Transformação digital no varejo: uma análise das tensões gerenciais no supermercado inteligente (2020-2025)

Autores

DOI:

https://doi.org/10.18800/360gestion.202611.003

Palavras-chave:

Inteligência artificial, Supermercados inteligentes, Varejo automatizado, Aprendizado de máquina

Resumo

A transformação digital do varejo é um dos processos de mudança organizacional mais rápidos da economia atual. No entanto, a literatura de gestão sobre esse fenômeno permanece fragmentada, uma vez que os estudos analisam separadamente a eficiência operacional, a experiência do cliente e a governança das tecnologias emergentes. Para preencher essa lacuna, foi realizada uma revisão sistemática da literatura com síntese narrativa, examinando 41 estudos publicados em cinco bases de dados acadêmicas entre 2020 e 2025. A análise destaca três tensões centrais de gestão: uma lacuna entre a maturidade técnica dos sistemas de IA e as capacidades organizacionais dos varejistas para operá-los de forma sustentável; um paradoxo da personalização, no qual os sistemas de recomendação algorítmica aprimoram a experiência do cliente, mas prejudicam a autonomia e criam riscos regulatórios; e a exclusão digital, que reproduz desigualdades de acesso entre os consumidores. A contribuição teórica é um quadro integrativo que posiciona o supermercado inteligente como um contexto privilegiado para estudar as tensões não resolvidas da transformação digital nos serviços ao consumidor.

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Referências

Abella, V., Initan, J., Perez, J. M., Astillo, P. V., Cañete, L. G., Jr. & Choudhary, G. (2024). Machine learning-assisted dynamic proximity-driven sorting algorithm for supermarket navigation optimization: a simulation-based validation. Future Internet, 16(8), 277. https://doi.org/10.3390/fi16080277

Ala, A., Sadeghi, A. H., Deveci, M. & Pamucar, D. (2024). Improving smart deals system to secure human-centric consumer applications: internet of things and Markov logic network approaches. Electronic Commerce Research, 24, 771-797. https://doi.org/10.1007/s10660-023-09787-1

Albayrak, Ö., Erkayman, B. & Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: a systematic review of the literature. Archives of Computational Methods in Engineering, 30, 2605-2625. https://doi.org/10.1007/s11831-022-09879-5

Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088

AlQahtani, A. A. S., Darrat, A. A., Turpin, L. & Alshayeb, T. (2025). Smart shelves: transforming retail stocking with internet of things and machine learning. Journal of Umm Al-Qura University for Engineering and Architecture, 16, 1864-1880. https://doi.org/10.1007/s43995-025-00213-1

Basheer, S., Vivekanadan, S., Panchatcharam, P. & Gandhi, U. D. (2022). Internet of Things-based automated shopping cart incorporated with virtual instrumentation using LabVIEW for control applications. International Journal of Grid and High Performance Computing, 14(1), 1-18. https://doi.org/10.4018/IJGHPC.301593

Batz, A., D’Croz-Baron, D. F., Vega Pérez, C. J. & Ojeda-Sanchez, C. A. (2025). Integrating machine learning into business and management in the age of artificial intelligence. Humanities and Social Sciences Communications, 12(235). https://doi.org/10.1057/s41599-025-04361-6

Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quarterly, 24(1), 169-196. https://doi.org/10.2307/3250983

Canhoto, A. I. & Clear, F. (2020). Artificial intelligence and machine learning as business tools: a framework for diagnosing value destruction potential. Business Horizons, 63(2), 183-193. https://doi.org/10.1016/j.bushor.2019.11.003

Chen, S., Choubey, B. & Singh, V. (2021). A neural network based price sensitive recommender model to predict customer choices based on price effect. Journal of Retailing and Consumer Services, 61, 102573. https://doi.org/10.1016/J.JRETCONSER.2021.102573

Chen, C. -H., Chung, C. -R., Yang, H. -Y., Yeh, S. -C., Wu, E. H. -K. & Ting, H. -J. (2024). Virtual-reality-based supermarket for intellectual disability classification, diagnostics, and assessment. IEEE Transactions on Learning Technologies, 17, 404-412. https://doi.org/10.1109/TLT.2023.3261314

Coeckelbergh, M. (2023). Democracy, epistemic agency, and AI: political epistemology in times of artificial intelligence. AI and Ethics, 3, 1341-1350. https://doi.org/10.1007/s43681-022-00239-4

Farahani, M. K., Yazdi, M., Talaei, M. & Ghahnavieh, A. R. (2024). Enhancing energy efficiency in supermarkets: a data-driven approach for fault detection and diagnosis in CO2 refrigeration systems. Applied Energy, 377, 124479. https://doi.org/10.1016/j.apenergy.2024.124479

Forteza, N., Prades, E. & Roca, M. (2025). Analyzing VAT pass-through in Spain using web-scraped supermarket data and machine learning. SERIEs, 16, 137-189. https://doi.org/10.1007/s13209-025-00309-w

Grewal, D., Roggeveen, A. L. & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1-6. https://doi.org/10.1016/j.jretai.2016.12.008

Gutierrez, J. C., Polo Triana, S. I. & Leon Becerra, J. S. (2025). Benefits, challenges, and limitations of inventory control using machine learning algorithms: literature review. OPSEARCH, 62, 1140-1172. https://doi.org/10.1007/s12597-024-00839-0

Hauser, M., Flath, C. M. & Thiesse, F. (2021). Catch me if you scan: data-driven prescriptive modeling for smart store environments. European Journal of Operational Research, 294(3), 860-873. https://doi.org/10.1016/j.ejor.2020.12.047

Hermann, E. (2022). Leveraging artificial intelligence in marketing for social good: an ethical perspective. Journal of Business Ethics, 179, 43-61. https://doi.org/10.1007/s10551-021-04843-y

Hou, P. & Huang, S. (2025). BCSM-YOLO: an improved product package recognition algorithm for automated retail stores based on YOLOv11. IEEE Access, 13, 139665-139679. https://doi.org/10.1109/ACCESS.2025.3595175

Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K. & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57-71. https://doi.org/10.1016/j.intmar.2020.04.001

Huang, M. & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459

Jahan, I. & Sanam, T. F. (2024). A comprehensive framework for customer retention in e-commerce using machine learning based on churn prediction, customer segmentation, and recommendation. Electronic Commerce Research. https://doi.org/10.1007/s10660-024-09936-0

Kitchenham, B. & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University & Durham University.

Kumar, D., Soni, G., Ramtiyal, B. & Vijayvargy, L. (2024). Data-driven approach for rational allocation of inventory in a FMCG supply chain. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-024-02519-0

Lemon, K. N. & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420

Li, Y., Xu, Q., Wang, Y. & Liu, B. (2024). Genetic algorithms application for pricing optimization in commodity markets. Mathematics, 12(9), 1289. https://doi.org/10.3390/math12091289

Liu, B. (2024). A deep learning-based object representation algorithm for smart retail management. Journal of the Institution of Engineers (India): Series B, 105, 1121-1128. https://doi.org/10.1007/s40031-024-01051-w

Mahala, V. R. S., Garg, N. & Kumar, R. (2024). Unveiling marketing potential: harnessing advanced analytics and machine learning for gold membership strategy optimization in a superstore. SN Computer Science, 5(374). https://doi.org/10.1007/s42979-024-02700-z

Mäntymäki, M., Minkkinen, M., Birkstedt, T. & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2, 603-609. https://doi.org/10.1007/s43681-022-00143-x

Matt, C., Hess, T. & Benlian, A. (2015). Digital transformation strategies. Business & Information Systems Engineering, 57(5), 339-343. https://doi.org/10.1007/s12599-015-0401-5

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. & Floridi, L. (2016). The ethics of algorithms: mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Momanyi, R., Cygu, S. B., Kiragga, A., Odero, H., Ng'etich, M., Asiki, G. & Kavu, T. D. (2025). Analyzing demographic grocery purchase patterns in Kenyan supermarkets through unsupervised learning techniques. Inquiry: The Journal of Health Care Organization, Provision and Financing, 62, 1-17. https://doi.org/10.1177/00469580251319905

Muñoz, J., Sánchez, A. & Kemper, G. (2024). End-to-end solution for automatic beverage stock detection in supermarkets based on image processing and convolutional neural networks. International Journal of Cognitive Computing in Engineering, 5, 453-474. https://doi.org/10.1016/j.ijcce.2024.09.001

Narayanan, L., Sudhakaran, D., Grandhe, S., Iqbal, N. & James, J. (2020). A deep learning enabled smart shopping cart. Bioscience Biotechnology Research Communications, 13(13), 247-251. http://dx.doi.org/10.21786/bbrc/13.13/36

Nguyen, S. P. (2021). Deep customer segmentation with applications to a Vietnamese supermarkets' data. Soft Computing, 25, 7785-7793. https://doi.org/10.1007/s00500-021-05796-0

Ou, T. -Y., Fu, H. -P. & Wu, M. -Z. (2025). Optimize a chain convenience store location prediction model by using MTS-machine learning methodology. Scientific Reports, 16, 1056. https://doi.org/10.1038/s41598-025-30650-w

Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N. & Chrissikopoulos, V. (2018). Explaining online shopping behavior with fsQCA: the role of cognitive and affective perceptions. Journal of Business Research, 69(2), 794-803. https://doi.org/10.1016/j.jbusres.2015.07.010

Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M. & Britten, N. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC Methods Programme. Lancaster University. https://doi.org/10.13140/2.1.1018.4643

Raju, B. A. N., Ghai, D., Tripathi, S. L., Nanda, S. K. & Islam, S. M. N. (2024). Predictive analytics for marketing and sales of products using smart trolley with automated billing system in shopping malls using LBPH and Faster R-CNN. En N. Singh, S. Birla, M. D. Ansari & N. K. Shukla (Eds.), Intelligent techniques for predictive data analytics (pp. 105-122). Wiley. https://doi.org/10.1002/9781394227990.ch6

Rao, S. & Zhang, L. (2021). The algorithms that make Instacart roll: how machine learning and other tech tools guide your groceries from store to doorstep. IEEE Spectrum, 58(3), 36-42. https://doi.org/10.1109/MSPEC.2021.9370062

Ratha, A. K., Devi, A. G., Sethy, P. K., Barpanda, N. K., Behera, S. K. & Nanthaamornphong, A. (2025). Deep learning-powered precision: a CNN-based approach for postharvest classification of Indian banana varieties in supermarket supply chains. Food Science and Technology, 13(2), 165-177. https://doi.org/10.13189/fst.2025.130205

Rodrigues, Z., Pinheiro, L., Marcolin, C., Matheus, R., Saxena, S. & Morais, M. (2024). Artificial intelligence in supermarkets: a multiple analysis about tasks, jobs, and automation. Disruptive innovation in a digitally connected healthy world (I3E 2024) (Lecture Notes in Computer Science, Vol. 14907) Springer, 90-102. https://doi.org/10.1007/978-3-031-72234-9_8

Saengsikhiao, P., Prapaipornlert, C. & Taweekun, J. (2024). The optimization of chillers air-conditioning in Thailand supermarkets using a retail energy management system (REMS). Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 118(2), 62-73. https://doi.org/10.37934/arfmts.118.2.6273

Saetra, H. S. (2020). Privacy as an aggregate public good. Technology in Society, 63, 101422. https://doi.org/10.1016/j.techsoc.2020.101422

Shrinidhi, M., Yeshvanthini, K., Yogitha, S. & Hephzipah, J. J. (2025). A remunerative self-checkout system designed for small scale supermarkets. En P. D. Sivakumar, R. Ramachandran, C. Pasupathi & P. Balakrishnan (Eds.), Computing Technologies for Sustainable Development, (pp. 311-320). Communications in Computer and Information Science, 2361, Springer. https://doi.org/10.1007/978-3-031-82383-1_24

Shrivastava, R. & Dubey, S. K. (2025). The bottom line of personalization: unravelling the power of algorithms and segmentation through a systematic review. Vision: The Journal of Business Perspective. https://doi.org/10.1177/09722629241313004

Stylianou, T. & Pantelidou, A. (2025). Big data and consumer behavior: a macroeconomic perspective through supermarket analytics. Quantitative Finance and Economics, 9(3), 682-712. https://doi.org/10.3934/QFE.2025024

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007

Tsai, C., Chen, C., Wu, E. H., Chung, C., Huang, C., Tsai, P. & Yeh, S. (2021). A machine-learning-based assessment method for early-stage neurocognitive impairment by an immersive virtual supermarket. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 2124-2132. https://doi.org/10.1109/tnsre.2021.3118918

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N. & Haenlein, M. (2021). Digital transformation: a multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022

Vial, G. (2019). Understanding digital transformation: a review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003

Wang, W., Zhang, P., Sun, C. & Feng, D. (2024). Smart customer service in unmanned retail store enhanced by large language model. Scientific Reports, 14, 19838. https://doi.org/10.1038/s41598-024-71089-9

Wijethunga, R., Nouraei, H., Zych, C., Samarabandu, J. & Sadhu, A. (2024). Precision leak detection in supermarket refrigeration systems integrating categorical gradient boosting with advanced thresholding. Energies, 17(3), 732. https://doi.org/10.3390/en17030736

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S. & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907-931. https://doi.org/10.1108/JOSM-04-2018-0119

Zhang, Y., Jin, S., Wu, Y., Zhao, T., Yan, Y., Li, Z. & Li, Y. (2020). A new intelligent supermarket security system. Neural Network World, 30(2), 113-131. https://doi.org/10.14311/NNW.2020.30.009

Zhou, B., Zha, W., Ye, L. & He, Z. (2022). A dynamic material handling scheduling method based on elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) and knowledge base (KB) for line-integrated supermarkets. Soft Computing, 26, 763-785. https://doi.org/10.1007/s00500-021-06385-x

Zhu, C., Jia, J. & Arslan, T. (2025). FVOR-YOLO: a real-time model for fruits and vegetables detection in complex supermarket self-checkout environments. IEEE Internet of Things Journal, 13(7), 13872-13887. https://doi.org/10.1109/JIOT.2025.3643299

Publicado

2026-05-25

Como Citar

Cepeda Cavero, L. E., & Véliz Soto, M. P. (2026). Transformação digital no varejo: uma análise das tensões gerenciais no supermercado inteligente (2020-2025). 360: Revista De Ciências Da Gestão, (11), 1–22. https://doi.org/10.18800/360gestion.202611.003

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