Predictive Performance Model in Collaborative Supply Chain Using Decision Tree and Clustering Technique

Ridha Derrouiche (1, 2) , Holimchayachotikul Pongsak (3) , Leksakul Komgrit (3)

1 EVS - Environnement, Ville, Société
2 Ecole Supérieure de Commerce de Saint-Etienne
3 CAMT - College of Arts, Media and Technology

This paper proposes an integrated framework between B2B supply chains (B2B-SC) and performance evaluation systems. This framework is based on data mining techniques, enabling the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. The results are deployment for collaborative performance guidelines, which were validated by the domain experts in terms of its real practical usage efficiency. This framework enables managers to develop systematic manners to predict future collaborative performance and recognize latent problems in their relationship. Its usages and difficulties were also discussed. Furthermore, the final predictive results and rules contain vital information relating to SC improvement in the long term

Mots clés: Business to Business (B2B); Supply chain (SC); Data Mining; Multi Attribute Decision Making; Performance Measurement

Domaines: Modélisation et simulation

Ridha Derrouiche, Holimchayachotikul Pongsak, Leksakul Komgrit. Predictive Performance Model in Collaborative Supply Chain Using Decision Tree and Clustering Technique. 4th International Conference on Logistics (IEEE-LOGISTIQUA'2011), May 2011, Hammamet, Tunisia. pp.412-417. ⟨hal-00961617⟩