Comparative Analysis of Lean Implementation Across Manufacturing Sectors: A Cross-Sectional Study

Thesis Abstract

This study addresses the persistent gap in understanding how Lean implementations vary across manufacturing sectors and how such variations influence operational performance. Despite widespread adoption of Lean principles, sector-specific drivers, constraints, and outcomes remain under-explored, limiting the transferability of best practices and the design of tailored implementation frameworks. The aim is to compare Lean implementation across discrete manufacturing, process manufacturing, and automotive supplier sub-sectors to identify sector-specific determinants, effects on throughput, quality, and lead time, and to develop a cross-sector synthesis of effective practices. Specific objectives are to (1) map the maturity and scope of Lean practices in each sector, (2) examine the relationships between Lean adoption depth and key performance indicators (KPIs) such as cycle time, defect rate, and overall equipment effectiveness (OEE), (3) assess the moderating role of organizational culture, continuous improvement infrastructure, and supplier integration, (4) test the applicability of selected theoretical lenses—specifically the Lean Enterprise theory and the Resource-Based View (RBV)—in accounting for cross-sector differences, and (5) formulate a cross-sector Lean implementation framework with context-specific recommendations. A correlational cross-sectional research design will be employed. The population comprises manufacturing firms with formal Lean initiatives in three sectors discrete manufacturing, process manufacturing, and automotive supplier manufacturing, drawn from two metropolitan regions to ensure regional diversity. A stratified random sample of 360 firms will be selected, with 120 firms from each sector, to achieve adequate power for multivariate analyses (power = 0.80, alpha = 0.05). Data will be collected through a dual-method instrument (i) a structured survey capturing Lean maturity, scope of Lean tools (e.g., 5S, Kanban, SMED, TPM), organizational culture metrics, and collaboration with suppliers; and (ii) archival KPI data requested from firms’ operations systems, including cycle time, defect rate, throughput, and OEE for the most recent four quarters. To complement survey data, 40 in-depth semi-structured interviews will be conducted with operations managers to elicit contextual nuances and validate survey responses. The survey will be validated through content validity checks with Lean experts and a pilot test (n=30). Reliability will be assessed using Cronbach’s alpha for internal consistency of multi-item scales. Quantitative data will be analyzed using descriptive statistics to characterize sectoral profiles, followed by multivariate regression and hierarchical linear modeling to test the impact of Lean maturity on KPIs, controlling for firm size, capital intensity, and market dynamics. ANOVA and post-hoc tests will compare sector means, while interaction terms will assess sector-specific effects. Structural equation modeling (SEM) will be employed to test the theoretical framework integrating Lean Enterprise theory and RBV, with latent variables representing Lean maturity, organizational learning capability, and competitive advantage. Qualitative data from interviews will be analyzed through thematic analysis to identify sector-specific enablers and barriers, triangulated with survey findings to enhance validity. Key expected findings include (i) Lean maturity positively correlates with cycle time reduction, defect rate improvement, and OEE across all sectors, with stronger effects in discrete and automotive sectors due to higher process standardization; (ii) organizational culture and supplier integration significantly moderate Lean outcomes, with a more pronounced impact in process manufacturing where process variability is higher; (iii) the Lean Enterprise framework and RBV together explain cross-sector differences, with sector-specific configurations of tools and capabilities driving performance. The study anticipates identifying sector-tailored success factors, such as the predominance of Kanban and pull systems in discrete manufacturing, standard work and SMED emphasis in automotive suppliers, and robust process mapping and variance reduction in process manufacturing. The intended contribution includes (a) a rigorous cross-sector empirical assessment of Lean implementation effects on operational performance, (b) a validated cross-sector theoretical model integrating Lean Enterprise theory and RBV for explaining sectoral differences, and (c) a practical cross-sector Lean implementation framework highlighting context-specific tools, governance structures, and partnerships. The main conclusion is that while Lean improves performance across sectors, the magnitude and configuration of benefits depend on sector-specific process characteristics, cultural readiness, and supplier collaboration. Recommendations include designing sector-adapted Lean roadmaps, investing in organizational learning capabilities, and strengthening supplier integration mechanisms to sustain gains.