Enhancing Retail Operational Efficiency through Intelligent Inventory Planning and Customer Flow Optimization: A Data-Centric Approach

Abstract

The retail industry is changing rapidly with sophisticated technologies enabling firms to gather volumes of data. This data provides firms with insights into their customers and their consumption and buying patterns; however, the cumulative data can be overwhelming. Today’s retail firms cannot operate solely based on intuition or experience. They need, instead, to improve operational efficiency by leveraging data and employing sophisticated analytical techniques in business decision making. These techniques help retailers gain insights into pregnant consumption trends. Building on these insights, intelligent inventory planning and customer flow optimization solutions are developed. The proposed solutions and corresponding results are discussed to illustrate how they can enhance operational efficiency in the intensely competitive retail industry. Before any implementation of an analytical solution, data acquisition and data preparation are crucial steps. Data acquisition involves gathering data from the relevant sources, while data preparation consists of selecting and cleaning the relevant data. These steps are usually time-consuming but critical. Data acquisition gathers on-line transaction data at Point-of-Sales (POS) as operands to the analytical solution to be proposed. In addition to the POS data, data from the other two relevant systems—the data warehouse and Load and Data Replication (LDR)—are also extracted and cleaned to prepare a consolidated data set for retail analysts to derive insights. Data preparation concerns identifying the store location type (i.e., Touchpoint location type) for each of the store-under-consideration. Store location type identification is an important task as it dictates the analytics to be conducted.