Inventory Control Using Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System under Uncertain Conditions

Authors

Ali Abdulmajeed Ali, Faculty of Engineering - University of Aden
Arzaq Mohammed Ali Kulaib, Faculty of Engineering University of Aden

Abstract

Supply chain management plays a significant role for running business efficiently, as it integrates management of materials and information flows between supply chain parties Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy inference system (ANFIS) are expert systems widely used to deal with imprecise and vague data. In this paper FIS and ANFIS are implemented to deal with the uncertainty regarding demand, lead time and inventory level in continuous inventory control system in order to obtain the optimal order quantity and reorder point. These two models are compared with Economic Order Quantity (EOQ) model at the different service level to study their impacts on the inventory costs. A case Study of Yemen Company for Industry and Commerce has been selected in this paper. The simulation results showed the superiority and efficiency of the proposed FIS and ANFIS models in comparison to stochastic EOQ model with7 %saving of total inventory cost and no shortages with expectation of raising the customers’ loyalty.

Recommended Citation

Ali, Ali Abdulmajeed and Ali Kulaib, Arzaq Mohammed (2020) "Inventory Control Using Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System under Uncertain Conditions," Hadhramout University Journal of Natural & Applied Sciences: Vol. 17: Iss. 2, Article 3.
Available at: https://digitalcommons.aaru.edu.jo/huj_nas/vol17/iss2/3