This paper reviews the research literature on forecasting retail demand. We begin by
introducing the forecasting problems that retailers face, from the strategic to the operational …

Research interests in machine learning (ML) and supply chain management (SCM) have
yielded an enormous amount of publications during the last two decades. However, in the …

With the advent of Big Data, nowadays in many applications databases containing large
quantities of similar time series are available. Forecasting time series in these domains with …

Recently, incorporating sustainability into the buyer-supplier sourcing decisions has
achieved a considerable amount of attentions among researchers and industrial enterprises …

Spare parts demand forecasting has received considerable attention over the last fifty years
as it is a challenging problem for many companies. This paper provides a critical review and …

The combination of forecasts resulting from an ensemble of neural networks has been
shown to outperform the use of a single “best” network model. This is supported by an …

This paper investigates the gap between research and practice in spare parts management,
with specific reference to durable goods addressed to private or professional customers. The …

Abstract Information sharing and coordination between the agents of a supply chain are
considered to be an effective strategy for improving its global performance. This paper …

We study ex ante information sharing in a supply chain consisting of a downstream retailer
and a make-to-stock upstream manufacturer. The retailer has imperfect demand information …

Tactical forecasting in supply chain management supports planning for inventory,
scheduling production, and raw material purchase, amongst other functions. It typically …