IntroductionAssembly 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.MethodsA 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statistics, correlation analysis and exploratory factor analysis.ResultsThe results showed that lack of modern tools or outdated machinery was the most critical barrier with 97.0% total agreement and the highest mean score of 4.60.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 ConclusionThe 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.