Application of Combination Forecasting Model in Service Parts Demand of the PC Industry
Abstract
This paper puts forward combination-forecast model for service parts demand forecasting of PC firms, based on data analysis in the information system. The data need to collect and information system for demand forecasting is specifically designed. This paper holds the idea that correct selection of single forecasting model can improve the accuracy of the combination forecast model. Through in-depth analysis of service parts demand in PC enterprise, selecting regression analysis method based on failure rate, improved model of commercial enterprise existing prediction models, and grey prediction as single prediction model. Then, put forward the combination forecast model based on the least absolute value of error. Through the empirical analysis and verification, combination-forecasting model can improve the prediction accuracy, and can be used as spare parts demand forecasting tools.
Keywords
Service parts demand Data analysis Regression prediction Grey forecast combination forecast modelReferences
- Fu Y, Cai ZQ (2002) Research on spare part of logistic operation. China Logist Procure 22(1):28–30Google Scholar
- Kamath K Rajashree, TPM Pakkala (2002) A Bayesian approach to a dynamic inventory model under an unknown demand distribution. Computers & Operations Research, 29(4):403–422Google Scholar
- Lu SW, Liu WL (2008) Fast algorithm on least squares estimation. Appl Probabil Stat 24(6):621–629Google Scholar
- Sleptchenko A, Van der Heijden, Van Harten A (2005) Using repair priorities to reduce stock investment in spare part networks. European Journal of Operational Research, 163(3):733–735Google Scholar
- Theodore B, Trafalis F, Grant H (2007) Dynamic optimization of service part inventory control policy through applied data mining and simulation. University of Oklahoma Norman Campus, Oklahoma: 660 Parrington Oval, NormanGoogle Scholar
- Willemain TR, Smart CN, Schwarz HF (2004) A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20(3):375–387Google Scholar
- Willemain TR, Smart CN (2004) A new approach to forecasting intermittent demand for service parts inventories. Int J Forecasting 20(1):375–387CrossRefGoogle Scholar
- Xu JP, Wei J (2012) Comparing improvement strategies for inventory inaccuracy in a two-echelon supply chain. Eur J Oper Res 22(1):213–221CrossRefGoogle Scholar
- Yao Y (2008) Design and implementation of service parts requirement forecast system. Shanghai Jiaotong University, ShanghaiGoogle Scholar