Paper Title: Optimization of multi-echelon multi-product supply chains using discrete event simulation
Authors: Ovundah King Wofuru-Nyenke, Daniel Osezua Aikhuele
Corresponding Author: Ovundah King Wofuru-Nyenke (ovundah.wofuru-nyenke@ust.edu.ng)/ Nigeria
Abstract
This study details the application of a novel multi-echelon, multi-product hybrid supply chain optimization framework known as the Optimizing Known Warehouses (OKW) framework, which consists of mathematical and computer simulation models, to analyze the effects of changes in inventory policies for products delivered by two (2) different case-study manufacturing supply chains. The methods used are the Bayesian optimization algorithm and linear programming to optimize inventory and routing in the supply chains. Furthermore, Arena software was used to develop a discrete event simulation model to compare the current and optimized scenarios of the supply chains. The results show that the optimization framework effectively reduces backorders and increases order fulfillment within the manufacturing supply chains analyzed, and can be easily applied to other Fast-Moving Consumer Goods (FMCG) manufacturing supply chains. For the water bottling supply chain, the optimal inventory policies reduced backorders of the 50cl, 75cl, and 150cl bottled water products by 8.7%, 18.7%, and 34.7%, respectively. For the noodles manufacturing supply chain, backorders for the 70g, 100g, 120g, 180g, and 280g noodle products were reduced by 3.5%, 12%, 20.6%, 7.1%, and 11.9%, respectively. This study provides a procedure for inventory and routing optimization using Bayesian optimization and linear programming, respectively, and a Discrete Event Simulation model for analyzing various supply chain configurations.
