Design and Evaluation of a Lean-Driven Hybrid Assembly Line Model for SMEs

Thesis Abstract

This study investigates how lean-driven, hybrid assembly line configurations can enhance operational performance in small and medium-sized enterprises (SMEs) facing demand variability, limited capital, and skilled-labor shortages. The aim is to design, implement, and evaluate a hybrid assembly line model that integrates lean principles with flexible automation to improve throughput, lead times, and first-time yield while maintaining cost-efficiency. Specific objectives include (i) characterizing current assembly line performance in a sample of 12 SMEs across electronics and consumer goods sectors, (ii) developing a hybrid line design that combines standardized work, kanban pull, and modular automation, (iii) implementing the proposed model in two pilot lines and (iv) evaluating performance using a pre-post design over 6 months. The methodology adopts a mixed-methods, action-research design anchored in the Theory of Constraints and the Toyota Production System as the theoretical lens. The population comprises assembly-line operations within SMEs in the Southeast region, with a purposive sample of 12 firms, narrowed to 4 pilot lines for in-depth study, and 8 additional lines for cross-sectional benchmarking. Data collection employs (i) quantitative instruments including time-and-motion studies, line balancing metrics, throughput, work-in-process, and cost data, collected for 12 months, and (ii) qualitative instruments including semi-structured interviews with line supervisors and operators, and a documental audit of production records. Validity and reliability are ensured through triangulation, pilot testing of measurement protocols, and inter-rater reliability checks with an expected Cronbach alpha above 0.80 for survey components. Data analysis uses descriptive statistics, paired t-tests and repeated-measures ANOVA to assess changes in throughput, cycle time, deviation rates, and overall equipment effectiveness (OEE); regression analysis identifies predictors of improvement, while a discrete-event simulation model (using Arena) validates the operational impact under varying demand scenarios. The qualitative data are analyzed thematically using a framework approach to extract insights on change management, worker engagement, and barriers to adoption, with findings triangulated against the quantitative results. Anticipated findings indicate that the lean-driven hybrid model can achieve a 15–25% reduction in cycle time, a 10–20% increase in OEE, and a 5–12% reduction in operating costs across pilot lines, particularly in scenarios with moderate demand volatility and high setup time. The study is expected to reveal that the integration of standardized work and kanban within a modular automation envelope reduces changeover complexity and enables rapid reconfiguration, while lessons from worker involvement and supervisory coaching contribute to sustained gains. The contribution to knowledge lies in (i) providing an implementable blueprint for SMEs that bridges lean principles with selective automation in a hybrid line context, (ii) empirical evidence on performance drivers and constraints of hybrid lines in resource-constrained settings, and (iii) a validated simulation model and analytic framework that practitioners can adapt for different product families and market conditions. The main conclusion anticipates that a carefully designed lean-driven hybrid assembly line yields substantial performance improvements without compromising flexibility or capital discipline, provided that change management, operator training, and continuous improvement mechanisms are embedded from the outset. Recommendations include developing a phased rollout with clear governance for standardization and modular automation, investing in operator upskilling and cross-training, adopting real-time visibility through digital dashboards, and conducting ongoing post-implementation audits to sustain gains. Suggestions for future research point to exploring cross-industry applicability, long-term sustainability impacts, and the integration of advanced analytics for predictive maintenance and demand-shaping within hybrid line environments.