Juan Ramón Trapero Arenas

Universidad de Castilla-La Mancha

Supply chain forecasting special issue

Supply chain forecasting special issue

I am pleased to inform you that Dr. Gokham Egilmez and myself are organizing a topical collection about supply chain management forecasting to be published in Forecasting (ISSN 2571-9394). Please, see this link for further information. In case you are interested, please feel free to let either of us know.

Forecasting is one of the most important and most challenging strategic and operational management level exercises, which is critical and influential for the survival and sustainable growth of business organizations. The pandemic and its aftermath will only increase the subject’s importance, since supply chains have been disrupted significantly across various service and manufacturing organizations worldwide. The food, retail, and tourism industries in particular were highly impacted during the so-called new normal. The connection between effective supply chain management and timely and effective forecasting has become a central topic of interest everywhere. Today, business organizations continuously dedicate increasing emphasis and resources on data-driven decision making as a result of the recent developments as well as the need to reduce uncertainty across the supply chains. Therefore, decision-making parameters across the supply chains including final and intermediate demand, lead times, inventories, transportation, supplier partnerships, etc. have been studied to be integrated into forecasting procedures and applications. This Special Issue invites authors to submit state-of-the-art manuscripts that address challenges in supply chain forecasting from a business analytics perspective. Both theoretical and practice-oriented papers are accepted with strong preference to practical studies. We welcome authors from academic, government, non-government, and industrial organizations. The focus of this Topical Collection is (but is not limited to):

  • Machine learning/artificial intelligence techniques applied to demand forecasting;
  • Demand forecasting under promotional campaigns;
  • Judgmental forecasting;
  • Intermittent demand forecasting;
  • Censored demand forecasting;
  • Hierarchical forecasting;
  • Automatic identification of demand time series;
  • Implications of demand forecasting on inventory and spare parts management;
  • Forecasting considering safety stock, lead times, and bullwhip effect;
  • Volatility and quantile demand forecasting;
  • Forecasting models applied to supply chain collaborations;
  • Forecasting error/costs metrics;
  • Nonparametric methods applied to demand forecasting;
  • Forecasting with system dynamics;
  • Simulation-based forecasting;
  • Empirical case studies.

Dr. Gokhan Egilmez
Dr. Juan Ramón Trapero Arenas
Guest Editors

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