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L公司服務(wù)器配件需求預(yù)測研究

發(fā)布時(shí)間:2018-04-04 08:38

  本文選題:服務(wù)器 切入點(diǎn):配件 出處:《北京交通大學(xué)》2017年碩士論文


【摘要】:近年來,國家大力發(fā)展信息化建設(shè),我國信息產(chǎn)業(yè)實(shí)現(xiàn)跨越式發(fā)展,以金融、電信、交通、教育等為代表的重點(diǎn)行業(yè)對(duì)服務(wù)器的需求迅速增長。L公司為國內(nèi)知名的服務(wù)器制造商,以L公司為中心的服務(wù)器供應(yīng)鏈中,向上游供應(yīng)商采購服務(wù)器配件是影響公司效益的重要環(huán)節(jié)之一。本文將對(duì)L公司服務(wù)器配件需求預(yù)測予以研究,目的在于通過選擇更加符合L公司實(shí)際情況的配件分類和預(yù)測模型,使得服務(wù)器配件分類更加細(xì)致,需求預(yù)測結(jié)果更加準(zhǔn)確。L公司采購部門可以依照優(yōu)化后的預(yù)測結(jié)果,對(duì)服務(wù)器配件進(jìn)行采購。本文在相關(guān)理論研究的基礎(chǔ)上,分析了L公司服務(wù)器配件需求預(yù)測現(xiàn)狀,主要包括配件分類現(xiàn)狀和配件預(yù)測現(xiàn)狀。在分類問題上,L公司服務(wù)器配件存在分類交叉、客戶滿意度不高的問題。由于分類不合適,以及預(yù)測方法亟待改進(jìn),導(dǎo)致配件需求預(yù)測結(jié)果與實(shí)際值偏差較大。針對(duì)L公司服務(wù)器配件需求預(yù)測存在的問題,本文主要從兩個(gè)方面開展工作:一是對(duì)配件分類進(jìn)行優(yōu)化;二是在優(yōu)化配件分類的基礎(chǔ)上,對(duì)配件需求預(yù)測進(jìn)行優(yōu)化。在現(xiàn)狀分析的基礎(chǔ)上,本文建立了需求預(yù)測優(yōu)化的兩階段模型。第一階段對(duì)L公司服務(wù)器配件建立分類模型,在傳統(tǒng)帕累托分類的基礎(chǔ)上新增了訂單周期、采購時(shí)間和下單次數(shù)等影響因素,求解算法采用了 ID3算法。第二階段建立了ARIMA-BP神經(jīng)網(wǎng)絡(luò)模型,該模型在傳統(tǒng)的時(shí)間序列預(yù)測模型上,增加了對(duì)非線性特征數(shù)據(jù)的預(yù)測,使預(yù)測結(jié)果更加接近實(shí)際值。模型以優(yōu)化配件分類和采購需求的預(yù)測結(jié)果為目標(biāo),對(duì)L公司服務(wù)器配件進(jìn)行需求預(yù)測。本文結(jié)合L公司實(shí)際調(diào)研得到的數(shù)據(jù)對(duì)所建立的模型進(jìn)行求解。首先利用MATLAB軟件對(duì)第一階段服務(wù)器配件的分類模型進(jìn)行求解,得到更加細(xì)致的服務(wù)器配件分類結(jié)果和具有預(yù)測意義的配件種類;其次利用SPSS軟件求解ARIMA模型;利用MATLAB軟件求解BP神經(jīng)網(wǎng)絡(luò)。最后,將線性預(yù)測數(shù)據(jù)和非線性預(yù)測數(shù)據(jù)匯總得到優(yōu)化后的預(yù)測結(jié)果。根據(jù)模型結(jié)果,對(duì)L公司服務(wù)器配件采購需求預(yù)測優(yōu)化效果進(jìn)行分析。一方面檢驗(yàn)?zāi)P偷膶?shí)用性,另一方面驗(yàn)證優(yōu)化結(jié)果對(duì)服務(wù)器配件采購需求預(yù)測的改善作用。研究結(jié)果表明,優(yōu)化后的需求預(yù)測結(jié)果比優(yōu)化前的預(yù)測結(jié)果更加接近實(shí)際值。
[Abstract]:In recent years, the state has made great efforts to develop information construction, and the information industry in our country has developed by leaps and bounds to finance, telecommunications, transportation,Education and other key industries represented by the rapid growth of demand for servers. L is a well-known domestic server manufacturers, L company as the center of the server supply chain,Purchasing server accessories from upstream suppliers is one of the important links that affect the efficiency of the company.The purpose of this paper is to make the server accessories classification more detailed by selecting the fitting classification and forecasting model which is more in line with L company's actual situation.The demand forecast result is more accurate. L purchasing department can purchase the server accessories according to the optimized forecast result.On the basis of relevant theoretical research, this paper analyzes the current situation of L company server accessories demand forecasting, mainly including the status of accessories classification and accessories forecasting.In the classification of the company's server accessories there is a cross-classification, customer satisfaction is not high.Because the classification is not suitable, and the forecasting method needs to be improved urgently, the forecast result of spare parts demand deviates greatly from the actual value.Aiming at the problems of L company's server accessories demand forecasting, this paper mainly carries out the work from two aspects: one is to optimize the accessories classification; the other is to optimize the spare parts demand prediction on the basis of optimizing the accessories classification.Based on the analysis of the present situation, a two-stage model of demand forecasting optimization is established in this paper.In the first stage, the classification model of L company's server accessories is established. Based on the traditional Pareto classification, the influence factors such as order cycle, purchase time and order number are added. The ID3 algorithm is used to solve the problem.In the second stage, the ARIMA-BP neural network model is established. In the traditional time series prediction model, the prediction of nonlinear characteristic data is increased, and the prediction results are closer to the actual value.The model aims at optimizing the classification of accessories and forecasting the purchasing demand, and forecasts the demand of L company's server accessories.In this paper, the established model is solved by combining the data obtained from the actual investigation of L Company.First, the classification model of the first stage server accessories is solved by using MATLAB software, and the more detailed classification results of server accessories and the kinds of accessories with predictive significance are obtained. Secondly, the ARIMA model is solved by using SPSS software.BP neural network is solved by MATLAB software.Finally, the linear prediction data and the nonlinear prediction data are summarized to obtain the optimized prediction results.According to the result of the model, this paper analyzes the effect of forecasting and optimizing the purchasing demand of server accessories in L Company.On the one hand, the practicability of the model is tested; on the other hand, the optimization results are verified to improve the demand prediction of server accessories.The results show that the optimized demand forecasting results are closer to the actual values than those before the optimization.
【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)】:F274;F49

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