Title: A self-learning approach to improving service quality in outsourcing of engineering design using operational data
Authors: Vandana Srivastava; A. Sharfuddin; Subhash Datta
Addresses: Operations and Information Management Area, IILM Institute for Higher Education, 3, Lodhi Institutional Area, New Delhi 110003, India ' Department of Mathematics, Jamia Millia Islamia, New Delhi 110025, India ' Center for Inclusive Growth & Sustainable Development, M-134, II Floor, South City I, Gurgaon 122007, Haryana, India
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.
Keywords: high-end services; design outsourcing; engineering design; service quality; knowledge extraction; artificial intelligence; simulation; knowledge management; self-learning systems; modelling; natural language processing; NLP; rule-based reasoning; knowledge creation.
International Journal of Computer Applications in Technology, 2013 Vol.48 No.4, pp.307 - 320
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 18 Dec 2013 *