A large-scale real-world comparative study using pre-COVID lockdown and post-COVID lockdown data on predicting shipment times of therapeutics in e-pharmacy supply chains

Abstract

PurposeThe purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.Design/methodology/approachThe researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.FindingsThe proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.Research limitations/implicationsThe implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.Practical implicationsE-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.Originality/valueThe present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Guest editorial: The “new normal”: rethinking supply chains during and post-COVID-19 global business environment

“…After several rounds of revisions, four papers were accepted for this special issue. These are authored by, in alphabetical order, Acar et al (2022), Mariappan et al (2022), Nikookar and Yanadori (2022) and Spieske et al (2022). A total of 15 authors contributed to these four papers who are affiliated with institutions from six countries, namely Australia, France, Germany, India, Japan and Turkey.…”

“…Two of the papers are co-authored representing two countries, namely Australia–Japan and India–France. Accepted articles employed several research methodologies, including a questionnaire survey (Nikookar and Yanadori, 2022; Spieske et al , 2022), secondary or archival data (Mariappan et al , 2022), and a case study (Acar et al , 2022). An analysis of the keywords deployed by the authors show that the accepted articles emphasised several keywords of which COVID-19, supply chain resilience and supply chain disruption were the most commonly deployed.…”

“…The special issue article by Mariappan et al (2022) focuses on building a proactive strategy through an absorption mechanism for supply chain resilience. Through a machine learning-based framework for predicting shipment times, these authors suggest developing a new capability for e-pharmacy organisations to work on disruption-proof shipment time prediction of therapeutics, diagnostics and vaccines.…”

“…Through a machine learning-based framework for predicting shipment times, these authors suggest developing a new capability for e-pharmacy organisations to work on disruption-proof shipment time prediction of therapeutics, diagnostics and vaccines. Mariappan et al (2022) suggest the application of big data and analytics for demand assessment and shipment planning and decision support systems in this respect.…”

“…Change is one of the key drivers of agility (Abdelilah et al , 2018), while flexibility and speed are the two main elements of agility (Kidd, 1994). The article by Mariappan et al (2022) emphasised redesigning the supply chain structure to achieve a greater level of agility and adoption of different technologies to enhance the flexibility and responsiveness in supply chain processes. Spieske et al (2022) suggested developing agility through supply chain collaboration, manufacturing localisation and visibility and information exchanges beyond the first-tier ASCN suppliers.…”