Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach to Raw Material Inventory Control at Pt XYZ

Abstract

Raw material inventory control is important thing for food companies. The adaptive inventory control is able to adapt to changes in the environment, maintain system performance and stability in the face of various problems in the industry, one of which can be applied to controlling raw material inventories. PT XYZ is a jelly drink industry that has a high level of raw material inventory. In 2016 the investment costs incurred by PT XYZ for carrageenan raw materials ranged from 55–566 million IDR. Costs incurred are quite high and require considerable handling in their maintenance. Therefore, a solution is needed to minimize inventory costs without disturbing the business process. The purpose of this study is to analyse the raw materials inventory PT XYZ for jelly drink product. The analysis is carried out on the main raw material, namely agar (carrageenan) which has three suppliers. The method used is BPMN 1.0 and ANFIS (Adaptive Neuro Fuzzy Inference Systems) which form rules of raw material control rules. This method is an alternative decision making and accommodates flexibility in the form of frame work that accommodates uncertainty of information or data that is less accurate. This study uses four input parameters, namely production demand, raw material arrival, usage and stock. The output obtained is in the form of inventory costs. The results of the study are information regarding the role of each actor who stores important data as a sequence of decision making. The application of ANFIS to design a raw material inventory control system using epoch 50 produces 90 rules of rules with testing errors. The average test for the training dataset is 0, 00077412 and the test and examination dataset is 0, 0006903. Rules of rules obtained can be applied to control raw material inventories.