臺灣博碩士論文加值系統

傳統的管理預測系統往往僅根據過去歷史資料為基礎，用以預測未來事件發展之可能性與方向性，一旦外在動態環境（dynamic environment）產生不確定性變化（uncertain change），如東南亞金融風暴，便無法有效的即時快速反應（quick response just in time） 與預測出事件發生的軌跡。如此一來，企業體必須承擔極高的失敗風險機率及為此付出龐大的營運損失成本。
是故本文將以產品生命週期（product life cycle, PLC）為基礎架構，導入PLC供應鏈（supply chain）與PLC需求鏈（demand chain）之整合觀念。首先利用專家訪談法將消費者行為模式做定性分析，並將產品與市場特徵因素以變數型態（variable form）導入PLC預測模式中。另外，利用模式反轉解析（inverse computing）出隸屬消費者需求因子係數與生產者供應因子係數，並利用內部因子（internal factor）與外部因子（external factor）之轉折點（turning point）現象界定出產品生命週期區間。由於產品生命週期在不同階段時對消費者與生產者具有相互不同程度影響變化，故利用簡單迴歸法（regression method）求出隸屬各階段之內部特徵值與外部特徵值，代表各階段在消費者需求鏈與生產者供應鏈之特徵值。另外產品生命週期之成長過程應受到各階段特徵值不同程度之影響，故利用權重法（weight method）賦予不同程度之變化影響，可代表其真實世界當中產品銷售成長過程。最後將可在PLC各階段特徵值、PLC創新模仿係數（innovation & imitation coefficient）及PLC階段隸屬權重值上賦予不同策略性多目標（strategic multi-objective），對決策者經營企業及決策分析制訂上，成為強而有力之決策支援系統。

Traditional management prediction system is always only basic on past historical information. It is used to forecast possibilities and direction of future developing happenings. Once the uncertain change has come to dynamic environment such as Southeast Financial Storm, we can’t do effective quick response just in time and forecast the happenings. Therefore, enterprises have to take the extreme failure risks and pay much lost running cost.
Therefore, this article will be basic on product life cycle (PLC) and lead into the supply chain and demand chain of PLC. At first, we visit expert to analyze the Module of Consumers’ behavior and lead the feature factors of products and markets into the forecasting module of PLC with variable form. Besides, use inverse computing to analyze the requirement factor coefficient of consumers and supply factor of producers, and use the turning point of internal factor and external factor to analyze product life area. Because PLC has different effect and change to consumers and producers, we use regression method to gain internal and external characteristic value that belong to every phase and to indicate the characteristic value of every phase in consumer demand chain and producer supply chain. Moreover, the growing process of product life cycle should be affected differently by every phase characteristic value, so utilizing weighting method to give different kinds of effect. It can indicate product sale growth process in the real world. At last, we can give different strategic multi-object in each PLC characteristic value, PLC innovation and imitation coefficient, and PLC stage weight. Therefore, the policy-maker will become strong and powerful decision support system in running and managing business and enacting and analyzing policy.

第一章、緒論1
1.1 前言1
1.2 研究動機2
1.3 研究目的4
1.4 研究方向5
1.5 研究方法6
1.6 研究步驟7
1.7 論文章節架構8
第二章、文獻探討9
2.1 產品生命週期9
2.1.1 前言9
2.1.2 產品生命週期定義11
2.1.3 產品生命週期各階段研究25
2.1.4 產品生命週期的階段界定理論29
2.1.5 產品生命週期的爭議及限制34
2.2 商情供需環境預測理論36
2.2.1 前言36
2.2.2 企業經營環境與商情構面37
2.2.3 預測理論之發展41
2.2.4 預測的意義44
2.2.5 預測的分類45
2.2.6 預測的方法47
第三章、研究方法53
3.1 研究架構53
3.2 研究範圍55
3.3 消費者行為與需求鏈56
3.3.1 消費者行為56
3.3.2 影響消費者購買行為因素58
3.3.3 產品生命週期對消費者之影響61
3.4 生產者行為與供應鏈63
3.4.1 生產者行為63
3.4.2 生產者在產品生命週期階段行為66
3.3.3 不同型態產品生命週期之生產者行為69
3.5 多目標決策分析71
3.5.1 決策分析71
3.5.2 多目標規劃73
3.5.3 多目標規劃一般式76
3.6 預測模式建立77
3.6.1 預測模式雛形77
3.6.2 預測模式完整型78
3.6.3 預測子模式80
3.6.4 預測模式權重分析81
3.6.5 預測模式之應用85
3.6.6 預測模式導入步驟88
第四章、實證研究92
4.1 個案描述92
4.2 個案分析93
4.2.1 產品生命週期─歷史資料93
4.2.2 產品生命週期─階段區間界定作法94
4.2.3 產品生命週期─階段特徵係數作法101
4.2.4 產品生命週期─階段權重分配作法105
4.2.5 產品生命週期─階段模式整合作法108
第五章、結論與建議112
5.1 結論112
5.2 建議113
參考文獻114
附錄120

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