Forecasting supply chain sporadic demand with nearest neighbor approaches

Abstract

One of the biggest challenges in Supply Chain Management (SCM) is to forecast sporadic demand. Our forecasting methods arsenal includes Croston s method, SBA and TSB as well as some more recent non-parametric advances, but none of these can identify and extrapolate patterns existing in data; this is essential as these patterns do appear quite often, driven by infrequent but nevertheless repetitive managerial practices. One could claim such patterns could be picked up by Artificial Intelligence approaches, however these do need large training datasets, unfortunately non-existent in industrial time series. Nearest Neighbors (NN) can however operate in these latter contexts, and pick up patterns even in short series. In this research we propose applying NN for supply chain data and we investigate the conditions under which these perform adequately through an extensive simulation. Furthermore, via an empirical investigation in automotive data we provide evidence that practitioners could benefit from employing supervised NN approaches. The contribution of this research is not in the development of a new theory, but in the proposition of a new conceptual framework that brings existing theory (i.e. NN) from Computer Science and Statistics and applies it successfully in an SCM setting.

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

“…KNN can also be integrated into regression analysis problems [78] for dimensionality reduction of the data [79]. In the realm of demand forecasting in SC, Nikolopoulos et al [80] applied KNN for forecasting sporadic demand in an automotive spare parts supply chain. In another study, KNN is used to forecast future trends of demand for Walmart's supply chain planning [81].…”

Forecasting of Sporadic Products: Practical and Theoretical Considerations

“…For products with low coefficient of variation ( CV DE ) and average interdemand interval (p) the Croston method is recommended, or in extreme positive cases traditional time series analysis. The proposed method has been generalized recently in the field of information technology (Chovanec and Breznická, 2016;Nikolopoulos et al, 2016;Prestwich et al, 2014).…”

Marketing Practices and Profitability of a Company: A Study by Machine Learning Method

“…Исследователи соглашаются с мнением, что одним из основных преиму ществ использования KNN является его применимость для небольших размеров выборки. Так, для оптимизации алгорит му достаточно иногда два три наблюдения [Nikolopoulos, Babai, Bozos, 2016]. Даже на небольших выборках метод часто обла дает высокой точностью прогнозирования данных и малым размером ошибки при классификации.…”