This paper presents a systematic review of studies related to artificial intelligence (AI)
applications in supply chain management (SCM). Our systematic search of the related …

Collections of time series formed via aggregation are prevalent in many fields. These are
commonly referred to as hierarchical time series and may be constructed cross-sectionally …

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 …

This article studies monthly volatility forecasting for the copper market, which is of practical
interest for various participants such as producers, consumers, governments, and investors …

Studies in air pollution epidemiology are of paramount importance in diagnosing and
improve life quality. To explore new methods or modify existing ones is critical to obtain …

Demand forecasting performance is subject to the uncertainty underlying the time series an
organization is dealing with. There are many approaches that may be used to reduce …

Developments in data mining techniques have significantly influenced the progress of
Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the …

Recent advances have demonstrated the benefits of temporal aggregation for demand
forecasting, including increased accuracy, improved stock control and reduced modelling …

The forecasting of monthly seasonal streamflow time series is an important issue for
countries where hydroelectric plants contribute significantly to electric power generation …

The intersection of Artificial Intelligence (AI) and sustainability within the manufacturing
supply chain has emerged as a significant area of interest for both researchers and industry …