DOI QR코드

DOI QR Code

Safety Critical I&C Component Inventory Management Method for Nuclear Power Plant using Linear Data Analysis Technic

  • 투고 : 2020.05.14
  • 심사 : 2020.06.15
  • 발행 : 2020.06.30

초록

This paper aims to develop an optimized inventory management method for safety critical Instrument and Control (I&C) components. In this regard, the paper focuses on estimating the consumption rate of I&C components using demand forecasting methods. The target component for this paper is the Foxboro SPEC-200 controller. This component was chosen because it has highest consumption rate among the safety critical I&C components in Korean OPR-1000 NPPs. Three analytical methods were chosen in order to develop the demand forecasting methods; Poisson, Generalized Linear Model (GLM) and Bootstrapping. The results show that the GLM gives better accuracy than the other analytical methods. This is because the GLM considers the maintenance level of the component by discriminating between corrective and preventive.

키워드

참고문헌

  1. N. M. Scala, "Spare parts management for nuclear power generation facilities," Prentice Hall ; Service Tech Press, 2016.
  2. NEI, "Materials and Services Process Description and Guideline," AP-908, Rev.2, 2003.
  3. F.Rahn, "Inventory Optimization in Support of the EPRI Work Process," EPRI, Final Report, Nov. 1998.
  4. J.-H. Park, et al., "Inventory Control of Spare Parts for Operating Nuclear Power plants."
  5. A. Wild, "Best practice in inventory management," 3rd Edition. New York: Routledge, 2018.
  6. T. L. O. Steven Nahmias, "Production and operations analysis," Waveland Press, Inc., 2015.
  7. T. R. Willemain, et al., "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, vol. 10, no. 4, Dec. 1994.
  8. J. D. Croston, "Forecasting and Stock Control for Intermittent Demands," Operational Research Quarterly, vol. 23, no. 3, Sep. 1972.
  9. A. A. Syntetos and J. E. Boylan, "On the bias of intermittent demand estimates," International Journal of Production Economics, vol. 71, no. 1, 2001.
  10. V. Varghese and M. D. Rossetti, "A Parametric Bootstrapping Approach to Forecast Intermittent Demand," 2008.
  11. IEC, "Spare parts provisioning," IEC 62550.
  12. G. A. Rob J Hyndman, "Forecasting: Principles and Practice," Monash University, Australia.
  13. F. A. Haight, "Handbook of the Poisson distribution," New York: Wiley, 1967.
  14. K. N. Amirkolaii, A. Baboli, M. K. Shahzad, and R. Tonadre, "Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI)," IFAC-Papers OnLine, vol. 50, no. 1, 2017.
  15. J. H. Bookbinder and A. E. Lordahl, "Estimation of Inventory Re-Order Levels Using the Bootstrap Statistical Procedure," IIE Transactions, vol. 21, no. 4, 1989.
  16. M.-C. Wang and S. Subba Rao, "Estimating reorder points and other management science applications by bootstrap procedure," European Journal of Operational Research, vol. 56, no. 3, 1992.
  17. T. R. Willemain, C. N. Smart, and H. F. Schwarz, "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, vol. 20, no. 3, 2004.
  18. Andrea Callegaro, "Forecasting methods for spare parts demand," University of Padua, Italy, 2010.
  19. K. Grace-Martin, "Assessing the Fit of Regression Models," The Analysis Factor, 08-Dec-2008. [Online]