Search results

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.

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.

Globally, there is a growing proportion of disabled people as a result of different circumstances. This growth generates attention and leads to ways to integrate the affected population into society. Addressing such disability and integration is particularly important at buildings level, enabling and expanding the scope of activities for people with disabilities (PWDs). The rising number of PWDs and the need to integrate them into society create a need for action to improve their living condition and integration into society. This study aims to examine the issue of accessibility for PWDs in higher education facilities in Qatar.

Addressing accessibility at buildings level is particularly important in higher education because it enables inclusion in training and education and increases the potential for productive engagement in society. The study aims to develop an objective tool to assess and measure accessibility in educational institutions. Five selected buildings were examined and evaluated at Qatar University based on proximity, multi-use, vertical and horizontal circulation availability. The survey respondents were randomly selected. An existing assessment method was used in surveying respondents, including those with and without disabilities.

A comparative study was conducted to explore the discrepancy between facility users with and without disability, indicating the gap in existing tools.

The developed tool generates the same outcome when conducted by different assessors, indicating the level of compliance and percentage met as a benefit, not a focus. It allows professionals and non-professionals with minimal experience to conduct the assessment.

This paper introduces the YouWalk-UOS mobile application, a tool that revolutionises the assessment of urban open spaces (UOS). The paper demonstrates how integrating real-time, on-ground observations with users’ reactions into a digital platform can transform the evaluation of urban open spaces. It seeks to address the existing shortcomings of traditional UOS assessment methods and underscore the need for innovative, adaptable and inclusive approaches.

Emphasizing the necessity of UOS for mental and physical health, community interaction and social and environmental resilience in cities, the methodology involves a comprehensive analysis of a number of theoretical frameworks that have historically influenced urban open space conceptualisation, design and assessment. The approach includes a critical review of traditional UOS assessment methods, contrasting them with the capabilities of the proposed YouWalk-UOS application. Building on the reviewed theoretical frameworks, the methodology articulates the application’s design, which encompasses 36 factors across three assessment domains: functional, social and perceptual and provides insights into how technology can be leveraged to offer a more holistic and participatory approach to urban space assessment.

YouWalk-UOS application represents an important advancement in urban space assessment, moving beyond the constraints of traditional methods. The application facilitates a co-assessment approach, enabling community members to actively participate in the evaluation and development of their urban environments. Findings highlight the essential role of technology in making urban space assessment more user-centred, aligning more closely with community needs and aspirations.

The originality lies in the focus on the co-assessment approach and integration of mobile technology into urban open space assessment, a relatively unexplored area in urban design literature. The application stands out as an innovative solution, offering a new perspective on engaging communities in co-assessing their environments. This research contributes to the discourse on urban design and planning by providing a fresh look at the intersection of technology, user engagement and urban space assessment.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

In the continuous development of high-speed railways, ensuring the safety of the operation control system is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportation interruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances on signaling equipment and establishing evaluation methods for the correlation between EMI and safety is urgently needed.

This paper elaborates on the necessity and significance of studying the impact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railway operations and continuous development. The current status of research methods and achievements from the perspectives of standard systems, reliability analysis and safety assessment are examined layer by layer. Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMI and signaling safety.

Despite certain innovative achievements in both domestic and international standard systems and related research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitative correlation between EMI and safety has yet to be established. On this basis, this paper proposes considerations for research methods pertaining to the correlation between EMI and safety.

This paper overviews a series of methods and outcomes derived from domestic and international studies regarding railway signaling safety, encompassing standard systems, reliability analysis and safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact of EMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as a bridge to establish the correlation between EMI and signaling safety is proposed.

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.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This study aims to develop and implement an initial framework for assessing progress in lean implementation within an higher education institution (HEI). It includes developing preliminary findings regarding the impacts of lean implementation in the HEI case organization and comparing outcomes from this assessment to outcomes from other published sources.

Existing literature on lean, particularly in an HEI context, was used to develop a preliminary lean assessment framework for HEIs. Quality and continuous improvement literature were also compared to the proposed assessment framework to further validate the approach. This assessment framework was then utilized to evaluate lean implementation at a large public university (LPU) in the U.S.

The paper presents the framework as well as the major findings from the Large Public University (LPU)’s assessment. The assessment findings are further compared to other HEI quality measures and lean assessments done in other industries. Overall, the findings suggest that the assessment framework provides valuable insight to HEI organizations implementing lean.

The research intends to support lean assessment standardization efforts by proposing a preliminary lean assessment framework for the HEI, grounded in research trends, research findings, identified gaps in the research, and case study outcomes. To the research team's knowledge, this is the first lean assessment framework proposed for HEIs and also contributes to research gaps related to service industry frameworks and those containing both practices and outcomes. The framework can be used by other researchers as a foundation for additional conceptual and empirical developments on the topic and by researchers and practitioners seeking to understand and assess lean implementation progress in the HEI.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.