An anticipation approach of inventory control with simulation model in a real case study is presented to cope with unpredictable business environment to minimize the total inventory cost without stockouts occurring and inventory capacity being exceeded. Fuzzy inventory control algorithm (FZY) is presented and compared to classical inventory control algorithms. The algorithms are tested on historic data of a selected sample of representative items. The results show that FZY outperforms other algorithms and significantly reduces costs (up to 40%).
COBISS.SI-ID: 21607207
The modern business environment contains many factors that are more or less reliable. One of them is an unreliable supplier in the supply chain. This paper introduces a system dynamics approach to the inventory control model for replenishment process optimization regarding inventory total cost. Also, two delivery algorithms are presented for supplier's delivery: a) orders are delivered in one batch and b) orders are delivered in several batches. The impact of unreliable delivery on a replenishment strategy and on inventory total costs is presented and discussed.
COBISS.SI-ID: 6376467
The presented paper is dealing with knowledge discovery from data of the stochastic production plan to determine an adequate inventory control algorithm on a selected time interval with regard to given cost function and restrictions. In this preliminary research, machine learning methods, e.g. neural networks, decision trees and Bayes classifier, were used. Methods were learning on production plan characteristics, e.g. mean and variance, and periods, determined by Fourier analysis. The classification accuracy is presented together with comparison of machine learning methods.
COBISS.SI-ID: 6066451