Search results

Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude.

Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation.

The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references.

When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks.

The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations.

Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.

The purpose of this paper is to propose a cost-effective, time-saving and easy-to-use failure modes and effects analysis (FMEA) system applied on the quality control of supplied products. The traditional FMEA has been modified and adapted to fit the quality control features and requirements. The paper introduces a new and revised FMEA approach, where the “failure concept” has been modified with “defect concept.”

The typical FMEA parameters have been modified, and a non-linear scale has been introduced to better evaluate the FMEA parameters. In addition, two weight functions have been introduced in the risk priority number (RPN) calculus in order to consider different critical situations previously ignored and the RPN is assigned to several similar products in order to reduce the problem of complexity.

A complete procedure is provided in order to assist managers in deciding on the critical suppliers, the creation of homogeneous families overcome the complexity of single product code approach, in RPN definition the relative importance of factors is evaluated.

This different approach facilitates the quality control managers acting as a structured and “friendly” decision support system: the quality control manager can easily evaluate the critical situations and simulate different scenarios of corrective actions in order to choose the best one. This FMEA technique is a dynamic tool and the performed process is an iterative one. The method has been applied in a small medium enterprise producing hydro massage bathtub, shower, spas and that commercializes bathroom furniture. The firm application has been carried out involving a cross-functional and multidisciplinary team.

The purpose of this paper is to encourage the integration of Lean principles with reliability models to sustain Lean efforts on a long‐term basis. It seeks to present a modified FMEA that will allow Lean practitioners to understand and improve the reliability of Lean systems. The modified FMEA approach is developed based on the four critical resources required to sustain Lean systems: personnel, equipment, materials, and schedules.

A three‐phased methodology approach is presented to enhance the reliability of Lean systems. The first phase compares actual business and operational conditions with conditions assumed in Lean implementation. The second phase maps potential deviations of business and operational conditions to their root cause. The third phase utilizes a modified Failure Mode and Effects Analysis (FMEA) to prioritize issues that the organization must address.

A literature search shows that practical methodologies to improve the reliability of Lean systems are non‐existent.

The knowledge database involves many tedious calculations and hence needs to be automated.

The paper has defined Lean system reliability, developed a conceptual model to enhance the Lean system reliability, developed a knowledge base in the form of detailed hierarchical root trees for the four critical resources that support our Lean system reliability, developed a Risk Assessment Value (RAV) based on the concept of effectiveness of detection using Lean controls when Lean designer implements Lean change, developed modified FMEA for the four critical resources.

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.

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.

Due to the warming at the automotive market in the last years and consequently the growth of vehicle production has been moved and placed emphasis on the segment. In recent years, some have known, for example, as the earthquake that struck Japan in 2011 was able to disrupt the suppliers of the country. Due to these events, supply chain risk management has become essential to the supply chain operations success. The purpose of this paper is to evaluate the supplier’s systematic selection at the automotive industry compared with the identified models in the literature.

A case study applied at the automotive to propose a method for selecting suppliers considering the risk management.

These results indicate that the organization has established criteria for suppliers selection, this systematic aims to identify the potential risks in the supply chain before the supplier award the project but it’s also the current practice can be improved using as reference the comparative method as applied in this study.

The risk management, a few discussed topic but on the rise among researches, show that the companies, especially those inserted in the automotive segment has been used different techniques for selecting suppliers to focus in a better supply chain control. Establish criteria for selecting suppliers means identifying in advanced the potential risks that the suppliers may offer to the organization during the supply to avoid any interruptions to supply.

The purpose of this paper is to introduce an integrated approach using failure modes and effects analysis (FMEA), multiple-criteria decision making (MCDM), mathematical modeling and quality function deployment (QFD) techniques, for risk assessment and service quality enhancement in coronary artery bypass grafting (CABG) as a treatment for cardiovascular diseases (CVDs).

First, the disruptions in the CABG process are identified and prioritized following FMEA instructions, using two MCDM techniques, called analytic hierarchy process (AHP) and TOPSIS. Consequently, several corrective activities are identified and weighted on the basis of QFD. Finally, a mathematical model is established to determine the most cost-effective activities for implementation. The approach is developed in a fuzzy environment to reflect the uncertainty and ambiguity of human reasoning.

Regarding the CABG process disruption, a total of 30 failure modes in four main categories were identified and prioritized. Moreover, eight corrective activities were devised and ranked according to their impact on the failure modes. Finally, considering a limited amount of budget, a sensitivity analysis on the mathematical model’s objective function indicated that using 30 percent of the total budget, required to implement all corrective activities, was enough to cover more than 70 percent of the effects of corrective activities on the failure modes.

This paper contributes to the quality risk assessment knowledge by introducing an integrated approach to evaluate and improve healthcare services quality. Also, the case study conducted on the CABG process has not been done by other related studies in the literature.

The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes.

A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes.

A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies.

The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method.

This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.

The purpose of this paper is to present a modified failure mode and effects analysis (FMEA) in order to make the assignment of the scores for the occurrence factor more robust, and to link the FMEA chart directly to the maintenance activities.

A well-known clustering algorithm (i.e. K-means), along with a normalisation approach, are applied and compared for the assignment of the occurrence scores. Subsequently, the relationship between failures and maintenance operations is made explicit by a correlation matrix. Finally, the K-means algorithm is applied to the maintenance operations again in order to sort them into priority classes.

It is found that this revised FMEA approach improves the standard one due to its more rigorous mathematical formulation and lean applicability in real operating environments.

The novel approach may be improved by a deeper statistical analysis and/or applying the fuzzy theory.

A real case study is introduced in order to show the applicability of this approach to the quality control of a blow moulding process. It is found that this approach reveals a high potentiality for dealing with real issues.

The paper provides a further step towards bridging the gap between theory and practical application of the FMEA approach.

Conventional Failure Mode and Effects Analysis (FMEA) may lead to wrong decisions in terms of a company's financial objectives. Thus, the aim of this paper is to develop an improved approach to prioritizing failures within the procedure of the FMEA.

First, the current “risk priority number” (RPN) being used within the FMEA is developed. Second, a case study is conducted in cooperation with an automotive supplier in order to test the applicability and benefits of the new approach.

By implementing the cost‐oriented FMEA, quality management can be improved. In order to decide on improvement actions, costs arising from faults detected by the customer, costs of faults detected within the boundaries of the company and costs associated with false positive inspection results should be included in the evaluation of potential failures. This case study confirms the benefits of the new approach.

In order to implement this modified FMEA, a precondition is to estimate failure costs precisely and comprehensively. This precondition could constitute a serious obstacle, because quality costs are often difficult to quantify. In addition, future research should focus on the potential to include interdependencies among various failure modes and effects.

The proposed approach enables managers and designers to prevent expensive faults and hence facilitates decisions, which make better use of resources in optimising products and processes.

The paper provides a new approach to FMEA and thus contributes to the development of cost‐oriented quality management.