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Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing

Published

Author(s)

David J. Lechevalier, Anantha Narayanan Narayanan, Sudarsan Rachuri

Abstract

Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the manufacturing system states. Many small and medium manufacturers do not have the infrastructure, technical capability or financial means to take advantage of predictive analytics. A domain-specific language and framework for performing predictive analytics for manufacturing and production frameworks can counter this deficiency. In this paper, we survey some of the applications of predictive analytics in manufacturing and we discuss the challenges that need to be addressed. Then, we propose a core set of abstractions and a domain-specific framework for applying predictive analytics on manufacturing applications. Such a framework will allow manufacturers to take advantage of predictive analytics to improve their production.
Proceedings Title
ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems
Conference Dates
September 28-October 3, 2014
Conference Location
Valencia

Keywords

domain specific modeling, data analytic, machine learning, manufacturing

Citation

Lechevalier, D. , , A. and Rachuri, S. (2014), Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing, ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems, Valencia, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=916274 (Accessed April 14, 2024)
Created October 3, 2014, Updated February 19, 2017