Search results

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

Clarifying the risk evolution mechanism of housing construction for work-safety management is essential. Existing studies have inadequately discussed the risk-accumulation process in housing construction. Therefore, this study aimed to use the complex network theory and risk allocation mechanisms to explore the evolution of risk factors.

The authors analysed a database of housing construction accidents in China from 2015 to 2020 to identify risk factors. Moreover, the causal relationship between risk factors was determined through a systematic analysis of the logical sequence of risk factors. A complex network was used to construct a risk network for housing construction accidents (RNHCA).

The risk matrix method was used to define the factor risk threshold, and a risk value was assigned based on the correlation between risk factors. This contributes to the examination of the evolution mechanism of risk networks in the process of risk factor transmission. The case verification results show that the RNHCA quantitative assessment model can better evaluate the system risk status of housing construction accidents. Furthermore, this model can identify the key risk factors and risk chains with high risk in the evolution of the risk network.

Accident investigation reports need to be classified and processed to analyse the evolution law of risk networks under different scales of construction project, such as high-rise buildings, middle-rise buildings, and low-rise buildings.

This study clarified the risk evolution process of complex systems in housing construction and provided a new method for analysing accidents.

This study clarifies the risk value allocation of risk factors in the transmission process and reveals the process of risk factor evolution in housing construction. This study explains the individual risk factors that form a systemic risk through the transmission chain. Moreover, this paper clarified the transformation relationship between system risk and accidents. The paper also provided a new perspective for risk analysis.

Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few methodologies can capture how physical risks impact businesses via the supply chains, yet outside the business literature, methodologies such as sustainability assessments can assess cascading impacts.

Adopting a scoping review framework by Arksey and O'Malley (2005) and the PRISMA extension for scoping reviews (PRISMA-ScR), this paper reviews 27 articles that assess climate risk in supply chains.

The literature on supply chain risks of climate change using quantitative techniques is limited. Our review confirms that no research adopts sustainability assessment methods to assess climate risk at a business-level.

Alongside the need to quantify physical risks to businesses is the growing awareness that climate change impacts traverse global supply chains. We review the state of the literature on methodological approaches and identify the opportunities for researchers to use sustainability assessment methods to assess climate risk in the supply chains of an individual business.

According to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.

The Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.

In this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.

This study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

Facilities management (FM) plays a key role in the performance of businesses to ensure the comfort of users and the sustainable use of natural resources over operation and maintenance. Nevertheless, reactive maintenance (RM) services are characterised by delays, waste and difficulties in prioritising services and identifying the root causes of failures; this is mostly caused by inefficient asset information and communication management. While linking building information modelling and the Internet of Things through a digital twin has demonstrated potential for improving FM practices, there is a lack of evidence regarding the process requirements involved in their implementation. This paper aims to address this challenge, as it is the first to statistically characterise RM services and processes to identify the most critical RM problems and scenarios for digital twin implementation. The statistical data analytics approach also constitutes a novel practical approach for a holistic analysis of RM occurrences.

The research strategy was based on multiple case studies, which adopted university campuses as objects for investigation. A detailed literature review of work to date and documental analysis assisted in generating data on the FM sector and RM services, where qualitative and statistical analyses were applied to approximately 300,000 individual work requests.

The work provides substantial evidence of a series of patterns across both cases that were not evidenced prior to this study: a concentration of requests within main campuses; a balanced distribution of requests per building, mechanical and electrical service categories; a predominance of low priority level services; a low rate of compliance in attending priority services; a cumulative impact on the overall picture of five problem subcategories (i.e. Building-Door, Mechanical-Plumbing, Electrical-Lighting, Mechanical-Heat/Cool/Ventilation and Electrical-Power); a predominance of problems in student accommodation facilities, circulations and offices; and a concentration of requests related to unlisted buildings. These new patterns form the basis for business cases where maintenance services and FM sectors can benefit from digital twins. It also provides a new methodological approach for assessing the impact of RM on businesses.

The findings provide new insights for owners and FM staff in determining the criticality of RM services, justifying investments and planning the digital transformation of services for a smarter provision.

This study represents a unique approach to FM and provides detailed evidence to identify novel RM patterns of critical service provision and activities within organisations for efficient digitalised data management over a building’s lifecycle.

This study aims to conduct the aircraft electrical wiring interconnection system (EWIS) safety risk assessment process abundantly and hierarchically and establish the assessment index system considering the weights and interrelationships of different levels of indices.

Due to the failure of EWIS being multifactorial, hidden and diverse, this paper divides the factors influencing the failure of EWIS into 3 primary indices, 13 secondary indices and 38 tertiary indices. Taking open circuit failure (OCF) and short circuit failure (SCF) as examples, calculate the weights of assessment indices based on the triangular fuzzy number analytic hierarchy process (TFNAHP) and triangular fuzzy number decision-making trial and evaluation laboratory (TFNDEMATEL). The cloud model (CM) divides the risk levels and obtains the safety risk assessment results. The comparative analyses of different weight calculation methods, different failure modes and different aircraft EWIS zones verify the effectiveness and practicability of the proposed method.

The results show that the proposed method aligns more with the actual situation than other methods. Also, the results identify key focus objects in EWIS safety risk assessment, such as the surrounding environmental factors among the primary indices having the most significant influence on OCF and SCF, the risk level of SCF being higher than that of OCF, etc.

This paper proposes a safety risk assessment index system for aircraft EWIS based on the cable parameters, surrounding environmental factors, installation and protection methods. The weight assignment is added to the assessment index system, and the safety risk assessment model is constructed by combining TFNAHP, TFNDEMATEL and CM.

How to identify the critical success factors (CSFs) of public health emergencies (PHEs) is of great practical significance to carry out a scientific and effective risk assessment. The purpose of this paper is to address this issue.

In this paper, the authors propose a new approach to identify the CSFs by hesitant fuzzy linguistic set and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. First, a larger group of experts are clustered into three groups according to similarity degree. Then, the weight of each cluster is determined by the maximum consensus method, and the overall direct influence matrix is obtained by clustering with hesitant fuzzy linguistic weighted geometric (HFLWG) operators. Finally, the overall direct influence matrix is transformed into the crisp direct impact matrix by the score function, and 11 CSFs of PHEs are identified by using the extended DEMATEL method.

In addition, an example of PHEs shows that the approach has good identification applicability. The approach can be used to solve the problems of fuzziness and subjectivity in linguistic assessments, and it can be applied to identify the customer service framework with the linguistic assessments process in emergency management.

This paper extends the above DEMATEL method to study in the hesitant fuzzy linguistic context. This proposed hybrid approach has a wider application in the high-risk area where disasters frequently occur.

In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.

In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.

The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.

The basis of safe flight is the management of risks. This paper aims to present a new process-based risk assessment model, with an approach to calculate the risk score.

Since thousands of minor changes occur within ground operations, it is difficult to calculate how much risk these variations will pose. This paper proposes a risk assessment model fed from analysis of ground operation processes using functional resonance analysis method (FRAM) and fuzzy logic.

FRAM is used to detect variations in ground operation. Using the FRAM analysis, it has been revealed how much risk the process steps described in the procedures involve. The risk score was calculated by combining the probability value obtained from the airline’s database and the severity assessment of the expert group in fuzzy logic. The risk level can be monitored dynamically with the transfer of events in the airline’s database to the process-based risk assessment model.

FRAM analysis, which is used to detect function variations before undesirable risk occurs, has brought a proactive approach to risk assessment. The process-based risk assessment model allows the creation of new safety parameter indicators to be followed to reduce the risk level of the function with a high-risk level. The proposed approach can be used for other operational areas in aviation as well.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.