TY - JOUR

T1 - Forecasting Supply Chain sporadic demand with Nearest Neighbor approaches

AU - Nikolopoulos, Konstantinos

AU - Babai, Mohammed Zied

AU - Bozos, Konstantinos

PY - 2016/7

Y1 - 2016/7

N2 - 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.

AB - 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.

KW - Demand Forecasting

KW - Nearest Neighbors

KW - Exponential Smoothing

KW - Logistics

KW - Supply Chain

U2 - 10.1016/j.ijpe.2016.04.013

DO - 10.1016/j.ijpe.2016.04.013

M3 - Article

VL - 177

SP - 139

EP - 148

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - July 2016

ER -