Resilience Optimization for Medical Device Distribution Networks Based on Node Failures

The Location of distribution centers for medical device is concerned with how to select distribution centers from a potential set so that the total cost is minimized and the resilience is maximized. In the paper, an optimization model for a resilient medical devices distribution networks is proposed based on node failure probability, node failure costs and other factors. Furthermore, the validity and feasibility of the model is explained with an exampl

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

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

Using complex network theory to model supply chain network resilience: a review of current literature

Traditionally, supply networks are modelled as multi-agent systems, in order to represent explicit communications between various entities involved. However, due to the increasingly complex and interconnected nature of the global supply networks, a recent trend of research work has focussed on modelling supply networks as complex adaptive systems. This approach has enabled researchers to investigate various topological properties which give rise to resilience characteristics in a given supply network. This paper presents a critical review of the published research work on this field. Key insights provided by this paper include; (1) the importance of defining the concepts of ‘resilience’ and ‘disruptions’ as measurable variables; (2) the limitations of existing network models to realistically represent supply networks; (3) potential improvements to the currently used growth mechanisms, which rely on node ‘degree’ to derive attachment probability instead of the more realistic and relevant node ‘fitness’; (4) importance of incorporating operational aspects, such as flows, costs, and capacities of connections between the nodes as well as topological aspects; and (5) derivation of a new set of resilience metrics capturing operational as well as topological aspects. Finally, a conceptual approach incorporating the above improvements to the existing supply network modelling approach is presented

Heuristic Approach to Network Recovery

This study addresses optimization modeling for recovery of a transportation system after a major disaster. In particular, a novel metric based on the shape of the recovery curve is introduced as the objective to minimize. This metric is computed as the distance from the pre-disaster system performance at a time immediately before disruption to the two-dimensional location of the centroid point of the area beneath the recovery curve. The recovery trajectories derived from optimization models with this new metric are considered along with two other recovery goals from literature, i.e., minimizing the total recovery time and minimizing the skew of the recovery trajectory. A genetic algorithm is implemented to search for optimal restoration schedules under each objective and empirical analysis is used to evaluate the corresponding quality of the solutions. Additionally, a particle swarm optimization algorithm is employed as an alternative metaheuristic and the quality of the recovery schedules, as well as the observed computational efficiency is analyzed

Supply Chain Resilience Among SMEs Manufacturer in Malaysia- A Survey

According to the 2018 report from Business Continuity Institute, supply chain disruption was listed as one of the top ten threats. There was a tremendous growth in academic research papers on harnessing resilience to face this growing business threat. The number of insolvent companies in Malaysia had increased 1,203% year-on-year from 2754 in the year of 2016 to 38093 in 2017 which is extremely worrying. This paper presents the familiarity of supply chain resilience among SMEs manufacturers in Malaysia including their recovery speed in their past experience in facing financial crisis and potential barriers they might face when creating supply chain resilience and lastly a list of performance measures for their “financial survival bag” was suggested. 800 questionnaires were distributed to SMEs manufacturers in Malaysia and 280 respondents’ responded to the survey. Majority of the respondents are directors/CEOs/owners/ financial decision makers. More than half of the SMEs manufacturers in Malaysia are familiar with resilient supply chain management. However, more than half of them have only a limited understanding and are unclear about the action to be taken to respond to disruption. In a nut shell, majority of the SMEs manufacturers in Malaysia have not implemented resilient supply chain management. Furthermore, there is a need to research on their business longevity and ensure their long term survivability in Malaysia

An empirical investigation in the automotive supply chain

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

Resilient Aircraft Sustainment: Quantifying Resilience through Asset and Capacity Allocation

Decision makers lack a clear, generalizable method to quantify how additional investment in inventory and capacity equates to additional levels of resilience. This research facilitates a deeper understanding of the intricacies and complex interconnectedness of organizational supply chains by monetarily quantifying changes in network resilience. The developed Area under the Curve metric (AUC) is used to quantify the level of demand that each asset allocation can meet during a disruptive event. Due to its applicability across multiple domains, the USAF F-16 repair network in the Pacific theater (PACAF) is modeled utilizing discrete event simulation and used as the illustrating example. This research uses various levels of production capacity and response time as the primary resilience levers. However, it is essential to simultaneously invest in inventory and capacity to realize the greatest impacts on resilience. Real-world demand and cost data are incorporated to identify the inherent cost-resilience relationships, essential for evaluating the response and recovery capabilities across the developed scenarios. Results indicate that recovery capacity and response time are the greatest drivers of recovery after a disruption. Additionally, numerous network designs employing various levels of design flexibility are evaluated and recommended for future capacity expansion

Agilidade e resiliência na gestão da cadeia de abastecimento. Proposta de uma framework

Dissertação para obtenção do grau de Mestre em Engenharia e Gestão IndustrialAtualmente tem vindo a verificar-se um aumento da volatilidade dos mercados e da incerteza associada à procura dos clientes. Aliado a estes fatores é reconhecido que as cadeias de abastecimento, em que as organizações estão inseridas, são cada vez mais complexas resultando, também, num aumento do risco a que as cadeias de abastecimento estão sujeitas e na sua maior vulnerabilidade face à ocorrência de distúrbios. Assim, considera-se importante estudar a aplicação dos paradigmas ágil e resiliente na gestão das cadeias de abastecimento esperando que estes lhes tragam vantagens competitivas, uma vez que o paradigma ágil contribui para uma maior capacidade de resposta rápida ao cliente e o paradigma resiliente contribui para uma maior capacidade de recuperação dos efeitos negativos de um distúrbio. Nesta dissertação foi feita uma revisão estruturada da literatura, com o objetivo de aprofundar o conhecimento sobre os tópicos em estudo, nomeadamente, no que diz respeito a atributos e práticas de gestão que caracterizam os paradigmas. Esta revisão permitiu reunir as definições de cada paradigma, existentes na literatura, e elaborar uma análise comparativa. Com base na revisão da literatura foi desenvolvido um questionário que permitiu recolher a opinião de especialistas e de estudantes em Engenharia e Gestão Industrial no que diz respeito ao conjunto de atributos e práticas de gestão utilizado na implementação de cada um dos paradigmas analisados. Por fim, com base nos resultados obtidos a partir do tratamento estatístico das respostas ao questionário e na análise da revisão da literatura foi proposta uma framework com atributos e práticas de gestão que relaciona os paradigmas ágil e resiliente da cadeia de abastecimento. Espera-se que a framework contribua para compreender melhor os paradigmas, os atributos e práticas que os caracterizam, e a sua interação. Poderá, ainda, ajudar potenciais interessados na sua implementação numa cadeia de abastecimento

Análise do Nível de Maturidade e Rotina de Monitorização Empresarial

A capacidade de gerar vantagem competitiva é, cada vez mais, um fator determinante para o sucesso das empresas e a única opção para evitar situações de falência e insolvência. Porém, a maturidade de monitorização e as rotinas utilizadas para atingir os objetivos traçados têm-se revelado insuficientes. Por forma a investigar estas considerações, foi desenvolvido pelo professor Nuno Martins Cavaco um sistema de competitividade estratégica, baseado nos conceitos de resiliência, inovação e sustentabilidade, que visa apoiar as empresas no seu processo de planeamento estratégico. Este sistema intitula-se de SuCEES (Sustainable Competitiveness Evaluation and Execution System) e assume-se como instrumento de suporte à criação de vantagem competitiva para as empresas, através da avaliação de 7 drivers de competitividade e por definição de prioridades de atuação que facilitam a implementação da estratégia, reduzindo o execution gap. Dada alguma complexidade e exigência da aplicação deste sistema, uma das particularidades do SuCEES é contemplar uma avaliação prévia que permite aferir acerca do estado de maturidade das empresas em termos da sua capacidade de monitorização (Monitoring Readiness Evaluation). Posto isto, o presente estudo tem como principal objetivo concluir sobre o nível de maturidade e rotinadas de monitorização empresarial, recorrendo a esta ferramenta Monitoring Readiness Evaluation. Para alcançar o propósito deste estudo, foram realizadas 4 etapas, iniciando com a procura do público alvo, seguindo-se com o desenvolvimento de uma plataforma eletrónica de questionário e posterior análise e tratamento de dados, finalizando com uma análise comparativa de parâmetros. Assim, foram analisadas 19 empresas de diversos sectores de mercado (principal limitação da investigação), com o intuito de ter uma visão mais transversal e comparativa do estado de maturidade e rotina de monitorização das empresas. Deste estudo, pode ser concluído que genericamente quanto mais desenvolvida, mais orçamento investido e maior volume de faturação por parte das empresas, mais elevado é o nível de maturidade e rotina de monitorização aplicado nos sectores em estudo e vice-versa. Por outro lado, o nível de maturidade e rotina de monitorização aplicado nas empresas é considerado ainda abaixo dos níveis apropriados para todas as empresas, sendo evidenciado que muitas empresas, mais precisamente, as pequenas empresas, não detém condições necessárias para a avaliação de monitorização segundo o sistema SuCEES. Por outro lado, as grande empresas, detém bastante conhecimento sobre estas práticas de monitorização, porém, o avanço tecnológico mostra-se bastante superior em relação à maturidade das práticas de monitorização aplicadas pelas mesmas

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions