Forecasting supply chain sporadic demand with nearest neighbor approaches

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.