發(fā)布時間：2018-01-25 13:35

  本文關(guān)鍵詞： 代收貨款 EMS 物流配送 用戶行為分析 回歸分析 社區(qū)發(fā)現(xiàn) 信用分析　出處：《河北科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,代收貨款業(yè)務(wù)已經(jīng)成為EMS企業(yè)的主要業(yè)務(wù)增長點(diǎn)之一,代收貨款業(yè)務(wù)的特殊性使其在傳統(tǒng)的物流過程中附加了資金流。資金流的引入使得企業(yè)可以掌握大量的資金,但也加大了管理的難度。同時,各大物流企業(yè)代收貨款業(yè)務(wù)的競爭也越發(fā)激烈,各企業(yè)也分別從人員配置、系統(tǒng)安全、物流配送、人性化服務(wù)等方面改進(jìn)代收貨款業(yè)務(wù)模式。本文以提升代收貨款業(yè)務(wù)同城物流配送成功率為目標(biāo),主要做了如下三個工作:(1)分析了代收貨款的產(chǎn)生背景、發(fā)展現(xiàn)狀、主要的業(yè)務(wù)模式以及當(dāng)前的機(jī)遇和挑戰(zhàn)。確定以代收貨款業(yè)務(wù)中的用戶行為分析為出發(fā)點(diǎn),改善其同城配送的服務(wù)質(zhì)量。(2)從兩個方面對用戶行為進(jìn)行了分析,其一是用戶信用分析,通過對大量交易記錄的整理,采用神經(jīng)網(wǎng)絡(luò)模型對代收貨款過程中的三類主要的參與者(攬投員,發(fā)貨方和收件方)建立信用評價體系;其二是用戶趨勢分析,針對發(fā)貨方的發(fā)貨量,通過關(guān)鍵點(diǎn)檢測方法發(fā)現(xiàn)用戶發(fā)貨量的時序數(shù)據(jù)中的關(guān)鍵點(diǎn),分析關(guān)鍵點(diǎn)的成因以加深對業(yè)務(wù)規(guī)律的理解,通過線性回歸方法對用戶未來一段時間的發(fā)貨量進(jìn)行一定程度的預(yù)測,為響應(yīng)發(fā)貨變化提供依據(jù)。(3)以信用評級和趨勢預(yù)測為前提,本文構(gòu)建了代收貨款的用戶網(wǎng)絡(luò),并在此網(wǎng)絡(luò)上使用社區(qū)發(fā)現(xiàn)算法,挖掘針對攬投員的物流配送服務(wù)子網(wǎng)絡(luò),通過引入個性化的配送體系,改善了代收貨款服務(wù)質(zhì)量。結(jié)合具體案例,驗證了該方法的有效性。
[Abstract]:In recent years, the collection of loans on behalf of the business has become one of the main business growth points of EMS enterprises. The particularity of the collection of payment for goods makes it attach capital flow to the traditional logistics process. The introduction of capital flow enables the enterprise to master a large amount of funds, but also increases the difficulty of management. At the same time. The competition between the major logistics enterprises to collect the payment for goods is becoming more and more fierce, and the enterprises are also from the personnel configuration, system security, logistics distribution. This paper aims at improving the success rate of logistics distribution in the same city, mainly doing the following three work: 1) analyzing the background of the generation of the collection of goods. Development status, the main business models and current opportunities and challenges. To determine the collection of loans in the business of user behavior analysis as the starting point. Improve the service quality of distribution in the same city. 2) analyze the user behavior from two aspects, one is the user credit analysis, through a large number of transaction records collation. The neural network model is used to establish a credit evaluation system for the three main participants in the process of receiving payment for goods (bid takers, shippers and recipients). The second is the user trend analysis, through the key point detection method to find the key points in the timing data of the user shipments, and analyze the causes of the key points in order to deepen the understanding of the business law. Through the linear regression method to predict the future shipments of users to a certain extent, to provide a basis for responding to changes in the delivery of goods, based on credit rating and trend prediction as the premise. In this paper, we construct the user network of collecting payment, and use community discovery algorithm on this network to mine the sub-network of logistics distribution service for the bid takers, and introduce the personalized distribution system. The quality of service is improved. The effectiveness of this method is verified by a case study.
【學(xué)位授予單位】：河北科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F259.2

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