Abstract
This article presents a study conducted in a vehicle dealership company located in Southern Brazil. The company has been operating in the market since 2018 and faces challenges related to the lack of an automated and quantitatively based flow for negotiation, vehicle valuation, and inventory management. The absence of an automated Production Planning and Control (PPC) system and advanced techniques such as Machine Learning makes it difficult for the company to establish efficient pricing strategies, manage inventory, and make accurate decisions, which can negatively impact overall business performance. This study aims to implement a model that utilizes PPC and Machine Learning techniques to predict the selling price of vehicles.
To achieve this goal, the research followed these steps: conducting a literature review on Machine Learning and PPC focused on Sales and Operations Planning (S&OP), analyzing the company’s current procedure through a BPMN diagram, collecting data from the top 5 vehicles with the highest turnover in the company.
The study resulted in the implementation of an automated PPC and Machine Learning flow, enhancing the company’s sales management.
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References
Serra, F., Nara, E., Gonçalves, M., Costa, S., Bortoluzzi, S.: Preliminary construct for decision making in organizations: a systemic approach. In: Deschamps, F., de Lima, E.P., da Costa, S.E.G., Trentin, M.G. (eds.) Proceedings of the 11th International Conference on Production Research – Americas: ICPR Americas 2022, pp. 639–646. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-36121-0_81
Baierle, I.C., Schaefer, J.L., Sellitto, M.A., Furtado, J.C., Nara, E.O.B.: Moona software for survey classification and evaluation of criteria to support decision-making for properties portfolio. Int. J. Strateg. Property Manag. 24(2), 226–236 (2020)
Nora, L.D.D., Siluk, J.C.M., Júnior, A.L.N., Nara, E.O.B., Furtado, J.C.: The performance measurement of innovation and competitiveness in the telecommunications services sector. Int. J. Bus. Excell. 9(2), 210–224 (2016)
Schaefer, J.L., Baierle, I.C., Sellitto, M.A., Furtado, J.C., Nara, E.O.B.: Competitiveness scale as a basis for Brazilian small and medium-sized enterprises. EMJ – Eng. Manag. J. 33(4), 255–271 (2021)
Hamasaki, K., Gonçalves, M.C., Junior, O.C., Nara, E.O.B., Wollmann, R.R.G.: Robust linear programming application for the production planning problem. In: Deschamps, F., de Lima, E.P., da Costa, S.E.G., Trentin, M.G. (eds.) Proceedings of the 11th International Conference on Production Research – Americas: ICPR Americas 2022, pp. 647–654. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-36121-0_82
Dias, I., Sampaio, R., Wollmann, R., Goncalves, M., Nara, E.: A decomposition scheme in production planning based on linear programming that incorporates the concept of a dynamic planning environment. In: International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics (2023)
Agostini, L., Filippini, R.: Organizational and managerial challenges in the path toward Industry 4.0. Eur. J. Innov. Manag. 22(3), 406–421 (2019). https://doi.org/10.1108/EJIM-02-2018-0030
Gonçalves, M.C., Nara, E.O.B., dos Santos, I.M., Mateus, I.B., do Amaral, L.M.B.: Comparative analysis of machine learning techniques via data mining in a railroad company. In: Deschamps, F., de Lima, E.P., da Costa, S.E.G., Trentin, M.G. (eds.) Proceedings of the 11th International Conference on Production Research – Americas: ICPR Americas 2022, pp. 655–664. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-36121-0_83
Usuga Cadavid, J.P., Lamouri, S., Grabot, B., Fortin, A.: Machine learning in production planning and control: a review of empirical literature. IFAC-PapersOnLine 52(13), 385–390 (2019). https://doi.org/10.1016/j.ifacol.2019.11.155
Goncalves, M., Pereira, A., Ferraz, T., Nara, E., Dias, I.: Predicting project sales prices using machine learning techniques: a case study in a project consultancy. In: International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics (2023)
Alvarez, P.P., Espinoza, A., Maturana, S., Vera, J.: Improving consistency in hierarchical tactical and operational planning using Robust Optimization. Comput. Ind. Eng. 139, 106112 (2020). https://doi.org/10.1016/j.cie.2019.106112
Zanjani, M.K., Nourelfath, M., Ait-Kadi, D.: A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. Int. J. Product. Res. 48(16), 4701–4723 (2010). https://doi.org/10.1080/00207540903055727
Chen, I.J.: Planning for ERP systems: analysis and future trend. Bus. Process Manag. J. 7(5), 374–386 (2001). https://doi.org/10.1108/14637150110406768
Gonçalves, M.C., Wollmann, R.R.G., Sampaio, R.J.B.: Proposal of a numerical approximation theory to solve the robust convex problem of production planning. Int. J. Operat. Res. 1(1), 1 (2022). https://doi.org/10.1504/IJOR.2022.10049618
Goncalves, M., Sampaio, R., Wollmann, R., Nara, E., Dias, I.: Using robust approach concept to solve the production planning problem in manufacturing systems. In: International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2022. Springer Proceedings in Mathematics and Statistics (2023)
Lourenço, F., Nara, E., Gonçalves, M., Canciglieri, O.: Preliminary construct of sustainable product development with a focus on the brazilian reality: a review and bibliometric analysis. In: Filho, W.L., Frankenberger, F., Tortato, U. (eds.) Sustainability in Practice: Addressing Challenges and Creating Opportunities in Latin America, pp. 197–220. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-34436-7_12
Morin, M., Gaudreault, J., Brotherton, E., Paradis, F., Rolland, A., Wery, J., Laviolette, F.: Machine learning-based models of sawmills for better wood allocation planning. Int. J. Product. Econ. 222, 107508 (2020). https://doi.org/10.1016/j.ijpe.2019.09.029
Chabanet, S., Thomas, P., El-Haouzi, H.B., Morin, M., Gaudreault, J.: A kNN approach based on ICP metrics for 3D scans matching: an application to the sawing process. IFAC-PapersOnLine 54(1), 396–401 (2021). https://doi.org/10.1016/j.ifacol.2021.08.045
Bertolini, M., Mezzogori, D., Neroni, M., Zammori, F.: Machine Learning for industrial applications: a comprehensive literature review. Expert Syst. Appl. 175, 114820 (2021). https://doi.org/10.1016/j.eswa.2021.114820
Baumung, W., Fomin, V.V.: Predicting production times through machine learning for scheduling additive manufacturing orders in a PPC system. In: Proceedings of 2019 IEEE International Conference of Intelligent Applied Systems on Engineering, ICIASE 2019, art. no. 9074152, pp. 47–50 (2019). https://doi.org/10.1109/ICIASE45644.2019.9074152
Ritto, T.G., Rochinha, F.A.: Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mech. Syst. Signal Process. 155, 107614 (2021). https://doi.org/10.1016/j.ymssp.2021.107614
Morin, M., Paradis, F., Rolland, A., Wery, J., Gaudreault, J., Laviolette, F.: Machine learning-based metamodels for sawing simulation. In: Proceedings – Winter Simulation Conference, 2016-February, art. no. 7408329, pp. 2160–2171 (2015). https://doi.org/10.1109/WSC.2015.7408329
Gonçalves, M.C., Canciglieri, A.B., Strobel, K.M., Antunes, M.F., Zanellato, R.R.: Application of operational research in process optimization in the cement industry. J. Eng. Technol. Ind. Appl. 6(24), 36–40 (2020). https://doi.org/10.5935/jetia.v6i24.677
De Faria, G.L., Gonçalves, M.C.: Proposition of a lean flow of processes based on the concept of process mapping for a Bubalinocultura based dairy. J. Eng. Technol. Ind. Appl. 5(18), 23–28 (2019). https://doi.org/10.5935/2447-0228.20190022
Junior, O.J.T., Gonçalves, M.C.: Application of quality and productivity improvement tools in a Potato Chips Production Line. J. Eng. Technol. Ind. Appl. 5(18), 65–72 (2019). https://doi.org/10.5935/2447-0228.20190029
FIPE: https://veiculos.fipe.org.br/. Last accessed 18 Jul 2023
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The authors would like to thank the Pontifical Catholic University of Paraná (PUCPR) and PPGEPS-PUCPR.
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Gonçalves, M.C., Machado, T.R., Nara, E.O.B., Dias, I.C.P., Vaz, L.V. (2023). Integrating Machine Learning for Predicting Future Automobile Prices: A Practical Solution for Enhanced Decision-Making in the Automotive Industry. In: Janssen, M., et al. New Sustainable Horizons in Artificial Intelligence and Digital Solutions. I3E 2023. Lecture Notes in Computer Science, vol 14316. Springer, Cham. https://doi.org/10.1007/978-3-031-50040-4_8
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