胡健斌

摘 要：針對(duì)汽車行業(yè)對(duì)質(zhì)量風(fēng)險(xiǎn)普遍依托定性評(píng)價(jià)和考核管理，缺乏定量評(píng)估和動(dòng)態(tài)監(jiān)測(cè)的有效方法的痛點(diǎn)，開(kāi)展供應(yīng)鏈質(zhì)量風(fēng)險(xiǎn)管控的研究?；诠?yīng)鏈鏈上節(jié)點(diǎn)企業(yè)運(yùn)營(yíng)過(guò)程的質(zhì)量風(fēng)險(xiǎn)具有相關(guān)性、積累性和傳遞性等特點(diǎn)，形成一套集成風(fēng)險(xiǎn)定義、采集、分析、評(píng)估、預(yù)警、應(yīng)對(duì)為一體的風(fēng)險(xiǎn)定向監(jiān)測(cè)與準(zhǔn)確預(yù)警的關(guān)鍵技術(shù)，包括質(zhì)量風(fēng)險(xiǎn)指標(biāo)體系、質(zhì)量風(fēng)險(xiǎn)算法模型、風(fēng)險(xiǎn)預(yù)警法則;整合和挖掘供應(yīng)鏈“產(chǎn)”、“銷”、“存”全過(guò)程的內(nèi)外部運(yùn)行數(shù)據(jù)，建立供應(yīng)鏈風(fēng)險(xiǎn)智能分析預(yù)測(cè)模型，開(kāi)發(fā)汽車供應(yīng)鏈質(zhì)量風(fēng)險(xiǎn)智能預(yù)警系統(tǒng)，利用信息技術(shù)實(shí)現(xiàn)數(shù)據(jù)采集、分析計(jì)算、評(píng)估匹配、預(yù)警應(yīng)對(duì)的自動(dòng)化智能監(jiān)測(cè)，突破原有的傳統(tǒng)預(yù)警技術(shù)的局限，實(shí)現(xiàn)供應(yīng)鏈質(zhì)量風(fēng)險(xiǎn)數(shù)據(jù)定量分析與趨勢(shì)預(yù)判，提高整車企業(yè)和零部件制造業(yè)的風(fēng)險(xiǎn)管控能力，降低質(zhì)量損失，為汽車行業(yè)的質(zhì)量風(fēng)險(xiǎn)管控提供經(jīng)驗(yàn)借鑒。

關(guān)鍵詞：汽車供應(yīng)鏈 風(fēng)險(xiǎn)預(yù)測(cè)模型 風(fēng)險(xiǎn)控制方法集 風(fēng)險(xiǎn)定向監(jiān)測(cè) 智能預(yù)警系統(tǒng)大數(shù)據(jù)

Development and Application of Intelligent Early Warning System for Quality Risk in Automobile Supply Chain

Hu Jianbin

Abstract：Aiming at the pain points that the automobile industry generally relies on qualitative evaluation and assessment and management of quality risks， and lacks effective methods for quantitative assessment and dynamic monitoring， research on supply chain quality risk management and control is carried out. Based on the characteristics of relevance， accumulation and transferability of quality risks in the operation process of node enterprises in the supply chain， a set of integrated risk definition， collection， analysis， evaluation， early warning， and response is formed to form a set of risk-oriented monitoring and accurate early warning. Key technologies include quality risk index system， quality risk algorithm model， and risk warning rules. The article integrates and mines the internal and external operating data of the entire supply chain "production"， "sales" and "storage"， and establishes a supply chain risk intelligent analysis and prediction model to develop an intelligent early warning system for quality risk in the automotive supply chain， and uses information technology to realize automatic intelligent monitoring of data collection， analysis and calculation， assessment matching， and early warning response， breaking through the limitations of the original traditional early warning technology， and realizing quantitative analysis and analysis of supply chain quality risk data， so as to improve the risk management and control capabilities of vehicle companies and parts manufacturing， reduce quality losses， and provide experience and reference for quality risk management and control in the automotive industry.

Key words：automotive supply chain， risk prediction model， risk control method set， risk-oriented monitoring， intelligent early warning system big data

在經(jīng)濟(jì)全球化的今天，汽車行業(yè)市場(chǎng)競(jìng)爭(zhēng)激烈，而汽車整車零部件成本占到整車成本近85%，供應(yīng)鏈的競(jìng)爭(zhēng)力成為了主機(jī)廠的核心競(jìng)爭(zhēng)優(yōu)勢(shì)，正如著名供應(yīng)鏈管理專家馬丁·克里斯托弗曾說(shuō)：“市場(chǎng)上只有供應(yīng)鏈而沒(méi)有企業(yè)，21世紀(jì)的競(jìng)爭(zhēng)不是企業(yè)和企業(yè)之間的競(jìng)爭(zhēng)，而是供應(yīng)鏈與供應(yīng)鏈之間的競(jìng)爭(zhēng)”。每個(gè)主機(jī)廠均擁有著龐大的供應(yīng)鏈體系，供應(yīng)鏈在主機(jī)廠經(jīng)營(yíng)活動(dòng)中的作用及影響越來(lái)越大，零部件質(zhì)量成為影響整車質(zhì)量水平的關(guān)鍵因素。汽車供應(yīng)鏈鏈上節(jié)點(diǎn)企業(yè)運(yùn)營(yíng)過(guò)程的不確定質(zhì)量風(fēng)險(xiǎn)，會(huì)對(duì)企業(yè)獲利、生存及發(fā)展產(chǎn)生影響。由于汽車制造業(yè)典型的供應(yīng)鏈?zhǔn)浇Y(jié)構(gòu)使得供應(yīng)鏈鏈上節(jié)點(diǎn)企業(yè)的質(zhì)量風(fēng)險(xiǎn)具有相關(guān)性、積累性和傳遞性等特點(diǎn)，因此汽車供應(yīng)鏈風(fēng)險(xiǎn)不僅指產(chǎn)品質(zhì)量本身，更指供應(yīng)商的制造能力、管理水平、變更控制等風(fēng)險(xiǎn)，而國(guó)內(nèi)外汽車領(lǐng)域?qū)?yīng)鏈質(zhì)量風(fēng)險(xiǎn)的研究集中在某個(gè)特定風(fēng)險(xiǎn)或供應(yīng)鏈管理過(guò)程的某個(gè)階段，普遍依托定性評(píng)價(jià)和考核管理，缺乏定量評(píng)估和動(dòng)態(tài)監(jiān)測(cè)的有效方法，對(duì)供應(yīng)鏈質(zhì)量風(fēng)險(xiǎn)的綜合研究較少，如何有效控制供應(yīng)鏈的質(zhì)量風(fēng)險(xiǎn)，確保產(chǎn)品質(zhì)量穩(wěn)定輸出，成為整車企業(yè)共同關(guān)注的課題。