Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic

Mahesh Babu Mariappan, Kanniga Devi, Yegnanarayanan Venkataraman, Ming K. Lim, Panneerselvam Theivendren

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Purpose: This paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach. Design/methodology/approach: The present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation. Findings: The findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week. Research limitations/implications: The implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain. Originality/value: The novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

Original languageEnglish
Pages (from-to)390-416
Number of pages27
JournalInternational Journal of Logistics Management
Volume34
Issue number2
Early online date12 Jan 2022
DOIs
Publication statusPublished - 14 Mar 2023

Bibliographical note

Publisher Copyright:
© 2021, Emerald Publishing Limited.

Keywords

  • Analytics
  • Artificial intelligence
  • COVID-19 pandemic
  • E-pharmacy supply chain
  • Machine learning
  • Shipment time prediction

ASJC Scopus subject areas

  • Business and International Management
  • Transportation

Fingerprint

Dive into the research topics of 'Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic'. Together they form a unique fingerprint.

Cite this