ABSTRACT
Purpose - The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers. Design/methodology/approach - To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers. Findings - The findings reveal that the major barriers are in a four-level hierarchical relationship where "Insufficient SC strategic plans to ensure agility during crisis" acts as the most critical barrier, followed by "Poor information structure among SC contributors," and "Inadequate risk management policy under pandemic." Finally, the MICMAC analysis validates the findings from the ISM approach. Originality/value - This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.
ABSTRACT
The healthcare supply chain is a complex and multifaceted entity. A poorly functioning healthcare supply chain can directly affect patient health and facility performance. The task of this paper is to examine the most significant aspects of supply chain risk management in the healthcare industry. The review was carried out by analyzing the literature for keywords in major databases. Based on the authors' literature research, the most important factors related to the supply chain are presented. In addition to supply chain risk assessment, other factors closely related to the supply chain are considered. Since the outbreak of the Covid-19 pandemic, the topic of supply chain risk management has received much more attention from scientists and researchers. Before, the field had not been the subject of much research activity. © 2023, Success Culture Press. All rights reserved.
ABSTRACT
Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product's shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices. © 2023 Elsevier Ltd
ABSTRACT
Existing literature on optimizing inventory levels in pharmaceutical supply chains has focused on a limited set of drivers. However, the global supply chain disruptions produced by the Covid-19 pandemic demonstrated the need for a more nuanced picture of the inventory management drivers in this sector to identify profitable inventory configurations while fulfilling demands and safety margins. To address this gap in the literature, this paper identifies key drivers impacting inventory levels and develops a framework for assessing inventory configurations in pharmaceutical supply chains. The framework is tested using a single case study approach. The case study showed that while external and downstream supply chain factors were recognized as being critical to pursuing inventory reduction initiatives, internal factors prevailed when making inventory management decisions. The framework developed in this paper may assist practitioners in identifying the most important factors impacting inventory levels within a specific pharmaceutical supply chain configuration and is in use in the industry today. © 2023, The Author(s) under exclusive licence to German Academic Society for Production Engineering (WGP).
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PurposeThis study aims to explore the effect of power-based behaviours on pharmaceutical supply chain (PSC) resilience.Design/methodology/approachThis study used a mixed-method approach to explore the role of power-based behaviours in PSC resilience. Qualitative interviews from 23 key PSC stakeholders, followed by thematic analysis, revealed the underlying perceptions regarding PSC resilience. Quantitative propositions were then developed based on the themes adopted from PSC resilience literature and the qualitative findings. These were tested via a survey questionnaire administered to 106 key stakeholders across the various levels in the PSC. Structural equation modelling with partial least squares was used to analyse the data.FindingsThe data analysed identified proactive and reactive strategies as resilience strategies in the PSC. However, power-based behaviours represented by quota systems, information and price control influenced these resilience strategies. From a complex adaptive system (CAS) perspective, the authors found that when power-based behaviours were exhibited, the interactions between PSC actors were mixed. There was a negative influence on reactive strategies and a positive influence on proactive strategies. The analysis also showed that PSC complexities measured by stringent regulations, long lead times and complex production moderated the effect of power-based behaviour on reactive strategies. Thus, the negative impact of power-based behaviours on reactive strategies stemmed from PSC complexities.Research limitations/implicationsThis research particularly reveals the role of power-based behaviours in building PSC resilience. By evaluating the nexus from a CAS perspective, the analysis considered power-based behaviours and the moderating role of PSC complexities in developing resilience strategies. This study considers the interactions of PSC actors. This study shows that power asymmetry is a relational concept that inhibits the efficacy of reactive strategies. This study thus advocates the importance of power in achieving a more resilient PSC from a holistic perspective by highlighting the importance of the decision-making process among supply chain (SC) partners. The findings are particularly relevant if PSC resilience is viewed as a CAS. All the interactions and decision-making processes affect outcomes because of their inherent complexities. Although this study focused on the PSC, its implications could be extended to other SCs.Practical implicationsThe authors identified that power-based behaviours influenced resilience strategies. It was detrimental to reactive strategies because of the complexities of the PSC but beneficial to proactive strategies through resource-sharing. PSC actors are therefore encouraged to pursue proactive strategies as this may aid in mitigating the impact of disruptions. However, power-based behaviours bred partner dissatisfaction. This dissatisfaction may occur even within strategic alliances indicating that power could be detrimental to proactive strategies. Therefore, it is pertinent to identify conditions that lead to dissatisfaction when pursuing strategic partnerships. This study provides insight into actual behaviours influencing resilience and quantifies their effects on the PSC. These insights will be valuable for all SC partners wanting to improve their resilience strategies.Originality/valuePrevious PSC management and resilience studies have not examined the role of power in building resilience in the PSC. This paper thus provides a unique contribution by identifying the role of power in PSC resilience, offers empirical evidence and a novel theoretical perspective for future practice and research in building PSC resilience strategies.
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As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.
ABSTRACT
Unplanned events such as epidemic outbreaks, natural disasters, or major scandals are usually accompanied by supply chain disruption and highly volatile demand. Besides, authors have recently outlined the need for new applications of artificial intelligence to provide decision support in times of crisis. In particular, natural language processing allows for deriving an understanding from unstructured data in human languages, such as online news content, which can provide valuable information during disruptive events. This article contributes to this research strand as it aims to leverage textual data from news through sentiment analysis and predict demand volatility of pharmaceutical products in times of crisis. As a result, (1) a deep-learning-based sentiment analysis model was developed to extract and structure information from medicines-related news;(2) a framework allowing for combining extracted information from unstructured data with structured data of medicines demand was defined;and (3) an approach combining efficient artificial intelligence techniques with existing forecasting models was proposed to enhance demand forecasting in times of disruption. Additionally, the framework was applied to two examples of disruptive events in France: a pharmaceutical scandal and the COVID-19 pandemic. Findings outlined that using sentiment analysis allowed for enhancing demand forecasting accuracy.
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In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.
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(1) Background: Any disturbance in the pharmaceutical supply chain (PSC) can disrupt the supply of medicines and affect the efficiency of health systems. Due to shortages in the global pharma supply chain over the past few years and the complex nature of free trade and its limitations when confronted by a major global health and humanitarian crisis, many countries have taken steps to mitigate the risks of disruption, including, for example, recommending the adoption of a plus one diversification approach, increasing safety stock, and nationalizing the medical supply chains. (2) Objective: To scope findings in the academic literature related to decision criteria to guide national policy decisions for the "Partial Nationalization of Pharmaceutical Supply Chain” (PNPSC) from the viewpoints of the three main stakeholders: industry, payers (government and health insurance), and patients. (3) Methods: These consist of a scoping review of the peer-reviewed literature. (4) Results: A total of 115 studies were included. For local manufacturing decisions, five criteria and 15 sub-criteria were identified. Weighting, decision-making, risk assessment, and forecasting were the main data analysis tools applied;(5) Conclusions: The findings could serve as a baseline for constructing PNPSC frameworks after careful adaptation to the local context.
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Despite the network and internet technologies development, cases of counterfeit medicine still exist and an unreliable pharmaceutical supply chain infrastructure is one of the key factors behind drug counterfeiting. Before reaching the patient, medicines are transferred from suppliers to wholesalers, distributors, and pharmacists. Currently, information is not exchanged between supply chain management systems. Therefore, there is no visibility on the drug supply chain. Drug counterfeiting issue has become more important during the side spread of coronavirus disease due to the high popularity of this disease's vaccine. Even though this vaccine can help millions of people to eliminate coronavirus, the fake vaccine might be killable for them. This paper is proposing a solution by using blockchain technology for developing a secure pharmaceutical supply chain management system. This technology can add visibility, traceability, security, and transparency to the supply chain system. It is considered that to store the transactions, a permissioned blockchain will be used for this system, and only trusted parties will be permissioned to push data to the blockchain. By the end of this paper, a secure blockchain-based drug supply chain management system will be proposed by the researcher. © 2022 IEEE.
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During the COVID-19 pandemic, the current operating environment of pharmaceutical supply chain (PSC) has rapidly changed and faced increasing risks of disruption. The Internet of Things (IoT) and blockchain not only help enhance the efficiency of PSC operations in the information technology domain but also address complex related issues and improve the visibility, flexibility, and transparency of these operations. Although IoT and blockchain have been widely examined in the areas of supply chain and logistics management, further work on PSC is expected by the public to enhance its resilience. To respond to this call, this paper combines a literature review with semi-structured interviews to investigate the characteristics of PSC, the key aspects affecting PSC, and the challenges faced by PSC in the post-pandemic era. An IoT-blockchain-integrated hospital-side oriented PSC management model is also developed. This paper highlights how IoT and blockchain technology can enhance supply chain resilience and provides a reference on how PSC members can cope with the associated risks.
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The global health products supply chain is negatively influenced by the COVID-19 pandemic. Moreover, the risks in the pharmaceutical supply chain (PSC) have increased. Assessment and mitigation risks in PSC are essential issues to control and counter these risks. In this study, a 2-Tuple ARAS-BWM approach, which combines ARAS and BWM methods under linguistic 2-Tuple environment, is proposed to evaluate and address various risks to the best miti L . :ion strategies in the pharmaceutical industry in Tunisia during COVID-19. Noted that the main risk identified in the PSC is related to supply and suppliers and its best mitigation strategy is reducing risk. Copyright (C) 2022 The Authors.
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The twin forces of privacy law and data breaches have fundamentally challenged how we collect, store, and share sensitive information. Within this landscape, healthcare information is sacrosanct - and intimately tied to identity and data ownership. Building on prior work with UCLA Health, Genentech (a member of the Roche Group), Sanofi, Amgen, Biogen, and others, we offer this opinion piece to promote the development of a standard for decentralized Verifiable Credentials (VCs). This will empower Authorized Trading Partners (ATPs) in the pharmaceutical supply chain to trade and exchange information in compliance with the US federal law. Starting with credentialing and interoperability for the ATP community, our ultimate goal was to chart a path to a global standard for all health care VCs - providing individuals and health-care professionals control over their own data. By sharing our results and releasing essential components of the work to the public domain, we hope to align and connect with other foundational efforts, thus evolving standards within a truly open framework with broad stakeholder involvement.
ABSTRACT
In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to 'Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.
ABSTRACT
Unplanned events such as epidemic outbreaks, natural disasters, or major scandals are usually accompanied by supply chain disruption and highly volatile demand. Besides, authors have recently outlined the need for new applications of artificial intelligence to provide decision support in times of crisis. In particular, natural language processing allows for deriving an understanding from unstructured data in human languages, such as online news content, which can provide valuable information during disruptive events. This article contributes to this research strand as it aims to leverage textual data from news through sentiment analysis and predict demand volatility of pharmaceutical products in times of crisis. As a result, (1) a deep-learning-based sentiment analysis model was developed to extract and structure information from medicines-related news;(2) a framework allowing for combining extracted information from unstructured data with structured data of medicines demand was defined;and (3) an approach combining efficient artificial intelligence techniques with existing forecasting models was proposed to enhance demand forecasting in times of disruption. Additionally, the framework was applied to two examples of disruptive events in France: a pharmaceutical scandal and the COVID-19 pandemic. Findings outlined that using sentiment analysis allowed for enhancing demand forecasting accuracy.
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No one knows what the future holds but Investors and Executives still need to make long term decisions. Some choose to extrapolate the past. In retrospect, landslide events like the dot com recession, the housing bubble and the financial crisis proved them wrong. More recent events such as Brexit, the Sino-American rift and the US elections add to an already strained situation caused by the COVID crisis. It is possible that the latter are the harbingers of a less globalized economy. CDMOs need to consider the opportunities and threats associated with this scenario. The article develops a framework for exploring contingency strategy planning in a less globalized world for CDMOs. © 2021 TeknoScienze. All rights reserved.
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BACKGROUND: During disasters or crises, the traditional models of supply chain encounter failure and skewedness under the inevitable and unknown pressures. The procurement and transformation of required equipment to the involved areas is considered as one of the main triggers of decreasing damages and losses during crisis. In this regard, a breakdown in pharmaceutical supply chain can lead to intensive, undesired consequences. METHODS: This was a qualitative study applying a grounded theory approach. The study was conducted with attending of 32 informant participants who were qualified in supply chain during natural disasters and crisis. In order to collect the data, deep semi-structured interviews were applied along with investigating the documents, observation, field notes and theoretical memos. For data analysis, a continuous comparison was used according to Corbin and Strauss method. RESULTS: Results of the study were categorized in 8 main categories as the main themes. "Wasting" appeared as the main factor of the resilience of pharmaceutical and consumable medical equipment supply chain. Wasting included two subthemes of loss of resources and wasting time. CONCLUSION: In order to make resilience in pharmaceutical and consumable medical equipment during disasters, it is necessary to reinforce the various dimensions of the resilience model to increase the rate of supply chain responsiveness. This study particularly contributes to broadening and deepening our understanding of how to mitigate the risk of undesirable outcomes of pharmaceutical supply chain during the disasters or crises.
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The Stability Community of the American Association of Pharmaceutical Scientists (AAPS) held a virtual workshop on "Vaccine Stability Considerations to Enable Rapid Development and Deployment", on March 24-25, 2021. The workshop included distinguished speakers and panelists from across the industry, academia, regulatory agencies, as well as health care leaders. This paper presents a review of the topics covered. Specifically the challenges in accelerating vaccine development and analytical characterization techniques to establish shelf-life were covered. Additionally, vaccine stability modeling using prior knowledge stability models and advanced kinetic analysis played a key in the EUA approaches discussed during the workshop. Finally, the role of stability studies in addressing the challenges of vaccine distribution and deployment during the pandemic were a focus of presentations and panel discussions. Although the workshop did not have any presentation topics directly dedicated to the mRNA vaccines, the techniques discussed are generally applicable. The mRNA vaccine developers were represented in the panel discussions, where experts involved in the EUA approval/deployment stages for this vaccine type could discuss the challenges as applied to their vaccines.
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Pharmaceutical supply chain (PSC) is one of the most important healthcare supply chains and the recent pandemic (COVID-19) has completely proved it. Also, the environmental and social impacts of PSCs are undeniable due to the daily entrance of a large amount of pharmaceutical waste into the environment. However, studies on closed-loop PSCs (CLPSC) are rarely considered real-world requirements such as competition among diverse brands of manufacturers, the dependency of customers' demand on products' price and quality, and diverse reverse flows of end-of-life medicines. In this study, a scenario-based Multi-Objective Mixed-Integer Linear Programming model is developed to design a sustainable CLPSC, which investigates the reverse flows of expired medicines as three classes (must be disposed of, can be remanufactured and can be recycled). To study the competitive market and deal with demand uncertainty, a novel scenario-based game theory model is proposed. The demand function for each brand depends on the price and quality provided. Then, a hybrid solution approach is provided by combining the LP-metrics method with a heuristic algorithm. Furthermore, a real case study is investigated to evaluate the application of the model. Finally, sensitivity analysis and managerial insights are provided. The numerical results show that the proposed classification of reverse flows leads to proper waste management, making money, and reducing both disposal costs and raw material usage. Moreover, competition increases PSCs performance and improves the supply of products to pharmacies. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-021-03961-0.