Safety Stock And Lead Time Optimization By Intelligent Water Drops Algorithm: A Case Study Of A Local B2B Company

Abstract

This thesis considers the problem of optimizing safety stocks in multi-echelon inventory systems with stochastic demand by guarantee service approach. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to demand uncertainties and to achieve predefined target service levels. In particular, the supply chain will be configured, the certain amount of safety stock and lead time will be placed on each stage to ensure that products are delivered to customers in guarantee service time. In order to achieve the objectives, the new nature-inspired swarm- based meta-heuristic called Intelligent Water Drops (IWD) was applied, which imitates some of the processes that happen in nature between the water drops of a river and the soil of the river bed. To demonstrate the utility of the proposed approach, an application of the approach at MCZ114, a domestic chemical and mechanical company, will be discussed