Predicting E-commerce Sales & Inventory Management using Machine Learning

Ramrao Adik Institute of Technology, Department of Computer Science, Mumbai, India

^* Corresponding author: div.agn.rt17@rait.ac.in

Abstract

Due to prevalent transition from visiting physical stores to online shopping, predicting customer behaviour in the e-commerce market is gaining increasing importance. Over the past few years, e-commerce marketplaces such as Flipkart and Amazon have observed a manifold increase in both sales and market share of retail products sold. During this period, many traditional retailers and wholesalers have set up their e-commerce portfolios to stay relevant in the marketplace. However, due to lack of technology penetration in the Medium, Small, & Micro Enterprises (MSMEs) sector, the technological tools required by them are largely absent. One key issue for small businesses in e-commerce has been to forecast how many products their business will sell, thereby introducing another big issue of predicting how much inventory of a product they need to hold, to match up with demand. This is partly due to the massive scale and opportunities opening for them by participating in an open, large marketplace such as e-commerce. The COVID-19 pandemic has also drastically changed consumer shopping trends and behaviours: consumers have changed their preference to buying online. Thus, Indian small businesses need to have the ability to forecast demand more accurately than ever before.