Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Cisse Sory Ibrahima (School of Management, Northwestern Polytechnical University, Xi'an, 710072, China)
Jianwu Xue (School of Management, Northwestern Polytechnical University, Xi'an, 710072, China)
Thierno Gueye (School of Mechanical engineering, Northwestern Polytechnical University, Xi'an, 710072, China)

Abstract


Forecasting is predicting or estimating a future event or trend. Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century. As the competitiveness between supply chains intensifies day by day, companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits. Excessive inventory (overstock) and stock outs are very significant issues for suppliers. Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory. Excess inventory can also lead to increased storage, insurance costs and labor as well as lower and degraded quality based on the nature of the product. Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store. If clients are unable to find the right products on the shelves, they may switch to another vendor or purchase alternative items. Demand forecasting is valuable for planning, scheduling and improving the coordination of all supply chain activities. This paper discusses the use of neural networks for seasonal time series forecasting. Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Keywords


Inventory management; Demand forecasting; Seasonal time series; Artificial neural networks; Transfer function

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References


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DOI: https://doi.org/10.30564/jmser.v4i2.3242

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