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

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 and Kanniga Devi, Yegnanarayanan Venkataraman, Ming K. Lim and Panneerselvam Theivendren

Medienart:	E-Artikel

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	Zur Gesamtaufnahme - volume:34
Enthalten in:	The international journal of logistics management - 34(2023), 2, Seite 390-416

Sprache:	Englisch

Beteiligte Personen:	Mariappan, Mahesh Babu [VerfasserIn] Devi, Kanniga [VerfasserIn] Venkataraman, Yegnanarayanan [VerfasserIn] Lim, Ming Kim [VerfasserIn] Theivendren, Panneerselvam [VerfasserIn]

Links:	www.emerald.com [lizenzpflichtig] doi.org [lizenzpflichtig]

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

doi:	10.1108/IJLM-05-2021-0300

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	1846190878

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982			\|2 26 \|1 00 \|x DE-206 \|b 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.

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 and Kanniga Devi, Yegnanarayanan Venkataraman, Ming K. Lim and Panneerselvam Theivendren

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