نمودار کنترل و رویکرد افزایش توان تشخیص برای پایش قابلیت فرآیند توزیع لجستیک

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه مهندسی صنایع، دانشکدۀ فناوری‌های صنعتی، دانشگاه صنعتی ارومیه، ارومیه، ایران

2 دانشجوی دکتری، گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کارابوک، کارابوک، ترکیه

چکیده

قابلیت فرآیند یکنواختی و تکرارپذیری یک فرآیند را باتوجه به نیازمندی­های مشتری و مشخصات محصول اندازه­گیری می­کند. توسعه نمودارهای کنترل برای قابلیت فرآیند، راه جامع­تری برای پایش عملکرد فرآیند ارائه می­دهد. در این تحقیق، یک نمودار کنترل برای پایش قابلیت فرآیند فرآیندهایی که از توزیع لجستیک پیروی می­کنند، ارائه شده است. در این نمودار کنترل، بازده فرآیند براساس پارامترهای توزیع فرآیند پایش می­شود. همچنین، در این تحقیق رویکردی برای افزایش توان تشخیص نمودار کنترل پیشنهاد شده است. مزیت نمودار کنترل پیشنهادی توانایی آن در پایش همزمان پارامترهای توزیع فرآیند است. عملکرد نمودار کنترل از طریق آزمایش‌های شبیه‌سازی براساس شاخص‌های میانگین طول اجرا (ARL) و انحراف استاندارد طول اجرا(SDRL)  ارزیابی می‌شود. باتوجه به نتایج شبیه‌سازی، نمودار کنترل پیشنهادی می­تواند عملکرد مؤثری در تشخیص شرایط خارج از کنترل داشته باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Control Chart and Detection Power Enhancement Procedure for the Monitoring of the Logistic Distribution Process Capability

نویسندگان [English]

  • Kamyar Sabri-Laghaie 1
  • Sevda Janalipour 2
1 Associate Professor, Industrial Engineering Department, Faculty of Industrial Technologies, Urmia University of Technology, Urmia, Iran
2 PhD Student, Industrial Engineering Department, Faculty of Engineering, Karabuk University, Karabuk, Turkey
چکیده [English]

Process capability measures the uniformity and repeatability of a process with regard to customer requirements and product specifications. Developing control charts for the capability of a process offers a more comprehensive way to monitor the process performance. In this research, a control chart is developed for the process capability of processes that follow Logistic distribution. In this control chart, process yield is monitored based on the process distribution parameters. A procedure is proposed to enhance the detection power of the control chart. The advantage of the proposed control chart is its ability to simultaneous monitoring of process distribution parameters. The performance of the control chart is evaluated through simulation experiments based on the average run length (ARL) and standard deviation of run length (SDRL) indices. According to simulation results, the proposed control chart can effectively detect out-of-control conditions.

کلیدواژه‌ها [English]

  • Process Capability
  • Process Yield
  • Control Chart
  • Logistic Distribution
  • Process Monitoring
  • Montgomery, D.C. (2007). Introduction to statistical quality control. John Wiley & Sons.
  • Kane, V.E. (1986). Process capability indices. Journal of quality technology, 18(1): 41-52.
  • Chen, K.S., Yu, K.T., Sheu, S. (2006). Process capability monitoring chart with an application in the silicon-filler manufacturing process. International Journal of Production Economics, 103(2): 565-571.
  • Juran, J., Godfrey, A.B. (1999). Quality handbook. Republished McGraw-Hill, 173(8).
  • Pearn, W. (1998). New generalization of process capability index Cpk. Journal of Applied Statistics, 25(6): 801-810.
  • Bothe, D.R. (1992). A capability study for an entire product. ASQC Quality Congress Transactions.
  • Chen, K., Huang, M., Li, R.K. (2001). Process capability analysis for an entire product. International Journal of Production Research, 39(17): 4077-4087.
  • Yang, J., Meng, F., Huang, S., Cui, Y. (2019). Process capability analysis for manufacturing processes based on the truncated data from supplier products. International Journal of Production Research, 1-17.
  • Kocherlakota, S., Kocherlakota, K., Kirmani, S. (1992). Process capability indices under non-normality. International Journal of Mathematical and Statistical Sciences, 1(2): 175-210.
  • Kocherlakota, S., Kocherlakota, K. (1994). Confidence intervals for the process capability ratio based on robust estimators. Communications in Statistics-Theory and Methods, 23(1): 257-276.
  • Peng, C. (2010). Estimating and testing quantile-based process capability indices for processes with skewed distributions. Journal of Data Science, 8(2): 253-268.
  • Kantam, R., Rosaiah, K., Subba Rao, R. (2010). Estimation of process capability index for half-logistic distribution. International Transactions in Mathematical Sciences and Computer, 3(1): 61-66.
  • Panichkitkosolkul, W. (2012). Bootstrap confidence intervals of the difference between two process capability indices for half-logistic distribution. Pakistan Journal of Statistics and Operation Research, 878-894.
  • RRL, K. (2012). Acceptance sampling plans for percentiles based on the inverse Rayleigh distribution. Electronic Journal of Applied Statistical Analysis, 5(2): 164-177.
  • Aslam, M. (2018). Statistical monitoring of process capability index having one-sided specification under repetitive sampling using an exact distribution. IEEE Access, 6: 25270-25276.
  • Kuo, T.I., Chuang, T.-L. (2023). Process Capability Control Charts for Monitoring Process Accuracy and Precision. Axioms, 12(9): 857.
  • Wang, D.S., Yang, H. Y., Koo, T. Y. (2020). Variable sample size control chart for monitoring process capability index Cpm. International Journal of Industrial and Systems Engineering, 36(1): 32-48.
  • Tomohiro, R., Arizono, I., Takemoto, Y. (2020). Economic design of double sampling Cpm control chart for monitoring process capability. International Journal of Production Economics, 221: 107468.
  • Liao, M.-Y., Wu, C.W. (2023). Process capability monitoring and change-point analysis for S-type quality characteristic. Quality Technology & Quantitative Management, 1-20.
  • Spiring, F.A. (1995). Process capability: a total quality management tool. Total Quality Management, 6(1): 21-34.
  • Spiring, F.A. (1991). Assessing process capability in the presence of systematic assignable cause. Journal of Quality Technology, 23(2): 125-134.
  • Boyles, R.A. (1991). The Taguchi capability index. Journal of Quality Technology, 23(1): 17-26.
  • Sarkar, A., Pal, S. (1997). Process control and evaluation in the presence of systematic assignable cause. Quality Engineering, 10(2): 383-388.
  • Subramani, J. (2004). Application of systematic sampling in process control, statistics and applications. Journal of Society of Statistics, Computer and Applications (New Series), 1: 7-17.
  • Subramani, J. (2010). Process control in the presence of linear trend. Model Assisted Statistics and Applications, 5(4): 273-282.
  • [26] Chen, K., Huang, H., Huang, C.T. (2007). Control charts for one-sided capability indices. Quality & Quantity, 41(3): 413-427.
  • Subramani, J., Balamurali, S. (2012). Control charts for variables with specified process capability indices. International Journal of Probability and Statistics, 1(4): 101-110.
  • Ahmad, L., Aslam, M., Jun, C.H. (2016). The design of a new repetitive sampling control chart based on process capability index. Transactions of the Institute of Measurement and Control, 38(8): 971-980.
  • Liao, M.Y. (2016). Process capability control chart for non-normal data–evidence of on-going capability assessment. Quality Technology & Quantitative Management, 13(2): 165-181.
  • Aslam, M., Rao, G. S., AL-Marshadi, A. H., Ahmad, L., Jun, C. H. (2019). Control Charts Monitoring Process Capability Index Using Median Absolute Deviation for Some Popular Distributions. Processes, 7(5): 287.
  • Ahmad, L., Aslam, M., Jun, C.H. (2014). Designing of X-bar control charts based on process capability index using repetitive sampling. Transactions of the Institute of Measurement and Control, 36(3): 367-374.
  • Janalipour, S., Sabri-Laghaie, K., Noorossana, R. (2020). Control charts for process capability monitoring based on the probability distribution parameters of the quality characteristics. Journal of Industrial Engineering Research in Production Systems, 7(15): 339-353.