Predictive Analytics for Brick Production Using LSTM and Norton-Bass
Yen-Kun Lin, Shan-Hung Wu, Yuan-Ding Hu, Chih-An Chen, Ming-Lun Li, Chaio-Hsuan Teng · 2024
In the context of the United Nations Sustainable Development Goals (SDGs), “Environmental Sustainability and Resource Reuse” is considered a key indicator. As bricks are non-renewable resources, contemporary brick manufacturing incorporates recycled materials as an alternative to traditional raw materials. This transition transforms the traditional brick industry into a circular economy. This makes brick production distinct from the energy consumption associated with conventional raw materials. Simultaneously, it increases economic value. However, the incorporation of social sustainability into traditional inventory management such as paper-based and Enterprise Resource Planning (ERP) systems may lack supply chains. Increased use of customized recycled materials in bricks requires more complex manufacturing methods and makes inventory management more challenging. Moreover, the state-of-the-art brick ERP has complex user interfaces. Therefore, predictive analytics for brick production using LSTM and Norton-Bass was proposed in this study. LSTM and Norton-Bass models were used for demand forecasting, to produce decision-making and planning. Additionally, a user-friendly cloud platform is established to facilitate inventory management, especially for recycled materials. This ensures that the brick industry meets the requirements of the construction industry and aligns with the SDGs of “Environmental Sustainability and Resource Recycling” in the circular economy.