Leveraging Natural Language Processing for Sustainable Supply Chain Optimization in Agriculture, Healthcare, and Asset Management

This chapter explores how Natural Language Processing (NLP) can be leveraged to optimize sustainable supply chains in agriculture, healthcare, and asset management. It highlights the transformative impact of advanced neural network models such as Feedforward Neural Networks (FFNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based architectures like BERT and GPT. By extracting meaningful insights from unstructured textual data—such as contracts, reports, and sensor feeds—these models support key applications like demand forecasting, risk assessment, inventory optimization, and regulatory compliance. The chapter emphasizes the role of attention mechanisms and pre-trained models in enhancing contextual understanding. Through NLP-driven innovation, organizations can achieve greater transparency, reduce waste, and advance ethical sourcing, contributing to long-term sustainability.