Transformation to lean manufacturing by an automotive component supplier
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Software for Data Analysis: Programming with R
Software for Data Analysis: Programming with R
Applied Survival Analysis: Regression Modeling of Time to Event Data
Applied Survival Analysis: Regression Modeling of Time to Event Data
| Hi-index | 0.98 |
The aim of the present paper is the analysis of the factors that have influence over the lead time of batches of metallic components of aerospace engines. The approach used in this article employs support vector machines (SVMs). They are a set of related supervised learning methods used for classification and regression. In this research a model that estimates whether a batch is going to be finished on the forecasted time or not was developed using some sample batches. The validity of this model was checked using a different sample of similar components. This model allows predicting the manufacturing time before the start of the manufacturing. Therefore a buffer time can be taken into account in order to avoid delays with respect to the customer's delivery. Further, some other researches have been performed over the data in order to determine which factors have more influence in manufacturing delays. Finally, conclusions of this study are exposed.