Single-hidden layer neural networks for forecasting intermittent demand

Summary form only given. Forecasting is the statement about the unknown results or outputs of events that will occur at a specific time in the future. To enhance the organizational competitive advantage in a constantly fluctuating environment, an organization's management must make the correct decision on a timely basis depending on the information available. This study presents an application of neural network methods for forecasting three different items that exhibit lumpiness in their trend pattern. Using sixty periods of simulated data for each time series, forecasts were prepared for the next thirteen periods.

Lumpy demand forecasting using neural networks

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A Comparison Between Three Neural Network Models for Classification Problems

Investigating the accuracy of cross-learning time series forecasting methods

Daily SKU demand forecasting is a challenging task as it usually involves predicting irregular series that are characterized by intermittency and erraticness. This is particularly true when forecasting at low cross-sectional levels, such as at a store or warehouse level, or dealing with slow-moving items. Yet, accurate forecasts are necessary for supporting inventory holding and replenishment decisions. This task is typically addressed by utilizing well-established statistical methods, such as the Croston’s method and its variants. More recently, Machine Learning (ML) methods have been proposed as an alternative to statistical ones, but their superiority remains under question. This paper sheds some light in that direction by comparing the forecasting performance of various ML methods, trained both in a series-by-series and a cross-learning fashion, to that of statistical methods using a large set of real daily SKU demand data. Our results indicate that some ML methods do provide better forecasts, both in terms of accuracy and bias. Cross-learning across multiple SKUs has also proven to be beneficial for some of the ML methods.

Comparison of statistical and machine learning methods for daily SKU demand forecasting

Accurate demand forecasting is one of the most keyissues in inventory management of spare parts. The problem ofmodeling future consumption becomes especially difficult for lumpypatterns, which characterized by intervals in which there is nodemand and, periods with actual demand occurrences with largevariation in demand levels. However, many of the forecastingmethods may perform poorly when demand for an item is lumpy.In this study based on the characteristic of lumpy demand patternsof spare parts a hybrid forecasting approach has been developed,which use a multi-layered perceptron neural network and atraditional recursive method for forecasting future demands. In thedescribed approach the multi-layered perceptron are adapted toforecast occurrences of non-zero demands, and then a conventionalrecursive method is used to estimate the quantity of non-zerodemands. In order to evaluate the performance of the proposedapproach, their forecasts were compared to those obtained by usingSyntetos & Boylan approximation, recently employed multi-layeredperceptron neural network, generalized regression neural networkand elman recurrent neural network in this area. The models wereapplied to forecast future demand of spare parts of ArakPetrochemical Company in Iran, using 30 types of real data sets. Theresults indicate that the forecasts obtained by using our proposedmode are superior to those obtained by using other methods

A. Nasiri Pour

Papers

A hybrid neural network and traditional approach for forecasting lumpy demand

A Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand