Inoperability Input-output Model Research Articles

Economic impact assessment of natural disaster with multi-criteria decision making for interdependent infrastructures

Natural disasters, such as flood, earthquake or hurricane, can cause environmental damage, infrastructural destruction, economic disruption and result in the loss of human lives. The goal of this paper is to assess vulnerability of the interdependent sectors and to determine the impact of a disaster. As part of our investigation, we explore the inoperability input–output model (IIM) to obtain ballpark estimate of losses. IIM can quantify the effect on production level and measure ripple effect of perturbation on the interdependent system by calculating the economic loss and inoperability level. Furthermore, PROMETHEE (preference ranking organization method for enrichment evaluations) is integrated with IIM, incorporating the criteria of economic system linkages, economic losses and percentage of the inoperability. It will help to analyze the effect of disaster and rank the most vulnerable sectors—based on the defined criterion. A case study is performed in Pakistan (an Asian developing country) which was hit by floods in 2011–2012, perturbing the demand in various sectors. To analyze the perturbation level and system linkages, an interdependent input–output matrix—based on the Pakistan’s economy—is constructed. Further research analysis gives perspicacity in terms of describing criticality and sensitivity of the independent economic system. It is concluded that the agriculture and service sectors have suffered with the highest inoperability level. The results also identify significant sectors of the economy after flood strikes; these sectors can be prioritized for policymaking activities to reduce the sector-specific impact in the aftermath of a catastrophic event.

Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran

Advancement in technology has contributed to increment in complexity of systems and infrastructures. Furthermore, it has complicated the management of systems to deal with natural hazards. Input–output inoperability model (IIM) is a simple method to characterize the impacts of natural hazards on interconnected infrastructures. In this paper, the impacts of a flood hazard on six critical infrastructures in Tehran metropolitan have been assessed by using IIM. The computational results show that energy and transportation infrastructures are the most influencing infrastructures, while emergency services and healthcare infrastructures are the most influenced infrastructures. All data required to evaluate this case study have been collected using questionnaires and converted to fuzzy interdependency values. To increase decision-making power, the developed fuzzy matrix has been arranged for different risk levels (from absolutely optimistic to absolutely pessimistic) and confidence levels (from absolutely confident to absolutely non-confident). Afterward, the interdependency matrix has been deffuzified, and inoperability of infrastructures has been calculated by the IIM for seven different initial conditions. Finally, a sensitivity analysis has been conducted to incorporate the risk levels and confidence levels to determine values of inoperability under the above-mentioned conditions. The ranking for both of the influencing and the influenced infrastructures has also been provided. This ranking helps decision makers to manage natural hazard risks effectively by appropriate resource allocation. It also helps to realize the interdependencies among infrastructures and to determine the inoperability of infrastructures before natural hazards. This would help decision makers to mitigate the risk and prepare the society well in advance.

A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems

Infrastructure systems play a critical role in delivering essential services that are important to the economy and welfare of society. To enhance the resilience of infrastructure systems after a large-scale disruptive event, determining where and when to invest restoration resources is a challenge for decision makers. Comprehensively considering the recovery time of infrastructure systems and the overall losses resulting from a disaster, this study proposes a two-stage restoration resource allocation model for enhancing the resilience of interdependent infrastructure systems. First, to evaluate the effect of resource allocation during the recovery process, dynamic resilience is selected as the criterion for the recovery of infrastructure systems. Second, taking into consideration the decision makers’ point of view, a two-stage resource allocation model is proposed. The objective of the first stage is to quickly recover the infrastructure systems’ dynamic resilience to meet the basic needs of the users. The second stage is aimed at minimizing the overall losses in the following recovery process. The effects of infrastructure interdependencies on resource allocation are incorporated in the model using the dynamic inoperability input–output model. Through a case study, the proposed approach is compared with other resource allocation strategies. The results show that: (1) the restoration resource allocation strategy obtained from the proposed approach balances the recovery time and the overall losses to infrastructure systems; and (2) the value of the usage cost of the unit restoration resource has a significant impact on the recovery time and the overall losses under different strategies. The proposed model is both effective and efficient in solving the post-disaster resource allocation problem and can provide decision makers with scientific decision support.

Modeling interdependencies of critical infrastructures after hurricane Sandy

This paper evaluates the level of inoperability and the resilience of the critical infrastructure networks of the New York Metropolitan Area affected by Hurricane Sandy in October 2012. The region analyzed in the case study includes New York City and some New Jersey counties. The highly concentrated critical infrastructures of this area are vulnerable to the direct impact of catastrophic events, such as hurricanes, as well as to the disruptive cascading effects that are spread through the existing interdependencies. The inoperability Input-Output model, developed by Haimes and Jiang, is selected to numerically define the degree of interconnection among these systems and quantify the effect of an external perturbation on the network's functionality. Based on the model's results, a new indicator called the “inoperability ratio” is introduced to identify some initiatives that policymakers can implement during the restoration process. These actions reduce the inoperability ratio to prevent cascading effects and to speed up the recovery process.

Analysis of Vulnerability and Recoverability of the Energy System in China Based On Inoperability Input-Output Model and a Dynamic Extension

In this paper, the Inoperability Input-Output Model (IIM) is used to simulate the degree of functional decline of the energy sectors under the perturbation of demand and supply. The Dynamic Inoperability Input-Output Model (DIIM) is used to simulate the time-varying operation state of the energy sectors. The paper then performs some case study that utilizes accounts from 2012 Input-Output Tables of China. The vulnerability and recoverability of energy system are analyzed by using relevant indicators in order to manage and develop the energy system.

Simulating the impact of inventory on supply chain resilience with an algorithmic process based on the supply-side dynamic inoperability input–output model

ABSTRACTThe interdependent nature of highly interconnected systems, such as supply chains, renders them incapable of quickly resuming operation following a disruptive event. In the context of supply chains, previous works have demonstrated the efficacy of inventory as a mitigation strategy to delay the impact of disruptive events. However, current methods tend to focus only on a demand-driven approach, which may be inappropriate for some systems where a value-added price perturbation represents an impact. Thus, this paper offers a methodology for understanding the role of inventory in the supply-side dynamic inoperability input–output model (DIIM). To demonstrate the applicability of the proposed model, an illustrative example is presented using data from the 2012 Philippines Input–Output (I-O) Table in five different scenarios. Given a predefined planning horizon, the results of this work can serve as guidelines for decision-makers for the provision of inventory that will keep the overall inoperability of an entire supply chain at a minimum.

Return of the inoperability

There has been unrest in the research community investigating the inoperability of an economic system under disaster situations. The inoperability input–output model (IIM), which is very popular in the risk management field, has become a center of argument, particularly from the input–output researchers, that IIM is a straightforward application of the standard Leontief input–output model. This paper revisits the concept of inoperability, rather than IIM, and proposes its new role in disaster impact analysis using a conventional tool, i.e. the RAS method, for illustrating how the inoperability of an economic system in the aftermath of disaster can be evaluated. The proposed framework is employed to examine the inoperability of industries resulting from the 1995 Kobe earthquake. The findings of the analysis reveal the usefulness of inoperability concept that can even incorporate resilience (gained operability) using the proposed framework of this paper.

System-level Prognostics Based on Inoperability Input-output Model

Nowadays, the modern industry is increasingly demanding the availability and reliability of production systems as well as the reduction of maintenance costs. The techniques to achieving these goals are recognized and discussed under the term of Prognostics and Health Management (PHM). However, the prognostics is often approached from a component point of view. The system-level prognostics (SLP), taking into account interdependencies and multi-interactions between system components, is still an underexplored area. Inspired from the inoperability input-output model (IIM), a new approach for SLP is proposed in this paper. The inoperability corresponds to the component’s degradation, i.e. the reduction of its performance in comparison to an ideal reference state. The interactions between component degradation and the effect of the environment are included when estimating the inoperability of components and also when predicting the system remaining useful life (SRUL). This approach can be applied to complex systems involving multi-heterogeneous components with a reasonable computational effort. Thus, it allows overcoming the lack of scope and scalability of the traditional approaches used in PHM. An illustrative example is presented and discussed in the paper to highlight the performance of the proposed approach.

Analysing economic impact on interdependent infrastructure after flood: Pakistan a case in point

ABSTRACTA disaster such as floods can have a drastic impact on interdependent infrastructure and economic sectors. The resilience or the ability of the critical sector to recover quickly from the disruption can also reduce the consequences of the disaster. In this paper, through resilience and recovery time Dynamic Inoperability Input-Output model (DIIM) is applied. Thus, Input-Output (I-O) table is constructed for Pakistan's economic system and a case study is performed on the flooding in Pakistan 2011–12. The purpose of this study is to provide a ballpark estimate of the system-wide impact and ripple effect on the sectors that lasted for several days after the disruption. Furthermore, to analyze the inoperability and economic loss in the sectors caused by the disaster in a developing country. The findings of the research show that most of the critical sectors are associated with agriculture and service sector in terms of inoperability and economic loss respectively. The outcome of the study will be essential for the policy makers, disaster management authorities and health departments to respond accordingly.

Comparing the Economic Impact of Natural Disasters Generated by Different Input-Output Models: An Application to the 2007 Chehalis River Flood (WA).

Due to the concentration of assets in disaster-prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single-region input-output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input-output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.

Novel Vulnerability Metrics for Interdependent System based on System Controllability

In this paper we develop novel vulnerability metrics for interdependent critical infrastructures or economic sectors based on the concept of controllability. Specifically, we consider the Input-Output Inoperability Model, that represents the dynamics of the dependencies and interdependencies among infrastructures or sectors during an adverse event or malicious attack, and we argue that the more the system is controllable via an external perturbation that represents the fault or attack, the more the system is vulnerable. Therefore, we analyze the vulnerability of the system in terms of the magnitude of the associated controllability matrix. Moreover, based on the proposed index, we develop a simple defensive strategy to reduce the effect of an attack. A validation of the approach with respect to real data concludes the paper.

Modeling the damage and recovery of interdependent critical infrastructure systems from natural hazards

Understanding the impact of disasters to civil infrastructure network will guide strategic pre-disaster hazard mitigation and post-disaster recovery planning of a community. The facilities in civil infrastructure network depend on each other to exchange product, information or services. When disaster happens, these dependencies would aggravate the initial damage and lead to cascading failures. Thus, understanding the dependencies among infrastructure facilities is essential in modeling the damage and recovery of a community under disruptive events. This study builds upon the Dynamic Inoperability Input–output Model to assess the recovery of the civil infrastructure facilities by considering the facility-level dependencies. The recovery of the integrated network was assessed utilizing the characteristic parameters from graph theory. This methodology was demonstrated with a hypothetical infrastructure network, consisting of power, water and telecommunication systems under a hurricane. The sensitivity of the network recovery to the dependency measures was investigated, which showed that the recovery time is sensitive to the level of dependency between different systems. The proposed methodology was compared with a conventional model with system-level interdependencies and showed that considering facility-level dependencies would yield more refined result. The model was then applied to simulate the power system restoration of Galveston City, Texas under Hurricane Ike.

Restoration resource allocation model for enhancing resilience of interdependent infrastructure systems

Enhancing the resilience of infrastructure systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent infrastructure systems. According to Inoperability Input–Output Model, a resilience metric for infrastructure systems is developed, in which the performance loss of infrastructure systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of infrastructure system; and (3) the significance of factors such as initial inoperability of infrastructure systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

Input-output Inoperability Model for the risk analysis of eco-industrial parks

An Eco-Industrial Park (EIP) is formed by a set of manufacturing and service businesses located together in a bounded geographic area and interconnected in Industrial Symbiosis (IS), aiming at enhancing environmental, economic and social performances of the EIP itself and its individual Industrial Actors (IA). Connections of IA to Critical Infrastructures (CI), such as for electricity and water supply, together with the newly created IS interconnections make EIP vulnerable to disruptive events propagation, whereby a failure in a CI outside EIP may propagate to EIP and its IA in a cascading effect. The interdependencies created by the interconnections of various CI and IS in the EIP should be closely investigated in order to predict the behaviour of the interconnected EIP, to evaluate the risk of disruptive events propagation and propose mitigation solutions. Different approaches exists for this, requiring different levels of details, model sophistication and complexity, data for parameters estimation. The present paper focuses on the expert-based Input-output Inoperability Model (IIM) and proposes a methodology for its complete deployment on the existing EIP of Kalundborg. The results obtained from the application to this EIP can be used to provide guidance for establishing measures of resilience to disruptive events, in terms of possible technological solutions and adjustment strategies to increase overall EIP resilience.

The spatial and temporal ‘cost’ of volcanic eruptions: assessing economic impact, business inoperability, and spatial distribution of risk in the Auckland region, New Zealand

Volcanic risk assessment has historically concentrated on quantifying the frequency, magnitude, and potential diversity of physical processes of eruptions and their consequent impacts on life and property. A realistic socio-economic assessment of volcanic impact must however take into account dynamic properties of businesses and extend beyond only measuring direct infrastructure/property loss. The inoperability input-output model, heralded as one of the 10 most important accomplishments in risk analysis over the last 30 years (Kujawaski Syst Eng. 9:281–295, 2006), has become prominent over the last decade in the economic impact assessment of business disruptions. We develop a dynamic inoperability input-output model to assess the economic impacts of a hypothetical volcanic event occurring at each of 7270 unique spatial locations throughout the Auckland Volcanic Field, New Zealand. This field of at least 53 volcanoes underlies the country’s largest urban area, the Auckland region, which is home to 1.4 million people and responsible for 35.3% (NZ$201481.2 billion) of the nation’s GDP (Statistics New Zealand 2015). We apply volcanic event characteristics for a small-medium-scale volcanic eruption scenario and assess the economic impacts of an ‘average’ eruption in the Auckland region. Economic losses are quantified both with, and without, business mitigation and intervention responses in place. We combine this information with a recent spatial hazard probability map (Bebbington and Cronin Bull Volcanol. 73(1):55–72, 2011) to produce novel spatial economic activity ‘at risk’ maps. Our approach demonstrates how business inoperability losses sit alongside potential life and property damage assessment in enhancing our understanding of volcanic risk mitigation.

On the limited usability of the inoperability IO model

ABSTRACTThis note shows that the inoperability input–output model (IIM) estimates only a part of mainly the negative indirect economic impacts of disasters, whereas it neglects most of the positive indirect impacts. This means that the IIM is not suited to prioritize industries for policy interventions that aim at reducing the negative impacts of such disasters. Besides, this note shows that the application of the IIM is problematic and tends to overestimate the subset of impacts that the model is able to quantify. Finally, we identify two approaches that much better capture the variety of different disaster impacts.

Novel approach for manufacturing supply chain risk analysis using fuzzy supply inoperability input-output model

Aside from becoming more complex and dynamic, manufacturing supply chains must be capable in adapting to disruptive events caused by natural and man-made disasters. Risk analysis aids in developing mitigation policies to achieve a resilient manufacturing supply chain. However, the uncertainty and vagueness of information along the supply chain pose a challenge to risk analysis. Previous approaches on the analysis of supply chain risks have been proposed but have drawbacks that may provide counterintuitive results. Thus, this study attempts to develop a methodological approach based on supply-driven input-output analysis with fuzzy parameters in order to address supply chain risk analysis. The motivation behind the adoption of such approach lies in the strength of I-O analysis in addressing interdependent systems and its ability to address uncertainty of information shared among members. The proposed approach was applied to an herbal manufacturing supply chain to illustrate the methodology.

Electricity Supply Interruptions: Sectoral Interdependencies and the Cost of Energy Not Served for the Scottish Economy

Modern economies and infrastructure sectors rely upon secure electricity supplies. Due to sectoral interdependencies, major interruptions cause cascading effects in the economy. This paper investigates the economic effects of major power supply disruptions taking such interdependencies into account. We apply a dynamic inoperability input-output model (DIIM) to 101 sectors, including households, of the Scottish economy in 2009 to explore the direct, indirect, and induced effects of supply interruptions. We estimate the societal cost of energy not supplied (SCENS) due to an interruption. The results show that the most economically affected industries, following an outage, are different from the most inoperable ones. The results also indicate that SCENS varies with the duration of a power cut, ranging from ਴,300/MWh for a one-minute outage to £8,100/MWh for a three-hour (and higher) interruption. The results can be used to design policies for contingencies and preventive investments in the power sector.

Supply-side inoperability input–output model (SIIM) for risk analysis in manufacturing systems

This paper proposes a methodological approach in risk analysis of interdependent manufacturing systems which is based on Ghosh model - a variation of the Leontief input-output model. The proposed approach is able to quantify the impact of supply perturbations in a manufacturing system in terms of cost-price increase in production output due to increase in prices of value-added input brought about by degraded supply resulting from natural or man-made disasters and sudden policy changes. Unlike demand-driven perturbation models presented in literature, the supply-driven inoperability input-output model (SIIM) appears to be more relevant particularly in make-to-order manufacturing systems as demand is usually pre-determined and production costs typically increase when prices of inputs increase. An actual case study was carried out in a furniture manufacturing firm in central Philippines and three scenarios were presented to illustrate the proposed approach: (1) a sudden log ban in the location of the supplier, (2) increase in labor costs and (3) metal shortage caused by severe weather condition. Results show that supply perturbation of upstream processes does not impact downstream processes as long as these processes remain independent of the perturbed upstream process as described in firm's system structure and topology. This also shows that the magnitude of impact of non-perturbed process depends on its nature of interdependence of the perturbed process. The proposed approach is highly relevant for manufacturing practitioners in formulating and implementing mitigation and adaptation policies.

A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that needs to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from the food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed.