The purpose of this paper is to develop and implement an efficient capacity and availability management tracker for information technology (IT) service delivery management that can be applied by analyzing base‐lined data, using quantitative project management and knowledge discovery techniques, for taking decisions on a monthly basis in resource allocation, optimum resource utilization and efficient service level management.
A ticket forecasting model has been developed. Also data were collected from fixed price running IT service delivery programs with about 200 or more full‐time employees working in each program, limited to four large service lines. Using Monte Carlo simulation, the data were base lined and applied to a capacity and availability management tracker. The results were then analyzed and conclusions drawn.
The findings suggest that the service provider was able to share the resources across the organization as needed based on demand, and overall the bench strength of the organization was drastically reduced. Also they were able to achieve better service level management. This has contributed to profit margin improvement in the organization.
The relatively small sample size (three programs and four service lines in one IT service organization) has impact on research implications.
The results have implications for practice in promoting IT tools and technique for capacity and availability management in an IT service provider. It is suggested that senior management may need to concentrate on adapting a positive approach promoting the usage of new IT tools and techniques in order to achieve IT service improvement, better performance and margin improvement.
This study has addressed the consequences of initiating an organization‐wide knowledge‐based process performance model for a popular strategic IT initiative. The study has answered the questions on better resource utilization and efficient service level management.
Bairi, J., Murali Manohar, B. and Kundu, G.K. (2012), "Capacity and availability management by quantitative project management in the IT service industry", Asian Journal on Quality, Vol. 13 No. 2, pp. 163-176. https://doi.org/10.1108/15982681211265472Download as .RIS
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