Comparative Analysis of Additive vs. Subtractive Manufacturing Efficiency in SMEs

Thesis Abstract

This study investigates the comparative efficiency of additive manufacturing (AM) and subtractive manufacturing (SM) within small and medium-sized enterprises (SMEs) to address rising productivity and cost-performance pressures in contemporary supply chains. The problem addressed is the lack of robust, context-specific evidence on how AM and SM differ in throughput, unit cost, lead time, and quality under real-world SME conditions, considering capital constraints, workforce capabilities, and product mix. The aim is to determine how AM and SM performance vary across product complexity, batch size, and setup time, and to identify conditions under which each manufacturing mode yields superior efficiency. Specific objectives are (1) to quantify throughput, unit cost, lead time, and defect rates for AM and SM processes across representative SME case studies; (2) to examine the influence of product complexity and batch size on process efficiency using interaction effects; (3) to assess the impact of capital expenditure, maintenance, and operator proficiency on overall efficiency; (4) to evaluate environmental and energy efficiency implications; and (5) to develop a decision framework for SMEs selecting between AM and SM for given production scenarios. The research adopts a mixed-methods design, combining quantitative cross-sectional data from 40 SME case studies (20 predominantly using AM and 20 predominantly using SM) with qualitative interviews of 24 managers and process engineers. Data collection instruments include structured process audits, time-motion studies, production cost sheets, defect logs, and semi-structured interviews. Validity and reliability are established through triangulation, pilot testing of instruments, inter-rater reliability checks on time measurements, and Cronbach’s alpha for survey items. Quantitative data will be analyzed using descriptive statistics, multivariate analysis of variance (MANOVA) to compare efficiency metrics, and multiple regression models to identify key determinants of performance, controlling for firm size, product mix, and capital intensity. Interaction effects between manufacturing mode and product complexity, as well as batch size, will be examined. A hierarchical linear model will account for nested data at the firm level. Qualitative data will be analyzed using thematic analysis to extract drivers and barriers related to process efficiency, technology maturity, workforce skill requirements, and supplier integration. The study will apply diffusion of innovations and technology-organization-environment (TOE) frameworks to interpret adoption and performance outcomes, complemented by a resource-based view to explain sustained efficiency gains. Expected findings indicate that AM demonstrates superior lead times and flexibility for high-complexity, low-to-medium volume parts, with mixed results on unit cost due to material usage and equipment depreciation. SM is anticipated to outperform AM in high-volume, low-complexity production with lower unit costs and shorter learning curves but limited design freedom and higher setup times for complex geometries. The interaction analyses are expected to reveal threshold batch sizes and complexity levels where AM’s benefits outweigh SM’s cost penalties, and vice versa. The study will contribute to knowledge by providing empirically grounded benchmarks for SME manufacturing efficiency, delineating the conditions under which AM or SM yields optimal performance, and offering a practical decision framework for process selection and capital budgeting. It will extend existing literature on SME manufacturing performance, supplier integration, and technology choice under resource constraints, addressing gaps related to contextualized comparative analyses and cross-case synthesis. The main conclusion anticipates that neither AM nor SM is universally superior; rather, efficiency outcomes depend on product complexity, batch size, and total cost of ownership, including equipment, maintenance, energy, and workforce investments. Recommendations include the development of SME-specific decision-support tools to guide process selection, staged capital investment plans aligned with product portfolio strategy, workforce upskilling programs for hybrid AM-SM environments, and policies to facilitate supplier ecosystems and energy-efficient practice adoption. Future research avenues include longitudinal studies to capture learning effects, expansion to multi-site SMEs, and integration of environmental lifecycle assessment with economic performance.