaire-based quantitative data to analyse machine, manpower, supply chain and quality-related hindrances through frequency analysis, descriptive statistics, correlation analysis and factor analysis. The study further aims to classify these barriers into meaningful dimensions for supporting productivity improvement decisions.

2 Methodology

2.1 Research design

The study adopted a quantitative research design to examine the factors affecting assembly line productivity in passenger car manufacturing units of the Pune region. The research focused on identifying and evaluating major productivity barriers related to infrastructure, manpower, supply chain and quality control. These areas were selected in line with the research objectives, which aimed to identify productivity and quality performance indicators, evaluate operational hindrances, analyze their relationship with productivity performance and propose a productivity enhancement framework for the Indian automobile sector. A structured questionnaire was used as the main research instrument. The questionnaire considered important concerns machine malfunctions, insufficient maintenance, power supply variations, obsolete machines, restrictions of layout, shortage of experienced workers, uneven burden of work, absence of employees, lack of training, poor coordination, delay in supply of materials, poor quality of materials supplied, shortage of inventory, problems relating to suppliers, problems within logistics, defective products, rework, poor quality control, delays of quality problems and customer complaints due to defects. This kind of research was well suited for this particular study since it involved quantifying perceptions and correlation between different productivity barriers.

2.2 Sampling strategy and data collection

The sampling frame comprised personnel working in passenger-car original equipment manufacturer plants located in the Pune automotive cluster. The Pune region was selected because it is a major automotive manufacturing hub with high-volume assembly operations, diverse vehicle models and complex production and supply networks. This setting provided an appropriate industrial context for examining assembly-line productivity constraints under comparable operating conditions. Data were collected from five passenger-car OEM plants using a purposive plant-based sampling strategy. The participating plants were coded as OEM-A, OEM-B, OEM-C, OEM-D and OEM-E to maintain industrial confidentiality. Respondents were selected on the basis of their direct involvement in assembly-line production, supervision, quality control, production planning, maintenance, logistics, engineering and operational decision-making. This approach ensured that the participants had practical knowledge of equipment-related interruptions, workforce issues, material-flow problems, quality failures and productivity constraints.

The sample included operational staff, supervisors, engineers, managers and senior management personnel with different levels of industrial experience as shown in Table 3. The questionnaire was administered across the five participating plants and measured barriers related to machine and infrastructure conditions, manpower and workforce coordination, supply-chain and logistics performance, and quality and defect control. Responses were recorded on a five-point Likert scale ranging from “Strongly disagree” to “Strongly agree.” After data screening, 535 valid responses were retained for analysis. Item-wise valid responses were used for frequency and descriptive analyses, whereas complete cases were used for exploratory factor analysis through listwise deletion.

2.3 Statistical analysis

The collected data were analyzed using SPSS. Frequency analysis was used to examine the response pattern for each questionnaire item. The valid percentage of agree and strongly agree responses was used to identify the most severe productivity barriers. Descriptive statistics were used to calculate mean and standard deviation values. Mean values helped to identify the most strongly perceived barriers while standard deviation values showed variation in respondent perception. Correlation analysis was applied to examine the association among selected productivity barrier variables. This helped to understand whether barriers related to machines, workforce, supply chain and quality were connected within the assembly line system. The objective-based plan also included the use of quantitative analysis to evaluate hindrance factors and their relationship with productivity performance. The present study examines the perceived severity and statistical association of assembly-line productivity barriers. The cross-sectional questionnaire design does not permit causal inference. Correlation analysis identifies the strength and direction of association between variables, while exploratory factor analysis identifies their shared underlying structure.

2.3.1 Validation and reliability

The questionnaire was developed based on previous studies related to automotive assembly-line productivity, machine-related losses, manpower issues, supply-chain interruptions and quality-related defects. The final instrument included 20 items grouped into four sections: machine and infrastructure barriers, manpower and workforce barriers, supply-chain and logistics barriers and quality and defect-related barriers. All items were measured using a five-point Likert scale ranging from “Strongly disagree” to “Strongly agree.” The internal consistency of the questionnaire was assessed using Cronbach’s alpha. The overall 20-item scale produced an alpha coefficient of 0.871, indicating good reliability. Factor-wise reliability was subsequently evaluated using the items assigned to each component on the basis of their highest rotated loading. Factor 1 and Factor 2 demonstrated good internal consistency, with alpha coefficients of 0.860 and 0.854, respectively. Factor 3 and Factor 4 produced lower coefficients of 0.567 and 0.581. These comparatively lower values may be attributed to the limited number of items within these factors, particularly Factor 4, which contained only two items. Therefore, the first two factors demonstrate strong reliability, whereas the latter factors should be interpreted cautiously and validated using additional items in future research.

Construct validity was examined through exploratory factor analysis as shown in Table 4. The Kaiser-Meyer-Olkin value was 0.863, indicating good sampling adequacy. Bartlett’s test of sphericity was statistically significant with χ² = 3842.438, df = 190, p < 0.001, confirming that the correlation matrix was suitable for factor analysis. The four extracted factors explained 58.223% of the total variance. These results support the reliability and construct validity of the questionnaire for analyzing assembly-line productivity barriers.

2.4 Factor extraction and interpretation

Exploratory factor analysis was used to reduce the selected productivity barrier variables into meaningful factor groups. Principal Component Analysis with Varimax rotation was applied for factor extraction and interpretation. The Kaiser-Meyer-Olkin test and Bartlett’s test of sphericity were used to check the suitability of data for factor analysis. The KMO value was 0.863, which indicated good sampling adequacy. Bartlett’s test was statistically significant with a chi-square value of 3842.438 at 190 degrees of freedom and a significance value of p < 0.001. Four major factors were retained and these factors explained 58.223% of the total variance. The extracted factors were interpreted according to the variables with the highest loadings. These factor groups represented workforce coordination and capability, machine and infrastructure stress, supply flow and logistics pressure and quality loss and defect control. These dimensions were further useful for developing a productivity enhancement framework based on survey results and expert-oriented improvement measures.

3 Frequency analysis

The frequency analysis was conducted to examine the response pattern for each barrier affecting assembly line productivity in passenger car manufacturing units of the Pune region. The responses were recorded on a five-point scale ranging from strongly disagree to strongly agree. The interpretation mainly focuses on the valid percentage of agree and strongly agree responses because these categories show the perceived severity of each productivity barrier. The barriers were grouped into four major sections for better presentation. These sections include machine and infrastructure-related barriers, manpower and workforce-related barriers, supply chain and logistics-related barriers and quality and defect-related barriers.

3.1 Machine and infrastructure-related barriers

Machines and infrastructure barriers include faulty machines, lack of maintenance of machines, electrical problems, old machines and factory design. These factors affect the physical flow of the assembly line. According to the findings, the condition of the machines and infrastructure plays a significant role in determining the productivity levels in passenger cars assembly plants.

Equipment breakdowns were identified as a major operational constraint, with 44.5% of respondents agreeing and 44.8% strongly agreeing, resulting in a combined agreement of 89.3%. Only 0.4% strongly disagreed and 0.9% disagreed, while 9.4% remained neutral as shown in Figure 1. The limited level of disagreement indicates substantial consistency in respondent perceptions across the participating plants. Equipment breakdowns may be associated with production interruptions, increased idle time, disruption of line balance, and additional pressure on maintenance and workforce scheduling. The result therefore indicates that machine reliability is closely associated with the continuity and stability of assembly-line operations.

FIGURE 1

Inadequate maintenance exhibited one of the strongest response patterns among the machine-related barriers as shown in Figure 2. A total of 34.7% of respondents agreed and 60.8% strongly agreed, producing an overall agreement of 95.5%. Only 0.2% strongly disagreed and 0.8% disagreed, whereas 3.6% remained neutral. The predominance of strong agreement suggests that maintenance deficiencies were perceived as a widespread operational concern. Inadequate preventive maintenance may be associated with repeated stoppages, extended repair time, reduced equipment availability, and increased variability in production schedules. These findings position maintenance capability as a critical determinant of stable operational performance rather than a purely corrective support function.

FIGURE 2

Power fluctuations generated a more varied response pattern than equipment breakdowns and inadequate maintenance as shown in Figure 3. Overall, 38.5% of respondents agreed and 12.5% strongly agreed, resulting in a combined agreement of 51.0%. In contrast, 38.3% remained neutral, while 2.1% strongly disagreed and 8.7% disagreed. The relatively high neutral proportion indicates that the severity of power-related disruptions may differ across plants, production areas, or backup-power arrangements. Power instability therefore appears to be a context-specific constraint rather than a uniformly experienced barrier. In affected facilities, however, it may be associated with machine stoppages, process resetting, schedule disruption, and potential variation in product quality.

FIGURE 3

Outdated machinery emerged as the most strongly perceived machine-related constraint as shown in Figure 4. A total of 33.5% of respondents agreed and 63.5% strongly agreed, yielding the highest combined agreement of 97.0%. Only 0.2% strongly disagreed and 0.4% disagreed, while 2.5% remained neutral. The exceptionally high level of agreement indicates that technological obsolescence was perceived as a common and critical operational issue across the surveyed plants. Outdated equipment may be associated with lower processing speed, reduced process control, frequent maintenance requirements, higher error rates, and limited compatibility with modern digital manufacturing systems. The finding suggests that productivity improvement requires both effective maintenance of existing assets and selective technology renewal based on operational criticality.

FIGURE 4

Responses concerning layout constraints were comparatively heterogeneous as shown in Figure 5. A total of 37.0% of respondents agreed and 9.6% strongly agreed, producing an overall agreement of 46.6%. Meanwhile, 32.8% remained neutral, 16.4% disagreed, and 4.2% strongly disagreed. This distribution indicates that layout-related constraints were relevant in some plants but were not experienced uniformly across the sample. The variation may reflect differences in plant age, floor-space availability, line configuration, material-handling systems, and product variety. Where present, layout constraints may be associated with excessive movement, longer transfer distances, congestion, and delays between workstations. The result therefore supports the need for plant-specific layout assessment rather than a uniform interpretation across all manufacturing units.

FIGURE 5

3.2 Manpower and workforce-related barriers

Skilled manpower shortage, workload imbalance, absenteeism, operator delay, improper training and poor worker coordination are other barriers that influence assembly line efficiency. Assembly line production depends on worker availability, skills, workload balance and coordination between workers. The findings clearly indicate that worker coordination and operator discipline play a major role in the efficient operation of assembly lines.

Skilled manpower shortage was perceived as a substantial workforce-related constraint as shown in Figure 6. A total of 36.2% of respondents agreed and 27.7% strongly agreed, resulting in a combined agreement of 63.9%. In comparison, 27.7% remained neutral, while 6.6% disagreed and 1.7% strongly disagreed. The relatively high agreement indicates that the availability of adequately skilled personnel is an important operational concern across the surveyed plants. At the same time, the sizeable neutral response suggests variation in skill availability among production units or shifts. A shortage of skilled personnel may be associated with slower task execution, greater dependence on experienced workers, reduced flexibility in job allocation and increased vulnerability to production interruptions.

FIGURE 6

Unequal workload distribution produced a comparatively divided response pattern as shown in Figure 7. A total of 23.3% of respondents agreed and 10.8% strongly agreed, yielding an overall agreement of 34.1%. Meanwhile, 27.5% remained neutral, 16.9% disagreed and 21.6% strongly disagreed. The combined disagreement of 38.5% exceeded the level of agreement, indicating that workload imbalance was not experienced uniformly across the sample. This variation may reflect differences in line balancing practices, task allocation methods, production volume and supervisory control. Where workload imbalance is present, it may be associated with operator fatigue, idle time, bottleneck formation and coordination difficulties. The result therefore suggests that workload distribution should be assessed at line and station levels rather than interpreted as a universal problem across all plants.

FIGURE 7

Operator absenteeism and delays were identified as a prominent workforce-related constraint as shown in Figure 8. A total of 35.3% of respondents agreed and 35.9% strongly agreed, producing a combined agreement of 71.2%. Neutral responses accounted for 22.4%, while the remaining responses represented disagreement, including 3.0% strong disagreement. The high level of agreement indicates that workforce availability is closely associated with the stability of assembly-line operations. Absenteeism and reporting delays may require immediate task reallocation, increase dependence on replacement workers and disturb planned cycle times. The neutral responses may reflect differences in staffing reserves, shift management and cross-training practices among plants.

FIGURE 8

Inadequate training generated a moderate and comparatively heterogeneous response pattern as shown in Figure 9. A total of 28.2% of respondents agreed and 17.7% strongly agreed, resulting in an overall agreement of 45.9%. In contrast, 28.0% remained neutral, while 15.4% disagreed and 10.7% strongly disagreed. The distribution indicates that training deficiencies were relevant in several units but were not perceived consistently across the entire sample. This variation may reflect differences in induction systems, refresher training, job complexity and access to skill-development programmes. Where training gaps exist, they may be associated with process errors, slower task completion, reduced confidence and greater dependence on experienced personnel. The finding therefore supports targeted, role-specific training rather than a uniform training intervention across all assembly operations.

FIGURE 9

Poor worker coordination emerged as one of the most strongly perceived workforce-related constraints as shown in Figure 10. A total of 37.8% of respondents agreed and 36.3% strongly agreed, producing a combined agreement of 74.1%. In comparison, 20.2% remained neutral, while 3.6% disagreed and 2.1% strongly disagreed. The high level of agreement indicates that coordination difficulties were widely recognized across the participating plants. Weak coordination may be associated with communication delays, task-sequence disruption, imbalance between workstations and slower responses to production or quality problems. The neutral proportion suggests that coordination effectiveness may vary according to team structure, supervisory practices and shift-level communication. The result indicates that workforce productivity depends not only on individual skill and availability but also on effective interaction among operators and functional teams.

FIGURE 10

3.3 Supply chain and logistics-related barriers

The supply chain and logistics-related barriers consist of raw material supply delay, inconsistency in material quality, inventory shortage, supplier problems and logistics. These barriers impact assembly line operation through material availability, supplier performance, inventory management and internal material handling. The findings reveal that supply chain and logistics act as essential factors for maintaining productivity within passenger car assembly units.

Delays in raw-material supply exhibited a moderate and heterogeneous response pattern as shown in Figure 11. A total of 32.8% of respondents agreed and 10.0% strongly agreed, producing a combined agreement of 42.8%. In comparison, 36.9% remained neutral, while 13.0% disagreed and 7.3% strongly disagreed. The relatively high neutral response indicates that the severity of supply delays may vary across plants, production schedules, supplier arrangements and material categories. Where such delays occur, they may be associated with interruptions in material availability, idle workstations, rescheduling of production and reduced continuity of assembly operations. The findings therefore suggest that raw-material delays represent a context-dependent constraint rather than a uniformly experienced problem.

FIGURE 11

Inconsistency in material quality was perceived as a prominent supply-related constraint as shown in Figure 12. A total of 45.5% of respondents agreed and 32.0% strongly agreed, resulting in a combined agreement of 77.5%. Only 1.1% strongly disagreed and 1.5% disagreed, while 19.9% remained neutral. The limited disagreement indicates substantial consistency in respondent perceptions regarding the operational significance of material-quality variation. Inconsistent incoming materials may be associated with inspection delays, process adjustments, rejection, rework and variation in finished-product quality. The result indicates that supply-chain performance depends not only on timely material availability but also on the uniformity and conformity of supplied materials.

FIGURE 12

Inventory shortages produced one of the most divided response patterns within the supply-chain category as shown in Figure 13. A total of 24.6% of respondents agreed and 9.0% strongly agreed, yielding a combined agreement of 33.6%. In contrast, 34.9% remained neutral, while 17.7% disagreed and 13.7% strongly disagreed. The combined disagreement of 31.4% was close to the agreement level, indicating substantial variation in inventory-management performance across the participating plants. This variation may reflect differences in safety-stock policies, supplier proximity, material planning and just-in-time practices.

FIGURE 13

Supplier-related issues were identified as a significant constraint associated with assembly-process continuity as shown in Figure 14. A total of 38.8% of respondents agreed and 27.5% strongly agreed, producing a combined agreement of 66.3%. Meanwhile, 24.5% remained neutral, 6.3% disagreed and 2.9% strongly disagreed. The predominance of agreement indicates that supplier reliability and coordination are important components of operational stability. Supplier-related problems may be associated with delayed deliveries, incomplete quantities, quality deviations and unplanned changes in production schedules. The neutral proportion suggests that the extent of supplier influence may vary according to sourcing arrangements, supplier capability and the effectiveness of buyer–supplier coordination.

FIGURE 14

Poor internal logistics emerged as the most strongly perceived barrier within the supply-chain and logistics category as shown in Figure 15. A total of 47.9% of respondents agreed and 32.6% strongly agreed, resulting in a combined agreement of 80.5%. Only 0.8% strongly disagreed and 2.8% disagreed, while 15.9% remained neutral. The high agreement and limited disagreement indicate that internal material movement was widely recognized as an important operational constraint. Inefficient internal logistics may be associated with congestion, delayed component delivery, excessive handling, workstation idle time and disruption of production sequencing. The result demonstrates that productivity depends not only on external supply availability but also on the timely and coordinated movement of materials within the plant.

FIGURE 15

3.4 Quality and defect-related barriers

Barriers related to quality and defects include product defects, rework or repair of defective products, lack of quality inspection, inability to resolve quality issues, delay in solving quality problems and customer returns due to product defects. These barriers affect assembly line efficiency through inspection work, rework, process adjustments and customer pressure. As a result, the findings indicate that defect prevention and quality control play a vital role in ensuring smooth assembly line flow.

Frequent product defects emerged as one of the most strongly perceived quality-related constraints as shown in Figure 16. A total of 42.7% of respondents agreed and 51.0% strongly agreed, producing a combined agreement of 93.7%. Only 0.4% strongly disagreed and 1.1% disagreed, while 4.7% remained neutral. The very high agreement and minimal disagreement indicate substantial consistency across the surveyed plants regarding the operational significance of product defects. Frequent defects may be associated with line interruptions, additional inspection, rework, material loss and disruption of planned production flow. The finding therefore identifies defect prevention as a central requirement for maintaining stable assembly-line performance.

FIGURE 16

Rework and repair produced a comparatively heterogeneous response pattern as shown in Figure 17. A total of 23.6% of respondents agreed and 19.4% strongly agreed, resulting in a combined agreement of 43.0%. Neutral responses accounted for 24.0%, while the remaining 33.0% represented disagreement. The relatively balanced distribution indicates that rework was not experienced uniformly across all production units. This variation may reflect differences in product complexity, defect-containment practices, process capability and quality-control effectiveness. Where rework is frequent, it may be associated with additional labour, extended cycle time, material consumption and reduced availability of production resources. The result therefore supports unit-specific investigation of rework sources rather than treating it as a universal constraint.

FIGURE 17

Inadequate quality inspection was perceived as a significant operational constraint as shown in Figure 18. A total of 34.2% of respondents agreed and 34.4% strongly agreed, yielding a combined agreement of 68.6%. In comparison, 22.8% remained neutral, while 4.6% disagreed and 4.0% strongly disagreed. The predominance of agreement indicates that inspection quality is closely associated with the effectiveness of assembly-line control. Weak inspection practices may permit defects to proceed to subsequent stages, where correction becomes more time-consuming and costly. The neutral response suggests that inspection capability may vary across plants, product lines or quality-control arrangements. The finding highlights the importance of early defect detection and consistent inspection standards.

FIGURE 18

Delays in resolving quality issues generated a moderate and varied response pattern as shown in Figure 19. A total of 31.8% of respondents agreed and 17.0% strongly agreed, resulting in an overall agreement of 48.8%. Neutral responses accounted for 31.4%, while 11.9% disagreed and 7.9% strongly disagreed. The relatively high neutral proportion indicates that the severity of quality-resolution delays may depend on plant-specific escalation systems, technical support and decision-making speed. Where resolution is delayed, production may remain interrupted, work-in-process may accumulate and corrective actions may be postponed. The finding therefore suggests that responsiveness to quality problems is an important but context-dependent element of assembly-line performance.

FIGURE 19

Customer complaints and returns arising from defective products were identified as a major quality-related constraint as shown in Figure 20. A total of 40.2% of respondents agreed and 45.1% strongly agreed, producing a combined agreement of 85.3%. Only 1.2% strongly disagreed and 1.2% disagreed, while 12.4% remained neutral. The high level of agreement indicates broad recognition that downstream quality failures are associated with manufacturing inefficiency. Customer complaints may lead to investigation, corrective action, product replacement, additional inspection and disruption of routine production activities. The result also demonstrates that quality-related productivity losses extend beyond the assembly line to warranty performance, customer satisfaction and organizational reputation.

FIGURE 20

3.5 Descriptive statistics and correlation analysis

The descriptive statistics were used to examine the average response level and variation for the twenty assembly line productivity barriers. The mean value shows the average level of agreement while the standard deviation shows the variation in respondent perception. The results support the frequency analysis and show that technical limitations and quality-related failures are the most serious productivity barriers in passenger car manufacturing units. Lack of modern tools or outdated machinery recorded the highest mean value of 4.60 with a standard deviation of 0.581 as shown in Figure 21. This indicates that outdated machinery was the most strongly perceived barrier. Inadequate maintenance of machines recorded the second highest mean value of 4.54 with a standard deviation of 0.632. Frequent defects in products also showed a high mean value of 4.42. Equipment breakdowns recorded a mean value of 4.32 and customer returns or complaints due to defects recorded a mean value of 4.26. These results show that machine condition, maintenance weakness and defect-related issues strongly affect assembly line productivity. Poor internal logistics also recorded a high mean value of 4.11. Inconsistency in material quality showed a mean value of 4.07. Poor coordination among workers recorded a mean value of 4.03 while absenteeism or delays from operators recorded a mean value of 3.98. Inadequate quality inspection recorded a mean value of 3.89. These results indicate that productivity is not affected by machines alone. Material movement, input quality, workforce coordination and inspection practices also influence assembly line performance.

FIGURE 21

Moderate mean values were observed for shortage of skilled manpower and supplier-related issues. Both recorded mean values of 3.82. Power fluctuations recorded a mean value of 3.51 while delays in resolving quality issues recorded 3.39. Space or layout constraints recorded 3.33 and lack of proper training recorded 3.30. Delays in raw material supply showed a mean value of 3.23. These barriers remained relevant but their effect was less intense than outdated machinery, poor maintenance and frequent defects. The lowest mean values were observed for unequal workload distribution at 2.87, inventory shortages at 2.97 and rework or repair of defective items at 3.08. Rework showed the highest standard deviation of 1.425 while unequal workload distribution showed 1.297. This indicates greater variation in respondent perception across plants or departments. Descriptive statistics confirm that machine condition, maintenance, product defects, internal logistics and material quality are the strongest productivity barriers.

Pearson’s correlation analysis was conducted for all twenty questionnaire variables to examine the associations among machine, workforce, supply-logistics and quality-related barriers. Missing responses were handled using pairwise deletion, and the complete correlation matrix is provided in Appendix A. The matrix showed predominantly positive relationships of varying magnitude, indicating that several productivity barriers tended to occur together within the assembly-line system. The strongest relationship was observed between unequal workload distribution and inadequate training (r = 0.681), followed by absenteeism or operator delays and poor worker coordination (r = 0.676). Poor worker coordination was also strongly associated with inadequate quality inspection (r = 0.602) and customer complaints due to defects (r = 0.590). Inadequate training showed a strong relationship with rework or repair activities (r = 0.593). These results indicate that workforce coordination, training and workload-related issues are closely connected with quality and operational performance. However, the coefficients represent statistical associations and should not be interpreted as evidence of causal relationships.

Figure 22 presents the strongest selected associations identified among the assembly-line productivity barriers. The highest correlation was observed between unequal workload distribution and inadequate training (r = 0.681), followed by operator absenteeism or delays and poor worker coordination (r = 0.676). Poor worker coordination was also associated with inadequate quality inspection (r = 0.602) and customer complaints due to defects (r = 0.590). These findings indicate that workforce-related constraints are closely connected with quality and operational problems.

FIGURE 22

3.6 Factor analysis

Factor analysis was applied to identify the underlying structure of the twenty questionnaire items related to assembly line productivity barriers. Principal Component Analysis with Varimax rotation was used for factor extraction and interpretation. The analysis retained 478 complete responses through listwise deletion as shown in Table 5. The suitability of the dataset was checked through the Kaiser-Meyer-Olkin test and Bartlett’s test of sphericity. The KMO value was 0.863, which indicates good sampling adequacy. Bartlett’s test was significant with a chi-square value of 3842.438 at 190 degrees of freedom and a significance value of p < 0.001. This confirms that the correlation matrix was suitable for factor extraction. KMO and Bartlett’s test confirm that the dataset is statistically suitable for factor analysis.

The scree plot indicated that four major factors should be retained. The slope became nearly stable after the fourth component as shown in Figure 23. This supports the selection of a four-factor solution for explaining assembly line productivity barriers. The communalities also supported the factor structure. The extraction values ranged from 0.405 to 0.775. Poor internal logistics recorded the highest communality value of 0.775. Poor coordination among workers recorded 0.707 and lack of proper training recorded 0.688. Frequent defects in products recorded 0.662 while unequal workload distribution recorded 0.645. The lowest value was found for space or layout constraints at 0.405. This value remained acceptable for interpretation. Communality values show that all selected variables contributed meaningfully to the extracted factor structure.

FIGURE 23

The total variance explained table shows that four components had eigenvalues greater than one as shown in Figure 24. These four factors together explained 58.223% of the total variance. The first factor explained 29.805% of the variance. The second factor explained 14.894%. The third factor explained 7.764% while the fourth factor explained 5.761%. The cumulative variance reached 58.223%. This shows that the four-factor model provides a meaningful summary of the productivity barriers affecting passenger car assembly lines. The total variance result supports retention of four major productivity barrier dimensions.

FIGURE 24

The rotated component matrix was examined to interpret the factor structure as shown in Figure 25. The first factor showed strong loadings for poor internal logistics at 0.717, delays in raw material supply at 0.719, supplier-related issues at 0.631, inadequate quality inspection at 0.761 and customer returns or complaints due to defects at 0.706. This factor mainly represents supply flow, logistics and quality-control barriers. The second factor showed notable loadings for inconsistency in material quality at 0.668 and rework or repair of defective items at 0.689. The third factor included frequent defects in products with a loading of 0.633. The fourth factor was dominated by delays in resolving quality issues with a loading of 0.855. Rotation converged in eight iterations.

FIGURE 25

Rotated factor loadings show that productivity barriers are grouped around supply flow, logistics, quality control, material quality, defect correction and quality-resolution delay. Overall, factor analysis indicates that the reported productivity barriers share broader underlying dimensions. The extracted factors indicate that perceived productivity loss is associated with broader combinations of supply continuity, internal logistics, quality inspection, material quality, rework, defect occurrence and delayed quality resolution. Some variables showed overlap, which indicates that productivity barriers are interrelated within the assembly system. Therefore, productivity improvement requires an integrated strategy instead of separate correction of individual barriers. Factor analysis supports an integrated interpretation of supply, logistics and quality-related barriers.

4 Discussion

4.1 Empirical findings

The findings indicate that assembly-line productivity in passenger-car manufacturing is shaped by the interaction of technical, workforce, supply-logistics and quality-related barriers. Therefore, the results should not be interpreted only as a ranking of individual constraints, but as evidence of a connected productivity-loss system. Among all identified barriers, lack of modern tools or outdated machinery emerged as the most critical issue, with 97.0% total agreement and the highest mean score of 4.60. Inadequate maintenance followed with 95.5% agreement and a mean score of 4.54, while equipment breakdowns recorded 89.3% agreement. These findings suggest that technical readiness is a central condition for assembly-line stability. Machine capability, maintenance discipline and equipment reliability directly influence cycle continuity, workstation performance and production flow. The high ranking of outdated machinery and inadequate maintenance can be explained by their direct and immediate effect on assembly-line continuity. Unlike indirect managerial or planning-related barriers, machine stoppages, obsolete tools and poor maintenance directly affect cycle time, station availability and output stability. This finding is consistent with earlier studies on TPM, OEE and equipment effectiveness, which show that machine availability, maintenance quality and equipment performance are central to productivity improvement in automobile and manufacturing systems (Gupta and Vardhan, 2016; Sharma, 2019; Fattah et al., 2017). The present findings therefore extend the existing literature by showing that even in contemporary passenger-car assembly plants, basic equipment reliability remains a stronger productivity concern than many softer or indirect operational barriers.

An important implication of this finding is that productivity improvement in passenger-car assembly plants cannot depend only on advanced manufacturing technologies. Although recent literature emphasizes digital twins, Industry 4.0 and artificial-intelligence-based production systems (Sharma et al., 2022; Papanagnou, 2019; Biswas et al., 2026), the present results show that basic operational issues such as outdated machinery, inadequate maintenance and equipment breakdowns remain dominant barriers. This indicates that digitalization can support monitoring, simulation and decision-making, but it cannot replace the need for reliable machines, preventive maintenance and stable shop-floor control. Therefore, technology upgradation should be understood as a staged process in which basic equipment reliability must be strengthened before advanced digital interventions can produce sustained productivity gains. Quality-related barriers formed the second major pattern in the study. Frequent product defects recorded 93.7% agreement and a mean score of 4.42, while customer complaints due to defects recorded 85.3% agreement and a mean score of 4.26. These results show that quality failures affect productivity not only through rejected output but also through inspection delays, rework pressure, line disturbance, process correction and customer-response requirements. The high ranking of product defects can be linked with the close relationship between quality performance and assembly-line flow. In assembly systems, defects do not remain confined to the quality department. They create rechecking, correction, reallocation of manpower, possible line stoppage and customer-related pressure. This supports earlier literature showing that lean, Six Sigma, process quality and waste-reduction practices are closely associated with productivity improvement (Janarthanam and Rao, 2024; Swarnakar et al., 2021; Bubber et al., 2025).

However, rework or repair of defective items showed a comparatively lower mean score of 3.08 and greater variation in perception. This lower ranking does not necessarily mean that rework is unimportant. Rather, it suggests that rework may be less visible to some respondents because it is often absorbed into routine correction loops, offline repair stations or departmental-level quality actions. In contrast, frequent defects and customer complaints are more visible because they affect workflow, inspection load and external performance outcomes. Therefore, the difference between high agreement for defects and lower agreement for rework reveals an important interpretation. Some productivity losses are visible at the line level, whereas others remain hidden within correction and quality-control routines. Thus, defect-related productivity loss should not be assessed only through visible rework activities. It should also be examined through hidden correction time, inspection load, line stoppage, customer complaints and repeated process adjustments. Supply-logistics barriers also showed a differentiated pattern. Poor internal logistics recorded 80.5% agreement and a mean score of 4.11, while material quality inconsistency recorded 77.5% agreement and a mean score of 4.07. In contrast, raw material supply delay and inventory shortage recorded lower mean scores of 3.23 and 2.97, respectively. The lower ranking of inventory shortage is particularly important. It may indicate that the studied OEM plants have relatively mature inventory-control mechanisms or buffer arrangements, but still face problems in internal material movement, material quality consistency and supplier-linked operational disturbance. This interpretation is consistent with studies showing that material-flow efficiency, in-house logistics and line-feeding practices strongly influence automotive assembly performance (Thanou and Matopoulos, 2021; Sternatz, 2015). Hence, the productivity issue is not only whether material is available, but whether the right material reaches the right workstation at the right time and with the required quality.

This indicates that the major supply-side issue in the studied assembly systems is not merely material non-availability. Rather, the more critical concern appears to be the reliability of material movement and consistency of input quality. Productivity loss can therefore occur even when materials are available, if internal logistics are poorly coordinated or if incoming materials do not meet required quality standards. This finding extends the interpretation of supply-chain productivity barriers beyond inventory adequacy and highlights the importance of internal logistics discipline, supplier quality control and synchronized material flow. It also aligns with the wider view that supply-chain competitiveness and adaptability influence automotive manufacturing performance (Joshi et al., 2013). Workforce-related findings further support the integrated nature of assembly-line productivity loss. Poor worker coordination recorded 74.1% agreement, while absenteeism or operator delays recorded 71.2% agreement. The strongest correlation was found between absenteeism or operator delays and poor worker coordination, with r = 0.685. Unequal workload distribution was also strongly associated with lack of proper training, with r = 0.671. These results indicate that workforce barriers are relational rather than purely individual. Absenteeism, training gaps, workload imbalance and operator delays disturb coordination across stations and reduce the stability of production flow. This interpretation is consistent with earlier studies that identify skills, motivation, teamwork, absenteeism and human factors as important contributors to manufacturing productivity (Sofi et al., 2025; Mohanty et al., 2017; Rengamani, 2019; Shree and Raman, 2016). The present study adds to this literature by showing that workforce issues are not isolated human-resource concerns; they function as coordination barriers that influence material handling, quality response and line stability.

The factor-analysis results support the interpretation that productivity barriers do not act independently. The KMO value of 0.863 and significant Bartlett’s test confirmed the suitability of the data for factor analysis. The four extracted factors explained 58.23% of the total variance, indicating that individual productivity barriers can be reduced into broader productivity-loss dimensions. At the same time, the factor structure shows overlap among supply, logistics, quality and workforce-related variables. This overlap is theoretically meaningful because assembly systems are tightly connected operational environments. A disturbance in one area may occur alongside difficulties in several other areas. For example, poor logistics may be associated with waiting time, poor material quality with inspection load, weak coordination with delayed defect response and inadequate maintenance with stoppages and schedule pressure. The four-factor structure has both theoretical and practical implications. Theoretically, it shows that assembly-line productivity barriers can be understood as broader latent dimensions rather than as independent operational problems. This supports a socio-technical and flow-based interpretation of productivity, where technical readiness, workforce coordination, supply-flow reliability and quality-control capability jointly shape assembly performance. Practically, the four-factor structure helps managers move from item-level problem solving to dimension-level intervention. Instead of treating each barrier separately, managers can design integrated improvement packages around equipment reliability, workforce coordination, logistics synchronization and defect-prevention capability. The study therefore contributes to productivity literature by framing assembly-line productivity constraints as an integrated socio-technical and flow-based system. Perceived productivity loss is not associated with machine inefficiency, manpower shortage, supply delay or quality failure in isolation. Rather, it is linked with the combined presence of technical, workforce, material-flow and defect-control constraints. This interpretation extends earlier studies that discuss lean implementation, equipment effectiveness, workforce performance or supply-chain barriers separately. The present findings show that these dimensions are empirically interrelated and should be managed as a connected productivity system.

4.2 Research implications

Although the study provides useful empirical evidence, the findings should be interpreted with methodological caution. First, the study is based on questionnaire responses and therefore reflects the perceptions of production managers regarding productivity barriers. It does not directly measure objective shop-floor performance indicators such as cycle time, takt-time adherence, downtime minutes, OEE, defect-per-unit rate, rework hours or line-stoppage frequency. Second, the study follows a cross-sectional design. As a result, the findings explain the perceived severity and association among barriers at one point in time, but they do not establish causal relationships. Correlation values show the degree of association between variables, but they do not confirm that one barrier directly causes another. Third, the study is regionally focused on passenger-car manufacturing units in the Pune automotive cluster. This regional focus is useful because Pune is an important automotive manufacturing location; however, the findings should be generalized cautiously to other automotive clusters, vehicle segments or manufacturing sectors. The regional concentration of the study limits the direct generalization of the findings beyond the Pune automotive cluster. Other automotive regions may differ in supplier networks, workforce composition, automation levels, plant size and production systems. The relative importance of machines, workforce, logistics and quality barriers may therefore vary across locations. The findings may be more applicable to passenger-car OEM plants operating under similar industrial and organizational conditions. Their relevance to commercial vehicles, two-wheelers, component manufacturers and non-automotive sectors should be established through further empirical validation. Future studies should include multiple automotive clusters and supplier tiers to assess the stability of the identified factor structure across different manufacturing contexts.

Fourth, exploratory factor analysis was used to identify the underlying structure of productivity barriers. Although the KMO value, Bartlett’s test and total variance explained support the adequacy of the factor solution, the extracted structure requires further confirmation through confirmatory factor analysis or structural equation modelling. Finally, complete cases were used for factor analysis through listwise deletion, reducing the factor-analysis sample from 535 valid responses to 478 complete cases. Although the retained sample remains adequate, future studies should examine whether missing-response patterns influence factor stability. Future research should combine questionnaire-based perception data with objective production data to strengthen the explanatory power of productivity-barrier analysis. Regression analysis, structural equation modelling or machine-learning models may be used to estimate the relative contribution of machine, workforce, logistics and quality barriers to measurable productivity outcomes. Longitudinal studies may also examine whether maintenance improvement, supplier-quality control, workforce training or logistics redesign leads to measurable reduction in productivity loss over time. Comparative studies across different automotive clusters may further clarify whether the barrier structure identified in this study is specific to the Pune region or applicable to other emerging manufacturing environments.

5 Recommendations and future scope

Outdated machinery was the most critical barrier. It recorded 97.0% agreement and a mean score of 4.60. Inadequate maintenance followed with 95.5% agreement and a mean score of 4.54. These findings show that equipment reliability needs immediate attention. Plants should rank machines based on downtime, repair frequency, cycle-time loss and production importance. Critical machines should be placed under regular preventive maintenance. Digital maintenance records and spare-parts checks should also be maintained. Equipment replacement should focus on machines that show repeated failure and high production loss. Frequent product defects recorded 93.7% agreement. Customer complaints due to defects recorded 85.3%. Inadequate inspection was also linked with customer complaints at r = 0.543 and with rework at r = 0.463. These results support a shift from inspection-based control to defect prevention. Plants should use station-level defect checklists, poka-yoke devices and first-piece approval. Repeated defects should be escalated without delay. Production, quality and supplier teams should review major defects together. Progress should be tracked through defect rate, rework hours, inspection delay and customer returns.

Poor internal logistics recorded 80.5% agreement and a mean score of 4.11. This shows that material movement is a major source of productivity loss. Plants should conduct regular material-flow audits. These audits should examine line-feeding frequency, travel distance, storage location and point-of-use inventory. Routes that cause congestion, waiting or repeated interruptions should be redesigned. Raw-material delay was also related to supplier issues at r = 0.503. Supplier evaluation should therefore include delivery adherence, rejection rate, response time and contribution to line stoppages. Poor worker coordination recorded 74.1% agreement. Absenteeism or operator delay recorded 71.2%. Their correlation was the strongest in the study at r = 0.685. Plants should maintain shift-level backup arrangements. Operators should be cross-trained for nearby stations. Shift-handover procedures should also be standardized. Supervisors should regularly review absenteeism, station coverage and line-rebalancing time. Unequal workload distribution was strongly related to inadequate training at r = 0.671. Workload balancing should therefore be supported by competency mapping and station-specific certification.

The four-factor structure explained 58.223% of the total variance. This shows that machine, workforce, logistics and quality barriers are closely connected. They should not be handled by separate departments in isolation. Each plant should form a cross-functional productivity committee. It should include members from production, maintenance, quality, logistics, procurement and human resources. The committee should review a common performance dashboard. It should also assign responsibility and deadlines for corrective actions. At the policy level, automotive cluster agencies should support shared training, testing, calibration and digital maintenance facilities. Such support is especially important for smaller suppliers. Technology-upgradation schemes should be linked with equipment condition, maintenance maturity and supplier quality performance. General automation support may not be effective where basic operational systems remain weak.

Implementation should follow a time-bound monitoring framework. Immediate actions should focus on maintenance, defect control and workforce coordination. Medium-term actions should address equipment replacement, supplier development and logistics redesign. Each intervention should have a responsible department, completion deadline and measurable performance indicator. Quarterly reviews should be conducted to assess progress and revise actions where expected improvements are not achieved. Future studies should use structural equation modelling to test the relationships among the identified productivity dimensions. This method can examine both direct and indirect effects on downtime, defect rate and cycle-time loss. Machine-learning methods may be used when large plant-level datasets are available. They can help identify nonlinear patterns and predict combinations of barriers associated with high productivity loss.

6 Conclusion

This study indicates that perceived assembly-line productivity constraints are associated with technical, workforce, logistics and quality-related barriers. The findings do not support causal claims regarding any individual barrier. Instead, they show that outdated machinery, inadequate maintenance, equipment breakdowns, poor internal logistics, inconsistent material quality, workforce coordination gaps and recurring product defects are reported as interconnected constraints. Among the identified barriers, outdated machinery and inadequate maintenance emerged as the most critical concerns. Frequent product defects and customer complaints were also strongly perceived as constraints on production performance. These results indicate that stable assembly-line operation depends on both equipment reliability and effective defect prevention. The findings related to internal logistics and material quality further suggest that productivity is determined not only by the availability of materials, but also by their timely movement, consistency and suitability for production. The correlation analysis provides a broader understanding of how these barriers interact. The strong association between absenteeism or operator delays and poor worker coordination shows that workforce-related issues extend beyond individual performance. They were associated with line balance, quality response and production continuity. Similarly, the relationship between workload distribution and training gaps indicates that skill development and task allocation should be considered together. The factor structure further indicates that the identified constraints share interconnected productivity dimensions rather than representing isolated operational problems.

The main contribution of the study lies in integrating machine, workforce, supply-logistics and quality-related barriers within a single empirical framework. This approach extends productivity analysis beyond the ranking of individual problems and highlights the need for coordinated managerial action. Sustainable improvement is therefore more likely when production, maintenance, quality, logistics and human-resource functions address these constraints collectively. The findings should nevertheless be interpreted within the limits of the study. The analysis is based on questionnaire responses from production managers in five passenger-car OEM plants located in the Pune region. The results therefore reflect managerial perceptions rather than direct shop-floor measurements. The cross-sectional design also limits causal interpretation. In addition, the factor structure was identified through exploratory analysis and requires further validation in other automotive clusters and manufacturing contexts. Despite these limitations, the study provides a useful basis for evidence-based productivity improvement in passenger-car assembly systems. It shows that long-term gains depend on the simultaneous improvement of equipment reliability, workforce coordination, material-flow stability and defect-control capability. Future studies should combine survey responses with objective indicators such as downtime, cycle time, OEE, defect rate and rework hours. Longitudinal and multi-plant studies would further strengthen the generalizability and practical relevance of the findings.

References

• 1

  AfridiF.DhillonA.SharmaS. (2024). The ties that bind us: social networks and productivity in the factory. J. Econ. Behav. and Organ.218, 470–485. 10.1016/j.jebo.2023.12.026

  • CrossRef
  • Google Scholar
  • View reference in article

• 2

  AnderssonC.BellgranM. (2015). On the complexity of using performance measures: enhancing sustained production improvement capability by combining OEE and productivity. J. Manuf. Syst.35, 144–154. 10.1016/j.jmsy.2014.12.003

  • CrossRef
  • Google Scholar
  • View reference in article

• 3

  AsadiN.JacksonM.FundinA. (2019). Implications of realizing mix flexibility in assembly systems for product modularity—A case study. J. Manufacturing Systems52, 13–22. 10.1016/j.jmsy.2019.04.010

  • CrossRef
  • Google Scholar
  • View reference in article

• 4

  BiswasP.KimT. M.KimW. S. (2026). Integration of digital twins and physical AI in cyber-physical systems. Intelligent Syst. Appl.30, 200649. 10.1016/j.iswa.2026.200649

  • CrossRef
  • Google Scholar
  • View reference in article

• 5

  BogdanovskáG.MolnárV.FedorkoG. (2025). Failure analysis of the assembly process of a car lock. Eng. Fail. Anal.180, 109835. 10.1016/j.engfailanal.2025.109835

  • CrossRef
  • Google Scholar
  • View reference in article

• 6

  BubberD.BabberG.ShashiJainR. K. (2025). Toward increased business productivity: interlinks between lean thinking, process quality, inventory management, and productivity. Glob. Knowl. Mem. Commun.74 (56), 1511–1531. 10.1108/GKMC-03-2023-0079

  • CrossRef
  • Google Scholar
  • View reference in article

• 7

  CaoX.YaoM.ZhangY.HuX.WuC. (2024). Digital twin modeling and simulation optimization of transmission front and middle case assembly line. Comput. Model. Eng. and Sci. (CMES)139 (3), 3233–3253. 10.32604/cmes.2023.030773

  • CrossRef
  • Google Scholar
  • View reference in article

• 8

  ChaudhuriA.KoudalP.SeshadriS. (2010). Productivity and capital investments: an empirical study of three manufacturing industries in India. IIMB Management Review22 (3), 65–79. 10.1016/j.iimb.2010.04.012

  • CrossRef
  • Google Scholar
  • View reference in article

• 9

  DzulkarnainM. F. A.RahamanW. E. W. A. (2017). Productivity improvement in automotive component company using line balancing. Pertanika J. of Sci. and Technol.25, 147–158.

  • Google Scholar
  • View reference in article

• 10

  FattahJ.EzzineL.LachhabA. (2017). Evaluating the performance of a production line by the overall equipment effectiveness: an approach based on best maintenance practices. Int. J. Eng. Res. Afr.30, 181–189. 10.4028/www.scientific.net/jera.30.181

  • CrossRef
  • Google Scholar
  • View reference in article

• 11

  GambhireG.AherN.JoshiA.RajurkarA.SarodeP. (2024). “Productivity improvement in refrigerator manufacturing plant by using Yamazumi—line balancing technique,” in Optimization of Production and Industrial Systems. CPIE 2023. Lecture Notes in Mechanical Engineering. Editors A. Bhardwaj, P. M. Pandey, A. Misra (Singapore:Springer). 10.1007/978-981-99-8343-8_15

  • CrossRef
  • Google Scholar
  • View reference in article

• 12

  GuptaP.VardhanS. (2016). Optimizing OEE, productivity and production cost for improving sales volume in an automobile industry through TPM: a case study. Int. Journal Production Research54 (10), 2976–2988. 10.1080/00207543.2016.1145817

  • CrossRef
  • Google Scholar
  • View reference in article

• 13

  JanarthanamV.RaoV. (2024). Implementation of hybrid artificial neural network and multi-criteria decision model for the ranking of criteria that affect productivity–a case study. South Afr. J. Industrial Eng.35 (1), 1–19. 10.7166/35-1-2906

  • CrossRef
  • Google Scholar
  • View reference in article

• 14

  JoshiD.NepalB.RathoreA. P. S.SharmaD. (2013). On supply chain competitiveness of Indian automotive component manufacturing industry. Int. J. Prod. Econ.143 (1), 151–161. 10.1016/j.ijpe.2012.12.023

  • CrossRef
  • Google Scholar
  • View reference in article

• 15

  KumarS. R.Nimesh NathanV.Mohammed AshiqueS.RajkumarV.Arun KarthickP. (2021). Productivity enhancement and cycle time reduction in toyota production system through jishuken activity–case study. Mater. Today Proc.37, 964–966. 10.1016/j.matpr.2020.06.181

  • CrossRef
  • Google Scholar
  • View reference in article

• 16

  KumarA.LuthraS.ManglaS. K.Garza-ReyesJ. A.KazancogluY. (2023). Analysing the adoption barriers of low-carbon operations: a step forward for achieving net-zero emissions. Resour. Policy80, 103256. 10.1016/j.resourpol.2022.103256

  • CrossRef
  • Google Scholar
  • View reference in article

• 17

  LafouM.MathieuL.PoisS.AlochetM. (2015). Manufacturing system configuration: flexibility analysis for automotive mixed-model assembly lines. IFAC-PapersOnLine48 (3), 94–99. 10.1016/j.ifacol.2015.06.064

  • CrossRef
  • Google Scholar
  • View reference in article

• 18

  MalhotraR.SoniA.GoyalA. (2022). “Numerical analysis of fatigue failure,” in AIP Conference Proceedings (Melville, NY:AIP Publishing). 10.1063/5.0080979

  • CrossRef
  • Google Scholar
  • View reference in article

• 19

  MncwangoB.MdungeZ. L. (2025). Unraveling the root causes of low overall equipment effectiveness in the kit packing department: a define–measure–analyze–improve–control approach. Processes13 (3), 757. 10.3390/pr13030757

  • CrossRef
  • Google Scholar
  • View reference in article

• 20

  MohantyM. K.MishraP. C.MallM. (2017). Who are hindering shop floor productive hours? An exploratory finding from discrete manufacturing industries of India. Int. J. Prod. Qual. Manag.22 (4), 485–498. 10.1504/ijpqm.2017.087865

  • CrossRef
  • Google Scholar
  • View reference in article

• 21

  NelfiyantiMohamedN.RashidM.RamadhanA. I. (2022). Parameters of effects in decision making of automotive assembly line using the analytical hierarchy process method. CIRP J. Manuf. Sci. Technol.37, 370–377. 10.1016/j.cirpj.2022.02.018

  • CrossRef
  • Google Scholar
  • View reference in article

• 22

  PadhiS. S.WagnerS. M.NiranjanT. T.AggarwalV. (2013). A simulation-based methodology to analyse production line disruptions. Int. J. Prod. Res.51 (6), 1885–1897. 10.1080/00207543.2012.720389

  • CrossRef
  • Google Scholar
  • View reference in article

• 23

  PapanagnouC. I. (2019). “A digital twin model for enhancing performance measurement in assembly lines,” in Digital Twin Technologies and Smart Cities (Springer), 53–66.

  • Google Scholar
  • View reference in article

• 24

  PrabhushankarG.KruthikaK.PramanikS.KadadevaramathR. S. (2015). Lean manufacturing system implementation in Indian automotive components manufacturing sector-an empirical study. Int. J. Bus. Syst. Res.9 (2), 179–194. 10.1504/ijbsr.2015.069442

  • CrossRef
  • Google Scholar
  • View reference in article

• 25

  RaneA. B.SunnapwarV. K. (2017). Assembly line performance and modeling. J. Industrial Eng. Int.13 (3), 347–355. 10.1007/s40092-017-0189-7

  • CrossRef
  • Google Scholar
  • View reference in article

• 26

  RaneA. B.SudhakarD.RaneS. (2015). “Improving the performance of assembly line: review with case study,” in 2015 International Conference on Nascent Technologies in the Engineering Field (ICNTE) (IEEE).

  • Google Scholar
  • View reference in article

• 27

  RathiR.SinghM.Kumar VermaA.Singh GurjarR.SinghA.SamanthaB. (2022). Identification of lean six sigma barriers in automobile part manufacturing industry. Mater. Today Proc.50, 728–735. 10.1016/j.matpr.2021.05.221

  • CrossRef
  • Google Scholar
  • View reference in article

• 28

  RawatP. S.SharmaS. (2021). TFP growth, technical efficiency and catch-up dynamics: evidence from Indian manufacturing. Econ. Model.103, 105622. 10.1016/j.econmod.2021.105622

  • CrossRef
  • Google Scholar
  • View reference in article

• 29

  RengamaniJ. (2019). Motivating factors of mechanical engineers in the automobile companies in Chennai–an empirical study. Int. J. Mech. Eng. Technol.10 (1), 735–744.

  • Google Scholar
  • View reference in article

• 30

  SharmaR. (2019). Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: a case study of automobile sector. Int. Journal Productivity Quality Management26 (2), 206–222. 10.1504/ijpqm.2019.097767

  • CrossRef
  • Google Scholar
  • View reference in article

• 31

  SharmaV.RautR. D.Hajiaghaei-KeshteliM.NarkhedeB. E.GokhaleR.PriyadarshineeP. (2022). Mediating effect of industry 4.0 technologies on the supply chain management practices and supply chain performance. J. Environ. Manag.322, 115945. 10.1016/j.jenvman.2022.115945

  • CrossRef
  • Google Scholar
  • View reference in article

• 32

  ShreeS. V.RamanG. P. (2016). A study on team work and performance of employees in enhancing the organisational productivity in automobile industry. Int. J. Appl. Bus. Econ. Res.14, 559–569.

  • Google Scholar
  • View reference in article

• 33

  SofiI. A.AhmedS.PazirD. (2025). Gender diversity at the workplace and industrial productivity: empirical evidence from Indian formal manufacturing sector. J. Quantitative Econ.23 (2), 561–576. 10.1007/s40953-024-00435-5

  • CrossRef
  • Google Scholar
  • View reference in article

• 34

  SowmyaC.RameshV.SavithaM.MallaradhyaH. (2025). Enhancing productivity through lean manufacturing in the automotive components industry: a case study in line balancing. J. Adv. Manuf. Syst.1–36. 10.1142/s0219686727500193

  • CrossRef
  • Google Scholar
  • View reference in article

• 35

  SternatzJ. (2015). The joint line balancing and material supply problem. Int. J. Prod. Econ.159, 304–318. 10.1016/j.ijpe.2014.07.022

  • CrossRef
  • Google Scholar
  • View reference in article

• 36

  SugimoriY.KusunokiK.ChoF.UchikawaS. (1977). Toyota production system and Kanban system materialization of just-in-time and respect-for-human system. International Journal Production Research15 (6), 553–564. 10.1080/00207547708943149

  • CrossRef
  • Google Scholar
  • View reference in article

• 37

  SwarnakarV.SinghA. R.TiwariA. K. (2021). Effect of lean six sigma on firm performance: a case of Indian automotive component manufacturing organization. Mater. Today Proc.46, 9617–9622. 10.1016/j.matpr.2020.07.115

  • CrossRef
  • Google Scholar
  • View reference in article

• 38

  ThanouE.MatopoulosA. (2021). Improving efficiency of material flows in an automotive assembly plant: a case study. CIRP J. Manuf. Sci. Technol.35, 959–967. 10.1016/j.cirpj.2021.10.008

  • CrossRef
  • Google Scholar
  • View reference in article

• 39

  UsubamatovR.Rahman RizaA.Nasir MuradM. (2012). A method for assessing productivity in unbuffered assembly processes. J. Manuf. Technol. Manag.24 (1), 123–139. 10.1108/17410381311287526

  • CrossRef
  • Google Scholar
  • View reference in article

• 40

  UsubamatovR.Alsalam AlsalamehA.AhmadR.Rahman RizaA. (2014). Analysis of buffered assembly line productivity. Assem. Autom.34 (1), 34–40. 10.1108/aa-11-2012-086

  • CrossRef
  • Google Scholar
  • View reference in article