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Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network
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This study evaluated and predicted production yield in a plastic manufacturing company using artificial neural network modeling. Predictive tools that improve manufacturing efficiency can reduce material waste and off-specification plastic products that may contribute to environmental plastic pollution.
The study focused on the evaluation and prediction of a production yield in Finoplastika plastic manufacturing industry. The study investigates the need of prediction and continuous improvement of production plastic yield in manufacturing industries. The literature reveals the related research works in manufacturing industries and found a gap in application of predictive tools to appraise the plastic production yield in the case company. The use of artificial neural network serves as the method of data analysis applied to achieve the aim of this study. The application of artificial neural network for the predicted solutions of the response variables of 110mm waste plastic pipe, 20mm pressure plastic pipe, 50mm waste plastic pipe and 32mm pressure plastic pipe are 31149, 45171, 13412, and 12891 respectively. The results for predicted solutions are recommended to the case company and other plastic companies for their wider use and applicability in other to achieve their optimal results and to support decision making during, inventory system, production process, production planning and control.
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