本文選題：汽車零部件 + 需求預(yù)測(cè)��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：最近幾年新車的盈利前景不被看好,新品牌車輛的銷售更是令人擔(dān)憂,而汽車售后市場(chǎng)方面的業(yè)務(wù)逐漸發(fā)展,成為了汽車4S店的重點(diǎn)發(fā)展核心。本文以單個(gè)新品牌汽車4S店為出發(fā)點(diǎn),發(fā)現(xiàn)該4S店零部件需求預(yù)測(cè)不當(dāng)引發(fā)的一系列問(wèn)題。本文旨在選擇適合該汽車4S店售后零部件的分類方法和需求預(yù)測(cè)模型,解決零部件需求預(yù)測(cè)問(wèn)題,為該4S店零部件庫(kù)存管理提供數(shù)據(jù)和模型支持。通過(guò)實(shí)地調(diào)研,發(fā)現(xiàn)該4S店售后零部件庫(kù)存管理不當(dāng),經(jīng)驗(yàn)主義較為嚴(yán)重,分類方法不合理、需求預(yù)測(cè)不準(zhǔn)確,導(dǎo)致其存儲(chǔ)很多不必要的零部件,同時(shí)還會(huì)采購(gòu)一些不必要的零部件。部分零部件缺貨現(xiàn)象時(shí)有發(fā)生,零部件庫(kù)存積壓現(xiàn)象較為嚴(yán)重,長(zhǎng)期這樣會(huì)造成庫(kù)存停滯的惡性局面,降低客戶滿意度,降低該4S店的盈利能力,影響該4S店的發(fā)展�？紤]到汽車零部件隨機(jī)性強(qiáng)且需求波動(dòng)較大,而需求的波動(dòng)是影響庫(kù)存決策的關(guān)鍵,因此,本文希望從優(yōu)化售后零部件的分類和需求預(yù)測(cè)角度來(lái)改善A汽車4S店零部件的庫(kù)存管理現(xiàn)狀。本文在相關(guān)理論研究的基礎(chǔ)上,對(duì)需求預(yù)測(cè)分兩個(gè)階段進(jìn)行研究。第一階段利用數(shù)據(jù)包絡(luò)分析法與聚類分析相結(jié)合的方法對(duì)該4S店的售后零部件進(jìn)行分類,找出維修零部件中具有預(yù)測(cè)意義的關(guān)鍵備件,提高零部件分類的有效性和零部件管理的針對(duì)性。通過(guò)與原有分類方法做比較,證明了該分類方法的有效與合理。第二階段利用自適應(yīng)變異粒子群參數(shù)尋優(yōu)的最小二乘支持向量機(jī)算法預(yù)測(cè)該4S店零部件的需求量,提高該4S店汽車零部件需求預(yù)測(cè)準(zhǔn)確率,為售后零部件的庫(kù)存計(jì)劃與管理優(yōu)化提供科學(xué)的數(shù)據(jù)支持,為降低庫(kù)存及物流成本、減少庫(kù)存的滯后和積壓、提高售后服務(wù)質(zhì)量與客戶滿意度提供科學(xué)的理論支持。通過(guò)與BP神經(jīng)網(wǎng)絡(luò)模型和多元回歸分析得出的需求預(yù)測(cè)值進(jìn)行比較,分析并比較其誤差,驗(yàn)證了最小二乘支持向量機(jī)用于預(yù)測(cè)4S店汽車零部件需求量的相對(duì)準(zhǔn)確性。研究結(jié)果表明,該方法可以用于4S店進(jìn)行零部件需求預(yù)測(cè),進(jìn)而改善零部件庫(kù)存管理現(xiàn)狀。本文包括圖16幅,表13個(gè),參考文獻(xiàn)54篇。
[Abstract]:In recent years, the profit prospects of new cars have been low, the sales of new brand vehicles are even more worrying, and the development of after-sale business has become the core of the development of 4S stores. In this paper, a series of problems caused by improper forecasting of spare parts demand in 4S shop of a new brand automobile are found as the starting point. The purpose of this paper is to select the classification method and demand forecasting model suitable for the after-sale parts of the 4S shop, to solve the demand forecasting problem of the parts, and to provide data and model support for the inventory management of the 4Sshop parts. Through field investigation, it was found that the 4S store had improper inventory management of after-sale parts, more serious empiricism, unreasonable classification methods and inaccurate demand prediction, which led to the storage of many unnecessary parts. At the same time will also purchase some unnecessary parts. Part of the spare parts out of stock phenomenon occurs from time to time, parts inventory backlog phenomenon is more serious, such a long-term will lead to the vicious situation of inventory stagnation, reduce customer satisfaction, reduce the profitability of the 4S store, affect the development of the 4S store. Considering the randomness of automobile parts and the large fluctuation of demand, the fluctuation of demand is the key to the decision-making of inventory. This paper hopes to improve the inventory management status of the parts in 4S shop from the point of view of optimizing the classification of aftermarket parts and forecasting the demand. On the basis of relevant theoretical research, this paper studies demand forecasting in two stages. In the first stage, the method of data envelopment analysis and cluster analysis is used to classify the after-sale parts of the 4S shop, and to find out the key parts in the maintenance parts which have predictive significance. Improve the effectiveness of parts classification and the pertinence of parts management. By comparing with the original classification method, it is proved that the method is effective and reasonable. In the second stage, the least squares support vector machine (LS-SVM) algorithm is used to predict the demand of the 4S shop parts, and to improve the accuracy of the 4Sshop auto parts demand prediction. It provides scientific data support for after-sale parts inventory planning and management optimization, scientific theoretical support for reducing inventory and logistics costs, reducing inventory lag and backlog, improving after-sales service quality and customer satisfaction. Compared with BP neural network model and multivariate regression analysis, the error is analyzed and compared, and the relative accuracy of least square support vector machine (LS-SVM) in predicting the demand of automobile parts in 4Shop is verified. The results show that this method can be used to predict the demand of parts in 4S shop and improve the inventory management situation. This paper includes 16 figures, 13 tables and 54 references.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F426.471

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