Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents an integrated store performance monitor framework that transforms retail operations management through multi-dimensional risk assessment and predictive analytics. The article addresses the limitations of traditional performance metrics by developing a comprehensive composite scoring methodology that synthesizes fulfillment accuracy, workforce stability, operational organization, and customer experience indicators into unified performance assessments. The framework employs advanced machine learning algorithms, including ensemble learning approaches, pattern recognition systems, and historical comparison analysis to identify at-risk stores before performance deterioration becomes evident through financial metrics. Implementation across retail networks demonstrates substantial improvements in early warning capabilities, enabling proactive intervention strategies that prevent operational failures and support store recovery efforts. The predictive modeling system successfully identified numerous stores requiring intervention, achieving superior detection accuracy compared to conventional manual review processes. Cost-benefit analysis reveals compelling return on investment through revenue protection, reduced emergency interventions, and optimized resource allocation. The integrated approach enables strategic labor allocation decisions, enhances executive decision-making processes through improved operational risk visibility, and facilitates the development of standardized intervention protocols based on successful recovery strategies.