Title :
Applying Analytics to Improve Hardware and Software Maintenance Support Services
Author :
Zhou Xin ; Feng Li ; Qi Cheng Li ; Sarkar, Soumitra Ronnie
Author_Institution :
IBM Res. - China, Beijing, China
Abstract :
Organizations providing large scale software and hardware maintenance support services typically capture detailed metrics of each service request (SR) for a customer. Examples of such metrics include the time taken to resolve the problem, success of the resolution, escalations across levels of support, field engineer site visit statistics, and parts replacement data -- the latter two for hardware maintenance only. For some SRs, targeted customer surveys may be conducted to elicit feedback about how effectively the end-to-end problem resolution process was performed. Application of analytics to such data to enable continuous improvement of the operational efficiencies of providing maintenance services is an open area of research. This paper describes the authors´ experience with several analytics projects in this domain. Improvement of maintenance support services can lead to faster and better problem resolution, leading to reduced down time and an increase in the overall resiliency of a computing environment.
Keywords :
software maintenance; software metrics; SR; analytics projects; computing environment resiliency improvement; continuous improvement; down time reduction; elicit feedback; end-to-end problem resolution process; hardware maintenance support service improvement; large-scale software; operational efficiencies; service request; software maintenance support service improvement; Analytical models; Data models; Hardware; Maintenance engineering; Measurement; Predictive models; Training data; analytics; customer satisfaction; machine learning; maintenance; service; technical support; trends;
Conference_Titel :
Services Computing (SCC), 2015 IEEE International Conference on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7280-0
DOI :
10.1109/SCC.2015.56