| 摘要: | 摘 要 有機蔬果是農產品中較特殊的部份，其生產條件需予嚴格限制，而不同於慣行農法所種植之產品；故更需要在供需間尋求平衡。然而大部分的業者卻常憑直覺及經驗法則預測需求，以作為生產規劃的基礎，在物流作業上也缺乏整體面的流程規劃，這將影響生產策略及產期規劃，甚至供應鏈各環節中的存貨或供應。 本研究應用「灰色理論」，透過GM(1,1)的數列預測模型以其容易處理非線性問題、計算簡易、少數據之特性來預測有機農產之需求量，並與傳統常用的迴歸分析、時間序列的比較，根據結果探討這些預測方法的適用性，於實證中GM(1,1)模型的準確性及樣本數等的比較上，顯示均優於傳統方式。 另外，透過專家訪談，歸納出影響有機農產物流中心的關鍵成功因素，再以灰色關聯分析法排序出重要性的關聯程度。在局部灰關聯中，以經營最大效益為參考目標，管理者應首重物流作業構面；而在整體灰關聯結果則顯示經營者應重視產品的檢驗及生產過程應具有公信力的有機認證。 有效的預測方法配合資訊科技的應用，可以幫助管理者對於生產控制的適當決策，以降低供應鏈中需求變動中的風險，而成功因素的探討則可以定位出有機農產物流公司的競爭力及方向，做為管理者決策參考。 Abstract Organic vegetables are the exceptional products in the agriculture industry with many strict restrictions and regulations defined on the organic practice, which distinguishes it from the conventional agriculture and increases the production cost. In order to raise profit margin, the high production cost should be subsidized by a better price, which does require a well-balanced market demand and supply. However, most of organic vegetable producers rely on their intuition and previous experiences to forecast the market demand for the production plan without an integral and systematic production flow chain. Consequently, this kind of practice will result in a significant impact on the production strategy and management in every link of the supply chain, which is usually reflected on the reduction of profit. Modern information technology in conjunction with effective prediction methods could assist mangers on decision making in production control to meet market demands and significantly reduce the risks of loss resulted from over-production. Sequential Grey Prediction of GM (1,1) model based on “Grey Theory” posses the properties of easy processing in non-linear problems, simple calculation and requiring less data points for prediction, therefore, GM(1,1) model has been well adopted for forecasting the demand on organic agriculture products. In this study, we particularly applied Sequential Grey Prediction of GM (1,1) model to forecast the market demand on organic agriculture products while compared to two conventional methods, Regression Model and Time-Series Analysis. After ascertaining the suitability of each method according to the analyzed results, it demonstrated that GM (1,1) model could produce much more accurate predictions and require smaller sample size than Regression Model and Time-Series Analysis could. In addition to prediction model comparison, this study categorized the Key Success Factors (KSFs) of organic agriculture product logistics by surveying the experts in the organic production to permute the relational importance grades of KSFs. The results of local grey relational analysis showed that managers should pay great attention on logistics operation dimension; and, the results of global grey relational analysis indicated that managers should emphasize on inspecting the quality of products to establish the trust among consumers on their organic agriculture practice. The outcomes of KSFs study should benefit organic company owners and managers on setting operation policies and marketing strategies to well position themselves in a dynamic and highly competitive market. |
