Application of machine learning methods in forecasting sales prices in a project consultancy


  • Alexandre dos Santos Pereira Pontifícia Universidade Católica do Paraná
  • Marcelo Carneiro Gonçalves Pontifícia Universidade Católica do Paraná (PUCPR)
  • Elpidio Oscar Benitez Nara Pontifícia Universidade Católica do Paraná (PUCPR)
  • Thales de Freitas Ferraz Pontifícia Universidade Católica do Paraná (PUCPR)



Machine learning, Gradient Boosting Machine, Computational Intelligence


The objective of this article is to apply a comparative analysis of machine learning techniques to predict project sales prices for a consulting company in the South of Brazil. The company is involved in various fields such as strategy, production, quality, and innovation. Due to this diverse range of projects, the company managers face challenges in accurately determining the sales value of new projects, as they deal with different types of predictor variables such as consultant type, project type, and number of hours. Hence, there is a need to utilize a method that can predict sales values through multivariate analysis and yield results close to the company's expectations. To achieve this goal, the article conducted a literature review on two research topics: Production Planning and Control (PPC) and machine learning techniques. Subsequently, the current sales prospecting process of the company was mapped out. Data were collected, analyzed, and prepared, followed by testing and selection of the best model. Finally, the proposed improvement was discussed with the organization. The results revealed that the application of Gradient Boosting Machine (GBM) technique achieved the lowest error rate among the tested machine learning techniques. The error rate was approximately 22%, which is deemed acceptable within the analyzed segment. Consequently, this study successfully met stakeholders' expectations by demonstrating the potential of utilizing computational algorithms for demand forecasting and project pricing.


Não há dados estatísticos.


ALSUBARI, et al. Data analytics for the identification of fake reviews using supervised learning. Computers, Materials and Continua, v. 70, n.2, 2022.

AYON, S.I.; ISLAM, M.M.; HOSSAIN, M.R. Coronary artery heart disease prediction: A comparative study of computational intelligence techniques. IETE Journal of Research, v. 68, n. 4, 2022.

Buettgen, John Jackson. Administração da produção. UNIASSELVI, p. 173-175 (2012).

CHINOSI, MICHELE; TROMBETTA, ALBERTO. BPMN: An introduction to the standard. Computer Standards & Interfaces, v. 34, p. 122-134, 2012.

Choi, T.-J., AN, H.-E., KIM, C.-B.: Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles. Life, v. 12, n. 9, p. 1443, 2022.

CHRISTIE, DAVID; NEILL, SIMON P. Measuring and Observing the Ocean Renewable Energy Resource. In: Comprehensive Renewable Energy. 2 ed. Elsevier, p. 149-175. cap. 8, 2022.


HALL, PETER; PARK, BYEONG U; SAMWORTH, RICHARD J. Choice of Neighbor Order in Nearest-Neighbor Classification. Annals of Statistics, p. 2135-2152, 2008.

HASTIE, TREVOR; TIBSHIRANI, ROBERT; FRIEDMAN, JEROME. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2 ed. Springer, 2016.

JAMES, GARETH. An Introduction to Statistical Learning: with Applications in R. New York (2013).

KAUR, H; KUMARI, V. Predictive modelling and analytics for diabetes using a machine learning approach. Applied Computing and Informatics, v. 18, p. 90-100, 2022.

LEE, IN; SHIN, YONG JAE. Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, v. 63, p. 157-170, 2020.

LORENA, ANA CAROLINA; CARVALHO, ANDRÉ C.P.L.F de. Uma Introdução às Support Vector Machines. Revista de Informática Teórica e Aplicada, v. 14, p. 43-67, 2007.

MARTINS, PETRÔNIO GARCIA; LAUGENI, FERNANDO P. Administração da Produção. 2nd edn, São Paulo, 2005.

MURPHY, KEVIN. P. Machine learning: a probabilistic perspective. MIT press, 2012.

NATEKIN, ALEXEY. KNOLL, ALOIS. Gradient Boosting Machines, A Tutorial. Frontiers in neurorobotics, 2013.

OLU-AJAYI. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering, v. 45, 2022.

QIU. Sensor combination selection strategy for kayak cycle phase segmentation based on body sensor networks. IEEE Internet of Things Journal, v. 9, n. 6, 2022.

SALMAN. A machine learning based framework for IoT device identification and abnormal traffic detection. Transactions on Emerging Telecommunications Technologies, v. 33, n. 3, 2022.

SHARIATI. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Engineering with Computers, v. 38, n. 1, p. 757-779, 2022.

SWAMIDASS, P. Mean Absolute Percentage Error (MAPE). Encyclopedia of Production and Manufacturing Management. Springer: Boston (2000).

THOMAS, D.R.E. “Strategy is different in service business”, Harvard Business Review, v. 56, p. 158-65, 1978.

TUBINO, DALVIO FERRARI. Planejamento e Controle da Produção: Teoria e Prática. São Paulo, 2007.

XU, X., FAIRLEY, C.K., CHOW, E.P.F., ZHANG, L., ONG, J.J.: Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages. Scientific Reports, v. 12, n. 1, p. 8757, 2022.

ZHANG. Slope stability prediction using ensemble learning techniques: A case study in yunyang county, chongqing, china. Journal of Rock Mechanics and Geotechnical Engineering, v. 14, n. 4, p. 1089-1099, 2022.

ZHAO. Clay content mapping and uncertainty estimation using weighted model averaging. Catena, v. 209, 2022.