A self-learning approach to improving service quality in outsourcing of engineering design using operational data Online publication date: Wed, 18-Dec-2013
by Vandana Srivastava; A. Sharfuddin; Subhash Datta
International Journal of Computer Applications in Technology (IJCAT), Vol. 48, No. 4, 2013
Abstract: Managing service quality in outsourcing requires a holistic approach to managing knowledge. This need is more pronounced in case of outsourcing of high-end tasks such as engineering designs. As complex tasks are carried out large amounts of multi-structured transactional data are captured during service delivery, more often appearing as text. This qualitative operational data has rich knowledge embedded in it. This paper aims to demonstrate a way of extracting knowledge from such operational data for improving service quality. The study uses simulation as a method of inductive research. Simulation model of a self-learning system for extracting knowledge from operational data are created. The proposed artificial intelligence system integrates natural language processing and rule-based reasoning for knowledge creation. Finally, with a view to demonstrate the potential of the proposed system, a real prototype industry application is described.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook