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PurposeThe study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India.Design/methodology/approachThe present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory.FindingsThe drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group.Originality/valueThe analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics.
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The pandemic crisis and the resulting global uncertainties have obviously had a severe impact on the healthcare supply chain (HSC), leading scholars, healthcare executives, and policymakers to focus on the sustainability of the HSC. Technologies have emerged and developed rapidly in recent years, especially in the healthcare industry, for coping with the pandemic crisis and supporting the "new normal" for humankind. Within this context, various new technologies have been implemented to maximize the supply chain process, ensure patient and healthcare worker safety, and improve the quality of care. Hence, the integration of a technological dimension with the traditional three pillars of sustainability may aid in attempts to define the potential attributes of these dimensions of sustainability. Therefore, this study aimed to identify the key attributes of a sustainable healthcare supply chain (SHSC), and this paper presents a new, four-dimensional model for SHSCs, consisting of social, environmental, economic, and technological dimensions. A systematic literature review was conducted, resulting in the identification of 35 potential SHSC attributes. The Fuzzy Delphi Method (FDM) was then applied to determine the appropriateness of these potential attributes according to the consensus of 13 experts, including healthcare workers in a variety of medical specialties, who profoundly understand HSC sustainability. The results yielded 22 appropriate attributes, which were then categorized across the four dimensions. Consequently, a new model of an SHSC, which prioritizes patient safety, was constructed and is proposed here. This SHSC model can be applied strategically to the healthcare industry to enhance the safety of both medical personnel and patients in a sustainable manner.
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During the COVID-19 pandemic, sectoral contributors to home healthcare supply chain (HHCSC) corporations highlighted the role of home care services. Pharmacies are located where patients are allocated to them, and nurses are routed and scheduled according to their patients' needs. It is the first study to propose an integrated location-allocation-routing model, which includes all preliminaries necessary to make these decisions. We implement the LP-metric and epsilon-constraint methods to solve this model, and then we discuss the results of these methods. A comparison is also made regarding the objective function values and the time taken to solve the problem. The average, mean ideal distance (MID) (3.74;3.19), the rate of achievement of two objectives simultaneously (RAS) (1.71;3.56), and computational time (CPU time) (1.92;24.92) for two ɛ-constraint and LP-Metric methods is calculated. The superior technique is finally selected by utilizing the TOPSIS. To solve the study's mathematical model, the LP-metric method is worth implementing. Based on these results, the suggested model for HHCSC companies, and employees' performance, is efficient during the COVID-19 pandemic.
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This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.
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The coronavirus (COVID-19) pandemic has had a huge impact all over the world. Healthcare industry is one that has been greatly affected by Global supply chain disruption, including shortages of critical medical equipment and drugs, insufficiency of diagnostic, and inadequacy of medical personnel. The aforementioned problems directly affect human health in the dimension of 'patient safety' which may cause life-threatening situations. Therefore, it is important to learn how to strengthen the healthcare supply chain (HSC) and increase safety, particularly for patients. There are various researchers who studied HSC performance in developed countries but the research in developing countries especially southeast Asia countries e.g., Cambodia is scarce. Therefore, this research aims to identify and synthesize HSC performance for patient safety and provide a novel model of HSC performance in developing countries' healthcare settings. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted using a patient safety definition from WHO, International Patient Safety Goals from Joint Commission International (JCI), and the Hospital Accreditation institute (HA) from Thailand. The HSC performances are categorized under the easily recognizable heading SIMPLE (stands for patient safety goals in 6 domains as follows: Safe surgery, Infection Prevention & Control, Medication safety, Patient care process, Laboratory & blood product safety, and Emergency preparedness and response). The novel model of HSC performance for patient safety is provided to demonstrate the factors that can enhance the performance of HSC management in developing countries. Moreover, this study contributes to the meta-analysis by analyzing existing research and proposing new future research opportunities in HSC performance for patient safety that leads to social sustainability. © 2022 IEEE.
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This research investigates the mediation of resilience abilities on the relationship between Industry 4.0 technologies adoption and healthcare supply chain performance during the COVID-19 outbreak in Brazil and India. We surveyed 179 practitioners from organizations at different tiers of the healthcare supply chain (e.g., manufacturers, distributors, and care providers) in July 2021. Multivariate data techniques are used to the collected data to verify the hypotheses anchored on concepts from resource dependence theory. We identify two constructs of Industry 4.0 technologies (named after their predominant roles) and two constructs of resilience abilities (named according to the main abilities encompassed). Our findings indicate that resilience abilities mediate the impact of Industry 4.0 technologies on the performance of the healthcare supply chain since the beginning of the COVID-19 pandemic. However, the role played by adaptive and restorative abilities seems more prominent than the one played by anticipation and monitoring abilities. Further, sensing and communication technologies directly affect the healthcare supply chain's performance. Our study brings together three emerging topics related to the literature on the healthcare supply chain (Industry 4.0 adoption, resilience abilities development, and the disruptions caused by the COVID-19 pandemic). Although digitalization of the healthcare supply chain does improve its performance, our research indicated that its impact could be significantly enhanced when resilience abilities are concurrently developed, particularly in the Indian and Brazilian contexts. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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Technological acclimatization in today's healthcare industry is a subject of new inventions. The worldwide Covid-19 epidemic has led to increase in the use of technology for healthcare supply chain, patient data management, and claims settlement. Data management in healthcare industry is a complex structure where multiple organizations provide proper supply chain services in day to day life. Improper data management disrupts the supply chain, which has a long-term impact on the healthcare sector. Various issues in the present supply chain must be addressed. Blockchain-based crypto-currencies are well-known nowadays for their ability to create safe and traceable solutions. With the growing use of crypto-currencies, it also governs new range of applications and opportunities, including healthcare applications. Blockchain-based solutions are effective in the health sector for secure data retrieval and storage, resulting in more effectual product creation and tracking. Such system can provide data provenance, promotes genuine healthcare sector demands, and ensures the immutability of multi-direction transactions. In this study, we contribute a thorough overview of the literature on how Blockchain technology is changing the way healthcare supply chains operate. We looked at 61 papers from 2019 to 2021 that highlighted various difficulties with the traditional healthcare supply chain. We scrutinized different barriers and opportunity of Blockchain-based healthcare supply chain at the end of the research. © 2022 The Authors
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The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.
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The article examines what medical suppliers and providers learned about facial protection products after the Covid-19 pandemic of 2020-2022. Topics discussed include remarks from Jason Burnham, Senior Director of Facial Protection at Owens & Minor, evidence of pandemic-relaxed behaviors morphing into workflow acceptance, and statement from Gary Harris, Vice President of Sales and Marketing at Prestige Ameritech, about emergency use guidelines for personal protective equipment (PPE).
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The COVID-19 (Corona virus disease 2019) pandemic continues to slash through the entire humanity on the earth causing an international health crisis and financial uncertainty. The pandemic has formed a colossal disruption in supply chain networks. It has caused piling higher mortality in patients with comorbidities and generated a surging demand for critical care equipment, vaccines, pharmaceuticals, and cutting-edge technologies. Personal protective equipment, masks, ventilators, testing kits, and even commodities required for daily care have been scarce as lockdown and social distancing guidelines have kicked in. Amidst COVID-19, implementing and executing key processes of the healthcare supply chain (HSC) in a secured, trusted, effective, universally manageable, and the traceable way is perplexing owing to the fragile nature of the HSC, which is susceptible to redundant efforts and systemic risks that can lead to adverse impacts on consumer health and safety. Though the crisis shone a harsh light on the cracks and weaknesses of the HSC, it brings some significant insights into how HSC can be made more resilient and how healthcare industries figure out solutions to mitigate disruptions. While there are innumerable experiences learned from the disruption of this crisis, in this paper, five important areas to analyze the most vital and immediate HSC enhancements including building a resilient supply chain, thinking localization, implementing reliable reverse logistics, breaking down extant silos to achieve end-to-end visibility, and redesigning HSC using digitalization are emphasized. This work identifies important features related to CoT and HSC. Also, this study links these lessons to a potential solution through Chain of Things (CoT) technology. CoT technology provides a better way to monitor HSC products by integrating the Internet of Things (IoT) with blockchain networks. However, such an integrated solution should not only focus on the required features and aspects but also on the correlation among different features. The major objective of this study is to reveal the influence path of CoT on smart HSC development. Hence, this study exploits (i) fuzzy set theory to eliminate redundant and unrelated features; (ii) the Decision-Making and Experimental Evaluation Laboratory (DEMATEL) method to handle the intricate correlation among different features. This fuzzy-DEMATEL (F-DEMATEL) model attempts to direct CoT technology towards smart HSC by identifying the most influencing factors and investors are recommended to contribute to the development of application systems. This work also demonstrates how CoT can act a vital role in handling the HSC issues triggered by the pandemic now and in the post-COVID-19 world. Also, this work proposes different CoT design patterns for increasing opportunities in the HSC network and applied them as imperative solutions for major challenges related to traditional HSC networks.
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The severe scarcity of critical medical supplies caused by the COVID-19 pandemic led to considerable pro-curement challenges in the healthcare supply chain (HCSC). As ensuring the availability of such supplies during disruptions is critical, the debate on how to increase supply chain resilience in healthcare has gained new mo-mentum. We present empirical evidence from a multi-tier case study spanning nine European medical supplies manufacturers and hospital groups. Based on the resource dependence theory, we investigated procurement -related strategies to improve medical supplies availability. We conducted semi-structured interviews with 39 procurement and supply chain management experts and derived seven propositions on buffering and bridging approaches for managing evolving resource dependencies and thereby strengthening supply chain resilience in a pandemic. Overall, we confirm the resource dependence theory's applicability for explaining companies' miti-gation measures in a pandemic disruption. We find that bridging measures within the healthcare supply base, such as offering procurement support for suppliers or leveraging long-term buyer-supplier relationships, are more effective for securing medical supplies than buffering measures. Complementing bridging with buffering, such as extended upstream procurement or resource sharing among hospitals, can lead to superior risk mitigation as capacities of the present supplier base may not suffice. Furthermore, we extend the resource dependence theory by showing that the severity of disruptions caused by a pandemic triggers new forms of buffering external to the HCSC. Both traditional and new buffering measures establish novel flows of medical supplies in the HCSC that can enable higher supply security in a pandemic.
ABSTRACT
COVID-19 is recognized as an infectious disease generated by serious acute respiratory syndrome coronavirus 2. COVID-19 has rapidly spread all over the world within a short time period. Due to the coronavirus pandemic transmitting quickly worldwide, the impact on global healthcare systems and healthcare supply chain management has been profound. The COVID-19 outbreak has seriously influenced the routine and daily operations of healthcare facilities and the entire healthcare supply chain management and has brough about a public health crisis. As making sure the availability of healthcare facilities during COVID-19 is crucial, the debate on how to take resilience actions for sustaining healthcare supply chain management has gained new momentum. Apart from the logistics of handling human remains in some countries, supplies within the communities are urgently needed for emergency response. This study focuses on a comprehensive evaluation of the current practices of healthcare supply chain management in Hong Kong and the United States under COVID-19 settings. A wide range of different aspects associated with healthcare supply chain operations are considered, including the best practices for using respirators, transport of life-saving medical supplies, contingency healthcare strategies, blood distribution, and best practices for using disinfectants, as well as human remains handling and logistics. The outcomes of the conducted research identify the existing healthcare supply chain trends in two major Eastern and Western regions of the world, Hong Kong and the United States, and determine the key challenges and propose some strategies that can improve the effectiveness of healthcare supply chain management under COVID-19 settings. The study highlights how to build resilient healthcare supply chain management preparedness for future emergencies.
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Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.
ABSTRACT
Purpose: The study aims to identify and analyse the drivers of resilient healthcare supply chain (HCSC) preparedness in emergency health outbreaks to prevent disruption in healthcare services delivery in the context of India. Design/methodology/approach: The present study has opted for the grey clustering method to identify and analyse the drivers of resilient HCSC preparedness during health outbreaks into high, moderate and low important grey classes based on Grey-Delphi, analytic hierarchy process (AHP) and Shannon's information entropy (IE) theory. Findings: The drivers of the resilient HCSC are scrutinised using the Grey-Delphi technique. By implementing AHP and Shannon's IE theory and depending upon structure, process and outcome measures of HCSC, eleven drivers of a resilient HCSC preparedness are clustered as highly important, three drivers into moderately important, and two drivers into a low important group. Originality/value: The analysis and insights developed in the present study would help to plan and execute a viable, resilient emergency HCSC preparedness during the emergence of any health outbreak along with the stakeholders' coordination. The results of the study offer information, rationality, constructiveness, and universality that enable the wider application of AHP-IE/Grey clustering analysis to HCSC resilience in the wake of pandemics. © 2022, Emerald Publishing Limited.
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Many challenges lie ahead when dealing with COVID-19, not only related to the acceleration of the pandemic, but also to the prediction of personal protective equipment sets consumption to accommodate the explosive demand. Due to this situation of uncertainty, hospital administration encourages the excess stock of these materials, over-stocking products in some hospitals, and provoking shortages in others. The number of available personal protective equipment sets is one of the three main factors that limit the number of patients at a hospital, as well as the number of available beds and the number of professionals per shift. In this scenario, we developed an easy-to-use expert system to predict the demand for personal protective equipment sets in hospitals during the COVID-19 pandemic, which can be updated in real-time for short term planning. For this system, we propose a naive statistical modeling which combines historical data of the consumption of personal protective equipment sets by hospitals, current protocols for their uses and epidemiological data related to the disease, to build predictive models for the demand for personal protective equipment in Brazilian hospitals during the pandemic. We then embed this modeling in the free Safety-Stock system, which provides useful information for the hospital, especially the safety-stock level and the prediction of consumption/demand for each personal protective equipment set over time. Considering our predictions, a hospital may have its needs related to specific personal protective equipment sets estimated, taking into account its historical stock levels and possible scheduled purchases. The tool allows for adopting strategies to control and keep the stock at safety levels to the demand, mitigating the risk of stock-out. As a direct consequence, it also enables the interchange and cooperation between hospitals, aiming to maximize the availability of equipment during the pandemic.
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Healthcare supply chains are becoming increasingly complex and characterized by rapid and unpredictable changes, particularly during the Covid-19 pandemic. This unpredictability means supply chains are challenged from all levels. Patients, employees and society are all sources of uncertainty resulting with the need for supply chains to be healthier. This research explores the need for healthcare supply chains to be more adaptable and flexible. A literature informed design science approach was adopted as the methodology. We propose a systems view of an adaptive and flexible healthcare supply chain. Furthermore, we build system dynamic models to illustrate an unhealthy healthcare supply chain and a healthy healthcare supply chain. Theoretical supply chain conceptual frameworks and information systems concepts were synthesized to propose models that look to solve some of the supply chain problems arising from the Covid-19 pandemic. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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Motivated by a real-world healthcare supply case of a medical implant company, this paper studies a supply network configuration problem that integrates warehouse selections for vendor managed inventory (VMI), inventory policy, and delivery routing optimization together. The problem is a variant of the classic location-inventory-routing problem (LIRP) with both deterministic demand and uncertain demand, where multi-product, multi-period, multi-type delivery, delivery time limit and VMI are considered. Two types of delivery are used: one is the scheduled bulk delivery to the VMI warehouses and the other is direct shipping for hospitals. To address the problem, first, a deterministic MILP model is presented for the integrated LIRP. Then, to deal with the uncertainty in demand, we propose a robust optimization model and transform it into a tractable linear equivalent formulation. Further, considering the effect of COVID-19 pandemic on the demand and delivery time, a new robust model is proposed to account for this special situation. Numerical experiments are conducted to verify the advantage of the proposed robust optimization models. The sensitivity analysis provides some interesting managerial insights, and a real-world case of medical implant supply configuration with 78 hospitals is solved.
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This article empirically examines the effect of big data analytics (BDA) on healthcare supply chain (HSC) innovation, supply chain responsiveness, and supply chain resilience under the moderating effect of innovation leadership in the context of the COVID-19 pandemic. The scanning interpretation-action-performance model and organization information processing theory are used to explain BDA, HSC innovation, responsiveness, and resilience relationships. First, the hypotheses were tested using data collected from 190 experienced respondents working in the healthcare industry. Our structural equation modeling analysis using the partial least squares (PLS) method revealed that BDA capabilities play a pivotal role in building a responsive HSC and improving innovation, which has contributed to resilience during the current pandemic situation. High innovation leadership strengthens the effect of BDA capabilities on HSC innovation. High innovation leadership also increases the effect of BDA capabilities on responsiveness. Second, we validated and supplemented the empirical research findings using inputs collected in 30 semistructured qualitative questionnaires. Our article makes a unique contribution from the perspective of innovation leaderships. In particular, we argue that the role of innovative leadership in the COVID-19 pandemic situation is critical as it indirectly affects HSC resilience when BDA is in place.
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Healthcare is considered one basic necessity to sustaining life;thereby, assessing the character of a healthy and resilient supply chain can help a nation develop ideas to combat the healthcare crisis. COVID-19 has led to a long-term strain on the healthcare supply chain (HCSC) and has resulted in a lack of basic healthcare necessities. It has become apparent that supply chain disruptions and increased usage has led to a lack of medical supplies needed to provide the proper care to patients. Multicriteria decision-making (MCDM) will help to indicate what characteristics contribute to resilient healthcare supply chains. To assess the characteristic of a resilient supply chain, significant healthcare supply chains will help indicate significant characteristics. A case study on the medical supplies’ supply chains is presented. A rank reversal proximity index MCDM method ranks criteria to assist with decision making. The proximity index will reduce the chances of the rank reversal phenomenon that results in incorrect rankings from occurring. Results show that redundancy, collaboration, and robustness are key indicators of a resilient supply chain while, supply chain design, communication capabilities, and supply chain risk management become comparatively less important during the COVID-19 pandemic. Furthermore, a cluster analysis is conducted to group the resilience indicators of the respective supply chain. Through this study, the best way to combat disruptions in the healthcare supply chain due to large-scale pandemics is to share information quickly, reduce reliance on the design of the supply chain, and track the usage of necessary medical supplies. Alternatively, we validated our study by comparing a Preference Selection Index (PSI) to the proposed method.