Application of Neural Networks in Perishable Inventories Management

Abstract:

Classifying inventories into different groups based on the importance of each category of material is necessary for inventory management when there are a large number of inventories to be managed. In order to plan and determine effective policies in the management of each material, it is essential that the inventories be properly classified. One of the most popular methods used in classifying inventories is the ABC analysis, which is the classification of inventories based on their actual values. In the food-processing industry, for example, where inventories are often of perishable goods, the quality of inventories will decrease with storage time. Storage time is therefore considered a major factor when managing this inventory. In this research, the criterion of storage time was considered alongside others, including prices of materials per unit, amount of use, worth of use, and duration. However, since the classification of the inventories in this study was based on various complicated criteria, neural networks were applied. By using previous classifications as the input variables, we were able to apply a neural network to produce output variables and classify each inventory category into group A, B, or C. Neural networks were used to manage 105 inventories of the processing and product developing plant of the Royal Project Foundation. The findings showed that the neural network could effectively classify those inventories into groups A, B, and C, and that the accuracy of this classification was 84.35%.

• Periodical: Applied Mechanics and Materials (Volumes 752-753)
• Pages: 1424-1429
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.752-753.1424
• Online since: April 2015
• Authors: Wichittra Assawawongmethee, Wimalin Laosiritaworn*
• Keywords: ABC Analysis, Inventory Management, Neural Network, Perishable Inventories

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