The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform

Lingyau Chan - One of the best experts on this subject based on the ideXlab platform.

  • a two stage decision procedure for monitoring processes with low fraction Nonconforming
    European Journal of Operational Research, 2003
    Co-Authors: Lingyau Chan
    Abstract:

    Abstract Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction Nonconforming. To overcome this difficulty, one may develop models based on the number of Items inspected until r (⩾1) Nonconforming Items are observed. The cumulative count control chart (CCC-chart) is such an example. Like many other control charts, the CCC-charts suggested in the literature are one-stage control charts in which a decision is made when a signal for out of control appears. A CCC-chart with a small value of r requires less Items inspected in order to obtain a signal for out of control, but is less reliable in detecting shifts of p than a CCC-chart with a large value of r (because the standard deviation of the number of Items inspected in order to observe the rth Nonconforming Item, when divided by the mean, is proportional to 1/ r ). In the present paper, inspired by the idea of double sampling procedures in acceptance sampling, a two-stage CCC-chart is proposed in order to improve the performance of the one-stage CCC-chart. Analytic expressions for the average number inspected (ANI) of this two-stage CCC-chart is obtained, which is important for further studies of the chart. As an application of this result, an economic model is used to calculate the optimal values of probabilities of false alarm set at the first and second stages of the two-stage CCC-chart so that an expected total cost can be minimized.

Hamzic Zlatan - One of the best experts on this subject based on the ideXlab platform.

  • Development of an Optimization Model to Determine Sampling Levels
    'Emerald', 2016
    Co-Authors: Cudney, Elizabeth A., Qin Ruwen, Hamzic Zlatan
    Abstract:

    Purpose: As the complexity of the multi-component products increases the quality of these products becomes increasingly difficult to control throughout the supply chain. The first step to manufacturing a quality product is to ensure that the product components from suppliers meet specifications. Product quality can be controlled through sampling inspection of the components. The paper aims to discuss these issues. Design/methodology/approach: The model presented in this paper was developed to determine the optimal sampling levels for incoming lots containing parts for production and assembly of multi-component systems. The main objective of the model is to minimize the expected cost that is associated with a Nonconforming Item reaching assembly. Findings: In this research, the results showed that even with limited time available for inspection, performing sampling inspection significantly reduced the expected cost of a Nonconforming Item reaching assembly. The model, solved by the evolutionary algorithm, was able to provide a meaningful, near optimal solution to the problem. Originality/value: In this model the time available for inspection is limited, the distribution of defects is assumed to follow the binomial distribution, and the distribution of accepting the lot with defects follows the hypergeometric distribution. In addition, the inspection is considered to be accurate and, if a Nonconforming Item is found in the inspected sample, the entire lot is rejected. An example is given with real world data and the results are discussed as they relate to supply chain management and quality

  • Development of an optimization model to determine sampling levels
    Scholars\u27 Mine, 2013
    Co-Authors: Hamzic Zlatan
    Abstract:

    As the complexity of multi-component products increases the quality of these products becomes increasingly difficult to control. The first step to manufacturing a quality product is making sure that the components of the product meet specifications. Product quality can be controlled through sampling inspection of the components. Two models were developed in this research to determine the optimal sampling levels for incoming lots containing parts for production and assembly of multi-component systems. The main objective of the first model is to minimize the expected cost that is associated with a Nonconforming Item reaching assembly. In this model the time available for inspection is limited. The main objective in the second model is to minimize total cost, which includes the appraisal cost (inspection cost) and the cost associated with nonconformance reaching assembly. In this model the time available is not a constraint. The distribution of defects is assumed to follow the binomial distribution, and the distribution of accepting the lot with defects follows the hypergeometric distribution. In addition, the inspection is considered to be accurate and, if a Nonconforming Item is found in the inspected sample, the entire lot is rejected. An example is given with real world data and the results are discussed --Abstract, page iv

Champ, Charles W - One of the best experts on this subject based on the ideXlab platform.

  • Using Runs Rules to Monitor an Attribute Chart for a Markov Process
    'Informa UK Limited', 2012
    Co-Authors: Shepherd, Deborah K., Rigdon, Steve E., Champ, Charles W
    Abstract:

    For some repetitive production processes, the quality measure taken on the output is an attribute variable. An attribute variable classifies each output Item into one of a countable set of categories. One of the simplest and most commonly used attribute variables is the one that classifies an Item as either conforming or Nonconforming. A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. In this paper, a sequence of random variables, Xi , i = 1,2,… , is used to classify an Item as conforming or Nonconforming under a stationary Markov chain model and under 100% sequential sampling. The sequence of random variables to be plotted on the control chart is Yi , i = 1,2,… , where Y1 counts the number of conforming Items before the first Nonconforming Item and Yi , i = 2,3,… counts the number of conforming Items between the (i-1)th and the ith Nonconforming Items. In the literature, Yi is called the CRL (Conforming Run Length). The contribution of this paper to the literature is to present runs rules for an attribute control chart of this type. The efficiency of these charts is evaluated using the average run length (ARL) of the charts. The supplemental runs rules that are presented are two out of three values of Yi , i 1,2, falling below determined lower limits, four out of five values of Yi , i =1,2,…falling below determined lower limits, and eight out of eight values of Yi , i =1,2,… falling below determined lower limits

Ly Chan - One of the best experts on this subject based on the ideXlab platform.

  • A two-stage decision procedure for monitoring processes with low fraction Nonconforming
    'Elsevier BV', 2003
    Co-Authors: Tn Goh, Xie M, Cd Lai, Ly Chan
    Abstract:

    Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction Nonconforming. To overcome this difficulty, one may develop models based on the number of Items inspected until r (⩾1) Nonconforming Items are observed. The cumulative count control chart (CCC-chart) is such an example. Like many other control charts, the CCC-charts suggested in the literature are one-stage control charts in which a decision is made when a signal for out of control appears. A CCC-chart with a small value of r requires less Items inspected in order to obtain a signal for out of control, but is less reliable in detecting shifts of p than a CCC-chart with a large value of r (because the standard deviation of the number of Items inspected in order to observe the rth Nonconforming Item, when divided by the mean, is proportional to Full-size image (

Shepherd, Deborah K. - One of the best experts on this subject based on the ideXlab platform.

  • Using Runs Rules to Monitor an Attribute Chart for a Markov Process
    'Informa UK Limited', 2012
    Co-Authors: Shepherd, Deborah K., Rigdon, Steve E., Champ, Charles W
    Abstract:

    For some repetitive production processes, the quality measure taken on the output is an attribute variable. An attribute variable classifies each output Item into one of a countable set of categories. One of the simplest and most commonly used attribute variables is the one that classifies an Item as either conforming or Nonconforming. A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. In this paper, a sequence of random variables, Xi , i = 1,2,… , is used to classify an Item as conforming or Nonconforming under a stationary Markov chain model and under 100% sequential sampling. The sequence of random variables to be plotted on the control chart is Yi , i = 1,2,… , where Y1 counts the number of conforming Items before the first Nonconforming Item and Yi , i = 2,3,… counts the number of conforming Items between the (i-1)th and the ith Nonconforming Items. In the literature, Yi is called the CRL (Conforming Run Length). The contribution of this paper to the literature is to present runs rules for an attribute control chart of this type. The efficiency of these charts is evaluated using the average run length (ARL) of the charts. The supplemental runs rules that are presented are two out of three values of Yi , i 1,2, falling below determined lower limits, four out of five values of Yi , i =1,2,…falling below determined lower limits, and eight out of eight values of Yi , i =1,2,… falling below determined lower limits