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回歸分析下的經(jīng)營(yíng)決策在澄西船廠的應(yīng)用研究

發(fā)布時(shí)間:2018-05-17 18:50

  本文選題:生產(chǎn)決策 + 船舶企業(yè) ; 參考:《江蘇科技大學(xué)》2015年碩士論文


【摘要】:船舶工業(yè)具有強(qiáng)大的綜合性,可以通過(guò)上下游行業(yè)的聯(lián)系對(duì)國(guó)民經(jīng)濟(jì)的發(fā)展產(chǎn)生巨大的推動(dòng)作用。根本原因在于船舶工業(yè)的核心技術(shù)包含了大型結(jié)構(gòu)件的加工裝配和對(duì)其他多種學(xué)科的綜合應(yīng)用。船舶工業(yè)涉及到的集成技術(shù)具有很強(qiáng)的延展性,除了能夠涉及制造船舶和船用設(shè)備,還可以擴(kuò)展到非船舶工業(yè)中去,滿足國(guó)民經(jīng)濟(jì)其他方面的發(fā)展。但受歐洲債務(wù)危機(jī)的拖累,船只的需求下降,加上船舶行業(yè)產(chǎn)能過(guò),F(xiàn)象的突出,國(guó)際航運(yùn)業(yè)遭受了嚴(yán)重的打擊。面對(duì)造船業(yè)利潤(rùn)下降、成本上升、訂單減少的局面,高利潤(rùn)的訂單已經(jīng)越來(lái)越少,低利潤(rùn)甚至負(fù)利潤(rùn)的訂單則逐日增多,利潤(rùn)的縮減導(dǎo)致船企不得不另尋出路,扭轉(zhuǎn)過(guò)去僅僅依靠毛利率來(lái)選擇訂單的思路,將視野投向與成本聯(lián)系更為緊密的邊際貢獻(xiàn)率。本文借助企業(yè)經(jīng)營(yíng)決策理論和變動(dòng)成本法理論,以回歸分析為基礎(chǔ)構(gòu)建了訂單定價(jià)模型,與特殊訂單的生產(chǎn)決策相結(jié)合,將澄西船廠作為研究實(shí)例,對(duì)訂單定價(jià)模型和特殊訂單的生產(chǎn)決策在澄西船廠的應(yīng)用等內(nèi)容進(jìn)行了深入研究。通過(guò)對(duì)相關(guān)成本理論概念和SPSS回歸分析方法的介紹,對(duì)船舶制造成本進(jìn)行分析,并運(yùn)用SPSS軟件對(duì)成本數(shù)據(jù)和訂單定價(jià)數(shù)據(jù)進(jìn)行逐步回歸分析和驗(yàn)證,從而推出船舶企業(yè)的訂單定價(jià)模型。結(jié)合訂單定價(jià)模型與邊際貢獻(xiàn)的相關(guān)理論,對(duì)船舶企業(yè)的特殊訂單進(jìn)行判定,詳盡分析出特殊訂單的生產(chǎn)決策模型。在此基礎(chǔ)上,結(jié)合澄西船廠的生產(chǎn)經(jīng)營(yíng)數(shù)據(jù),對(duì)訂單定價(jià)模型和特殊訂單生產(chǎn)決策模型在澄西船廠的應(yīng)用進(jìn)行了實(shí)例分析。借此希望可以對(duì)相關(guān)船舶企業(yè)在訂單生產(chǎn)決策上提供一些理論支持,減少訂單選擇的困難,改變企業(yè)的經(jīng)營(yíng)困境。
[Abstract]:The shipbuilding industry has a strong synthesis, which can promote the development of national economy through the connection of upstream and downstream industries. The fundamental reason is that the core technology of shipbuilding industry includes the machining and assembling of large structural parts and the comprehensive application of other disciplines. The integrated technology involved in the shipbuilding industry has a strong ductility. In addition to the shipbuilding and shipbuilding equipment, it can also be extended to the non-shipbuilding industry to meet the development of other aspects of the national economy. But the international shipping industry has been hit hard by the European debt crisis, falling demand for ships and overcapacity in the shipping industry. In the face of declining profits, rising costs and decreasing orders in the shipbuilding industry, the number of orders for high profits has become smaller and smaller, while orders for low and even negative profits are increasing day by day. As profits shrink, shipbuilding enterprises have to find another way out. This paper reverses the idea of only relying on gross profit margin to select orders, and focuses on the marginal contribution rate, which is more closely related to cost. In this paper, with the help of enterprise management decision theory and variable cost theory, based on regression analysis, an order pricing model is constructed, which is combined with the production decision of special orders. The Chengxi Shipyard is taken as an example. The application of order pricing model and production decision of special order in Chengxi Shipyard is studied. Through the introduction of related cost theory concept and SPSS regression analysis method, the ship manufacturing cost is analyzed, and the cost data and order pricing data are analyzed and verified step by SPSS software. Thus, the order pricing model of shipbuilding enterprises is put forward. Combined with the theory of order pricing model and marginal contribution, the special orders of shipbuilding enterprises are judged, and the production decision model of special orders is analyzed in detail. On this basis, combined with the production and management data of Chengxi Shipyard, the application of order pricing model and special order production decision model in Chengxi Shipyard is analyzed. It is hoped that this paper can provide some theoretical support to the shipbuilding enterprises concerned in the decision of order production, reduce the difficulty of order selection, and change the management difficulties of the enterprises.
【學(xué)位授予單位】:江蘇科技大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2015
【分類號(hào)】:F406.7;F426.474

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