Optimizing strategies to mitigate risk in a supply chain disruption

With the growth of globalization in the supply chain industry, manufacturing firms and suppliers are more susceptible to disruptions. There is a huge gap between optimization, simulation, and supply chain risk management. Our research is an attempt to bridge this gap, by improving a currently existing supply chain disruption model through Bayesian optimization technique. During a disruption, suppliers of manufacturing firms do not always have an option of moving their facility to an alternate location. This model optimizes a complex simulation to help identify the optimal risk management strategies for a firm who is planning for a severe supply chain disruption. The results of the model are depicted through an illustrative example based out of the 2011 Japanese earthquake and tsunami, and its robustness is tested through sensitivity analysis. Firms need to be prepared for disruptive events and may have the desire to maximize their profit and this model provides techniques to the decision maker to choose cost-effective strategies based on certain parameters

Analysis of the association between receivements from suppliers, shipments to clients and perception of external supply chain risks: A comparison between Portugal and Norway

The study of Supply Chain Management has been recently gaining interest in academia literature (Ayaviri & Saucedo, 2017). Organisations face an ever more elaborate environment, that is continuously shifting, rising supply chain risks, thus making companies vulnerable to disruptions in the supply chain (Munir et al., 2020). The goal of this dissertation is to understand the association between the perception of external risk factors in the supply chain, delays in deliveries and delays in the reception of materials from suppliers. The data used in this research originated from a scientific database providing evidence from 145 transforming companies of multiple industry sectors, based in two countries: Portugal and Norway. A quantitative study was conducted, resorting to the statistical analysis software tool IBM SPSS Statistics (version 27). The findings of this study suggest that the external risk in the supply chain is perceived differently in Portugal and Norway, under the scope of the sample used. Overall, the correlation coefficients measured for firms in Norway are relatively lower than those regarding Portuguese firms - the companies present in Portugal have demonstrated a higher propensity to perceive external micro risks. In both nations, there were no significant variations in the analysis of the link between delays in receivements and delays in shipments, and the results suggested no significant correlation among the two; this may indicate that the firms in the scope of the sample used have common perceptions of their capabilities to overcome potential upstream delays and avoid causing delays in shipments to their clients.O estudo da Gestão da Cadeia de Abastecimento tem, recentemente, despertado grande interesse na literatura académica (Ayaviri e Saucedo, 2017). As empresas enfrentam um meio envolvente que continuamente sofre transformações, incrementando os riscos da cadeia de abastecimento, traduzindo-se numa maior vulnerabilidade às disrupções. (Munir et al., 2020). O objetivo desta dissertação é estudar a associação entre a perceção de fatores de risco externos na cadeia de abastecimento, atrasos nas entregas a clientes e atrasos na receção de matérias de fornecedores. Os dados utilizados nesta pesquisa tiveram origem numa base de dados científica, contendo informação sobre 145 empresas de variados setores, presentes em dois países: Portugal e Noruega. Foi conduzido um estudo quantitativo, recorrendo ao software de análise estatística IBM SPSS Statistics (versão 27). Os resultados deste estudo sugerem diferenças entre países na perceção do risco externo na cadeia de abastecimento, no âmbito da amostra considerada. No geral, os coeficientes de correlação calculados para as empresas Norueguesas são relativamente inferiores do que aqueles relacionados com empresas Portuguesas – as firmas em Portugal demonstraram uma maior propensão na perceção de micro riscos externos. Em ambos os países, não foi verificada uma variação significativa na análise da correlação entre atrasos nos recebimentos e atrasos nos envios, sugerindo a não existência de uma relação significativa entre ambos; este facto poderá indicar que as empresas que constituem a amostra têm uma perceção semelhante nas suas capacidades para ultrapassar potenciais atrasos a montante e evitar que estes se relacionem com atrasos em envios para os seus clientes

Assessing Risk in Different Types of Supply Chains with a Dynamic Fault Tree

Supply chain risk analysis is an important field in operations management and logistics. Identifying those risks, assessing the probability of those risks, and understanding how those risks change if mitigation strategies are implemented contribute to better supply chain risk management. Reliability analysis has a long tradition of assessing the probability of failure, and fault trees are typically used to understand how the failure of individual components can lead to system failure within an engineered system. More recently, fault trees have been proposed to assess the probability of a supply chain failure. Dynamic fault trees, which are relatively new in reliability analysis, model the dependency among possible component failure and how these probabilities change over time. This paper applies dynamic fault trees to model supply chain risk for different types of supply chains. The dynamic fault tree allows a firm to model complex interactions among suppliers and understand how those interactions impact its risk. The model incorporates an information system that relays information about the status of suppliers to the firm, and this information system could also fail. A Markov chain model and Monte Carlo simulation are used to numerically assess supply chain risk as modeled by these dynamic fault trees.This is a manuscript of an article published as Lei, Xue, and Cameron MacKenzie. "Assessing Risk in Different Types of Supply Chains with a Dynamic Fault Tree." Computers & Industrial Engineering 137 (2019): 106061. DOI: 10.1016/j.cie.2019.106061. Posted with permission.</p

Assessing Risk in Different Types of Supply Chains with a Dynamic Fault Tree

Supply chain risk analysis is an important field in operations management and logistics. Identifying those risks, assessing the probability of those risks, and understanding how those risks change if mitigation strategies are implemented contribute to better supply chain risk management. Reliability analysis has a long tradition of assessing the probability of failure, and fault trees are typically used to understand how the failure of individual components can lead to system failure within an engineered system. More recently, fault trees have been proposed to assess the probability of a supply chain failure. Dynamic fault trees, which are relatively new in reliability analysis, model the dependency among possible component failure and how these probabilities change over time. This paper applies dynamic fault trees to model supply chain risk for different types of supply chains. The dynamic fault tree allows a firm to model complex interactions among suppliers and understand how those interactions impact its risk. The model incorporates an information system that relays information about the status of suppliers to the firm, and this information system could also fail. A Markov chain model and Monte Carlo simulation are used to numerically assess supply chain risk as modeled by these dynamic fault trees