Cost prediction in blockchain-enabled pharmaceutical supply chain under uncertain demand

Havaeji, Hossein, Dao, Thien-My et Wong, Tony. 2023. « Cost prediction in blockchain-enabled pharmaceutical supply chain under uncertain demand ». Mathematics, vol. 11, nº 22.

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Résumé

Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technologyenabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its importance, and we applied the FeatureWeighting approach to analyze their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a more substantial influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Dao, Thien-My Wong, Tony
Affiliation:	Génie mécanique, Génie des systèmes
Date de dépôt:	18 déc. 2023 19:03
Dernière modification:	08 janv. 2024 19:48
URI:	https://espace2.etsmtl.ca/id/eprint/28169

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