Identification Of Root Cause Research Articles

Root Cause Assessment for a Manufacturing Industry: A Case Study

Root-cause identification for quality related problems are key issues for manufacturing processes. It has been a very challenging engineering problem particularly in a multistage manufacturing, where maximum number of processes and activities are performed. However, it may also be implemented with ease in each and every individual set up and activities in any manufacturing process. Kaizen is aimed towards reduction in different types of losses i.e. Failure Loss/ Breakdown Loss, Minor stoppage, idling loss, Setup and adjustment loss etc. So as to improve quality and productivity.In this report, root-cause identification methodology has been adopted to eliminate the rejection of product manufactured by the enterprise and improving the life of product. Brainstorming and other Root Cause Assesmenttools have been used to find out the reasons of tube failure and vibration in tubular strander. Solutions of these problems have also given in this report. Kaizen activities have reduced the time consumed in daily activities of cleaning, lubricating, inspection etc. A detailed study has illustrated the effectiveness of the proposed methodology.

Application of root cause analysis in improvement of product quality and productivity

Root-cause identification for quality and productivity related problems are key issues for manufacturing processes. It has been a very challenging engineering problem particularly in a multistage manufacturing, where maximum number of processes and activities are performed. However, it may also be implemented with ease in each and every individual set up and activities in any manufacturing process. In this paper, root-cause identification methodology has been adopted to eliminate the dimensional defects in cutting operation in CNC oxy flame cutting machine and a rejection has been reduced from 11.87% to 1.92% on an average. A detailed experimental study has illustrated the effectiveness of the proposed methodology.

Application of root cause analysis in improvement of product quality and productivity

Root-cause identification for quality and productivity related problems are key issues for manufacturing processes. It has been a very challenging engineering problem particularly in a multistage manufacturing, where maximum number of processes and activities are performed. However, it may also be implemented with ease in each and every individual set up and activities in any manufacturing process. In this paper, root-cause identification methodology has been adopted to eliminate the dimensional defects in cutting operation in CNC oxy flame cutting machine and a rejection has been reduced from 11.87% to 1.92% on an average. A detailed experimental study has illustrated the effectiveness of the proposed methodology.

Entry, Descent, and Landing Communications for the2007 Phoenix Mars Lander

This paper addresses NASA's requirement on the 2007 Phoenix Mars Lander to provide spacecraft communications during entry, descent, and landing on Mars to allow the identification of probable root cause should any mission failure occur. The Phoenix mission launched on 4 August 2007 and will land on 25 May 2008 on the northern plains of Mars to conduct a three-month study of the Martian environment. The paper discusses the architectural trades in designing a communications link and surveys the entry, descent, and landing communications approaches taken by previous missions. It then discusses the Phoenix-specific constraints and degrees of freedoms and presents a novel and robust implementation approach to entry, descent, and landing communications. The overall methodology and conclusions described herein can serve as a pathfinder for the entry, descent, and landing communications architecture and implementation of future Mars landed missions.

Support Vector Machine-Based Method for Quality Characteristic Modeling

Root-cause identification and product quality characteristic modeling are key issues for improving product quality and the productivity of manufacturing process. The quality characteristic model, which indicates the influence of the dominate root-causes on the product quality characteristic, is the basis for quality control. In this paper an approach for quality characteristic modeling based on Support Vector Machine is presented. The selections of parameters and the Kernel function are discussed. The presented approach is applied for analyzing and predicting the quality of inertial gyroscope.

Potential problems in the identification of the root cause of superheater tube failures in 9Cr martensitic alloys

Potential problems in the identification of the root cause of superheater tube failures in 9Cr martensitic alloys

Statistical Estimation and Testing for Variation Root-Cause Identification of Multistage Manufacturing Processes

Root-cause identification for quality-related problems is a key issue in quality and productivity improvement for a manufacturing process. Unfortunately, root-cause identification is also a very challenging engineering problem, particularly for a multistage manufacturing process. In this paper, root-cause identification is formulated as a problem of estimation and hypothesis testing of a general linear mixed model. First, a linear mixed fault-quality model is built to describe the cause-effect relationship between the process faults and product quality. Then, the estimation algorithms developed for a general linear mixed model are adapted to estimate the process mean and variance. Finally, a hypothesis testing method is developed to determine if process faults exist in terms of statistical significance. A detailed experimental study illustrated the effectiveness of the proposed methodology.<br><br>Note to Practitioners-Economic globalization brings intense competition among manufacturing enterprises. The key to succeed in this competitive climate is to rapidly respond to fast-changing market demands with high-quality and competitively priced products. To achieve this, we need to quickly identify root causes of quality-related problems in a complicated manufacturing system. However, the current widely adopted quality-control techniques focus more on monitoring than on root-cause identification. These techniques can efficiently detect the changes in the process but the root cause identification is often left to the plant engineers or operators. In this paper, a systematic estimation and testing method is proposed to identify the variational root causes in multistage manufacturing processes. First, a linear model is built based on the design information to describe the cause-effect relationship between the process faults and product quality. Then, an algorithm is developed to estimate the mean and variance of the process faults from the quality measurements of products. Finally, a statistical testing method is developed to determine if process faults (i.e. root causes) exist in terms of statistical significance. A detailed experimental study illustrates the effectiveness of this method. The method presented in this paper is a new quality-control technique and can be used for quality improvement of multistage manufacturing processes.