Anticipation of Demand in Supply Chains

Youssef Tliche (), Atour Taghipour and Béatrice Canel-Depitre ()
Additional contact information
Youssef Tliche: NIMEC - Normandie Innovation Marché Entreprise Consommation - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - IRIHS - Institut de Recherche Interdisciplinaire Homme et Société - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université
Atour Taghipour: NIMEC - Normandie Innovation Marché Entreprise Consommation - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - IRIHS - Institut de Recherche Interdisciplinaire Homme et Société - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université
Béatrice Canel-Depitre: NIMEC - Normandie Innovation Marché Entreprise Consommation - UNICAEN - Université de Caen Normandie - NU - Normandie Université - ULH - Université Le Havre Normandie - NU - Normandie Université - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - IRIHS - Institut de Recherche Interdisciplinaire Homme et Société - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université

Post-Print from HAL

Abstract: The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

Date: 2019
References: Add references at CitEc
Citations:

Published in A. Taghipour (Ed). Hierarchical Planning and Information Sharing Techniques in Supply Chain Management, IGI Global, pp.1-45, 2019, 9781522572992. ⟨10.4018/978-1-5225-7299-2.ch001⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02173363

DOI: 10.4018/978-1-5225-7299-2.ch001

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().