Inteligência artificial aplicada a pequenas empresas: o uso da engenharia automática de recursos e do aprendizado de máquina para um planejamento mais preciso




Inteligência artificial, Engenharia automática de recursos, Aprendizado de máquina, Pequenas empresas, Empresas locais


O objetivo deste estudo é desenvolver um modelo preditivo que aumente a precisão do planejamento operacional de negócios usando dados de uma pequena empresa. A partir de técnicas de aprendizado de máquina (AM), são apresentadas estratégias de expansão, reamostragem e combinação que permitiram superar várias das limitações enfrentadas pelas pesquisas conduzidas até então. O estudo adotou uma nova técnica de engenharia de recursos que permitiu aumentar a precisão de um modelo preditivo, encontrando 10 novos recursos derivados dos originais, desenvolvidos automaticamente através das relações não-lineares encontradas entre eles. Por fim, foi criado um classificador com regras para prever, com alta precisão, a receita da pequena empresa. De acordo com os resultados apresentados, a abordagem proposta abre novas possibilidades para a pesquisa sobre a AM aplicada a pequenas e médias empresas.


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Aburto, L., & Weber, R. (2007). A sequential hybrid forecasting system for demand prediction. Lecture Notes in Computer Science, 4571, 518–532. DOI:

Albisua, I., Arbelaitz, O., Gurrutxaga, I., Lasarguren, A., Muguerza, J., & Pérez, J. M. (2013). The quest for the optimal class distribution: an approach for enhancing the effectiveness of learning via resampling methods for imbalanced data sets. Progress in Artificial Intelligence, 2(1), 45–63. DOI:

Andreyeva, T., Middleton, A. E., Long, M. W., Luedicke, J., & Schwartz, M. B. (2011). Food retailer practices, attitudes and beliefs about the supply of healthy foods. Public Health Nutrition, 14(6), 1024–1031. DOI:

Banks, G. P. (2013). Exploring small-business change and strategic adaptation in an evolving economic paradigm. Doctoral dissertation. Walden University.

Batuwita, R., & Palade, V. (2010). Efficient resampling methods for training support vector machines with imbalanced datasets. In: The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8).

Berry, L. R., Helman, P., & West, M. (2020). Probabilistic forecasting of heterogeneous consumer transaction–sales time series. International Journal of Forecasting, 36(2), 552–569. DOI:

Bonti-Ankomah, S., & Yiridoe, E. K. (2006). Organic and conventional food: a literature review of the economics of consumer perceptions and preferences. Organic Agriculture Centre of Canada, 59, 1–40.

Bousqaoui, H., Achchab, S., & Tikito, K. (2019). Machine learning applications in supply chains: Long short-term memory for demand forecasting. Lecture Notes in Networks and Systems, 49, 301–317. DOI:

Bowman, J. (2016). Walmart’s neighborhood market is crushing the competition. Business Insider. Retrieved May 15, 2018, from:

Brinckmann, J., Grichnik, D., & Kapsa, D. (2010). Should entrepreneurs plan or just storm the castle? A meta-analysis on contextual factors impacting the business planning--performance relationship in small firms. Journal of Business Venturing, 25(1), 24–40. DOI:

Burns, P. (2016). Entrepreneurship and small business. Palgrave Macmillan Limited.

Caspi, C. E., Pelletier, J. E., Harnack, L., Erickson, D. J., & Laska, M. N. (2016). Differences in healthy food supply and stocking practices between small grocery stores, gas-marts, pharmacies and dollar stores. Public Health Nutrition, 19(3), 540–547. DOI:

Christensen, C. M., & Bower, J. L. (1996). Customer power, strategic investment, and the failure of leading firms. Strategic Management Journal, 197–218.

Cohen, W. M., & Klepper, S. (1996). Firm size and the nature of innovation within industries: the case of process and product R&D. The Review of Economics and Statistics, 232–243.

Corkery, M. (2018). Grocery Wars Turn Small Chains Into Battlefield Casualties. The New York Times. Retrieved May 15, 2018, from:

Culkin, N., & Smith, D. (2000). An emotional business: a guide to understanding the motivations of small business decision takers. Qualitative Market Research: An International Journal, 3(3), 145–157. DOI:

Dagevos, H. (2016). Beyond the Marketing Mix: Modern Food Marketing and the Future of Organic Food Consumption. In: The Crisis of Food Brands: Sustaining Safe, Innovative and Competitive Food Supply, 255.

Dapp, T., & Slomka, L. (2015). Fintech reloaded - Traditional banks as digital ecosystems. Publication of the German Original, from:

Davenport, S., & Bibby, D. (1999). Rethinking a national innovation system: The small country as’ SME’. Technology Analysis & Strategic Management, 11(3), 431–462.

De Melo, V. V. (2014). Kaizen Programming. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation (pp. 895–902). New York, NY, USA: ACM. DOI:

Deb, S. (2017). Analytical ideas to improve daily demand forecasts: A case study. Lecture Notes in Computer Science, 10192, 23–32. DOI:

Dewhurst, M., & Willmott, P. (2014). Manager and machine: The new leadership equation. McKinsey Quarterly, 4, 1–8.

Doward, J. (2017). Organic food sales soar as shoppers put quality before price | Environment | The Guardian. Retrieved May 15, 2018, from:

Draganska, M., Klapper, D., & Villas-Boas, S. B. (2010). A larger slice or a larger pie? An empirical investigation of bargaining power in the distribution channel. Marketing Science, 29(1), 57–74. DOI:

Dunkley, B., Helling, A., & Sawicki, D. S. (2004). Accessibility versus scale: examining the tradeoffs in grocery stores. Journal of Planning Education and Research, 23(4), 387–401. DOI:

Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18–36. DOI:

Fadahunsi, A. (2012). The growth of small businesses: Towards a research agenda. American Journal of Economics and Business Administration, 4(1), 105. DOI:

Farhadi, H. (2018). Machine Learning: Advanced Techniques and Emerging Applications. BoD--Books on Demand.

Gil, J. M., Gracia, A., & Sanchez, M. (2000). Market segmentation and willingness to pay for organic products in Spain. The International Food and Agribusiness Management Review, 3(2), 207–226. DOI:

Gilbert, R. J. (2015). E-books: A tale of digital disruption. Journal of Economic Perspectives, 29(3), 165–184. DOI:

Gordon, W. L., & Key, J. R. (1987). Artificial intelligence in support of small business information needs. Journal of Systems Management, 38(1), 24.

Ha, A. Y. (1997). Inventory rationing in a make-to-stock production system with several demand classes and lost sales. Management Science, 43(8), 1093–1103.

Isidore, C., Wattles, J., & Kavilanz, P. (2018). Toys “R” Us will close or sell all US stores. Retrieved May 15, 2018, from:

Jones, C. (2004). An alternative view of small firm adaptation. Journal of Small Business and Enterprise Development, 11(3), 362-370. DOI:

Kolassa, S. (2013). Forecasting and optimisation for big data: Lessons from the retail business. In OR55 Keynotes and Extended Abstracts - 55th Conference of the Operational Research Society, 33–35. Retrieved from:

Lee, H. L., & Whang, S. (2000). Information sharing in a supply chain. International Journal of Manufacturing Technology and Management, 1(1), 79–93. DOI:

Lensink, R., Van Steen, P., & Sterken, E. (2005). Uncertainty and Growth of the Firm. Small Business Economics, 24(4), 381–391. DOI:

Leswing, K. (2017). Amazon Is Buying Whole Foods-Here’s Amazon’s Vision for the Grocery Store of the Future. Business Insider, from:

Li, Y., Su, Z., Liu, Y., & Li, M. (2011). Fast adaptation, strategic flexibility and entrepreneurial roles. Chinese Management Studies, 5(3), 256–271. DOI:

Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. Journal of Strategic Information Systems, 24(3), 149–157. DOI:

Love, R. R., & Hoey, J. M. (1990). Management science improves fast-food operations. Interfaces, 20(2), 21–29.

McFarlane, F. W. (1984). Information technology changes the way you compete. Harvard Business Review, Reprint Service.

Moore, C. W. (2008). Managing small business: An entrepreneurial emphasis. Cengage Learning EMEA.

Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555–572). Springer.

Ramentol, E., Verbiest, N., Bello, R., Caballero, Y., Cornelis, C., & Herrera, F. (2012). SMOTE-FRST: a new resampling method using fuzzy rough set theory. In Uncertainty Modeling in Knowledge Engineering and Decision Making (pp. 800–805). World Scientific.

Slimani, I., El Farissi, I., & Achchab, S. (2017). Configuration and implementation of a daily artificial neural network-based forecasting system using real supermarket data. International Journal of Logistics Systems and Management, 28(2), 144–163. DOI:

Slimani, I., Farissi, I. E., & Al-Qualsadi, S. A. (2016). Configuration of daily demand predicting system based on neural networks. In Proceedings of the 3rd IEEE International Conference on Logistics Operations Management. DOI:

Soper, T. (2017). Amazon reports $1.3B in physical store sales, breaking out brick-and-mortar business for first time, still dwarfed by $26.4B online sales. GeekWire. Retrieved May 15, 2018, from:

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., … Horvitz, E. (2016). Artificial intelligence and life in 2030: One hundred year study on artificial intelligence. Standford University, from:

Taylor, J. W. (2011). Multi-item sales forecasting with total and split exponential smoothing. Journal of the Operational Research Society, 62(3), 555–563. DOI:

Taylor, K., & Hanbury, M. (2018). Amazon is threatening these 8 industries. Business Insider. Retrieved May 15, 2018, from:

Thompson, G. D. (1998). Consumer demand for organic foods: what we know and what we need to know. American Journal of Agricultural Economics, 80(5), 1113–1118. DOI:

Tu, J. I. (2016). Costco gets creative to meet shoppers’ huge appetite for organics. The Seattle Times. Retrieved May 15, 2018, from:

Van Doorn, J., & Verhoef, P. C. (2011). Willingness to pay for organic products: Differences between virtue and vice foods. International Journal of Research in Marketing, 28(3), 167–180. DOI:

Wingfield, N., & de la Merced, M. (2017). Amazon to buy Whole Foods for 13.4 billion. The New York Times.



Como Citar

Nascimento, A. M. ., de Melo, V. V., Muller Queiroz, A. C., Brashear-Alejandro, T., & Meirelles, F. de S. (2020). Inteligência artificial aplicada a pequenas empresas: o uso da engenharia automática de recursos e do aprendizado de máquina para um planejamento mais preciso. Revista De Contabilidade E Organizações, 14, e171481.