A regression data mining approach in Lean Production

Nowadays, companies want technologies that are able to help them to make the best decision. Data Mining is an excellent tool to estimate the sales. It allows the company to optimize its production and reduce costs, eg, in storage. When these models are combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This case study followed the CRISP-DM methodology in order to create a model able to reduce and, if possible, eliminate wastage. Several statistics measures were applied to the dataset. Regression algorithms were induced with the goal to find which one of the models are less likely to make mistakes, in other words, what model correctly predict the target result. After executing the tests, the model M1 from the scenario C1 with RandomTree algorithm, average data grouping and average method class creation is the less likely one to give errors regarding regression, having produced an RAE of 6.75%.COMPETE, Grant/Award Number: POCI-01-0145-FEDER-007043; Fundacao para a Ciencia e Tecnologia (FCT), Grant/Award Number: UID/CEC/00319/201