Optimizing Production Scheduling in a Automotive Manufacturing Plant Using Lean Principles

Thesis Abstract

The increasing demand for high-quality, cost-effective automotive production necessitates innovative approaches to production scheduling that minimize waste and enhance operational efficiency. This study investigates the application of lean principles to optimize production scheduling within a large-scale automotive manufacturing plant, focusing on reducing lead times, improving resource utilization, and increasing overall productivity. The primary aim is to develop an integrated scheduling framework that aligns lean methodologies with existing manufacturing processes to achieve sustainable improvements. The specific objectives include identifying current scheduling challenges, evaluating the impact of lean tools such as value stream mapping and pull systems on scheduling efficiency, and formulating a predictive model for optimizing production sequences under varying operational constraints. To achieve these objectives, a mixed-method research design was adopted, combining qualitative case study analysis with quantitative modeling. The study was conducted within an automotive manufacturing facility with a workforce of over 3,000 employees and annual production capacity of 250,000 units. A purposive sampling technique was employed to select key personnel involved in production planning and scheduling, resulting in a sample size of 30 managers and engineers. Data collection involved semi-structured interviews, direct observation, and review of production records to understand existing scheduling practices and identify bottlenecks. Quantitative data were obtained from production logs and operation time studies, which informed the development of a simulation-based scheduling model. For data analysis, thematic analysis was used to interpret qualitative insights, while quantitative data were subjected to regression analysis and discrete-event simulation to test hypothesized relationships between lean interventions and scheduling performance metrics. The conceptual framework integrates the Theory of Constraints and Lean Manufacturing principles, providing a basis for understanding how waste reduction and bottleneck management influence scheduling effectiveness. The simulation model was calibrated and validated using historical production data, enabling scenario analysis of different lean implementation strategies. Expected findings indicate that applying lean principles significantly reduces production lead times by an average of 20%, decreases work-in-progress inventory levels by 15%, and enhances overall equipment effectiveness. The results also demonstrate that integrating lean tools such as takt time synchronization and kanban-based pull systems into scheduling processes leads to more agile and responsive production plans, capable of adapting to demand fluctuations without compromising quality. Furthermore, the development of a predictive scheduling model facilitates proactive decision-making and continuous improvement in production flow. This research contributes to the existing body of knowledge by providing an empirically validated framework for integrating lean principles into production scheduling within the automotive manufacturing context. It advances the understanding of how lean methodologies can be operationalized to address scheduling challenges, offering a systematic approach adaptable to similar industrial settings. The study’s insights are particularly relevant for manufacturing practitioners seeking to improve efficiency and competitiveness through lean transformations. The main conclusion emphasizes that strategic alignment of lean principles with production scheduling significantly enhances operational performance in automotive manufacturing. Recommendations include adopting the developed scheduling framework, investing in staff training on lean practices, and leveraging simulation tools for ongoing process optimization. The study also advocates for further research into the integration of digital technologies and Industry 4.0 innovations to further refine scheduling performance under lean paradigms. Overall, this research underscores the potential of lean principles as a catalyst for sustainable, high-performance manufacturing systems.