A Data Mining Approach for Inventory Forecasting: A Case Study of a Medical Store

Abstract

One of the factors that often result in an unforeseen shortage or expiry of medication is the absence of, or continued use of ineffective, inventory forecasting mechanisms. Unforeseen shortage of perhaps lifesaving medication potentially translates to a loss of lives, while overstocking can affect both medical budgeting as well as healthcare provision. Evidence from literature indicates that forecasting techniques can be a robust approach to address this inventory management challenge. The purpose of this study is to propose an inventory forecasting solution based on time series data mining techniques applied to transactional data of medical consumptions. Four different machine learning algorithms for time series analysis were explored and their forecasting accuracy estimates were compared. Results reveal that Gaussian Processes (GP) produced better results compared to other explored techniques (Support Vector Machine Regression (SMOreg), Multilayer Perceptron (MLP) and Linear Regression (LR)) for four weeks ahead prediction. The proposed solution is based on secondary data and can be replicated or altered to suit different constraints of other medical stores. Therefore, this work evidently suggests that the use of data mining techniques could prove a feasible solution to a prevalent challenge in medical inventory forecasting process. It also outlines the steps to be taken in this process and proposes a method to estimate forecasting risk that helps in deploying obtained results in the respective domain area.