Search results

One of the key elements of a maintenance system is risk analysis because the risk level of an engineering system will determine the maintenance policy required. Failure Mode and Effect Analysis (FMEA) is widely applied in evaluating risk of failure of engineering systems. However, the conventional FMEA used in analyzing risk by most industries has shortcomings such as an inability to utilize imprecise and interval data in prioritizing risk. To overcome these limitations, different variants of FMEA have been reported in the literature. However, these modified approaches are computationally intensive; hence, the purpose of this paper is to develop an efficient FMEA-based methodology that is easy to analyze and implement.

The proposed technique combines the Taguchi method with FMEA in order to analyze risk of engineering systems easily and effectively. The effectiveness of the approach is demonstrated with a case study of the fuel oil system of a marine diesel engine.

The results of the integrated Taguchi method and FMEA, when compared with well-known techniques, namely, VIKOR and compromise programming, from the literature are very similar. From the comparative analysis, it was evident that the proposed method is a viable option to the more computationally intensive approaches used in the literature.

The approach proposed is novel and simple and can be implemented more easily than approaches from the literature in analyzing risk.

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.

Presents a critical review of the method of FMEA and in particular of the risk priority number (RPN) used for ranking failure modes. Though the method itself is of great use, the calculation of the RPN lacks a proper model as a base and is thus internally inconsistent and potentially misleading. Proposes an alternative procedure, based on a simple model and using expected costs as the basis for ranking failure modes.

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.

To propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.

The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.

In this paper, we argued that not all the rules are actually required in the fuzzy RPN model. Eliminating some of the rules does not necessarily lead to a significant change in the model output. However, some of the rules are vitally important and cannot be ignored. The proposed GRRS is able to provide guidelines to the users which rules are required and which can be eliminated. By employing the GRRS, the users do not need to provide all the rules, but only the important ones when constructing the fuzzy RPN model. The results obtained from the case studies demonstrate that the proposed GRRS is able to reduce the number of rules required and, at the same time, to maintain the ability of the Fuzzy RPN model to produce predictions that are in agreement with experts' knowledge in risk evaluation, ranking, and prioritization tasks.

The proposed GRRS is limited to FMEA systems that utilize the fuzzy RPN model.

The proposed GRRS is able to simplify the fuzzy logic‐based FMEA methodology and make it possible to be implemented in real environments.

The value of the current paper is on the proposal of a GRRS for rule reduction to enhance the practical use of the fuzzy RPN model in real environments.

The purpose of this study is to propose a state‐of‐the‐art new approach to enhance FMEA assessment capabilities.

Through data envelopment analysis (DEA) technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish improving scales for SOD.

Through an illustrative example the proposed approach supports the proposition that DEA can not only complement traditional FMEA for improving assessment capability but also, especially, provide corrective information regarding the failure factors – severity, occurrence and detection. Further application of DEA Stratification also reveals that this methodology is useful for managing resource allocation and risk management.

It is shown that the proposed approach enables manager/designers to prevent system or product failures at an early stage of design. Moreover, the approach is able to provide managerial insight of SOD more effectively than justifying the efforts on RPN alone. Projection of each SOD is determined to help managers examine the scale of efforts. Finally, the stratification analysis offers the economical allocation of failure modes with respect to the incurred costs and the efficiency.

The paper proposes a unique new approach, robust, structured and useful in practice, for failure analysis. The methodology, within a firmed methodology, overcomes some of the largely known shortfalls of traditional FMEA: it takes into account multiple criteria and restricted weighted; and it analyses the failure modes' ranking considering not only the direct impacts of failure indices, but also the contribution of these indices.

Quality management tools such as failure mode and effects analysis (FMEA) have been implemented in various industries to improve quality. This report aims to demonstrate that FMEA can be applied as a performance improvement tool, based on case analysis of process improvement conducted for a drug discovery project.

The main points of the proposed FMEA process include: inclusion of an interface that makes it easy to visualize complicated processes in pharmaceutical research; identification of undesirable effects to indicate process defects; and a quantitative estimate of the undesirable effects related to quality and efficiency.

The effectiveness of the proposed FMEA process was evaluated based on in vivo screening/profiling during early drug discovery. The process targeted for improvement was visualized using a flow diagram. The undesirable effects identified included waiting, false operations, and errors in the decision‐making and reporting processes. The most serious flaws, determined by risk priority numbers for each category, were waiting and false operations.

The effectiveness of the proposed FMEA was demonstrated by applying the analysis to another in vivo profiling process. Quantitative evaluation of the undesirable effects determined that they were reasonable. This provides a benefit for scientists seeking to improve the drug discovery process.

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.

The aim of the present study is to overcome some of the limitations of the FMEA method by presenting a theoretical base for considering risk evaluation into its assessment methodology and proposing an approach for its implementation.

Fuzzy AHP is used to calculate the weights of the likelihood of occurrence (O), severity (S) and difficulty of detection (D). Additionally, the prospect-theory-based TODIM method was integrated with fuzzy logic. Thus, fuzzy TODIM was employed to calculate the ranking of potential failure modes according to their risk priority numbers (RPNs). In order to verify the results of the study, in-depth interviews were conducted with the participation of industry experts.

The results are very much in line with prospect theory. Therefore, practitioners may apply the proposed method to FMEA. The most crucial failure mode for a firm's attention is furnace failure followed by generator failure, crane failure, tank failure, kettle failure, dryer failure and operator failure, respectively.

The originality of this paper consists in integrating prospect theory with the FMEA method in order to overcome the limitations naturally inherent in the calculation of the FMEA's RPNs.

Describes a new technique for prioritizing failures for corrective actions in failure mode and effects analysis (FMEA). This technique extends the risk prioritization beyond the conventional risk priority number (RPN) method. A new scale has been defined. The ranks 1 through 1,000 are used to represent the increasing risk of the 1,000 possible severity‐occurrence‐detection combinations, called risk priority ranks (RPRs). The failures having a higher rank are given higher priority. This approach resolves some of the shortcomings in the traditional RPN technique. Traditionally, FMEA identifies the risk associated with a product failure through assignment of a standard RPN. A fundamental problem with FMEA is that it attempts to quantify risk without adequately quantifying the factors that contribute to risk. In particular cases, RPNs can be misleading. This deficiency can be eliminated by using the new technique. A methodology combining the benefits of matrix FMEA and the new technique as stated above is presented.