Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

Rad, Mahyar Habibi; Mojtahedi, Mohammad; Ostwald, Michael J.

doi:10.3390/buildings11090411

Cited by 40 publications

(23 citation statements)

References 119 publications

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“…This research adopts the SLR method, which is a reliable and well-defined methodology that follows a rigorous sequence of phases to develop robust outcomes [16,17]. The SLR method has previously been used to examine lean implementation [11,15,18,19] and provide answers to specific research questions. An SLR entails five stages: (i) question formulation; (ii) exploring the literature; (iii) study collection and assessment; (iv) analysis; and (v) reporting of results [16].…”

Section: Methodsmentioning

confidence: 99%

“…The descriptive analysis presents essential information associated with targeted studies and maps out the status of the publications in the research domain [15]. The findings of the descriptive analysis are listed in Table 1.…”

Section: Descriptive Analysis Of the Selected Studiesmentioning

confidence: 99%

“…First, there is a lack of understanding of the contributing components needed for the effective implementation of lean construction in infrastructure recovery projects. Habibi Rad et al [15], for example, note that there is insufficient evidence about which lean construction tools and techniques can enhance contractor performance in infrastructure recovery projects and organizational resilience of construction companies is missing from their decision-making procedures. Such research identifies the need for systematic theoretical development.…”

Section: Introductionmentioning

confidence: 99%

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A Conceptual Framework for Implementing Lean Construction in Infrastructure Recovery Projects

et al. 2022

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Natural hazards can have substantial destructive impacts on the built environment. Providing effective services in disaster areas is heavily reliant on maintaining or replacing infrastructure; thus, post-disaster reconstruction of infrastructure has attracted growing attention. Due to the complex and dynamic nature of infrastructure recovery projects, contractor companies engaged in this work have typically experienced poor performance. Furthermore, from a commercial perspective, the post-disaster reconstruction environment is characterized by fierce competition and market uncertainty, challenging the organizational resilience of companies undertaking this work. One approach for improving contractor performance is the implementation of lean construction, but the literature lacks consensus on its capability to affect organizational resilience. To respond to this problem, a conceptual framework applicable for lean implementation in infrastructure, which explicitly addresses organizational resilience, is required for recovery projects. In parallel, contributing components to effective implementation of lean-recovery and supportive theories for justifying the conceptual framework must be identified. Consequently, this paper proposes a conceptual framework to implement lean practices for the enhancement of organizational resilience. The framework is developed using a systematic research method, wherein 110 research documents were discovered initially, and following processing, 18 relevant documents were identified and analyzed. Through this process, contingency and Transformation-Flow-Value (TFV) theories were identified as an appropriate foundation for a framework to implement lean construction in infrastructure recovery projects.

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Section: Methodsmentioning

confidence: 99%

Section: Descriptive Analysis Of the Selected Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Conceptual Framework for Implementing Lean Construction in Infrastructure Recovery Projects

et al. 2022

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“…These cyber-attacks changed companies’ everyday perceptions, forcing them to believe that cyber-attacks in the absence of cybersecurity solutions can ruin the business (Ali et al, 2021 ). Many researchers have discussed the I4.0 implementation risks but lacked in recapitulating the specifics of risks impacting SMEs (Habibi Rad et al, 2021 ; Moeuf et al, 2020 ). As a result, in the absence of a clear risks framework, many organizations are still at the crossroad of decision-making, while others are extra cautious (Ghobakhloo & Iranmanesh, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Gadekar¹,

Sarkar

Gadekar

2022

Ann Oper Res

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Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization’s decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer’s, policymaker, and researchers’ viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption.

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“…Identifying and mitigating risks in SCs is accomplished not only at the company level, but also by focusing on the entire SC [ 9 ]. SCs are vulnerable to a variety of sources of uncertainty [ 10 , 11 ]. With the rise in uncertainties and risk events, supply chain risk management (SCRM) has received more attention than ever before and has become critical to business success [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Developing a Risk Reduction Support System for Health System in Iran: A Case Study in Blood Supply Chain Management

Sibevei

Azar

Zandieh

et al. 2022

IJERPH

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Health systems are recognised as playing a potentially important role in many risk management strategies; however, there is strong evidence that health systems themselves have been the victims of unanticipated risks and have lost their functionality in providing reliable services. Existing risk identification and assessment tools in the health sector, particularly in the blood supply chain, address and evaluate risks without taking into account their interdependence and a holistic perspective. As a result, the aim of this paper is to develop a new systemic framework based on a semi-quantitative risk assessment approach to measure supply chain risks, which will be implemented through a case study on the Iranian BSC. This paper identifies and assesses supply chain risks (SCRs) by employing a novel systemic process known as SSM-SNA-ISM (SSI). First, the supply chain and its risks are identified using Soft Systems Methodology (SSM). Then, given the large number of risks, the second stage uses Social Network Analysis (SNA) to identify the relationships between the risks and select the most important ones. In the third stage, risk levelling is performed with a more in-depth analysis of the selected risks and the application of Interpretive Structural Modelling (ISM), and further analysis is performed using the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). The study found that by using the new proposed approach, taking into account risk relationships, and taking a holistic view, various supply chain risks could be assessed more effectively, especially when the number of risks is large. The findings also revealed that resolving the root risks of the blood supply chain frequently necessitates management skills. This paper contributes to the literature on supply chain risk management in two ways: First, a novel systemic approach to identifying and evaluating risks is proposed. This process offers a fresh perspective on supply chain risk modelling by utilising systems thinking tools. Second, by identifying Iranian BSC risks and identifying special risks.

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Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

Cited by 40 publications

References 119 publications

A Conceptual Framework for Implementing Lean Construction in Infrastructure Recovery Projects

A Conceptual Framework for Implementing Lean Construction in Infrastructure Recovery Projects

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Developing a Risk Reduction Support System for Health System in Iran: A Case Study in Blood Supply Chain Management

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