面對不確定的顧客需求時，本研究提出一套決策支援系統，考慮不同發生機率下之可能情境。本系統考慮兩種輸入資料，一種是產品過去的需求資料，另一種則為經理人的主觀意見。本研究以層級分群流程處理需求資料，並以模糊隸屬函數及信賴區間的觀念表達為客觀隸屬函數（AMF）；同時透過訪問並量化決策者的意見為主觀隸屬函數（MMF）。隸屬函數可表達各需求點可能發生的程度，整合AMF與MMF為整合型隸屬函數（IMF）。各情境的機率經由一RFD法決定，RFD法改良自傳統的RFM法，R描述此情境上次出現時間，F描述此情境出現頻率，D描述此情境的偏移程度。總而言之，根據所提出之流程，即可經模糊邏輯推導出各種可能的情境，並經RFD法決定各種情境發生的機率。決策者可根據各可能情境發生的機率應用到不同的生產決策問題，如本研究已將結果應用到一生產系統之穩健規劃，即為示例。
為驗證模式的準確性，本研究利用本地一半導體科技公司的資料進行實證分析。在數值範例中，本研究推導出高中低三種需求狀況的情境，此情境以模糊理論的隸屬函數表達。隸屬函數可以看出各需求點隸屬於各情境的程度，相較於過去的研究，本研究提供更多資訊。經去模糊化的過程後，結合RFD法決定各情境機率。在預測高需求情境發生機率為59%的情況下，本研究的預測誤差為4.54%。結果顯示即使在數值範例相當不確定的情況下，本研究仍能得到相當好的結果。
本研究可應用於各種需要預測未來情境的企業，幫助推導出各種可能情境以協助決策者快速反應並做出一精準且有彈性的決策。本研究以穩健規劃為例說明所提出的方法如何將所得結果應用於穩健規劃模式，提供決策者更多有效的資訊。

In this study, we present a procedure to help Decision Maker (DM) realize the uncertain situations and provide a quantitative measure to project the DM’s perception to the situations. This procedure takes two types of input: historical data, and the DM’s subjective assessment. Based on this information, possible scenarios can be derived to describe and predict the uncertain situation. When the historical data are not sufficient, in this study, an Analytical Membership Function (AMF) is developed based on a hierarchical clustering procedure; whereas the DM’s perception can be derived from a dialogue with the concept of confidence interval and defined as a DM’s Membership Function (MMF). And then we combine them into a final Integrated Membership Function (IMF) to express the scenarios by an if-then operator. In each membership function, the membership degree expresses the degree of the point in interest belonging to each scenario. The probability of each scenario is determined by a Recency, Frequency, Deviation (RFD) method modified from Recency, Frequency, Monetary method (RFM) for application. In a word, a systematic approach to generate possible scenarios is proposed based on fuzzy set theory when the RFD method is incorporated to derive the probability of each scenario.
From a production case of an LED company, we have demonstrated that the generated scenarios can be used in a Robust Optimization (RO) model to help DM yield a robust solution. We have applied the procedure to an LED company and showed the promising results. The result of the illustration showed that this procedure generated three different scenarios of demand with high-, average-, low- demand in IMF. The scenarios could describe all situations of demand via membership function which is not only a better description than the past methodology when facing sparse data; but also give more information. The membership degree represents the degree of demand belonging to a scenario. We defuzzified each scenario from a fuzzy number into a scalar with probability decided by the RFD model. While our procedure predicted the right scenario with 59% of probability with 4.54% in error, the proposed procedure showed the promising results.
The proposed procedure can be applied to any enterprise that needs to predict the future situation with little historical data and help DM to make a flexible decision which is precise and with quick response. This study takes a robust optimization problem as an example to illustrate such application possibility.

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