全球競爭時代的來臨，使得產品生命週期變短且震盪幅度加劇，產業的需求不再是容易預測的線性趨勢，因此企業生存與成功的關鍵，必須仰賴組織對於環境變動的調整能力。在生產管理初期，往往僅能獲得有限的資料，在這樣的限制下，傳統的預測技術並無法產生理想的結果，但是決策者取得與學習管理知識的迫切性卻無法迴避，所以，小樣本資料的預測是我們必須正視且解決的問題。
灰預測模型是目前小樣本預測的重要方法之一，但是卻因為其採用固定的建模方式，無法依照樣本特性修正模型，而限制了模型的應用。本研究透過趨勢潛力追蹤法分析資料行為，擷取資料的隱含資訊，並利用資料的趨勢潛力值，在灰色系統理論的架構下，發展一種具有良好適應性的灰預測模型，做為小樣本資料的預測工具。經過實例驗證，本研究所提出的方法不但能依照樣本特性建構適合的模型，同時更成功地提高了小樣本資料的預測準確度。

In the age of global competition, product life cycles are shortened and the volatility increases. Industrial demands no longer follows linear trend which could be forecasted relatively easily. As a consequent, enterprise to survival depends on its capability to accommodating changes in manufacturing environment. Due to the rareness of data in the early stages of manufacturing management, traditional prediction techniques hardly obtain ideal results. The urgency of learning and acquirement of the management knowledge is unavoidable for decision makers. Therefore, small-data-set forecasting cannot be ignored and should be taken seriously.
Nowadays the grey forecasting model is one of the important methods for small-data-set forecasting. But its application is limited because the way of building model is fixed and could not be modified by the characteristics of data. This research explores the extra information with data analysis through trend and potency tracking method, and uses the generation of trend and potency value of each datum to develop the adaptive grey forecasting model as a tool of small-data-set forecasting based on the theory of grey system. The example verification displayed that the presented method can establish the suitable model according to the characteristics of data, and also improve the forecasting precision of the small data sets.

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