售后服務數據的運用模型

售后服務數據的運用模型

售后服務數據的運用模型
摘　要
本文以工廠提供的轎車某部件的千車故障數的數據表為研究對象,在分析了數據表的特征并剔除了表中不合理的數據后，根據數據的不同特點建立了多個模型，對其中3個生產批次的部件的質量狀況進行了預測，探討了如何改進數據表以進1步提高數據的有效性，并對廠家的質量管理提出了1系列建設性意見。
本文的主要工作有:
1對數據進行了分析，提出了原文中千車故障數的定義存在的幾種不合理性,并對其進行了修正,給出了更加合理的千車故障數的概念；
2 采用橫向最小2乘擬合與縱向卡爾曼濾波方法的聯合預測方法對數據表進行填充；
3 建立了灰色馬爾柯夫預測模型,并對0205批次使用月數18時﹑0306批次使用月數9時和0310批次使用月數12時的千車故障數進行了預測。
預測結果為:
0205批次使用月數18時的千車故障數為 79.65；
0306批次使用月數9時的千車故障數為  32.78；
0310批次使用月數12時的千車故障數為 12.57；
關鍵詞： 最小2乘法；灰色預測；馬爾柯夫鏈

 

Post-sale service data utilization model
Abstract
This paper factories car a 1000 car parts fault of a few data tables for study , After has analyzed the data sheet characteristic and has rejected in the table the unreasonable data, Has established many models according to the data different characteristic, Has carried on the forecast to 3 productions raid of part quality condition, How discussed improved the data sheet to further to enhance the data the validity, And gave a series of constructive comment to the factory quality control.
The main work:
1 on the data analysis, The original 1000 car fault the definition of several unreasonable, And its amendments, Is a more reasonable number of 1,000 cars the concept of fault.
2 horizontal and vertical least squares fitting method of Kalman filtering method of predicting the data sheet filler
3 Gray established three Markov chain model. 0205 installment also use a few of 18:00 ﹑ 0,306 installment on the use of several 0900 and 0310 installments on the use of a few 2:00 fault of a few thousand cars a prediction.
Forecast results:
0,205 batches of 18 for the use of 1,000 cars at 79.65 Fault
0,306 installment on the use of the 0900 number 1000 Trouble at 32.78
0,310 installment on the use of several 1000 12:00 Trouble at 12.57
4 after-use method to predict the test data
5 Finally, the re-tabulation of the proposals
Keywords: least squares; Gray forecast; markov

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