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Sequential application of fractional factorial and response surface designs in the regression modelling and optimisation of a multi‐parameter type manufacturing process is presented. In particular, the coating thickness variation of an acid copper plating process was minimised with high bath acidity, high cathodic current density and large anode‐cathode separation. Statistically designed experiments are shown to be highly effective in studying the effects and interactions of the various process factors.

The purpose of this research is to explore the views of individuals responsible for quality assurance in Hong Kong (HK) food and beverage companies with regards to their acceptance or rejection of the ISO 9000 quality management system or HACCP food safety system standards, along with the reasoning underlying such views. Thirty Hong Kong food or beverage manufacturing companies were approached and in‐depth interviews in the form of surveys were conducted with 11 companies. Participating companies included companies that had implemented both the ISO 9000 and HACCP standards, companies that had implemented only ISO 9000 or HACCP, and a company that had implemented neither. Half of the companies that participated in this study were large companies with 500 or more employees. The use of ISO 9000 was reported to improve the maturity of other quality systems. The use of HACCP was reported to improve the maturity of other food safety systems. While more companies used HACCP than the ISO 9000 standard to comply with customers’ requirements, the difficulties in the training of staff and added costs for documentation/data storage were reported as common to both standards.

Risk assessment is a very important step toward managing risks in various organizations and industries. One of the most extensively applied risk assessment techniques is failure mode and effects analysis (FMEA). In this paper, a novel fuzzy multiple-criteria decision-making (MCDM)-based FMEA model is proposed for assessing the risks of different failure modes more accurately.

In this model, the weight of each failure mode is considered instead of risk priority number (RPN). Additionally, three criteria of time, cost and profit are added to the three previous risk factors of occurrence (O), severity (S) and detection (D). Furthermore, the weights of the mentioned criteria and the priority weights of the decision-makers calculated by modified fuzzy AHP and fuzzy weighted MULTIMOORA methods, respectively, are considered in the proposed model. A new ranking method of fuzzy numbers is also utilized in both proposed fuzzy MCDM methods.

To show the capability and usefulness of the suggested fuzzy MCDM-based FMEA model, Kerman Steel Industries Factory is considered as a case study. Moreover, a sensitivity analysis is conducted for validating the achieved results. Findings indicate that the proposed model is a beneficial and applicable tool for risk assessment.

To the best of authors’ knowledge, no research has considered the weights of failure modes, the weights of risk factors and the priority weights of decision-makers simultaneously in the FMEA method.

The purpose of this paper is to evaluate cow-calf producer incentive to adopt innovations in traits with important environmental and economic implications for the beef supply chain.

A whole farm multi-year farm optimization model that tracks changes in discounted net returns and methane emissions from the use of newer DNA-related technologies to breed for feed efficient cattle is developed. The analysis is situated within the context of whole beef cattle supply chain. This allows for the derivation of the entire value and environmental impact of the innovation, and the decomposition of value by different participants. The impact of different policies that can stimulate producer uptake and the diffusion of the innovation is also addressed.

The results of the study showed that whilst the use of the breeding technology yielded positive economic and environmental benefits to all producers in the supply chain, primary adopters were unlikely to adopt. This paper finds evidence of the misalignment in incentives within the supply chain with a significant proportion of the additional value going to producers who do not incur any additional cost from the adoption of the innovation. The study also highlighted the role of both public and market-based mechanisms in the innovation diffusion process.

This paper is unique as it is the first study that addresses producer incentive to adopt genomic selection for feed efficiency across the entire beef cattle supply chain, and incorporates both economic and environmental outcomes.

The proposed hybridized framework provides a new performance optimization-based paradigm for analysing the failure behaviour of paneer unit (PU) in the dairy industry.

A novel fuzzy Jaya-based Lambda–Tau Optimization (JBLTO) approach-based mathematical modelling was developed for calculating various reliability indices of the considered unit. Failure mode and effect analysis (FMEA) was carried using qualitative information gathered from system's expert opinions. Fuzzy-complex proportional assessment (FCOPRAS) approach was integrated within FMEA to recognize the most critical failure causes associated with various subsystem/components.

The availability of the unit falls by 0.053% as the uncertainty level increases from ±15 to ±25% and further decreases to 0.323% as the uncertainty level increases from ±25 to ±60%. Failure causes, namely wearing in gears of gearbox (MST4), an impeller's cavitation and/or corrosion (CFP4), winding failure of electric motor (WS9), were recognized as the most critical failure causes with FCOPRAS final performance scores of 100, 100 and 100 and fuzzy combinative distance-based assessment (FCODAS) resultant assessment score of 0.5997, 1.1898 and 1.6135.

JBLTO approach-based reliability results were compared with traditional particle swarm optimization-based Lambda–Tau (PSOBLT) and traditional fuzzy Lambda–Tau (FLT) approaches for confirming the downward trend in the system's availability. The ranking results of qualitative analysis are compared with the implementation of FCODAS technique. Sensitivity analysis was executed to evaluate the robustness of the proposed hybridized framework.

Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.

We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.

The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.

The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.

This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Construction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.

The proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.

The results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.

The originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.

The Institute of Medicine (IOM) views Health Information Technology (HIT) as an essential organizational prerequisite for the delivery of safe, reliable, and cost-effective health services. However, HIT presents the proverbial double-edged sword in generating solutions to improve system performance while facilitating the genesis of novel iatrogenic problems. Incongruent organizational processes give rise to technological iatrogenesis or the unintended consequences to system integrity and the resulting organizational outcomes potentiated by incongruent organizational–technological interfaces. HIT is a disruptive innovation for health services organizations but remains an overlooked organizational development (OD) concern.

Recognizing the technology–organizational misalignments that result from HIT adoption is important for leaders seeking to eliminate sources of system instability. The Health Information Technology Iatrogenesis Model (HITIM) provides leaders with a conceptual framework from which to consider HIT as an instrument for organizational development. Complexity and Diffusion of Innovation theories support the framework that suggests each HIT adoption functions as a technological change agent. As such, leaders need to provide operational oversight to managers undertaking system change via HIT implementation. Traditional risk management tools, such as Failure Mode Effect Analysis and Root Cause Analysis, provide proactive pre- and post-implementation appraisals to verify system stability and to enhance system reliability. Reconsidering the use of these tools within the context of a new framework offers leaders guidance when adopting HIT to achieve performance improvement and better outcomes.

An inherent problem on risk priority number (RPN) value duplication of traditional failure modes and effect analysis (FMEA) also exists in two customer-oriented FMEAs. One has no unique value, and another has 1% unique values out of 4,000 possible values. The RPN value duplication has motivated the development of a new customer-oriented FMEA presented in this paper to achieve practically all 4,000 unique values and delivering reliable prioritization.

The drastic improvement is the result of power-law and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). By having all three risk factors in a power-law form, all unique values can be obtained, and by applying VIKOR to these power-law terms, the prioritization is more practical and reliable.

The proposed VIKOR power law-based customer-oriented FMEA can achieve practically all 4,000 unique values and is tested with two case studies. The results are more logical than the results from the other two customer-oriented FMEAs.

The evaluation has been done on two case studies for the service sector. Therefore, additional case studies in other industrial sectors will be required to confirm the effectiveness of this new customer-oriented RPN calculation.

Achieving all 1,000 unique values could only be done by having experts tabulate all possible combinations for the traditional FMEA. Therefore, achieving all 4,000 unique values will be much more challenging. A customer-oriented FMEA has been developed to achieve practically all 4,000 unique risk priority numbers, and that the prioritization is more practical and reliable. Furthermore, it has a connection to the traditional FMEA, which helps explain the traditional one from a broader perspective.

This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios.

To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method.

This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method.

This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.