SMART HYBRID MANUFACTURING: A COMBINATION OF ADDITIVE, SUBTRACTIVE, AND LEAN TECHNIQUES FOR AGILE PRODUCTION SYSTEMS

Abstract

Smart Hybrid Manufacturing (SHM)—the coordinated integration of additive, subtractive, lean, and smart control capabilities—has emerged as a central pathway for enabling responsive, high-quality, and resource-efficient production in modern manufacturing systems. Yet, empirical evidence explaining how these multidimensional capability foundations contribute to productivity, quality, efficiency, and agility remains limited. This study addresses this gap by developing and testing a comprehensive capability–performance model using data from a heterogeneous sample of industrial plants and hybrid manufacturing cells operating across multiple sectors, complexity classes, and volatility conditions. The research examines how additive and subtractive process maturity, lean–smart integration governance, and cyber-physical smart control jointly shape agile production system performance (APSP), while also assessing the mediating role of smart control capability and the moderating role of lean maturity. Additional multi-group comparisons evaluate whether SHM performance impacts differ across hybridization architectures and environmental turbulence. Measurement model evaluation demonstrated strong psychometric robustness. All constructs achieved acceptable levels of internal consistency (Cronbach’s α ≥ 0.85; CR ≥ 0.90), convergent validity (AVE ≥ 0.70), and discriminant validity across Fornell–Larcker, HTMT, and CFA criteria. The overall measurement structure supported a multidimensional representation of SHM capability, as well as second-order factors for SHM Capability (SHMC) and Agile Production System Performance (APSP). Model fit indices (CFI = 0.957; TLI = 0.948; RMSEA = 0.045; SRMR = 0.037) indicated strong alignment between data and the hypothesized measurement structure. Structural equation modeling results provided strong support for the hypothesized relationships. SHM Capability exhibited a significant and substantial effect on APSP (β = 0.62), explaining more than half of the observed variance in agile system performance. Each SHM sub-dimension demonstrated its expected directional relationship with specific performance outcomes: additive maturity predicted agility, subtractive finishing predicted quality, and lean–smart integration predicted productivity and efficiency. Smart Control Maturity partially mediated the SHM → quality relationship, confirming that in-situ sensing and closed-loop correction serve as essential mechanisms for enabling conformance and reducing variability. Lean maturity significantly moderated the SHM → productivity and SHM → agility relationships, indicating that SHM benefits are amplified under strong pull-flow discipline and structured continuous improvement.