A Hybrid Lean-Six Sigma Model with Automated Kaizen for Real-Time Quality Improvement

Abstract

This study addresses the problem that many enterprise quality improvement programs generate dashboards and alerts yet still fail to convert real-time deviations into owned corrective actions and verified closure, which sustains defect recurrence and process instability. The purpose was to test whether a Hybrid Lean Six Sigma (LSS) capability base, strengthened by Automated Kaizen execution workflows, predicts Real-Time Quality Improvement (RQI) in a quantitative, cross-sectional, case-based model. The sample comprised n = 210 valid responses collected from operational roles across cloud-enabled enterprise quality cases, including operators/technicians (35.2%), QA/QC staff (21.0%), supervisors (19.5%), engineers (15.2%), and CI or Lean Six Sigma team members (9.0%). Key variables were LSS capability, Automated Kaizen effectiveness, signal-to-action actionability, Kaizen closure confidence, DMAIC-stage functioning under automation, and RQI, measured on 5-point Likert scales and aggregated as composite indices. The analysis plan applied reliability testing (Cronbach’s alpha), descriptive statistics, Pearson correlations, and multiple regression models to estimate both baseline effects and incremental explanatory power. Measurement reliability was strong (α = .85–.90), including LSS α = .88, Automated Kaizen α = .90, and RQI α = .87. Descriptively, respondents reported high LSS capability (M = 3.94) and moderate-to-high Automated Kaizen (M = 3.78) and RQI (M = 3.83). Correlations supported the hypothesized relationships: LSS correlated with RQI (r = .62, p < .001) and with Automated Kaizen (r = .58, p < .001), while Automated Kaizen correlated more strongly with RQI (r = .67, p < .001). Regression results showed LSS predicting RQI (β = .62; R² = .38); when Automated Kaizen was added, it became the stronger predictor (β = .49) while LSS remained significant but reduced (β = .33), increasing explained variance to R² = .55, consistent with Automated Kaizen serving as a practical execution pathway. Mechanism evidence highlighted that signal-to-action actionability strongly related to RQI (r = .69; β = .41, p < .001) and automation’s highest perceived leverage appeared in the DMAIC Measure (M = 3.96) and Control (M = 3.91) stages. Implications indicate that enterprises seeking real-time quality gains should prioritize alert relevance, workflow routing integrity, clear ownership, and control-plan updates so that digital signals reliably trigger, track, verify, and standardize Kaizen actions at scale.