本文選題：多源數(shù)據(jù)　切入點(diǎn)：供應(yīng)鏈管理　出處：《合肥工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：經(jīng)濟(jì)全球化和改革開放推動(dòng)了我國汽車工業(yè)的發(fā)展,促使新經(jīng)濟(jì)時(shí)代來臨。在新經(jīng)濟(jì)時(shí)代,居民的消費(fèi)由“住房消費(fèi)”轉(zhuǎn)向“汽車消費(fèi)”,市場(chǎng)競(jìng)爭(zhēng)也演化成供應(yīng)鏈之間的競(jìng)爭(zhēng)。在以需求驅(qū)動(dòng)為主的供應(yīng)鏈管理模式下,需求成為供應(yīng)鏈的起點(diǎn)和動(dòng)力源泉。實(shí)時(shí)、精準(zhǔn)的需求預(yù)測(cè)不僅有利于消費(fèi)者合理選擇購買時(shí)機(jī),也成為供應(yīng)鏈管理模式下的戰(zhàn)略性問題。傳統(tǒng)模式下,消費(fèi)者在進(jìn)行購買決策調(diào)研過程中主要通過自身經(jīng)驗(yàn)、公開信息、逛商場(chǎng)等方式進(jìn)行,供應(yīng)鏈的需求信息在傳遞過程中具有一定的時(shí)延和扭曲。這種模式下的需求預(yù)測(cè)方法由于數(shù)據(jù)信息匱乏、實(shí)時(shí)性較差,因此其預(yù)測(cè)準(zhǔn)確率也較低,牛鞭效應(yīng)的現(xiàn)象在供應(yīng)鏈中也經(jīng)常存在,影響供應(yīng)鏈整體的效率。互聯(lián)網(wǎng)的深入發(fā)展使得消費(fèi)者可以很方便地在網(wǎng)上進(jìn)行購前調(diào)研,網(wǎng)上行為成為消費(fèi)者購買決策過程中的重要環(huán)節(jié)。這使得捕獲多源的大數(shù)據(jù)來進(jìn)行預(yù)測(cè)成為可能,同時(shí)也帶來了一些數(shù)據(jù)選擇和融合方面的挑戰(zhàn)�；诋�(dāng)前背景,為應(yīng)對(duì)數(shù)據(jù)選擇和融合方面的挑戰(zhàn),本文以多源數(shù)據(jù)為基礎(chǔ),對(duì)供應(yīng)鏈管理模式下的品牌汽車需求預(yù)測(cè)進(jìn)行研究。首先,提出一種基于多源數(shù)據(jù)信息共享供應(yīng)鏈協(xié)同預(yù)測(cè)框架,對(duì)其運(yùn)作流程和層次劃分進(jìn)行論證分析。然后,進(jìn)一步提出基于多源數(shù)據(jù)的預(yù)測(cè)模型方法。將獲取的多源數(shù)據(jù)按照汽車品牌進(jìn)行分類、標(biāo)注、關(guān)聯(lián)和統(tǒng)計(jì),提取出數(shù)據(jù)指標(biāo)。通過因素選擇模型來分析、篩選關(guān)聯(lián)度較大的數(shù)據(jù)指標(biāo)作為模型的輸入,結(jié)合選擇后的多源數(shù)據(jù)指標(biāo),綜合考慮時(shí)間序列和多種影響因素來進(jìn)行品牌汽車需求預(yù)測(cè)研究。本文的研究成果,針對(duì)新背景下的數(shù)據(jù)選擇和融合問題,考慮到理論和實(shí)踐意義,提出了解決方案,為供應(yīng)鏈管理模式下的汽車需求預(yù)測(cè)提供了新的思路,對(duì)需求預(yù)測(cè)的研究具有一定積極意義。
[Abstract]:Economic globalization and reform and opening to the outside world have promoted the development of China's automobile industry and promoted the advent of a new economic era. Residents' consumption has changed from "housing consumption" to "automobile consumption", and market competition has evolved into competition between supply chains. In the demand-driven supply chain management mode, demand becomes the starting point and power source of supply chain. Accurate demand forecasting is not only helpful for consumers to choose the right time to purchase, but also becomes a strategic problem in the mode of supply chain management. In the traditional mode, consumers mainly disclose information through their own experience in the process of purchasing decision research. The demand information in the supply chain has some delay and distortion in the transmission process. Because of the lack of data information and the poor real-time performance, the forecasting accuracy of the demand prediction method in this mode is also low. Bullwhip effect often exists in the supply chain, which affects the overall efficiency of the supply chain. The further development of the Internet makes it convenient for consumers to conduct pre-purchase research on the Internet. Online behavior has become an important part of consumer buying decisions. This has made it possible to capture multiple sources of big data to make predictions, and it has also brought some data selection and fusion challenges. In order to meet the challenges of data selection and integration, this paper studies the demand forecasting of brand cars based on multi-source data. Firstly, a collaborative forecasting framework for supply chain based on multi-source data sharing is proposed. Then, a prediction model method based on multi-source data is put forward. The obtained multi-source data is classified, labeled, correlated and counted according to the automobile brand. The data index is extracted and analyzed by the factor selection model, and the data index with high correlation degree is selected as the input of the model, and the selected multi-source data index is combined with the selected multi-source data index. The research results of this paper, aiming at the problem of data selection and fusion under the new background, considering the theoretical and practical significance, put forward a solution. It provides a new idea for automobile demand forecasting under the supply chain management mode, and has certain positive significance to the research of demand forecasting.
【學(xué)位授予單位】：合肥工業(yè)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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