Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

We are concerned with the interaction and integration between demand forecasting and
inventory control, in the context of supply chain operations. The majority of the literature is …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

Abstract Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating
demand requirements at one echelon. It involves complex issues such as supply chain …

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 …

Spare parts are held to reduce the consequences of equipment downtime, playing an
important role in achieving the desired equipment availability at a minimum economic cost …

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 …

Intermittent demand appears when demand events occur only sporadically. Typically such
time series have few observations making intermittent demand forecasting challenging …

The current study applies neural network (NN) modeling in forecasting lumpy demand. It is,
to the best of our knowledge, the first such study. Our study compares the performance of NN …

From its foundation, operational research (OR) has made many substantial contributions to
practical forecasting in organizations. Equally, researchers in other disciplines have …