Anticipation of Demand in Supply Chains

Youssef Tliche (1) , Atour Taghipour (1) , Béatrice Canel-Depitre (1)

1 NIMEC - Normandie Innovation Marché Entreprise Consommation

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.

Domaines: Gestion et management; Statistiques [stat]; Ingénierie, finance et science [cs.CE]; Recherche opérationnelle [math.OC]; Optimisation et contrôle [math.OC]; Modélisation et simulation

Youssef Tliche, Atour Taghipour, Béatrice Canel-Depitre. Anticipation of Demand in Supply Chains. 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⟩. ⟨hal-02173363⟩