AI-Driven Prediction of Equipment Availability in Production Systems for Supply Chain Reliability

Providing stable equipment availability is very essential for the overall efficiency of the supply chain. In this research, 11 different models of deep learning techniques, namely ANFIS, LSTM, GRU, Transformer, and DeepAR, are used for predicting the availability of equipment for the next day in the food processing industry. The research aims to improve the efficiency of the supply chain by integrating advanced predictive models and operation scheduling techniques. The proposed models are trained on three-year production data, considering Down Time, Idleness, and Production, and are used for predicting the value of Availability(t+1) using MAE and RMSE. The PyTorch LSTM model gives the best accuracy (MAE 0.0861, RMSE 0.1102) compared to other models, namely Darts LSTM and DeepAR. The research illustrates how artificial intelligence algorithms and predictive models can act as catalysts for optimized supply chain decision-making in Industry 4.0.