資料挖掘與關聯式分析在許多案例中都有成功的應用，而對於零售商來說，除了將關聯式分析的資訊應用在銷售策略上以獲得更好的利潤之外，若能在進行銷售預測時便考量此要素，也許能更有效的提升準確度；因此本研究提出考慮產品關聯性的銷售預測模型，除了希望能夠對預測有所幫助，此外也能瞭解產品關聯性對於銷售預測的影響程度。

本研究乃依據合理之外生變數預測值，應用統計方法及電腦快速、量大之特性，求得 一準確外生變數預測；俾便企業界據此作產銷協調、物料需求規劃、存貨管理之依據 。

由於隨著產品生命週期不斷的縮短，利用CPFR可幫助企業降低存貨管理的成本與提昇效率，然而有良好的銷售預測對降低存貨成本是極為重要的。因此，為了讓銷售預測更為準確，一個良好的例外管理機制，可以幫助企業有效率地辨識及處理銷售預測例外品項，對於銷售預測是有所幫助的。因此，本研究希望發展一個例外管理的風險評估模型，能夠讓企業在進行CPFR時銷售預測的例外管理活動時能夠了解組織需要承擔的風險來源有哪些，透過風險評估模型的計算可以事前估計因例外管理活動失效而造成銷售預測錯誤的機率有多高，進而作為企業進行例外管理活動之參考。本研究利用文獻探討及個案訪談從例外管理活動中篩選風險因子，並運用因素分析法及羅吉斯迴歸模型構建本研究之風險評估模型，以實際預測資料驗證本研究模型之正確性。

本篇目的---對於精實企業來說資訊預測的能力扮演舉足輕重的角色，如汽車製造商須要有可靠的資訊來完成各項重要的決策以保持企業競爭力，市場以及消費者的活動提供了新型態的資料可以透過現代科技來處理分，本篇論文希望從2008年至2016年整合的Google 搜尋趨勢資料來建構預測模型。 設計/方法論/方法---基於五階段消費者購買行為，此研究檢視整個過程中合適的Google關鍵字，並利用滯後變數模型和Google搜尋趨勢來驗證銷售和各種經濟變數之間的關係，預測的銷售會更進一步檢視其正確性。 結論與發現---用來檢視預測正確性的兩種最常見的方法指出Google搜尋趨勢可以作為有效的銷售預測依據，研究發現總體經濟變數和時間序列在預測上相較於Google搜尋趨勢在短期相對有效性小。 研究貢獻---僅有少許在汽車銷量預測上的研究將Google搜尋趨勢和合適的時間滯留列入考量，本篇研究提供消費者行為和銷售資料關係的新視角。 / Purpose – The role of forecasting in a lean enterprise is immense. It is crucial for car manufacturers to have reliable information about the future to make important decisions and stay competitive. Developing markets and consumers provide new types of data that demand modern approaches to be handled. This paper aims to create reliable forecasting models through integration of Google Trends data from 2008 to 2016. Design/methodology/approach – Building on the 5-stage-model of consumer buying behavior, the study identifies suitable Google keywords for this process. Autoregressive distributed lag models are used to examine the relationship between sales and macro-economic variables as well as Google Trends. Predicted sales are used to test for accuracy. Findings – Two most common evaluation measurements for forecasting accuracy suggest the use of Google Trends, as predictors for future sales, is outstanding. The finding concludes that macro-economic variables and seasonality are not as valuable as Google Trends in short-term, up to one year, forecasting. Value – Only little research on car sales forecasting takes Google Trends and their appropriate time lags into account. This analysis provides new insights into the linkage of consumer behavior and sales data.

協同規劃、預測與補貨(Collaborative Planning, Forecasting and Replenishment,CPFR)，在歐美經過一些企業的採用後已經有顯著的成效，目前國內已經有一些企業相繼採用或即將採用CPFR，期望能因此降低供應鏈作業成本及提升供應鏈作業績效，以提升企業競爭力。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同，且績效良好的的銷售預測具有關鍵的重要性，是管理決策與協同合作時的的重要依據；但是多數的企業並沒有一個結構化、系統化的預測流程及方法，而是各部門透過簡單時間序列方法、天真預測法或人為經驗法則估算需求，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。本研究結合時間序列、多元回歸模型與基因演算法發展出一個CPFR流程下之三階段混合預測方法，以買賣方直接之銷售資料、銷售計畫等資訊進行以「週」為單位之個別商品銷售預測。同時本研究中，亦以國內某製造業公司與其顧客（一國際大型零售連鎖店通路商）之產品銷售資料進行方法的驗證；實驗顯示，本研究所提出之預測方法之預測結果較Jeong等人(2002)所提結合多元回歸模型與基因演算法之二階段預測系統之預測結果佳；亦較傳統使用普通最小平方法求解之一般統計回歸方法預測結果佳。 / It has been verified in pilot projects by many European and American Corporations that Collaborative Planning, Forecasting and Replenishment (CPFR) can improve supply chain performance. Enterprises nowadays in Taiwan are implementing or going to implement CPFR, with hopes to reduce their supply chain operation cost, enhance logistic performance and increase their competition capability consequently. Under CPFR process and supply chain collaboration environment, a supply and demand both sides promised identical sales forecast with well forecasting performance for order decision making and cooperation is very important. Due to the dynamic complexities of both internal and external co-operate environment, many firms resort to qualitative, navie forecasting or other simple quantitative forecasting techniques and have many forecasts in their organization. However, these forecasting techniques lack the structure and extrapolation capability of quantitative forecasting models or without stable performance, while multi-forecasts providing different views of demand. Forecasting inaccuracies exist and typically lead to dramatic disturbances in sales order and production planning. This paper presents a hybrid forecasting model for sales forecasting requirements in CPFR. A three stage model is proposed that integrate the time series model, regression model and use genetic algorithm to determine its coefficients efficiently. Direct sales information and related planned events in both collaborated sides is used for individual product’s “week” sales forecasting. To verify this model, we experiment on two different products and produce forecasts with datum from one manufacturer in Taiwan and its international retailer. The results shows that the hybrid sales forecasting model has better forecasting performance than not only the causal-genetic forecasting model proposed by Jeong et al. (2002), but also ordinary regression model with no genetic training process.

現今市場競爭愈來愈激烈，迫使廠商紛紛至海外尋求產品消費市場，在跨國銷售的背景之下，需要有更多可以確定國家選擇、預測銷售及估計需求的方法。而其中可以滿足這些需求的方法之中，就是研究產品跨國擴散型態，藉以瞭解後進國家與領先國家中新產品如何擴散且會如何互相影響 (Douglas and Craig, 1992)。 在眾多的跨國產品中，本研究選擇好萊塢電影做為實證分析的對象。 經由集群分析，本研究發現（一）台灣高首週票房且口碑佳的電影，會遇到假日人潮、有很高的美國總票房、以及很高的美國首週票房；（二）美國影片在美國及台灣映演的每週票房趨勢有差異存在；（三）片商沒有做好影片在台灣映演的檔期歸劃；（四）三群電影中，在影片類型沒有明顯地區別。 經由十二個新產品銷售預測模型的建立：對數線性迴歸模式（LN-Regression Model）（不考慮新產品領先國擴散經驗）（以OLS估計）、卜瓦松迴歸模式(Poisson Regression Model) （不考慮新產品領先國擴散經驗）（以MLE估計）、負二項分配迴歸模式(Negative Binomial Distribution Regression Model) （不考慮新產品領先國擴散經驗）（以MLE估計）、Exponential Decay模式（以OLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、Exponential Decay模式（以OLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）、Exponential Decay模式＋層級貝氏迴歸模式（考慮新產品領先國擴散經驗）、Bass連續型擴散模式（以NLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗（以SUR估計）、Bass連續型擴散模式（以NLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗（以SUR估計）、Bass離散型擴散模式（以OLS估計）＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、Bass離散型擴散模式（以OLS估計）＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）、層級貝氏BASS離散型擴散模式＋迴歸方程式體系（不考慮新產品領先國擴散經驗）（以SUR估計）、層級貝氏BASS離散型擴散模式＋迴歸方程式體系（考慮新產品領先國擴散經驗）（以SUR估計）。本研究發現：（一）在考慮影響後進國的新產品擴散速度時，領先國的擴散經驗為絕對必要的考慮因子；（二）必須使用Bass連續型擴散模式做為建構新產品銷售預測模型的基礎；（三）必須使用Bass連續型擴散模式的NLS估計法估計Bass模型的創新係數p、模仿係數q及市場潛量m。

協同規劃、預測與再補貨(Collaborative Planning, Forecasting and Replenishment; CPFR)，是目前供應鏈管理下重要的討論議題；台灣近年來由於加入WTO與製造業外移使競爭壓力加劇，全球運籌需求提升，使廠商間的合作更加密切，且近年來企業資訊環境與基礎建設逐漸成熟，有助於協同商務之發展。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同且績效良好的銷售預測具有關鍵的重要性，是管理決策與協同合作時的重要依據；但是多數的企業並沒有一個結構化、有系統化的預測流程及方法，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。 在CPFR流程下，強調買賣雙方透過完整、即時資訊的交流，進行短期、單一銷售預測，以提供雙方後續訂單預測、訂單補貨等決策的依據。本研究利用演算法（類神經網路和演化策略法）找出更適合混合性預測架構的解釋變數，再以較適合於實數解之演化策略法於修改黃蘭禎（2004）的三階段之預測模型架構，最後採用實驗方法，進行模型績效驗證。 / Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. Because of the severer competition resulted from entrance into WTO and industry integration, cooperation between Taiwanese companies becomes more intensely; enterprises’ information environment and foundation construction attain to maturity also boost the development of collaboration business. In CPRF process and supply chain operation environment, it is critical that a good performance sale forecasting collaborated by both supplier and buyer sides, and it is also the basis of policy decision and collaboration. However, the majority of the companies lack for a structural and systematical forecasting process to proceed with a multi-points forecasting with different methods. This kind of sale forecasting is less of stable quality and is harder to provide the managers a reasonable statistics explanation. Under the CPRF process, both buyers and sellers are able to obtain the short-term and single sale forecasting by real time information communication. Furthermore, the follow-up order forecasting and replenishment strategy decision can be also established through this process. This research finds the variables that are more suitable to the mixed structure by usage of the algorithms, ANN and Evolution Strategy. And this research uses Evolution Strategy that is more suitable to real question to improve the mixed structure of Huang (2004). In the end, experimentation is adopted in order to verify the performance of the model.