Yield forecasting is a very important task to a semiconductor manufacturing factory. To enhance both the precision and accuracy of semiconductor yield forecasting, a fuzzy-neural system incorporating unequally important expert opinions is constructed in this study. In the proposed methodology, multiple experts construct their own fuzzy yield learning models from various viewpoints to predict the yield of a product. Besides, these expert opinions can also be considered unequally important. To aggregate these fuzzy yield forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy yield forecasts into a polygon-shaped fuzzy yield forecast, in order to improve the precision of yield forecasting. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy yield forecast and to generate a representative/crisp value, so as to enhance the accuracy. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, all approaches were applied to the practical data of three products in a real semiconductor manufacturing factory. According to experimental results, the proposed methodology improved both the precision and accuracy of semiconductor yield forecasting by 48% and 38%, respectively.

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As a major enabling factor for Industry 4.0, three‐dimensional (3D) printing faces technical and managerial concerns that may hinder its sustainable development. In this study, four technical challenges are reviewed as follows: time‐consuming 3D object design, limited types of usable materials, low precision, and low productivity. Seven managerial concerns are also discussed as follows: 3D object database management, intellectual property rights of 3D printing, business innovation, ubiquitous manufacturing, lean manufacturing, globalization and deglobalization, and feasibility evaluation and optimization. Then, this study asserts that technical challenges should be addressed to ensure the feasibility of a 3D printing application in a manufacturing context, whereas managerial concerns should be addressed to advance and optimize a 3D printing application. Based on the discussion, to maximize profit, a smart manufacturing system based on 3D printing should continually provide 3D objects of interest to customers, or join as many ubiquitous manufacturing networks as possible.