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Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand

Author

Listed:
  • Hossein Havaeji

    (Mechanical Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Thien-My Dao

    (Mechanical Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Tony Wong

    (Mechanical Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

Abstract

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 Technology-enabled 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 Feature Weighting 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.

Suggested Citation

  • Hossein Havaeji & Thien-My Dao & Tony Wong, 2023. "Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand," Mathematics, MDPI, vol. 11(22), pages 1-27, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4669-:d:1281772
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    References listed on IDEAS

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    1. Abdul Jabbar & Samir Dani, 2020. "Investigating the link between transaction and computational costs in a blockchain environment," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3423-3436, June.
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