Analytics‑Driven Continuous Improvement and Its Impact on Business Excellence

Omar Sharafeldin Mohamed Abdelfatah

No 954sa_v1, SocArXiv from Center for Open Science

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.

Date: 2026-04-07
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:954sa_v1

DOI: 10.31219/osf.io/954sa_v1

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