Material cost reduction using cutting optimization Pro5 and enhanced N-BEATS deep learning forecasting: a case study

Abstract

Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L and 120 L electric water heaters. That is, Cutting Optimization Pro5 was used to specify optimal sheet sizes and cutting plans, minimizing waste and reducing production costs. At the same time, the Enhanced N-BEATS deep learning model was implemented to forecast future production quantities. The optimization results showed a significant reduction in wasted materials and production costs, allowing lower product prices. Thus, the forecasting results proved that the performance of the Enhanced N-BEATS model was much better than that of SARIMAX and Simple Linear Regression since the model showed the lowest prediction errors (pMAPE value for the 120 L product is equal to 0.040 and nMSE is equal to 0.016, while pMAPE for the 80 L product equals 0.091, and nMSE equals 0.007). The results show that combining cutting optimization and deep learning can create an efficient decision-support framework to improve production efficiency, lower production costs, and enhance production planning in industrial environments.