Modeling and Measuring Resilience: Applications in Supplier Selection and Critical Infrastructure

Nowadays, infrastructure systems such as transportation, telecommunications, water supply, and electrical grids, are considerably facing the exposure of disruptive events such as natural disasters, manmade accidents, malevolent attacks, and common failures due to their size, complexity, and interconnectedness nature. For example fragile design of supply chain infrastructure might collapses because the consequences of a failure can propagate easily through the layers of supply chains, especially for large interconnected networks. Previously, owners and operators of infrastructure systems focused to design cost-efficient, competitive and sustainable ones; however the need for design of resilient infrastructure systems is inevitable. Infrastructure systems must be designed in such a way so that they are resistant enough to withstand and recover quickly from disruptions. The consequences of disruptive events on infrastructures ranging from energy systems (e.g., electrical power network, natural gas pipeline) to transportation systems (e.g., food supply chain, public transportation) cannot only impacted on individuals, but also on communities, governments and economics. The goal of this dissertation is to (i) identify the resilience capacities of infrastructure systems; in particular inland waterway ports, and supply chain systems, (ii) quantify and analyze the resilience value of critical infrastructure systems (CIs), (iii) improve the resilience of CIs by simulating different disruptive scenarios, and (iv) recommend managerial implications to help owners and operators of CIs for timely response, preparedness, and quick recovery against disruptive events. This research first identifies the resilience capacity of CIs, in particular, inland waterway, supply chain and electrical power plant. The resilience capacity of CIs is modeled in terms of their absorptive capacity, adaptive capacity and restorative capacity. A new resilience metric is developed to quantify the resilience of CIs. The metric captures the causal relationship among the characteristics of CIs and characteristics of disruptive events including intensity and detection of disruption likelihood of disruptive events. The proposed resilience metric is generic, meaning that can be applied across variety of CIs. The proposed metric measures the system resilience as the sum of degree of achieving successful mitigation and contingency strategies. The resilience metric accounts for subjectivity aspect of disruptive events (e.g., late disruption detection, very intense disruption, etc.). Additionally, the proposed resilience metric is capable of modeling multiple disruptive events occurring simultaneously. This research study further explores how to model the resilience of CIs using graphical probabilistic approach, known as Bayesian Networks (BN). BN model is developed to not only quantify the resilience of CIs but also to predict the behavior of CIs against different disruptive scenarios using special case of inference analysis called forward propagation analysis (FPA), and improvement scenarios on resilience of CIs are examined through backward propagation analysis (BPA), a unique features of BN that cannot be implemented by any other methods such as classical regression analysis, optimization, etc. Of interest in this work are inland waterway ports, suppliers and electrical power plant. Examples of CIs are inland waterway ports, which are critical elements of global supply chain as well as civil infrastructure. They facilitate a cost-effective flow of roughly $150 billion worth of freights annually across different industries and locations. Stoppage of inland waterway ports can poses huge disruption costs to the nation’s economic. Hence, a series of questions arise in the context of resilience of inland waterway ports. How the resilience of inland waterway ports can be modeled and quantified? How to simulate impact of potential disruptive events on the resilience of inland waterway ports? What are the factors contributing to the resilience capacity of inland waterway ports? How the resilience of inland waterway can be improved

Review of Quantitative Methods for Supply Chain Resilience Analysis

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

Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports

A Decision Support System Based On Machined Learned Bayesian Network for Predicting Successful Direct Sales Marketing

This paper proposes a decision support system based on a machine-learned Bayesian network (BN) to predict the success rate of telemarketing calls for long-term bank deposits. Telemarketing is one of the most common interactive techniques of direct marketing, widely used by financial institutions such as banks to sell long-term deposits. In this study, we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit, which is considered an output variable. The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach, a well-known supervised learning algorithm. The impact of each client\u27s attribute on the likelihood of subscribing is predicted. Further, we carry out multiple simulation scenarios using BN’s unique features (forward and backward propagation) to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics

The implementation of eco-labelling for construction materials in Malaysian construction industry

In response to sustainable development and green initiatives, many tools have been developed for building development. Eco-labelling (Green labeling or Environmental labeling) began as part of “Green Revolution” in marketing with non-food products. Environmental labeling refers to labels that inform consumers that a labeled product is more environmentally friendly relative to the other products in the same category. Eco-labels are designed to inform consumers that the labeled product is more environmentally friendly than most typically setting standards. In addition, Eco-labels are increasingly facilitating manufacturers, retailer, consumers, government officials, and other interested parties with information in their purchasing decision. Eco-labelling is one of the assessment tools that comes as third-party and has a serious role to verify eco-friendly products and compatible with the environment. This study therefore interested to identify the suitable strategy need to be employed in construction industry and suggest the solution for solving the problems of the current eco-labelling innovation in construction materials. This study aims to study the current gaps and potential linkages in implementation of Eco-labelling in Malaysia. Therefore, pilot survey conducted through the questionnaire and interview process with developers, suppliers, contractors, consultant and other construction companies that are regarded as consumers of eco-labelling materials. As a result, the cost of implementation, irrelevant standards for certificating, lack of users' awareness, poor coordination and consistency between rating tools and regulation are as critical barriers and problems. In overall, reliability & quality of rating system for construction materials, leadership & responsibility of conducting eco-labelling schemes in construction industry, and stakeholders involvement are most critical gaps toward eco-labelling

A Multi-Layer Bayesian Network Method for Supply Chain Disruption Modelling In the Wake of the COVID-19 Pandemic

While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance

Simulation-Based Assessment of Supply Chain Resilience With Consideration of Recovery Strategies In the COVID-19 Pandemic Context

In the wake of the COVID-19 pandemic, many firms lacked a strategy to cope with disruptions and maintain resiliency. In this study, we develop a measurement method to evaluate the impact of resilience strategies in a multi-stage supply chain (SC) in the presence of a pandemic. For the first time, we propose a method to deduce quantitative resilience assessment from simulation. We implement two resilience strategies, i.e., prepositioning extra-inventory and a backup supplier, and then we simulate its impact on SC resilience and financial performance. The simulation results indicate that the extra inventory leads to a higher resilience than a backup supplier but costs more for the given contextual setting. Finally, we examine the demand fulfillment and observe that the extra-inventory strategy allows for a higher service level, confirming our resilience simulations. We discuss the managerial implications of these findings on the descriptive and predictive analysis levels. Decision-makers can utilize our model and findings to develop a response plan in the occurrence of a pandemic or any long-duration high magnitude disruption. Also, scholars and managers can use our proposed method to measure SC resiliency from simulation in any disruption

A New Resilience Measure for Supply Networks With the Ripple Effect Considerations: A Bayesian Network Approach

This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case-study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed