A self-learning approach to improving service quality in outsourcing of engineering design using operational data

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.