Retailer stock levels optimization tool

Ndana, J.

Retailer stock levels optimization tool

Files

Retailer stock levels optimization tool.pdf(6.22 MB)

Date

2023

Authors

Ndana, J.

Publisher

Strathmore University

Abstract

The purpose of this research was to design a statistical model that allows a retailer to optimize stock levels based on stock related parameters such as demand, lack of stock, stock replenishment lead time, service level, maintenance cost, and costs of replenishing stock. The study adopted applied research to solve the business challenge on optimization of stock. The study utilized secondary historical data relating to stock obtained from a supermarket in developing the optimization model. Additionally, the study applied prototyping methodology to design, develop and test the prototype. The web application was developed using HTML, JavaScript and Java Server Pages in Netbeans IDE. The server applications were developed based on Java Programming language. Apache Kafka was used for ingestion. Spark was used for data streaming while YugabyteDB was used for storage. The model, based on parameter values of a high moving product, yielded a service level at 95.2%. This was a positive indicator that the retailer would not hit a stock-out during the subsequent replenishment cycle for this product. On a probability scale of 0.01 to 0.99, the probability of running out of stock was 0.048. The model yielded an optimum order quantity of 15 units, against an average of 17 units on supplies made. Moreover, the model computed an optimum safety stock level within the 10-20% range of 14 units, which allows the retailer to cater for varying vendor delivery periods, as well as meet the changing consumer demands. Based on these values, the model computed an optimum stock level of 21 units, which allows the retailer to only reorder when the cycle stock, computed at 7 units, nears depletion. Similarly, the retailer can further inform decision making in the reorder placements based on the computed average lead time, such that the delivery is made every 6 days. The model was further tested on another high moving product, yielding a service level of 96.5%, implying that approximately 96% of the periods the model is able to cover for the customer demand for the given product.