【摘要】：隨著市場和科學(xué)技術(shù)的快速發(fā)展，消費(fèi)者需求呈現(xiàn)出多樣化和細(xì)分化的趨勢，制造企業(yè)逐步轉(zhuǎn)化為多品種小批量的生產(chǎn)方式。紡機(jī)企業(yè)面臨著在滿足客戶多樣化需求的前提下，降低生產(chǎn)成本，減少浪費(fèi)，提高紡機(jī)產(chǎn)品質(zhì)量的巨大挑戰(zhàn)，訂單作為客戶需求的體現(xiàn)和企業(yè)的生命，研究在復(fù)雜多變的市場環(huán)境下對做出準(zhǔn)確的訂單預(yù)測可以有效的解決上述問題。 隨著信息化技術(shù)的發(fā)展，紡織機(jī)械制造企業(yè)通過采用信息管理系統(tǒng)，在數(shù)據(jù)庫中存儲了大量的銷售訂單歷史數(shù)據(jù)，本文依據(jù)企業(yè)的銷售數(shù)據(jù)，分析客戶需求的變化特征和影響因素，以此作為歷史訂單數(shù)據(jù)的研究切點(diǎn)，從而建立訂單預(yù)測模型。本文主要工作如下： （1）介紹了制造行業(yè)的背景和產(chǎn)品特點(diǎn)，在此基礎(chǔ)上詳細(xì)介紹了訂單預(yù)測的相關(guān)知識和研究現(xiàn)狀，，指出了常見訂單預(yù)測方法的不足，提出了面向客戶需求建立紡機(jī)企業(yè)的訂單預(yù)測模型。 （2）闡述了客戶需求與訂單的辯證關(guān)系，在紡機(jī)行業(yè)的歷史銷售數(shù)據(jù)規(guī)范完整的基礎(chǔ)上，分析了客戶需求對于訂單預(yù)測的重要意義，提出了從傳統(tǒng)時(shí)間序列預(yù)測方法轉(zhuǎn)為針對客戶需求的訂單預(yù)測方法。 （3）依據(jù)實(shí)時(shí)銷售數(shù)據(jù)，并結(jié)合紡機(jī)市場分析客戶需求的變動(dòng)特征，提出相關(guān)的客戶需求模糊影響因子，構(gòu)建一種新的基于客戶需求模糊影響因子的時(shí)間序列分解訂單預(yù)測方法，并對本文所采用的模型建立方法和預(yù)測原理進(jìn)行了相關(guān)的闡述。與此同時(shí)提出結(jié)合實(shí)際終端客戶訂單情況進(jìn)行對比分析，在對比分析的基礎(chǔ)上，對訂單預(yù)測方法作出評價(jià)。 （4）結(jié)合具體紡機(jī)企業(yè)，對訂單預(yù)測模型進(jìn)行軟件開發(fā)與驗(yàn)證。對紡機(jī)企業(yè)的訂單管理與預(yù)測系統(tǒng)進(jìn)行了需求和目標(biāo)分析，研究系統(tǒng)的框架和開發(fā)環(huán)境，并依據(jù)模型原理進(jìn)行功能結(jié)構(gòu)設(shè)計(jì)，最后在案例中對紡機(jī)企業(yè)的訂單管理與預(yù)測系統(tǒng)進(jìn)行實(shí)例應(yīng)用，為后續(xù)生產(chǎn)活動(dòng)的開展提供有效可靠的訂單信息。 最后對全文的研究內(nèi)容進(jìn)行總結(jié)，分析其存在的不足，并對課題的后續(xù)研究進(jìn)行展望。
[Abstract]:With the rapid development of market and science and technology, the consumer demand shows a trend of diversification and differentiation. Spinning machine enterprises are faced with the huge challenge of reducing production cost, reducing waste and improving the quality of spinning machine products under the premise of meeting the diversified needs of customers. Order is the embodiment of customer demand and the life of enterprises. The research can solve the above problems effectively by making accurate order forecasting in the complex and changeable market environment. With the development of information technology, textile machinery manufacturing enterprises store a large number of historical data of sales orders in the database by adopting information management system. The changing characteristics and influencing factors of customer demand are analyzed, which is used as the research point of historical order data, and then the order prediction model is established. The main work of this paper is as follows: (1) the background and product characteristics of manufacturing industry are introduced. On this basis, the related knowledge and research status of order forecasting are introduced in detail, and the shortcomings of common order forecasting methods are pointed out. In this paper, the order forecasting model of spinning machine enterprises is proposed to meet customer demand. (2) the dialectical relationship between customer demand and order is expounded. On the basis of the standardization and integrity of historical sales data of spinning machine industry, This paper analyzes the importance of customer demand to order forecasting, and puts forward an order forecasting method from traditional time series forecasting method to customer demand forecasting method. (3) according to the real time sales data, Based on the analysis of changing characteristics of customer demand in spinning machine market, a new forecasting method of time series decomposing order based on fuzzy influence factor of customer demand is proposed. The modeling method and prediction principle used in this paper are also expounded. At the same time, the paper puts forward a comparative analysis of the actual end-customer order, and evaluates the forecasting method of the order on the basis of the comparative analysis. (4) combined with the specific spinning machine enterprise, The order prediction model is developed and validated. The requirements and objectives of the order management and prediction system of spinning machine enterprises are analyzed, the framework and development environment of the system are studied, and the functional structure is designed according to the principle of the model. Finally, an example is given to the order management and prediction system of spinning machine enterprises, which provides effective and reliable order information for the subsequent production activities. Finally, the paper summarizes the research content, analyzes its shortcomings, and looks forward to the future research.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F426.81

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