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Analytics‑Driven Continuous Improvement and Its Impact on Business Excellence

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  • Abdelfatah, Omar Sharafeldin Mohamed

Abstract

Continuous improvement (CI) has been the cornerstone of operational excellence frameworks across industries for decades, yet traditional CI methodologies Lean, Six Sigma, and Kaizen have relied predominantly on periodic, sample-based measurement and human-facilitated root-cause analysis. The emergence of advanced analytics capabilities including real-time process mining, predictive quality modelling, prescriptive optimisation engines, and AI-augmented root-cause identification has fundamentally expanded the scope and velocity of continuous improvement, enabling organisations to identify performance gaps, attribute root causes, and deploy corrective interventions at a scale and speed that surpasses the limitations of conventional CI practice. This study examines the impact of analytics-driven continuous improvement (Analytics-CI) systems on business excellence performance across manufacturing, retail, FMCG, and professional services organisations. Employing a mixed-methods convergent parallel research design, primary data were collected from 204 operations, quality, and analytics professionals through a structured questionnaire, supplemented by 22 semi-structured executive interviews. Findings demonstrate that organisations deploying Analytics-CI capabilities achieve statistically significant improvements across all measured business excellence dimensions: a 36.2% average gain in Continuous Improvement Performance Effectiveness (CIPE) scores, accompanied by a 29.4% reduction in process defect rates, a 23.8% reduction in cycle times, a 27.1% improvement in first-pass yield, and a 19.7% increase in customer satisfaction scores, compared to organisations relying on traditional CI methods alone. Regression analysis identifies analytics model sophistication, process data richness, and cross-functional integration depth as the primary determinants of business excellence outcomes. The study proposes a three-stage Analytics-CI Maturity Model and introduces the validated Analytics–Business Excellence Performance (ABEP) framework. Theoretical contributions extend the Dynamic Capabilities and Information Processing theories to the analytics-augmented quality and operational excellence domain.

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  • Abdelfatah, Omar Sharafeldin Mohamed, 2026. "Analytics‑Driven Continuous Improvement and Its Impact on Business Excellence," SocArXiv 954sa_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:954sa_v1
    DOI: 10.31219/osf.io/954sa_v1
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    References listed on IDEAS

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