Risk Prioritization using A FUZZY BASED Approach in Software Development Design Phase

The success of a software project's objective is directly proportional to the degree to which it satisfies all of the stakeholders' concerns regarding the project's requirements, including the budget, schedule, and overall performance. Risks can occur throughout the software development lifecycle (SDLC) phases and affect every phase. The design phase of the SDLC yields an overview of the software and can be defined as the software's blueprint. Different types of software have their own unique design phases and have different types of risks. With the high number of interacting components, complex systems have a greater propensity to be more volatile, which increases the risk. It is necessary to prioritize the risks in order of their severity levels. The issue at hand is the lack of effective methods to prioritize and mitigate the risk. Recent studies have suggested several methods for prioritizing risks, but it is clear that few of these have been implemented. These methods are overly complicated, time-consuming, prone to inconsistency, and challenging to put into practice. This paper proposes a novel Fuzzy-based approach to risk prioritization in the software design phase using MATLAB software. Fuzzy-based models have been shown to be more accurate than other techniques when using standard datasets to prioritize risks. Fuzzy-based methods that have been proposed take into account the characteristics of risks by modelling those characteristics as fuzz

A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis

This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA. © 1963-2012 IEEE

A new two-stage fuzzy inference system-based approach to prioritize failures in failure mode and effect analysis

This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA

Economic impact failure mode and effects analysis

Failure mode and effects analysis (FMEA) is a method for reducing or eliminating failure modes in a system. A failure mode occurs when a system does not meet its specification. While FMEA is widely used in different industries, its multiple limitations can cause the method to be ineffective. One major limitation is the ambiguity of the risk priority number (RPN), which is used for risk prioritization and is the product of three ordinal variables: severity of effect, probability of occurrence, and likelihood of detection. There have been multiple attempts to address the RPN's ambiguity, but more work is still needed. Any new risk prioritization method needs to have a decision-support system to determine when to implement a corrective action or improvement.This research addresses some of the shortcomings of traditional FMEA through the creation of a new method called Economic Impact FMEA (EI-FMEA). EI-FMEA replaces the three ordinal values used in the RPN calculation with a new set of variables focusing on the expected cost of a failure occurring. A detailed decision-support system allows for the evaluation of corrective actions based on implementation cost, recurring cost, and adjusted failure cost. The RPN risk prioritization metric is replaced by the economic impact value (EIV) risk prioritization metric which ranks risks based on the impact of the corrective action through the largest reduction in potential failure cost. To help with resource allocation, the EIV only ranks risks where the corrective actions are economically sustainable.A comparison of three FMEA methods is performed on a product, and the risk prioritization metrics for each method are used to determine corrective action implementation. An evaluation of the FMEA methods are shown, based on the expected failure cost reduction, using the decision-support criteria of each method.The EI-FMEA method contributes to the body of knowledge by addressing the ambiguity of the RPN in FMEA by creating the EIV risk prioritization metric. This allows the EI-FMEA method to reduce failure cost by providing a decision-support system to determine when to implement a corrective action when both finite and infinite resources are available