PROACTIVE INDUSTRIAL COST MANAGEMENT WITH ARTIFICIAL INTELLIGENCE: ARCHITECTURE, PREDICTIVE MODELLING AND SCENARIO ANALYSIS IN THE MANAUS INDUSTRIAL HUB
Abstract
Industrial cost control in complex manufacturing plants — particularly in the Manaus Industrial Hub (MIH) under the differentiated tax regime of the Zona Franca de Manaus (ZFM) — is far more than an accounting challenge: it is a problem of decision intelligence in highly uncertain environments. This paper describes the architecture and technical-scientific foundations of FLEXOR, an intelligent platform for proactive industrial and operational cost management, integrating predictive time-series modelling, automated machine-learning-based business rule induction, and macroeconomic and operational scenario simulation. The solution combines supervised and unsupervised learning algorithms with a cyber-physical architecture connected to ERP and legacy systems via APIs. Expected outcomes include a MAPE below 5% for short-term forecasts, a substantial reduction in the cost-analysis cycle, and the ability to simulate optimistic, realistic and pessimistic scenarios with measurable impact on EBITDA, gross margin and ROI. The work contributes to the field of smart manufacturing and Industry 4.0, with emphasis on AI-based decision support systems for OEM companies operating under special fiscal regimes.
Author Biographies
Bachelor’s Degree in Computer Science. Federal University of Amazonas.
Bachelor’s Degree in Software Engineering. Federal University of Amazonas.
Bachelor’s Degree in Accounting Sciences. Federal University of Amazonas.
Master’s Degree in Computer Science. Federal University of Amazonas.
References
BONCZEK, R. H.; HOLSAPPLE, C. W.; WHINSTON, A. B. Foundations of Decision Support Systems. New York: Academic Press, 1981. DOI: https://doi.org/10.1016/B978-0-12-113050-3.50010-5
BREIMAN, L. Random forests. Machine Learning, v. 45, n. 1, p. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
CAVALCANTE, I. M. et al. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, v. 49, p. 86–97, 2019. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.03.004
CHANDOLA, V.; BANERJEE, A.; KUMAR, V. Anomaly detection: a survey. ACM Computing Surveys, v. 41, n. 3, p. 1–58, 2009. DOI: https://doi.org/10.1145/1541880.1541882
CHEN, T.; GUESTRIN, C. XGBoost: a scalable tree boosting system. In: ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 22., 2016, San Francisco. Proceedings... New York: ACM, 2016. p. 785–794. DOI: https://doi.org/10.1145/2939672.2939785
COOPER, R.; KAPLAN, R. S. Profit priorities from activity-based costing. Harvard Business Review, v. 69, n. 3, p. 130–135, mai./jun. 1991.
CRESWELL, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4. ed. Thousand Oaks: Sage, 2014.
DOSHI-VELEZ, F.; KIM, B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608, 2017.
FRANK, A. G. et al. Industry 4.0 technologies: implementation patterns in manufacturing companies. International Journal of Production Economics, v. 210, p. 15–26, 2019. DOI: https://doi.org/10.1016/j.ijpe.2019.01.004
GORRY, G. A.; SCOTT MORTON, M. S. A framework for management information systems. Sloan Management Review, v. 13, n. 1, p. 55–70, 1971.
HOLSAPPLE, C. W.; WHINSTON, A. B. Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing, 1996.
HORNGREN, C. T.; DATAR, S. M.; RAJAN, M. V. Cost Accounting: A Managerial Emphasis. 15. ed. Upper Saddle River: Pearson, 2015.
HYNDMAN, R. J.; ATHANASOPOULOS, G. Forecasting: Principles and Practice. 2. ed. Melbourne: OTexts, 2018. Disponível em: https://otexts.com/fpp2/ Acesso em: 10 jan. 2025.
JORDAN, M. I.; MITCHELL, T. M. Machine learning: trends, perspectives, and prospects. Science, v. 349, n. 6245, p. 255–260, 2015. DOI: https://doi.org/10.1126/science.aaa8415
KAGERMANN, H.; WAHLSTER, W.; HELBIG, J. Recommendations for implementing the strategic initiative Industrie 4.0. Munich: National Academy of Science and Engineering (Acatech), 2013.
KAPLAN, R. S.; ANDERSON, S. R. Time-Driven Activity-Based Costing. Boston: Harvard Business School Press, 2007.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, v. 521, n. 7553, p. 436–444, 2015. DOI: https://doi.org/10.1038/nature14539
LIU, F. T.; TING, K. M.; ZHOU, Z.-H. Isolation forest. In: IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 8., 2008, Pisa. Proceedings... Washington: IEEE Computer Society, 2008. p. 413–422. DOI: https://doi.org/10.1109/ICDM.2008.17
LU, Y. et al. Industry 4.0: a survey on technologies, applications and open research issues. Journal of Industrial Information Integration, v. 6, p. 1–10, 2017. DOI: https://doi.org/10.1016/j.jii.2017.04.005
LUNDBERG, S. M.; LEE, S.-I. A unified approach to interpreting model predictions. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, 30., 2017, Long Beach. Proceedings... Red Hook: Curran Associates, 2017. p. 4765–4774.
MAKRIDAKIS, S.; SPILIOTIS, E.; ASSIMAKOPOULOS, V. Statistical and machine learning forecasting methods: concerns and ways forward. PLOS ONE, v. 13, n. 3, e0194889, 2018. DOI: https://doi.org/10.1371/journal.pone.0194889
OLIVEIRA, G. A.; PACHECO, R. C. dos S. Zona Franca de Manaus: dinâmica produtiva, inovação e perspectivas para a Indústria 4.0. Revista de Administração Pública, v. 53, n. 5, p. 879–903, 2019.
PORTER, M. E.; HEPPELMANN, J. E. How smart, connected products are transforming competition. Harvard Business Review, v. 92, n. 11, p. 64–88, 2014.
POWER, D. J. A brief history of decision support systems. DSSResources.COM, 2007. Disponível em: http://dssresources.com/history/dsshistory.html Acesso em: 5 fev. 2025.
RIBEIRO, M. T.; SAMANTA, S.; GUESTRIN, C. "Why should I trust you?": explaining the predictions of any classifier. In: ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 22., 2016, San Francisco. Proceedings... New York: ACM, 2016. p. 1135–1144. DOI: https://doi.org/10.1145/2939672.2939778
SCHUH, G. et al. Industrie 4.0 Maturity Index: Managing the Digital Transformation of Companies. Munich: Herbert Utz, 2020.
SCHWAB, K. The Fourth Industrial Revolution. Geneva: World Economic Forum, 2016.
SHANK, J. K.; GOVINDARAJAN, V. Strategic Cost Management: The New Tool for Competitive Advantage. New York: Free Press, 1993.
SIMON, H. A. The New Science of Management Decision. Englewood Cliffs: Prentice Hall, 1977.
SUFRAMA. Relatório de Indicadores do Polo Industrial de Manaus. Manaus: Superintendência da Zona Franca de Manaus, 2022.
TURBAN, E.; ARONSON, J. E.; LIANG, T.-P. Decision Support Systems and Intelligent Systems. 7. ed. Upper Saddle River: Pearson Prentice Hall, 2005.
WANG, S. et al. Implementing smart factory of Industrie 4.0: an outlook. International Journal of Distributed Sensor Networks, v. 12, n. 1, p. 1–10, 2016. DOI: https://doi.org/10.1155/2016/3159805
License
Copyright (c) 2026 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
This work is licensed under a Creative Commons Attribution 4.0 International License.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.