ForecastGAN-LSTM A Hybrid Deep Learning Framework for Intelligent Production Forecasting in Smart Manufacturing Systems

Production forecasting serves as the backbone for optimum manufacturing operations, waste minimization, and timely decisions within intelligent factories. Actual manufacturing operational data, however often characterized by significant noise, non-linearity, and small quantities, renders reliable forecasting ever difficult. The current research work intends to make a step towards an intelligent forecasting mechanism, which would be able to capture theoretical and complex temporal patterns, for increased predictive accuracy, all in a dynamic manufacturing environment. Such practice differs from some old approaches of time-series modeling: here, this research introduces a new hybrid approach that combines LSTM networks with Generative Adversarial Networks (GANs). One inline activity of GANs is that they improve variability of the data while there remains noise reduction; LSTM further models the complex temporal dependency with an aim to better forecasting possibilities accuracy. The proposed system which is called GAN-LSTM ForecastNet has the generator that generates realistic sequences of production and the discriminator that tests the authenticity of the data. The cleaned data finally flow into an LSTM network for final production forecasts. Results present experimental validations realized on a real-world manufacturing dataset that demonstrated an increase of 12 to 18 percent improvements in forecast accuracy over classical LSTM and ARIMA models. Mean absolute percentage error (MAPE) for the GAN-LSTM model was found to be 5.2% on extra validation cases, highly outperforming the baseline models in both short and long terms, effectively demonstrating its generic prediction capability across various production conditions. The results on the combined application of both GANs and LSTM networks could create a great leap within the domain of production forecasting since they are highly accurate, very adaptable, and more resilient to data irregularities than other models. The model will be very suitable for real-time purposes in intelligent manufacturing systems towards data-driven operational excellence.