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    Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming

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    [EN] In today's volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation.This work was supported by the Spanish State Research Agency (Agencia Estatal de Investigacion) under the Reference No. RTI2018-101344-B-I00-AR.Sanchis, R.; Duran-Heras, A.; Poler, R. (2020). Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming. Mathematics. 8(9):1-29. https://doi.org/10.3390/math8091596S12989Day, J. M. (2013). Fostering emergent resilience: the complex adaptive supply network of disaster relief. International Journal of Production Research, 52(7), 1970-1988. doi:10.1080/00207543.2013.787496Kumar, S., & Anbanandam, R. (2019). 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    Review of Quantitative Methods for Supply Chain Resilience Analysis

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    Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

    Food supply chain network robustness : a literature review and research agenda

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    Today’s business environment is characterized by challenges of strong global competition where companies tend to achieve leanness and maximum responsiveness. However, lean supply chain networks (SCNs) become more vulnerable to all kind of disruptions. Food SCNs have to become robust, i.e. they should be able to continue to function in the event of disruption as well as in normal business environment. Current literature provides no explicit clarification related to robustness issue in food SCN context. This paper explores the meaning of SCN robustness and highlights further research direction

    RISK AND RESILIENCE THIRD PARTY LOGISTIC IN FREIGHT FORWARDER COMPANY

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    ABSTRACT Kumalasari, Evi, 2019. Risk and Resilience Third Party Logistic (3PL) In Freight Forwarder Company.Thesis, Departement of Industrial Engineering, Faculty Engineering, University of Islamic Majapahit (UNIM). Pembimbing I: Pipit Sari Puspitorini,ST.,MT Pembimbing II: Andhika Cahyono Putra, ST.,MT Third-party logistics (3PL) is a logistics service provider that can offer a variety of services such as logistics transportation, warehousing management, inventory management, packaging and product return services. Therefore, the demand for Third Party Logistics (3PL) services is increasing because companies want to focus on their business processes so that freight forwarders are required to perform services in accordance with customer expectations. But it certainly will not be separated from a threat as a causal factor that raises the risk that must be faced by freight forwarders. In this case the freight forwarder company must increase the resilience of Third Party Logistics to be able to survive in the face of disruption. The purpose of this study is to determine the priority of risk by creating a model using a dynamic system that will be used by the company to do risk mitigation and determine the level of resilience Third Party Logistic (3PL) using the Interpretive Structural Modeling (ISM) method and to determine the location of the enabler in the 1st quadrant, 2, 3 or 4 using MICMAC analysis. The results showed that the highest risk priority was product risk with a value of 2 out of the average predetermined criteria. Whereas the Third Party Logistic (3PL) endurance level is obtained with the highest level, namely N2 (Velocity of handling outbound overweight product) and the one with the highest driver power and dependence value, namely N7 (Velocity of shipping tracking). Key word : Third Party Lgistic (3PL), Freight Forwarder, Dynamic System, Interpretive Structural Modelling (ISM)

    Assessing the Consequences of Natural Disasters on Production Networks: A Disaggregated Approach

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    This article proposes a framework to investigate the consequences of natural disasters. This framework is based on the disaggregation of Input-Output tables at the business level, through the representation of the regional economy as a network of production units. This framework accounts for (i) limits in business production capacity; (ii) forward propagations through input shortages; and (iii) backward propagations through decreases in demand. Adaptive behaviors are included, with the possibility for businesses to replace failed suppliers, entailing changes in the network structure. This framework suggests that disaster costs depend on the heterogeneity of losses and on the structure of the affected economic network. The model reproduces economic collapse, suggesting that it may help understand the difference between limited-consequence disasters and disasters leading to systemic failure.Natural disasters, Economic impacts, Economic Network

    Measuring the metallurgical supply chain resilience using fuzzy analytic network process

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    The article presents a methodology for measuring the metallurgical supply chain resilience, which enables the ascertainment of key resilience capabilities and measurable criteria, and determining a level of the resilience. The methodology is based on Analytic Network Process (ANP), which is used to solve the complex decision-making problems, whose structures can be mapped as non-linear networks. Since ambiguous pairwise comparisons expressed by fuzzy sets are considered, the Fuzzy Analytic Network Process (FANP) is applied. The methodology is verified on the generalised model of a metallurgical supply chain. The SuperDecisions software was used for the application. The experiments performed demonstrate the high level of suitability of the FANP approach for measuring metallurgical supply chain resilience.Web of Science55478678

    Resilience Measurement – Financial Survival Bag Concept Using Rough Fuzzy Set Approach

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    The current Covid-19 pandemic has starkly revealed the importance of being resilient to enable an organization to stay in business. A resilient performance measurement model to constantly measure an organization’s financial resilience is thus necessary to ensure the continued survivability of the organization. The purpose of this research is to develop a resilience measurement model to measure and unify various metrics into a single unit-less index. This paper is an extension of work on the financial survival bag concept and the measures and metrics from [1].  The financial resilience measurement model was developed using the rough fuzzy set method for any participating SME manufacturer. This model intends to solve the research gaps from previous research conducted on resilience measurement to estimate the duration an organization can survive based on its current resilience result and to gauge the interaction of risk/ disruption with resilience capabilities.  A case study was conducted and the evaluation concurred with the findings of the proposed model as the results reflected their current resilience level. In essence, this research has managed to offer a new way of measurement for resilience to evaluate the financial resilience of any SME manufacturer in Malaysia

    An empirical investigation in the automotive supply chain

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    Funding Information: The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI) and project KM3D (PTDC/EME-SIS/32232/2017). Publisher Copyright: © 2022 Elsevier LtdSupply chains around the globe are susceptible to disturbances that negatively impact their performance. Generally, supply chain disturbances lead to failure modes that impact the ability of the supply chain to deliver the promised goods and services on time. Therefore, companies operating in different supply chains are willing to become resilient to disturbances and their ensuing failure modes to be able to deliver on time and remain competitive. In light of this willingness, this study aims to propose an index that enables companies to assess their resilience of on-time delivery to supply chain failure modes based on the resilience practices they deploy. To this end, drawing on the knowledge derived from case study data analysis and literature, eight propositions and an explanatory framework are put forward that theorize the identified relationships between supply chain disturbances, failure modes, resilience practices, and on-time delivery as the primary indicator for measuring supply chain performance. Next, considering the resilience practices companies tend to deploy, an index capable of assessing the companies’ resilience of on-time delivery to two prevalent supply chain failure modes, namely capacity shortage and material shortage is modelled and tested using a case study in an upstream automotive supply chain in Portugal. The results indicate high resilience levels of on-time delivery to the aforementioned failure modes, mainly due to the high cost of production halt in the automotive industry. Additionally, a set of supply chain capabilities and their related resilience practices and supply chain state variables are identified that can be deployed and controlled to improve supply chain resilience.publishersversionpublishe

    Sustainable Assessment in Supply Chain and Infrastructure Management

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    In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management
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