[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119810-en":3,"doc-seo-119810-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119810,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Application of machine learning methods in forecasting sales prices in a project consultancy - Research report","This article applies a comparative analysis of machine learning techniques to predict project sales prices for a consulting company in the South of Brazil. The organization delivers services across strategy, production, quality, and innovation, making sales-price determination challenging due to heterogeneous predictor variables such as consultant type, project type, and number of hours. After mapping the firm’s sales prospecting process, the study collected, prepared, tested, and selected predictive models. Results show Gradient Boosting Machine (GBM) yields the lowest error rate (about 22%), supporting stakeholder expectations and demonstrating computational algorithms’ value for demand forecasting and project pricing.","PRODUTO & PRODUÇÃO, vol. 24, n.2, p. 1-12. 2023*.  \nApplication of machine learning methods in forecasting sales prices in a  \nproject consultancy  \nAlexandre dos Santos Pereira  \nPontifícia Universidade Católica do Paraná (PUCPR)  \ne-mail: [ale952@gmail.com](ale952@gmail.com)  \nMarcelo Carneiro Gonçalves  \nPontifícia Universidade Católica do Paraná (PUCPR) e-mail: [carneiro.marcelo@pucpr.br](carneiro.marcelo@pucpr.br)  \nElpidio Oscar Benitez Nara  \nPontifícia Universidade Católica do Paraná (PUCPR) e-mail: [elpidio.nara@pucpr.edu.br](elpidio.nara@pucpr.edu.br)  \nThales de Freitas Ferraz  \nPontifícia Universidade Católica do Paraná (PUCPR)  \ne-mail:  [thalesdefreitasferraz@gmail.com](thalesdefreitasferraz@gmail.com)  \n* RECEBIDO em 26/09/2022 . ACEITO em 09/06/2023 .  \nAbstract  \nThe 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.  \nKeyword: Machine Learning; Gradient Boosting Machine; Computational Intelligence.  \n1. Introduction.  \nIn the current global economic landscape, numerous companies in the service industry face challenges in determining the appropriate pricing for their offerings. Pricing services is complex as it involves assessing the value perceived by customers rather than focusing solely on costs, as seen in manufacturing companies. The perceived value is influenced by customer beliefs about the worth of the service and the pricing strategies employed by competitors (THOMAS, 1978) .  \nIn Brazil, the service sector played a significant role, contributing 73.4% to the country's GDP in 2020. This highlights the importance of enhancing the pricing and presentation of services to customers, considering their substantial economic impact (CNC, 2020) .  \nIn addition to delivering high-quality services to generate customer value, companies must also address internal demands by efficiently allocating and organizing activities and employees. Production Planning and Control (PPC) plays a crucial role in balancing the interests of demand and supply within an organization. PPC aligns the activities of commercial and production departments to optimize company performance (BUETTGEN, 2012) .  \nForecasting, an essential aspect of PPC, involves utilizing statistical, mathematical, or econometric models to project future data. Accurate forecasting enables companies to better prepare for future demand and facilitate more precise and agile pricing strategies (MARTINS et al., 2005) .  \nMachine learning techniques are employed to make predictions based on these statistical models.","cbCailigrrAqj4YD","https://ap.wps.com/l/cbCailigrrAqj4YD","pdf",408741,1,12,"English","en",105,"# Introduction\n## Pricing challenges in service industries\n## Production Planning and Control (PPC) and forecasting\n## Machine learning for prediction and decision-making\n## Research objective and guiding question","[{\"question\":\"What problem does the article address?\",\"answer\":\"It addresses the difficulty a consulting company faces in accurately determining sales prices for new projects when pricing depends on many different predictor variables.\"},{\"question\":\"Which machine learning method performed best in the study?\",\"answer\":\"Gradient Boosting Machine (GBM) achieved the lowest error rate among the tested machine learning techniques.\"},{\"question\":\"How is the study carried out to select the best model?\",\"answer\":\"It maps the company’s current sales prospecting process, collects and prepares data, then tests and selects the best predictive model before discussing the improvement with the organization.\"}]","Application of machine learning methods in forecasting sales prices in a project consultancy - 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