AI-Powered: Leveraging Teachable Machine for Real-time Scanner - Marcelly, Frizca Fellicita; Rianto, Yan (Institut Riset dan Publikasi Indonesia, 2025)
Effective inventory control is essential in optimizing profitability through cost control and efficiency expectations. Conventional inventory techniques frequently find it difficult to adjust to the fast-changing restaurant setting, resulting in surplus stock, inventory deficits, and unnecessary food waste. Nonetheless, a notable shift is approaching, as the incorporation of artificial intelligence (AI) may help address this issue. AI-powered inventory management systems help restaurants optimize stock levels, reduce waste, and predict demand more accurately, leading to improved efficiency and increased profitability. This study explores how AI-driven inventory management enhances efficiency, reduces waste, and automates restocking in the restaurant sector, with a particular focus on TastyGo's integration of Teachable Machine and TensorFlow Lite. The suggested solution uses picture recognition for real-time inventory tracking, and machine learning models to predict demand and replenishment automation. TastyGo can expedite supply chain management, save waste through predictive analytics, and improve its inventory by employing these AI techniques. This study shows how AI-driven solutions may boost decision-making, reduce food waste, and greatly increase operational efficiency, all of which can result in higher profitability. The findings highlight how AI technologies have the potential to revolutionize conventional inventory management systems in the restaurant industry.