Designs of control charts with optimal manpower deployment

被引:16
|
作者
Wu, Z. [1 ]
Shamsuzzaman, M.
Wang, Q.
机构
[1] Nanyang Technol Univ, Sch Mech & Prod Engn, Singapore 639798, Singapore
[2] Nanyang Technol Univ, Nanyang Business Sch, Singapore 639798, Singapore
关键词
quality control; production economics; statistical process control; control chart; manpower deployment; quality cost;
D O I
10.1080/00207540500478413
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article proposes an algorithm for managing and deploying manpower ( equipped with adequate measuring instrument) to a statistical process control (SPC) scheme so that the resultant control chart ( referred to as M-chart) minimizes the expected total cost Ctotal incurred in the implementation of SPC. Unlike in the economic chart designs, most input specifications required by the design of an M-chart can be easily determined by practitioners. The design of an M- chart also takes into account the probability distribution of the random process shifts. The results of an example and a comprehensive study show that the M- chart can reduce the total cost by about 66%, on average, compared with the conventional control charts. It is also found that, in most SPC schemes, the allocated manpower is far less than needed and, consequently, the total cost can be significantly reduced by making some managerial arrangement to increase SPC manpower. Some useful guidelines are provided in this article to aid the management and determination of the appropriate amount of manpower for a particular application. Even though the M- chart is discussed in detail only for the X chart detecting mean shifts, the general idea can be applied to many other charts ( e. g. the CUSUM and EWMA charts) and to monitoring both process mean and variance.
引用
收藏
页码:2119 / 2132
页数:14
相关论文
共 50 条
  • [41] A hybrid Nelder–Mead simplex and PSO approach on economic and economic-statistical designs of MEWMA control charts
    Farnaz Barzinpour
    Rassool Noorossana
    Seyed Taghi Akhavan Niaki
    Mohammad Javad Ershadi
    The International Journal of Advanced Manufacturing Technology, 2013, 65 : 1339 - 1348
  • [42] Efficient control charts for monitoring the process mean using different paired double ranked set sampling designs
    Noor-ul-Amin, Muhammad
    Tayyab, Muhammad
    Hanif, Muhammad
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2021, 50 (10) : 2211 - 2223
  • [43] A Review on Fuzzy Control Charts for Monitoring Attribute data
    Jahromi, Seyed Mojtaba Zabihinpour
    Saghaei, Abbas
    Ariffin, M. K. A.
    ADVANCED MANUFACTURING TECHNOLOGY AND SYSTEMS, 2012, 159 : 23 - +
  • [44] Process Monitoring Using Robust Regression Control Charts
    Neto, Eufrasio de Andrade Lima
    Rauber, Cristine
    Borges, Hozana Francielle do Nascimento
    Lima-Filho, Luiz Medeiros Araujo
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2025,
  • [45] Comparing quality measurements part 2: control charts
    Kottner, Jan
    Hauss, Armin
    PFLEGE, 2013, 26 (02): : 119 - 127
  • [46] Construction of Control Charts based on the concept of Taguchi method
    Sun, Xiaoxia
    Kanzaki, Norie
    Kanagawa, Akihiro
    ICIM 2006: PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANAGEMENT, 2006, : 28 - 32
  • [47] Markovchart: an R package for cost-optimal patient monitoring and treatment using control charts
    Balázs Dobi
    András Zempléni
    Computational Statistics, 2022, 37 : 1653 - 1693
  • [48] New memory-type dispersion control charts
    Ali, Rizwan
    Haq, Abdul
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2017, 33 (08) : 2131 - 2149
  • [49] Linking EWMA p Charts and the Risk Adjustment Control Charts
    Sparks, Ross
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2017, 33 (03) : 617 - 636
  • [50] Statistical process control using shewhart control charts with supplementary runs rules
    Koutras, M. V.
    Bersimis, S.
    Maravelakis, P. E.
    METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY, 2007, 9 (02) : 207 - 224