Healthcare supply chain resilience framework: antecedents, mediators, consequents

Senna, Pedro; Reis, Augusto da Cunha; Dias, Ana Cláudia; Coelho, Ormeu; Guimarães, Júlio César Ferro de; Severo, Eliana Andréa

doi:10.1080/09537287.2021.1913525

Cited by 27 publications

(16 citation statements)

References 131 publications

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“…That also implies that healthcare can benefit from automation where standardized processes and routine operations are followed. In this regard, to obtain maximum benefits from automation, it is reasonable to first optimize the process through LH or Six Sigma [ 154 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Tlapa

Tortorella

Fogliatto

et al. 2022

IJERPH

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Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean–Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications.

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Section: Discussionmentioning

confidence: 99%

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Tlapa

Tortorella

Fogliatto

et al. 2022

IJERPH

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“…This study is part of a large research project on shortages of generic medicines and focuses on off-patent generics. Although they are most often in shortage (Bogaert et al 2015; WHO 2016; FDA, 2019), there is a paucity of risk management research within this context (c.f., Ergun Senna et al 2021). In the present study, we adopt a case study approach and focus on the paracetamol supply chain because it exemplifies a commonly used essential generic medicine (WHO, 2021).…”

Section: To Reduce Generic Medicine Shortage and Which Came In Place ...mentioning

confidence: 99%

Supply chain risk management strategies in normal and abnormal times: policymakers' role in reducing generic medicine shortages

Ahlqvist

Dube²,

Jahre³

et al. 2022

IJPDLM

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PurposeThis paper links supply chain risk management to medicine supply chains to explore the role of policymakers in employing supply chain risk management strategies (SCRMS) to reduce generic medicine shortages.Design/methodology/approachUsing secondary data supplemented with primary data, the authors map and compare seven countries' SCRMS for handling shortage risks in their paracetamol supply chains before and during the first two waves of the COVID-19 pandemic.FindingsConsistent with recent research, the study finds that policymakers had implemented few SCRMS specifically for responding to disruptions caused by COVID-19. However, shortages were largely avoided since multiple strategies for coping with business-as-usual disruptions had been implemented prior to the pandemic. The authors did find that SCRMS implemented during COVID-19 were not always aligned with those implemented pre-pandemic. The authors also found that policymakers played both direct and indirect roles.Research limitations/implicationsCombining longitudinal secondary data with interviews sheds light on how, regardless of the level of preparedness during normal times, SCRMS can be leveraged to avert shortages in abnormal times. However, the problem is highly complex, which warrants further research.Practical implicationsSupply chain professionals and policymakers in the healthcare sector can use the findings when developing preparedness and response plans.Social implicationsThe insights developed can help policymakers improve the availability of high-volume generic medicines in (ab)normal times.Originality/valueThe authors contribute to prior SCRM research in two ways. First, the authors operationalize SCRMS in the medicine supply chain context in (ab)normal times, thereby opening avenues for future research on SCRM in this context. Second, the authors develop insights on the role policymakers play and how they directly implement and indirectly influence the adoption of SCRMS. Based on the study findings, the authors develop a framework that captures the diverse roles of policymakers in SCRM.

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“…Senna et al. propose a framework for analyzing the relationships between the antecedent factors, mediators and consequents of healthcare supply chain resilience (HCSCRes) and scales for its factors (Senna et al. , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Mariappan

Devi

Yegnanarayanan

et al. 2022

IJLM

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PurposeThis 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/approachThe 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.FindingsThe 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/implicationsThe 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/valueThe 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.

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Healthcare supply chain resilience framework: antecedents, mediators, consequents

Cited by 27 publications

References 131 publications

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Effects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review

Supply chain risk management strategies in normal and abnormal times: policymakers' role in reducing generic medicine shortages

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

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