Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network

Akaolisa Chukwuebuka C., Iweriolor Sunday, Uzochukwukanma M. C., Ezeliora C. D., Umeh Maryrose N.
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Abstract

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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利用人工神经网络评估和预测塑料制造业的产量
这项研究的重点是评估和预测 Finoplastika 塑料制造业的产量。该研究探讨了预测和持续改进制造业塑料产量的必要性。文献揭示了制造业中的相关研究工作,并发现案例公司在应用预测工具评估塑料产量方面存在差距。为实现本研究的目的,采用了人工神经网络作为数据分析方法。应用人工神经网络对 110 毫米废塑料管、20 毫米压力塑料管、50 毫米废塑料管和 32 毫米压力塑料管的响应变量的预测解分别为 31149、45171、13412 和 12891。建议该案例公司和其他塑料公司更广泛地使用预测结果,以达到最佳效果,并在库存系统、生产过程、生产计划和控制过程中为决策提供支持。
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