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    Research on Risk Management for Healthcare Supply Chain in Hospital

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    Purpose: Unlike the commercial industries, the risks arising from the healthcare industry’s internal system and the surrounding environment may cause serious consequences, even the patients’ health. Concerning the increasing emphasis on risk management in the healthcare supply chain environment, there is an urgent demand for a novel decision support method that supports supply chain risk management in the hospital setting. As the topic is still in the early stage and only a few systematic academic studies on this topic can be found over the last decades. This research aims to propose a novel comprehensive framework and integrated risk management model that takes explicit account of multiple types of risk factors in aiding decision-making as well as compares and ranks the current implemented alternative risk mitigation strategies using fuzzy set theory and multiple criteria decision analysis (MCDA) methods. Methodology: In pursuit of meeting the requirements of the research objectives, this research conducts empirical studies from both China and UK healthcare industries and follows three steps of risk management procedure based on the proposed framework to conduct risk factors identification, assessment and risk mitigation strategies identification. In order to ensure that the analysis is systematic and inclusive, various types of risk factors are identified through a related systematic literature review and are validated through a set of empirical studies. Risk assessment is conducted through two stages of questionnaire surveys and evaluated through Fuzzy Analytic Hierarchy Process (AHP) and Interpretive Structural Modelling (ISM). Thereafter, risk mitigation strategies are identified through conducted empirical studies and evaluated through Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Research Implications: This is the first study which has developed a comprehensive risk management framework in the healthcare supply chain that effectively integrates supply chain risk factors identification, risk assessment as well as mitigation strategy identification and evaluation. The novelty of the developed framework lies in the fact that a systematic and practical decision making tools are proposed supporting hospital managers making strategic decisions on healthcare supply chain risk management. Furthermore, compared with several studies using secondary data, this thesis uses empirical data to conduct the identification and evaluation of risk mitigation strategies, enabling the results closes to the reality of the situation in the healthcare setting. Practical Implications: The profile of risk sources, the priority weighting and inter-relationship among these risks and, the ranking of mitigation strategies provide a guideline for hospital managers to anticipate and proactively deal with potential risks. The proposed framework applies to both the UK and China healthcare industries, the finding can also be applied in other countries and regions

    Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

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    [EN] Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of "dependence" among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport.Mzougui, I.; Carpitella, S.; Certa, A.; El Felsoufi, Z.; Izquierdo Sebastián, J. (2020). Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA. Processes. 8(5):1-22. https://doi.org/10.3390/pr8050579S12285Tian, Q., & Guo, W. (2019). Reconfiguration of manufacturing supply chains considering outsourcing decisions and supply chain risks. Journal of Manufacturing Systems, 52, 217-226. doi:10.1016/j.jmsy.2019.04.005Wu, Y., Jia, W., Li, L., Song, Z., Xu, C., & Liu, F. (2019). 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    A conceptual framework for crop-based agri-food supply chain characterization under uncertainty

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    [EN] Crop-based Agri-food Supply Chains (AFSCs) are complex systems that face multiple sources of uncertainty that can cause a significant imbalance between supply and demand in terms of product varieties, quantities, qualities, customer requirements, times and prices, all of which greatly complicate their management. Poor management of these sources of uncertainty in these AFSCs can have negative impact on quality, safety, and sustainability by reducing the logistic efficiency and increasing the waste. Therefore, it becomes crucial to develop models in order to deal with the key sources of uncertainty. For this purpose, it is necessary to precisely understand and define the problem under study. Even, the characterisation process of this domains is also a difficult and time-consuming task, especially when the right directions and standards are not in place. In this chapter, a Conceptual Framework is proposed that systematically collects those aspects that are relevant for an adequate crop-based AFSC management under uncertainty.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015Alemany Díaz, MDM.; Esteso, A.; Ortiz Bas, Á.; Hernández Hormazabal, JE.; Fernández, A.; Garrido, A.; Martin, J.... (2021). A conceptual framework for crop-based agri-food supply chain characterization under uncertainty. Studies in Systems, Decision and Control. 280:19-33. https://doi.org/10.1007/978-3-030-51047-3_2S1933280Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. 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    Multi-objective optimization model for risk assessment in the supply chain of a closed close loop under uncertainty conditions in parameters: Using a Constrained Risk Value Approach (CVaR)

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    In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The aim of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. Results show that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.IntroductionToday, the necessity and importance of corporate responsibility and the social impact of companies have led managers and planners to give special attention to these aspects in their organization's missions, visions, and strategies. Corporate social responsibility encompasses the influence of a company's activities on various social groups, including employee rights, workplace safety, favorable working conditions, and job creation, among others. Furthermore, the significance of environmental standards and organizations' efforts to reduce pollution and promote efficient waste management and recycling practices have become crucial for organizational success, considering legal requirements and customer expectations. In recent years, the integration of reverse logistics, social responsibility, and environmental objectives in supply chain management has gained increasing attention due to factors such as resource reduction, pollution mitigation, environmental pressures, customer demands, and transportation costs in a competitive market. This integration, known as the closed-loop supply chain network, aims to ensure sustainability. Additionally, risk management within the supply chain has become a vital concern for supply chain management, considering the uncertainties prevailing in the global economy and trends such as increased outsourcing and advancements in information technology. The growing interest in achieving sustainability as an effective strategy for addressing challenges in the global supply chain has led to extensive research in the field of sustainable closed-loop supply chain management. However, previous studies in this area have lacked a comprehensive measure for assessing risk. Therefore, it is essential to address this issue, which involves considering stability goals in a closed-loop supply chain alongside risk management in uncertain conditions. The necessity for such research is evident, given the complexity of global supply chains and the increased vulnerability and risk exposure faced by organizations.Materials and MethodsGiven the existing gaps in the literature and the presence of uncertainty in real-world data, a mathematical model was proposed to help decision-makers reduce risk by considering identified risks and utilizing a comprehensive and effective risk measurement scale. In the designed model and forward network, suppliers are responsible for procuring raw materials. The manufactured products are then delivered to the market's customers through distributor networks. In the reverse flow of products, returned items are categorized into two groups: separable and non-separable products, after collection and inspection. Products that can be disassembled are sent to separation centers where they are transformed into components. The components are further divided into recoverable and non-recoverable categories. Non-recoverable components are transferred to disposal centers for safe disposal, while recoverable components are sent to inspection, cleaning, and sorting centers. After inspection and cleaning, the products are classified into repairable, remanufacturable, and recyclable groups. In the remanufacturing process, reusable components, after inspection, cleaning, and sorting, are sent to factories based on the production center's capacity. They are then combined with other parts to create new products that reenter the distribution cycle. In the recycling process, separated recyclable components are transported to recycling centers for direct production of raw materials, based on the capacity of the recycling centers, after collection and inspection.Discussion and ResultsModel 1 represents the initial approach, where scenario analysis for future conditions is not utilized, and the average values of uncertain parameters are taken into account. On the other hand, Model 2 incorporates various scenarios of future conditions. It is a linear model that considers possible future conditions as well. Model 1 exhibits lower costs compared to Model 2. The predictability of this problem arises from the fact that the risk associated with future market conditions was largely disregarded in Model 1. However, in Model 2, the consideration of introduced triple conditions for possible future outcomes necessitates a higher cost. Nevertheless, this higher cost brings us closer to real-world approximation and facilitates better decision-making in supply chain management when confronted with risks.ConclusionIn this article, we conducted a literature review on the topic of risk models in supply chains and identified existing gaps. We found that most of the work in this field has certain weaknesses. Firstly, the focus has primarily been on risks in conventional and single-objective supply chains, neglecting the consideration of new risks and uncertainties that may arise in sustainable supply chains. To address this, we proposed a model for risk management in sustainable closed-loop supply chains. Secondly, we noticed that most of the existing studies lack a suitable and effective scale for measuring risk, particularly in the design of sustainable closed-loop supply chains. Drawing from the financial literature, we introduced the CVaR scale to fill this gap. Lastly, we developed and analyzed a model based on research gaps, using a case study in the home appliance industry as an example. The examination of the model's results, along with comparisons to real-world outcomes and previous research, validates the credibility of the proposed model

    Supply chain uncertainty:a review and theoretical foundation for future research

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    Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)

    Applying performance measures to support decision-making in supply chain operations: a case of beverage industry

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    Performance measurement systems (PMS) have commonly been applied to evaluate and reward performances at managerial levels, especially in the context of supply chain management. However, evidence suggests that the effective use of PMS can also positively influence the behaviour and improve performance at an operational level. The motivation is to accomplish organisational goals, namely to increase supply chain flexibility by responding to evermore-varying customer demands in a timely manner. The purpose of the study described in this paper was to develop a conceptual framework that adopts performance measures for ex-ante decision-making at an operational level within the supply chain. To guide the research, five questions were asked and subsequently key gaps have been identified. In an attempt to fill the gaps, a case study at a major global brand beverage company has been carried out, and as a result, a conceptual framework of the PMS has been developed. Overall, the research offers a foundation of the applicability and impact of PMS in the supply chain and provides a framework that attends to some of the potential uses of PMS that so far have not been practically applied. The outcomes from the testing indicate that the initial gaps identified in the literature have been addressed and that the framework is judicious with scope for practical applicability. The framework is deemed worthy of further testing in different operational contexts of the supply chain

    Feasibility of Warehouse Drone Adoption and Implementation

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    While aerial delivery drones capture headlines, the pace of adoption of drones in warehouses has shown the greatest acceleration. Warehousing constitutes 30% of the cost of logistics in the US. The rise of e-commerce, greater customer service demands of retail stores, and a shortage of skilled labor have intensified competition for efficient warehouse operations. This takes place during an era of shortening technology life cycles. This paper integrates several theoretical perspectives on technology diffusion and adoption to propose a framework to inform supply chain decision-makers on when to invest in new robotics technology
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