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

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.

A modified Teunter-Syntetos-Babai method for intermittent demand forecasting

Intermittent demand refers to the specific demand pattern with frequent periods of zero demand.It occurs in a variety of industries including industrial equipment,automotive and specialty chemicals.In some industries or some sectors of industry,even majority of products are in intermittent demand pattern.Due to the usually small and highly variable demand sizes,accurate forecasting of intermittent demand has always been challenging.However,accurate forecasting of intermittent demand is critical to the effective inventory management.In this study we present a band new method-modified TSB method for the forecasting of intermittent demand.The proposed method is based on TSB method,and adopts similar strategy,which has been used in m SBA method to update demand interval and demand occurrence probability when current demand is zero.To evaluate the proposed method,16289 daily demand records from the M5 data set that are identified as intermittent demands according to two criteria,and an empirical data set consisting three years’monthly demand history of 1718 medicine products are used.The proposed m TSB method achieves the best results on MASE and RMASE among all comparison methods on the M5 data set.On the empirical data set,the study shows that m TSB attains an ME of 0.07,which is the best among six comparison methods.Additionally,on the MSE measurement,m TSB shows a similar result as SES,both of which outperform other methods.