AI-Powered: Leveraging Teachable Machine for Real-time Scanner
DOI:
https://doi.org/10.57152/malcom.v5i3.1931Keywords:
Artificial Intelligence, Inventory Optimization, Machine Learning, Teachable Machine, TensorFlow LiteAbstract
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.
Downloads
References
V. Zatsu et al., “Revolutionizing the food industry: The transformative power of artificial intelligence-a review,” Food Chem. X, vol. 24, p. 101867, 2024, doi: https://doi.org/10.1016/j.fochx.2024.101867.
D. L. Roberts and M. Candi, “Artificial intelligence and innovation management: Charting the evolving landscape,” Technovation, vol. 136, p. 103081, 2024, doi: https://doi.org/10.1016/j.technovation.2024.103081.
L. Lu, M. Hua, X. Sun, R. Zou, and B. Lin, “AI robots over sommeliers? Exploring the service provider effect on diners’ wine ordering decisions at restaurants,” Int. J. Hosp. Manag., vol. 122, p. 103879, 2024, doi: https://doi.org/10.1016/j.ijhm.2024.103879.
S. K. Jauhar, S. Sethi, S. S. Kamble, S. Mathew, and A. Belhadi, “Artificial intelligence and machine learning-based decision support system for forecasting electric vehicles’ power requirement,” Technol. Forecast. Soc. Change, vol. 204, p. 123396, 2024, doi: https://doi.org/10.1016/j.techfore.2024.123396.
M. Schmitt, “Automated machine learning: AI-driven decision making in business analytics,” Intell. Syst. with Appl., vol. 18, p. 200188, 2023, doi: https://doi.org/10.1016/j.iswa.2023.200188.
W. Villegas-Ch, A. M. Navarro, and S. Sanchez-Viteri, “Optimization of inventory management through computer vision and machine learning technologies,” Intell. Syst. with Appl., vol. 24, p. 200438, 2024, doi: https://doi.org/10.1016/j.iswa.2024.200438.
J. Gopal and M. Muthu, “Handheld portable analytics for food fraud detection, the evolution of next-generation smartphone-based food sensors: The journey, the milestones, the challenges debarring the destination,” TrAC Trends Anal. Chem., vol. 171, p. 117504, 2024, doi: https://doi.org/10.1016/j.trac.2023.117504.
M. JEBBAR, A. MAIZATE, and R. A. I. T. ABDELOUAHID, “Moroccan’s Arabic Speech Training And Deploying Machine Learning Models with Teachable Machine,” Procedia Comput. Sci., vol. 203, pp. 801–806, 2022, doi: https://doi.org/10.1016/j.procs.2022.07.120.
L. Wang, Y. Cui, J. Sun, J. Liu, D. Wei, and C. Gu, “Determinants of consumer adoption of multilingual self-service ordering systems in fast food restaurants,” Acta Psychol. (Amst)., vol. 245, p. 104216, 2024, doi: https://doi.org/10.1016/j.actpsy.2024.104216.
B. Apriyansyah, N. F. Malak, G. W. Bhawika, H. Agustin, and I. F. Himma, “The Effect of Orange Color Usage on the Brand Element of the Attributes of Shopee Food Driver Partners Towards Brand Personality,” Procedia Comput. Sci., vol. 234, pp. 909–919, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.079.
A. Bucher, E. S. Blazek, and C. T. Symons, “How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review,” Mayo Clin. Proc. Digit. Heal., vol. 2, no. 3, pp. 375–404, 2024, doi: https://doi.org/10.1016/j.mcpdig.2024.05.007.
S. Venkataramanan, A. K. R. Sadhu, L. Gudala, and A. K. Reddy, “Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems,” Aust. J. Mach. Learn. Res. Appl., vol. 4, no. 1, pp. 267–287, 2024.
I. Benito-Altamirano, D. Martínez-Carpena, H. Lizarzaburu-Aguilar, C. Fàbrega, and J. D. Prades, “Reading QR Codes on challenging surfaces using thin-plate splines,” Pattern Recognit. Lett., vol. 184, pp. 37–43, 2024, doi: https://doi.org/10.1016/j.patrec.2024.06.004.
J. Nunez-Yanez, A. Otero, and E. de la Torre, “Dynamically reconfigurable variable-precision sparse-dense matrix acceleration in Tensorflow Lite,” Microprocess. Microsyst., vol. 98, p. 104801, 2023, doi: https://doi.org/10.1016/j.micpro.2023.104801.
A. Derossi, E. Di Palma, J. A. Moses., P. Santhoshkumar, R. Caporizzi, and C. Severini, “Avenues for non-conventional robotics technology applications in the food industry,” Food Res. Int., vol. 173, p. 113265, 2023, doi: https://doi.org/10.1016/j.foodres.2023.113265.
M. Borhani, G. S. Gaba, J. Basaez, I. Avgouleas, and A. Gurtov, “A critical analysis of the industrial device scanners’ potentials, risks, and preventives,” J. Ind. Inf. Integr., vol. 41, p. 100623, 2024, doi: https://doi.org/10.1016/j.jii.2024.100623.
M. Mohd Ali and N. Hashim, “Development of deep learning based user-friendly interface for fruit quality detection,” J. Food Eng., vol. 380, p. 112165, 2024, doi: https://doi.org/10.1016/j.jfoodeng.2024.112165.
Z. Wu, A. Mohammed, and I. Harris, “Food waste management in the catering industry: Enablers and interrelationships,” Ind. Mark. Manag., vol. 94, pp. 1–18, 2021.
Y. He, Y. Gao, K. Liu, and W. Han, “Database, prediction, and antibacterial research of astringency based on large language models,” Comput. Biol. Med., vol. 184, p. 109375, 2025, doi: https://doi.org/10.1016/j.compbiomed.2024.109375.
R. K. Patil and C. Szocs, “Effects of bitten food images in marketing communications,” Appetite, vol. 200, p. 107566, 2024, doi: https://doi.org/10.1016/j.appet.2024.107566.
S. Phiphitphatphaisit and O. Surinta, “Multi-layer adaptive spatial-temporal feature fusion network for efficient food image recognition,” Expert Syst. Appl., vol. 255, p. 124834, 2024, doi: https://doi.org/10.1016/j.eswa.2024.124834.
N. Fogolari et al., “Influence of qualitative menu labeling on diners’ food choices: A controlled quasi-experiment in self-service buffet restaurants,” Appetite, vol. 203, p. 107698, 2024, doi: https://doi.org/10.1016/j.appet.2024.107698.
N. Veena, M. Prasad, S. Aruna Deepthi, B. Swaroopa Rani, M. Nayak, and S. Someshwar, “An Optimized Recurrent Neural Network for re-modernize food dining bowls and estimating food capacity from images,” Entertain. Comput., vol. 50, p. 100664, 2024, doi: https://doi.org/10.1016/j.entcom.2024.100664.
D. Elhani, A. C. Megherbi, A. Zitouni, F. Dornaika, S. Sbaa, and A. Taleb-Ahmed, “Optimizing convolutional neural networks architecture using a modified particle swarm optimization for image classification,” Expert Syst. Appl., vol. 229, p. 120411, 2023, doi: https://doi.org/10.1016/j.eswa.2023.120411.
Y. Liu, J. Xue, D. Li, W. Zhang, T. K. Chiew, and Z. Xu, “Image recognition based on lightweight convolutional neural network: Recent advances,” Image Vis. Comput., vol. 146, p. 105037, 2024, doi: https://doi.org/10.1016/j.imavis.2024.105037.
S. Liu, J. Zhao, H. Wang, R. Li, and Z. Lu, “Using Convolutional Neural Networks (CNN) to Realize Deep Recognition Analysis of Power Technology Standard Images,” Procedia Comput. Sci., vol. 243, pp. 323–331, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.040.
K. Filus and J. Doma?ska, “Software vulnerabilities in TensorFlow-based deep learning applications,” Comput. Secur., vol. 124, p. 102948, 2023, doi: https://doi.org/10.1016/j.cose.2022.102948.
M. F. Tuysuz, M. Ucan, and R. Trestian, “A real-time power monitoring and energy-efficient network/interface selection tool for android smartphones,” J. Netw. Comput. Appl., vol. 127, pp. 107–121, 2019, doi: https://doi.org/10.1016/j.jnca.2018.11.013.
N. Rajesh Mavani, M. A. Ismail, N. Abd Rahman, and J. Mohd Ali, “Fuzzy logic-based barcode scanning system for food products halal identification,” Food Control, vol. 168, p. 110926, 2025, doi: https://doi.org/10.1016/j.foodcont.2024.110926.
G. Califano and C. Spence, “Assessing the visual appeal of real/AI-generated food images,” Food Qual. Prefer., vol. 116, p. 105149, 2024, doi: https://doi.org/10.1016/j.foodqual.2024.105149.
L. Marty, F. Teil, C. Lange, V. Bellassen, and M. Visalli, “Development and validation of a web application to collect food supply data associated with their nutritional composition and environmental impacts,” MethodsX, vol. 13, p. 102891, 2024, doi: https://doi.org/10.1016/j.mex.2024.102891.
V. T. Le, Y.-H. Tseng, Y.-C. Liu, M. S. Malik, and Y.-Y. Ou, “VesiMCNN: Using pre-trained protein language models and multiple window scanning convolutional neural networks to identify vesicular transport proteins,” Int. J. Biol. Macromol., vol. 280, p. 136048, 2024, doi: https://doi.org/10.1016/j.ijbiomac.2024.136048.
J. C. Crane, F. W. Crawford, and S. J. Nelson, “Grid enabled magnetic resonance scanners for near real-time medical image processing,” J. Parallel Distrib. Comput., vol. 66, no. 12, pp. 1524–1533, 2006, doi: https://doi.org/10.1016/j.jpdc.2006.03.009.
Z.-M. Kuo, K.-F. Chen, and Y.-J. Tseng, “MoCab: A framework for the deployment of machine learning models across health information systems,” Comput. Methods Programs Biomed., vol. 255, p. 108336, 2024, doi: https://doi.org/10.1016/j.cmpb.2024.108336.
T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, pp. 24–49, 2021, doi: https://doi.org/10.1016/j.isprsjprs.2020.12.010.
Z. Li and Y. Sun, “Parameter efficient finetuning of text-to-image models with trainable self-attention layer,” Image Vis. Comput., vol. 151, p. 105296, 2024, doi: https://doi.org/10.1016/j.imavis.2024.105296.
S.-C. Huang and T.-H. Le, “Chapter 1 - Introduction to TensorFlow 2,” S.-C. Huang and T.-H. B. T.-P. and L. for D. L. Le, Eds., Academic Press, 2021, pp. 1–26. doi: https://doi.org/10.1016/B978-0-323-90198-7.00014-8.
M. L. Buchaillot et al., “Framework for deep learning diagnosis of plant disorders in horticultural crops: From data collection tools to user-friendly web and mobile apps,” Ecol. Inform., vol. 84, p. 102900, 2024, doi: https://doi.org/10.1016/j.ecoinf.2024.102900.
C. El Baou et al., “Effect of inhaled corticosteroid particle size on asthma efficacy and safety outcomes: A systematic literature review and meta-analysis,” BMC Pulm. Med., vol. 17, no. 1, Feb. 2017, doi: 10.1186/s12890-016-0348-4.
Y. M. Tang, W. T. Kuo, and C. K. M. Lee, “Real-time Mixed Reality (MR) and Artificial Intelligence (AI) object recognition integration for digital twin in Industry 4.0,” Internet of Things, vol. 23, p. 100753, 2023, doi: https://doi.org/10.1016/j.iot.2023.100753.
L. Madarász, L. A. Mészáros, Á. Köte, A. Farkas, and Z. K. Nagy, “AI-based analysis of in-line process endoscope images for real-time particle size measurement in a continuous pharmaceutical milling process,” Int. J. Pharm., vol. 641, p. 123060, 2023, doi: https://doi.org/10.1016/j.ijpharm.2023.123060.
R. Rosati, P. Senesi, B. Lonzi, A. Mancini, and M. Mandolini, “An automated CAD-to-XR framework based on generative AI and Shrinkwrap modelling for a User-Centred design approach,” Adv. Eng. Informatics, vol. 62, p. 102848, 2024, doi: https://doi.org/10.1016/j.aei.2024.102848.
P. Karadi, J. Amann, J. S. Bachiller, P. Seiler, and J. Wursten, “Price setting on the two sides of the Atlantic - Evidence from supermarket scanner data,” J. Monet. Econ., vol. 140, pp. S1–S17, 2023, doi: https://doi.org/10.1016/j.jmoneco.2023.05.009.
N. Vipond, A. Kumar, J. James, F. Paige, R. Sarlo, and Z. Xie, “Real-time processing and visualization for smart infrastructure data,” Autom. Constr., vol. 154, p. 104998, 2023, doi: https://doi.org/10.1016/j.autcon.2023.104998.
N. Zhou, B. Shang, J. Zhang, and M. Xu, “Modeling load distribution for rural photovoltaic grid areas using image recognition,” Glob. Energy Interconnect., vol. 7, no. 3, pp. 270–283, 2024, doi: https://doi.org/10.1016/j.gloei.2024.06.002.
X. Tang, B. Long, and L. Zhou, “Real-time monitoring and analysis of track and field athletes based on edge computing and deep reinforcement learning algorithm,” Alexandria Eng. J., vol. 114, pp. 136–146, 2025, doi: https://doi.org/10.1016/j.aej.2024.11.024.
Y. Liang et al., “AI nutritionist: Intelligent software as the next generation pioneer of precision nutrition,” Comput. Biol. Med., vol. 178, p. 108711, 2024, doi: https://doi.org/10.1016/j.compbiomed.2024.108711.
T. Gu, J. Wang, D. Tang, J. Wang, and X. Jiang, “A novel reconstruction method with robustness for polluted measurement dataset,” Adv. Eng. Informatics, vol. 62, p. 102834, 2024, doi: https://doi.org/10.1016/j.aei.2024.102834.
M. N. A. Ramadan, M. A. H. Ali, S. Y. Khoo, and M. Alkhedher, “AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging,” Results Eng., vol. 24, p. 103277, 2024, doi: https://doi.org/10.1016/j.rineng.2024.103277.
A. Chauhan, A. Sharma, and R. Mohana, “A Pre-Trained Model for Aspect-based Sentiment Analysis Task: using Online Social Networking,” Procedia Comput. Sci., vol. 233, pp. 35–44, 2024, doi: https://doi.org/10.1016/j.procs.2024.03.193.
G. dos Santos de Souza, F. J. Lanferdini, F. E. Puntel, C. E. Rossato, C. B. Mota, and A. F. da Silveira, “Modified Star Excursion Balance test: Prototype development, correction, and reliability of measurements,” J. Bodyw. Mov. Ther., vol. 40, pp. 2053–2061, 2024, doi: https://doi.org/10.1016/j.jbmt.2024.10.071.
C. N. Udanor et al., “An internet of things labelled dataset for aquaponics fish pond water quality monitoring system,” Data Br., vol. 43, p. 108400, 2022, doi: https://doi.org/10.1016/j.dib.2022.108400.
J. Zeng and X. Jia, “Systems Theory-Driven Framework for AI Integration into the Holistic Material Basis Research of Traditional Chinese Medicine,” Engineering, vol. 40, pp. 28–50, 2024, doi: https://doi.org/10.1016/j.eng.2024.04.009.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Frizca Fellicita Marcelly, Yan Rianto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright © by Author; Published by Institut Riset dan Publikasi Indonesia (IRPI)
This Indonesian Journal of Machine Learning and Computer Science is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
