• DocumentCode
    3528593
  • Title

    Research on improving prediction of demand for common components through aggregation effect—Simulation based on linear trend of ols method

  • Author

    Wei, Yu

  • Author_Institution
    Coll. of Eng., Nanjing Agric. Univ., Nanjing, China
  • Volume
    Part 3
  • fYear
    2011
  • fDate
    3-5 Sept. 2011
  • Firstpage
    1488
  • Lastpage
    1492
  • Abstract
    The shift the prediction object from the final product to the common component makes better use of the aggregation effect. In this study, the mutually independent demands for all the products with linear increasing trend is generated by the method of Monte Carlo Stochastic Simulation. Using the prediction method of ordinary least square, the comparison is made with two prediction results. One of which is to predict final products demand, then the total demand for common components of all the products is calculated based on the Bill of Materials, and the other is to predict the demand of common components directly. The following conclusion is drawn that the direct prediction of the demand for common components could take advantage of the aggregation effect more sufficiently. This study also discusses the influences of correlations of the product demands, the fluctuation degree, and the number of the common components, etc. on the aggregation effect.
  • Keywords
    Monte Carlo methods; bills of materials; demand forecasting; least squares approximations; stochastic processes; Monte Carlo stochastic simulation; OLS method; aggregation effect; bill of materials; demand prediction; ordinary least square; product demands; Bills of materials; Correlation; Fluctuations; Supply chains; Uncertainty; Washing machines; Aggregation Effect; Common Components; Demand Prediction; Stochastic Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-61284-446-6
  • Type

    conf

  • DOI
    10.1109/ICIEEM.2011.6035442
  • Filename
    6035442