Design and Evaluation of a Lean Manufacturing Workflow Optimization Model

Thesis Abstract

In the contemporary manufacturing environment, achieving operational efficiency and competitiveness necessitates continuous process improvement, with lean manufacturing principles serving as a pivotal approach to minimize waste and optimize workflows. Despite widespread adoption, many manufacturing facilities encounter persistent challenges in effectively designing and implementing workflow models that truly enhance productivity, reduce lead times, and ensure sustainable practices. This study aims to develop and empirically evaluate a comprehensive lean manufacturing workflow optimization model, specifically tailored to medium-sized automotive component manufacturing firms. The primary objectives include identifying key waste dimensions within existing workflows, designing a model integrating Value Stream Mapping (VSM), Kaizen events, and takt time analysis, and assessing the model’s impact on process efficiency, throughput, and waste reduction. Additionally, the research seeks to establish a framework for continuous improvement and workflow adaptation. The research adopts a mixed-methods approach, combining qualitative and quantitative data collection and analysis. A sequential exploratory design guides the study, with initial qualitative insights collected through semi-structured interviews with 25 process engineers, production managers, and shop floor workers at three manufacturing plants. These insights inform the development of the lean workflow model, which is then implemented and evaluated quantitatively across a larger sample of 15 similar firms, utilizing surveys, process performance data, and waste logs over a six-month period. Data collection instruments include structured questionnaires devised following the criteria of the Manufacturing Enterprise Solutions Association (MESA) and direct observational tools. The validity and reliability of the questionnaires are ensured through Cronbach’s alpha testing (? > 0.85) and pilot studies. Data analysis employs descriptive statistics to profile existing workflows, followed by regression analysis and paired t-tests to measure pre- and post-implementation effects on key performance indicators such as cycle time, throughput, and waste levels. The technical analytical framework is grounded in the Theory of Constraints and the Lean Manufacturing principles as articulated by Womack and Jones, with the new model’s efficacy evaluated via process simulation using Arena Software. The study also incorporates thematic analysis of interview transcripts to understand contextual challenges and facilitators of the model's adoption. Expected findings indicate that the implementation of the tailored lean workflow optimization model will lead to a statistically significant reduction in cycle times (by approximately 20%), waste (by roughly 25%), and work-in-progress inventory levels (by about 15%). Additionally, improvements in overall equipment effectiveness (OEE) and process flexibility are anticipated. The model is projected to facilitate continuous flow, reduce non-value-adding activities, and promote a culture of ongoing improvement among stakeholders. This research contributes to knowledge by providing an empirically validated, adaptable workflow optimization framework rooted in lean principles, combining theoretical rigor with practical relevance. It extends existing literature on lean implementation in medium-sized manufacturing settings and offers a replicable model for similar contexts. The study highlights the importance of integrating employee engagement, technology, and real-time data analytics in workflow redesigns. The main conclusion underscores that systematic lean workflows, when properly tailored and supported by training and monitoring mechanisms, significantly enhance manufacturing efficiency and sustainability. It recommends that manufacturing firms adopt the proposed model with a structured change management approach, emphasizing staff involvement and continuous data-driven evaluation. Future research directions include exploring digitalization impacts on lean workflows, integrating Industry 4.0 technologies, and examining long-term sustainability of workflow improvements across diverse manufacturing sectors.