A framework for intelligent inventory prediction in small and medium- scale enterprise.

Obot, Okure; George, Uduak; James, Imaobong

Abstract

The aim of this research is to apply an intelligent technique to predict optimal inventory quantity in small and medium-scale enterprise. This is in view of the fact that the conventional models such as the EOQ model use only deterministic while some decision variables are non-deterministic in nature. Forecasted average demand of items for ten months in a small-scale retail outlet was collected and trained using an Artificial Neural Networks (ANN) of 5 neurons in the input layer with eight neurons in the first hidden layer and four neurons in the second hidden layer. Two feed-forward training algorithms of quasi-newton and quick propagation were employed in the training with the results of fuzzy logic technology found in the literature as the target output. Results obtained show that the quasi-newton algorithm covaries stronger with the fuzzy logic results than the quick propagation results. The objective and subjective feelings of the inventory manager were also captured to optimise the results of the training. The study is at a framework stage and will proceed to implementation level when more datasets are collected. Data collection in a small-scale outlet is a daunting task as record keeping is hardly done. The inclusion of non-deterministic circumstances such as emotional and objective feelings of the inventory manager to predict inventory is novel considering the fact that studies in the available intelligent inventory prediction have not employed such variables in their predictions.

Citation

OBOT, O., GEORGE, U. and JAMES, I. 2021. A framework for intelligent inventory prediction in small and medium-scale enterprise. European journal of business and management [online], 13(2), pages 21-28. Available from: https://doi.org/10.7176/EJBM/13-2-03

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2020
• Online Publication Date: Jan 31, 2021
• Publication Date: Jan 31, 2021
• Deposit Date: Oct 5, 2021
• Publicly Available Date: Oct 5, 2021
• Journal: European Journal of Business and Management
• Print ISSN: 2222-1905
• Electronic ISSN: 2222-2839
• Publisher: International Institute for Science, Technology and Education (IISTE)
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 2
• DOI: https://doi.org/10.7176/EJBM/13-2-03
• Keywords: Artificial neural networks; Fuzzy logic; Quasi newton; Quick propagation; EOQ; Inventory; Forecast
• Public URL: https://rgu-repository.worktribe.com/output/1456771