Güç transformatörleri üretiminde adam-saat tahmini için Gaussian proses regresyon, destek vektör makineleri ve ANFIS modellerinin karşılaştırılması

Production times affect the product valuations because it is directly relevant to the production cost. Therefore, accurate estimation of the production times is one of the key problems to be able to make correct product valuations and pricing. The man-hour unit is widely used for this purpose and it is taken into consideration while making product valuations before tendering phase of projects. It is especially important in tailor-made production which generally has a labor-intensive manufacturing environment because each product is a different project. When the man-hours are predicted by experts instead of systematic tools derived from local data, they often result in incorrect predictions which affect the ex-factory cost of the product. If the incorrect prediction has a negative deviation from the actual man-hour, it may result in a reduction of the profit margin which is a serious problem for all kinds of businesses or if the man-hour prediction is over-calculated to prevent this problem then another problem emerges and the cost-effectiveness may be lost for customers in tendering. Hence, the prediction of the man-hour without excess under-estimations and over-estimations based on systematic methods in labor-intensive manufacturing environments is important to be able to keep the overall profitability of factories. There have been several kinds of research to overcome the problems resulting from incorrect expert estimations in different industries such as shipbuilding and the aircraft industry. Also, it has been researched under the title of "effort estimation" in the software development field. However, there has not been any study directed to the Power Transformer manufacturing which has a tailor-made production mentality. In this study, man-hour predictions in Power Transformers manufacturing have been studied based on data-driven methods and machine learning applications. The results showed that the proposed forecasting system can be a good alternative to the existing ones.