Density forecasting for failure probability in manufacturing

Ilmari Juutilainen, Satu Tamminen and Juha Röning

European Journal of Industrial Engineering, 2015, vol. 9, issue 4, 432-449

Abstract: Density forecasting is a subfield of multivariate regression aimed at accurate prediction of full conditional distributions. This article presents methods for improving product quality by deploying density forecast-based failure probability predictors that predict the risk of failure to meet the requirements of qualification tests and specification limits. Algorithms that efficiently deploy failure probability predictors in target optimisation problems and in process monitoring, planning and control operations are provided. In one of the three case applications, density forecast methods decreased production costs more efficiently than the reference method, i.e., point prediction for mean. In two case applications, density forecast methods did not provide additional value. To promote exploitation of density forecasting, the article presents ideas and prototype implementations for integrating density forecast-based failure probability predictors into software applications employed to improve the production efficiency of manufacturing processes. [Received 17 March 2013; Revised 22 October 2013; Revised 8 March 2014; Accepted 6 April 2014]

Keywords: applied probability; manufacturing industry; quality control; failure probability; failure prediction; probability density function; density forecasting. (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=70320 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:eujine:v:9:y:2015:i:4:p:432-449

Access Statistics for this article

More articles in European Journal of Industrial Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().