Frizca Fellicita Marcelly, Yan Rianto

AI-Powered: Leveraging Teachable Machine for Real-time Scanner

Introduction

Ai-powered: leveraging teachable machine for real-time scanner . Leverage AI-powered inventory management for restaurants using Teachable Machine & TensorFlow Lite. Optimize stock, reduce waste, predict demand, and boost profitability with real-time scanning.

Abstract

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.

Review

This paper presents a timely and relevant exploration into leveraging AI for enhancing inventory control within the dynamic restaurant sector. The abstract clearly articulates the pressing need to move beyond conventional inventory techniques, which frequently lead to issues like surplus stock, deficits, and food waste. By proposing an AI-powered inventory management system, the study posits a compelling solution to optimize stock levels, improve demand prediction, and ultimately boost efficiency and profitability. The focus on integrating Teachable Machine and TensorFlow Lite for real-time scanning underscores a practical and accessible approach to applying advanced AI technologies to a common industry problem. The proposed solution outlines a methodical application of AI, specifically employing picture recognition for real-time inventory tracking and machine learning models for predictive demand and automated replenishment. The mention of "TastyGo" as a case study or specific context grounds the theoretical benefits in a practical application, suggesting a tangible demonstration of how these AI techniques can expedite supply chain management and reduce waste through predictive analytics. This focus on specific tools like Teachable Machine and TensorFlow Lite indicates an emphasis on developing an implementable and potentially scalable system, aiming to revolutionize decision-making and operational efficiency in a sector notoriously prone to inefficiencies. While the abstract effectively highlights the potential benefits and the AI technologies employed, a comprehensive review of the full paper would benefit from greater detail on the practical implementation and empirical validation. The abstract primarily describes a "suggested solution" and "explores how" AI enhances efficiency, which implies a conceptual or early-stage study. A stronger paper would present concrete experimental results, quantitative performance metrics (e.g., accuracy of demand prediction, actual percentage reduction in waste, ROI), and a thorough discussion of the methodology, including data collection, model training, and evaluation. Addressing potential challenges in integration, user adoption, and scalability across different restaurant sizes would also strengthen the practical applicability and overall impact of this promising research.

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