SMART CONTINUOUS IMPROVEMENT WITH ARTIFICIAL INTELLIGENCE, BIG DATA, AND LEAN TOOLS FOR ZERO DEFECT MANUFACTURING SYSTEMS

Abstract

This study addresses the practical problem that many Industry 4.0 oriented manufacturers invest separately in artificial intelligence, big data analytics, and lean tools without clear empirical evidence of how their integrated deployment supports smart continuous improvement and zero defect manufacturing outcomes. The purpose is to test a model linking AI capability, big data analytics capability, lean tools deployment, smart continuous improvement performance, and zero defect results using a quantitative cross-sectional, case-study based survey of 214 professionals from ten enterprise manufacturing plants (response rate 71.3 percent). Key variables are measured on five-point Likert scales and analyzed using descriptive statistics, reliability analysis, Pearson correlations, and multiple regression. All scales show strong internal consistency (Cronbach’s alpha 0.88 to 0.92). Lean tools deployment has the highest mean (3.94), followed by smart continuous improvement (3.78) and zero defect outcomes (3.65), while AI and big data capabilities are moderately developed (3.46 and 3.59). Smart continuous improvement is strongly predicted by AI, big data, and lean (R² = 0.582), with lean as the dominant driver (β = 0.36). Smart continuous improvement in turn explains 41.6 percent of the variance in zero defect performance (β = 0.65), and the full model combining all capabilities plus smart continuous improvement explains 53.8 percent of zero defect variance, indicating partial mediation. These results imply that lean and continuous improvement should be treated as the backbone of zero defect programs, with AI and big data designed explicitly to enhance data driven problem solving and defect prevention routines rather than operating as isolated digital initiatives.