Big Data Analytics and Operational Management Efficiency: An Empirical Study

Abstract

Big Data Analysis can help make operating decisions, but this help will only be achieved if it is actually applied. Based on the data from the 160 operating units, this paper studies the effect of analytical ability on operational management efficiency. Design stands for Design Ability, Data Governance, Organisation Scale and Demand Fluctuations, etc., and many other factorial combinations. Operational efficiency is the proportion of orders that are processed on time according to the plan. Descriptive comparisons and Ordinary Least Squares regression quantify associations by means of explicit data generation. Increase the level of analysis by 20 and then raise the efficiency by 6 percentage points. The adjusted coefficient is 0.300 and the conditional 95% confidence interval is 0.260-0.340. Governance, Size and Volatility all increase the explained variance from 40.0% to 72.2%. These are the estimations of the built analysis environment and not observations of business improvement or causality. Therefore, it has been decided that the variables, reproducible calculations and transparent uncertainties are all suitable for operational interpretation.