Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Long-Term Demand Forecasting Using Agent-Based Model : Application on Automotive Spare Parts

Agent-Based Model을 활용한 자동차 예비부품 장기수요예측

  • Received : 2015.01.27
  • Accepted : 2015.02.27
  • Published : 2015.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

Keywords

References

  1. Achorn, E., Integrating agent-based models with quantitative and qualitative research methods. in Australian Association for Research in Education 2004 Conference Papers ACH04769, 2004.
  2. Croston, J.D., Forecasting and Stock Control for Intermittent Demands. Oper. Res. Q. 1970-1977, 1972, Vol. 23, No. 3, pp. 289-303. https://doi.org/10.1057/jors.1972.50
  3. Gutierrez, R.S., Solis, A.O., and Mukhopadhyay, S., Lumpy demand forecasting using neural networks. Int. J. Prod. Econ., 2008, Vol. 111, No. 2, pp. 409-420. https://doi.org/10.1016/j.ijpe.2007.01.007
  4. Hesselbach, J., Mansour, M., and Graf, R., Reuse of components for the spare parts management in the automotive electronics industry after end-of-production. in 9th CIRP International Seminar, Erlangen, Germany, 2002.
  5. Hong, J.-S., Ahn, J.-K., and Hong, S.-K., Development of the Forecasting Model for Parts in an Automobile. J. Korean Inst. Ind. Eng., 2001, Vol. 27, No. 3, pp. 233-238.
  6. Inderfurth, K. and Mukherjee, K., Decision support for spare parts acquisition in post product life cycle. Cent. Eur. J. Oper. Res., 2008, Vol. 16, No. 1, pp. 17-42. https://doi.org/10.1007/s10100-007-0041-z
  7. Kaki, A., Forecasting in End-Of-Life Spare Parts Procurement. Master's Thesis, Helsinki University of Technology, 2007.
  8. Kim, M.J., Lee, S.Y., Park, K.H., Park, W.Y., and Park, S.Y., Agent Oriented Software Modeling Methodology. J. KOREA Inf. Sci. Soc., 2000, Vol. 27, No. 10, pp. 1015-1027.
  9. Kwak, D.H. and Kim, S.B., Comparison of Parameter Estimation Methods for Weibull Distribution Using Interval Censored Data. Proc. Korean Soc. Qual. Manag., 2013, No. 1, pp. 102-103.
  10. Leven, E. and Segerstedt, A., Inventory control with a modified Croston procedure and Erlang distribution. Int. J. Prod. Econ., 2004, Vol. 90, No. 3, pp. 361-367. https://doi.org/10.1016/S0925-5273(03)00053-7
  11. Ministry of Transportation, Automotive Control Act Enforcement Rules, 2014.
  12. Ord, K., Snyder, R., and Beaumont, A., Forecasting the Intermittent Demand for Slow-Moving Items. Monash University, Department of Econometrics and Business Statistics, Monash Econometrics and Business Statistics Working Paper 12/10, 2010.
  13. Parunak, H.V.D., Savit, R., and Riolo, R.L., Agentbased modeling vs. equation-based modeling : A case study and users' guide. in Multi-agent systems and agent-based simulation, 1998, pp. 10-25.
  14. do Rego, J.R. and de Mesquita, M.A., Spare parts inventory control : a literature review, Producao, 2011, Vol. 21, pp. 656-666.
  15. Sherbrooke, C.C., Optimal inventory modeling of systems : multi-echelon techniques, 2004, Vol. 72.
  16. Snyder, R., Forecasting sales of slow and fast moving inventories. Eur. J. Oper. Res., 2002, Vol. 140, No. 3, pp. 684-699. https://doi.org/10.1016/S0377-2217(01)00231-4
  17. Syntetos, A.A. and Boylan, J.E., On the bias of intermittent demand estimates. Int. J. Prod. Econ., 2001, Vol. 71, No. 1, pp. 457-466. https://doi.org/10.1016/S0925-5273(00)00143-2
  18. Teunter, R.H., Syntetos, A.A., and Zied Babai, M., Intermittent demand : linking forecasting to inventory obsolescence. Eur. J. Oper. Res., 2011, Vol. 214, No. 3, pp. 606-615. https://doi.org/10.1016/j.ejor.2011.05.018
  19. Volpato, G. and Stocchetti, A., Managing product life cycle in the auto industry : evaluating carmakers effectiveness. Int. J. Automot. Technol. Manag., 2008, Vol. 8, No. 1, pp. 22-41. https://doi.org/10.1504/IJATM.2008.018766
  20. Wallstrom, P. and Segerstedt, A., Evaluation of forecasting error measurements and techniques for intermittent demand. Int. J. Prod. Econ., 2010, Vol. 128, No. 2, pp. 625-636. https://doi.org/10.1016/j.ijpe.2010.07.013
  21. Willemain, T.R., Smart, C.N., and Schwarz, H.F., A new approach to forecasting intermittent demand for service parts inventories. Int. J. Forecast., 2004, Vol. 20, No. 3, pp. 375-387. https://doi.org/10.1016/S0169-2070(03)00013-X

Cited by

  1. 포뮬러 형태 자작자동차의 경량화 및 주행 성능 향상을 위한 최적설계에 관한 연구 vol.18, pp.10, 2015, https://doi.org/10.5762/kais.2017.18.10.725
  2. 간헐적 수요예측을 위한 이항가중 지수평활 방법 vol.41, pp.1, 2015, https://doi.org/10.11627/jkise.2018.41.1.050
  3. 머신러닝 기법을 활용한 대용량 시계열 데이터 이상 시점탐지 방법론 : 발전기 부품신호 사례 중심 vol.43, pp.2, 2015, https://doi.org/10.11627/jkise.2020.43.2.033