Enhancing Operational Efficiency Through Data-Driven Strategies: An Analytical Approach to Operations Management

“Enhancing Operational Efficiency Through Data-Driven Strategies: An Analytical Approach to Operations Management”

HARSHIKA RAI

SCHOOL OF BUSINESS, GALGOTIAS UNIVERSITY

Abstract

In today’s digital landscape, operational efficiency is no longer driven by manual optimization alone, but by intelligent, data-driven strategies. This study explores the transformative role of data analytics in improving operational efficiency across manufacturing, logistics, retail, and healthcare sectors. Using a mixed-method approach, the study combines literature synthesis, surveys, interviews, and case analysis to propose and validate a practical analytical framework.

The findings show that the integration of descriptive, predictive, and prescriptive analytics enhances inventory management, downtime reduction, demand forecasting, and decision-making at scale. Key enablers include system integration, employee skill development, and leadership alignment. The proposed five-stage framework—data acquisition, preparation, modeling, decision support, and continuous monitoring—offers a structured approach adaptable across industries.

This paper contributes to operations management literature by bridging data science with operational theory and provides actionable recommendations for organizations aiming to become data-driven enterprises.