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Siva Canagassababady - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, M. Munir Ahmad, Siva Canagassababady
    Abstract:

    Abstract The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks’ study after implementing changes, there was a 9% reduction in defects.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-Integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, Munir Ahmad, Siva Canagassababady
    Abstract:

    The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks' study after implementing changes, there was a 9% reduction in defects. ?? 2006 Elsevier Ltd. All rights reserved.

Nasreddin Dhafr - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, M. Munir Ahmad, Siva Canagassababady
    Abstract:

    Abstract The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks’ study after implementing changes, there was a 9% reduction in defects.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-Integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, Munir Ahmad, Siva Canagassababady
    Abstract:

    The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks' study after implementing changes, there was a 9% reduction in defects. ?? 2006 Elsevier Ltd. All rights reserved.

Brian Burgess - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, M. Munir Ahmad, Siva Canagassababady
    Abstract:

    Abstract The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks’ study after implementing changes, there was a 9% reduction in defects.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-Integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, Munir Ahmad, Siva Canagassababady
    Abstract:

    The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks' study after implementing changes, there was a 9% reduction in defects. ?? 2006 Elsevier Ltd. All rights reserved.

Munir Ahmad - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of quality performance in manufacturing organizations by minimization of production defects
    Robotics and Computer-Integrated Manufacturing, 2006
    Co-Authors: Nasreddin Dhafr, Brian Burgess, Munir Ahmad, Siva Canagassababady
    Abstract:

    The work in this paper will present a developed methodology for quality improvement in manufacturing organizations. This methodology comprises a model for the identification of various sources of quality defects on the product; this model would include an analysis tool in order to calculate defect probability, a statistical measurement of quality, and a lean manufacturing tool to prevent the presence of defects on the product. The attribution of defects to their source will lead to a fast and significant definition of the root cause of defects. The techniques described in this paper were developed for an improvement project in a plastic parts painting manufacturing facility of a first-tier supplier to the automotive industry. Data were collected from the manufacturing plant, which indicated that the daily defect rates were significant, ranging between 10% and 15%. These figures gave a clear indication that the number of defects could be significantly reduced to a few parts within the total production. This could be achieved if appropriate manufacturing practices were adopted with the aim of reducing the effect of manufacturing system variables that affect overall quality. A process Attribute Chart (H-PAC) has been introduced to monitor the defects every hour. Upper and lower control limits were given and an SPC graph is plotted every hour for the three major defects. If the defects go above the upper control limits, the team meets to solve the issues. Over ten weeks' study after implementing changes, there was a 9% reduction in defects. ?? 2006 Elsevier Ltd. All rights reserved.

Peter Worthington - One of the best experts on this subject based on the ideXlab platform.

  • why traditional statistical process control Charts for Attribute data should be viewed alongside an xmr Chart
    BMJ Quality & Safety, 2013
    Co-Authors: Mohammed A Mohammed, Peter Worthington
    Abstract:

    The use of statistical process control (SPC) Charts in healthcare is increasing. The general advice when plotting SPC Charts is to begin by selecting the right Chart. This advice, in the case of Attribute data, may be limiting our insights into the underlying process and consequently be potentially misleading. Given the general lack of awareness that additional insights may be obtained by using more than one SPC Chart, there is a need to review this issue and make some recommendations. Under purely common cause variation the control limits on the xmr -Chart and traditional Attribute Charts (eg, p -Chart, c -Chart, u -Chart) will be in close agreement, indicating that the observed variation ( xmr -Chart) is consistent with the underlying Binomial model ( p -Chart) or Poisson model ( c -Chart, u -Chart). However, when there is a material difference between the limits from the xmr -Chart and the Attribute Chart then this also constitutes a signal of an underlying systematic special cause of variation. We use one simulation and two case studies to demonstrate these ideas and show the utility of plotting the SPC Chart for Attribute data alongside an xmr -Chart. We conclude that the combined use of Attribute Charts and xmr -Charts, which requires little additional effort, is a useful strategy because it is less likely to mislead us and more likely to give us the insight to do the right thing.