Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting

Paul, S, Azeem, A and Ghosh, A 2015, 'Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting', International Journal of Business Information Systems, vol. 18, no. 3, pp. 268-284.


Document type: Journal Article
Collection: Journal Articles

Title Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting
Author(s) Paul, S
Azeem, A
Ghosh, A
Year 2015
Journal name International Journal of Business Information Systems
Volume number 18
Issue number 3
Start page 268
End page 284
Total pages 17
Publisher Inderscience Publishers
Abstract Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system (ANFIS) has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network (ANN), to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting.
Subject Logistics and Supply Chain Management
Neural, Evolutionary and Fuzzy Computation
Keyword(s) inventory level forecasting
adaptive neuro-fuzzy inference systems
ANFIS
artificial neural networks
ANNs
fuzzy logic
inventory levels
inventory modelling
raw material prices
raw material demand
holding cost
setup cost
supplier reliability
lead times.
DOI - identifier 10.1504/IJBIS.2015.068164
Copyright notice © 2015 Inderscience Enterprises Ltd.
ISSN 1746-0972
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