Improving the resolution time performance of an application support process using Six Sigma methodology

Abstract

Purpose

This paper is a case study on the successful application of Six Sigma methodology in the information technology industry. The purpose of this paper is to improve the resolution time performance of an application support process.

Design/methodology/approach

Through brainstorming, the potential factors influencing the resolution time are identified. From the potential factors, the important factors, namely, day-wise ticket volume, team’s software engineering skill and domain expertise are shortlisted using test of hypothesis, correlation, etc. Then a model is developed using principal component regression, linking the critical to quality characteristic with the root causes or important factors. Finally, a solution methodology is developed using the model to obtain the team composition and size with optimum software skill and domain expertise to resolve the tickets within the required time.

Findings

The implementation of the solution resulted in improving the process performance significantly. The process performance index increased from 0.00 to 1.2 and parts per million reduced from 501366.31 to 153. 33.

Practical implications

The software engineers can use the similar approach to improve the performance of core software activities such as coding, testing and bug fixing. The approach can also be used for improving the performance of other skill-based operations such as error reduction in medical diagnostics.

Originality/value

This is one of the rare Six Sigma case studies on improving skill-based processes such as software development. The study also demonstrates the usefulness of the Six Sigma methodology for solving dynamic problems whose solution needs to be continuously adjusted with the changes in the input or process conditions.

Citation

John, B. and Kadadevaramath, R.S. (2020), "Improving the resolution time performance of an application support process using Six Sigma methodology", International Journal of Lean Six Sigma, Vol. 11 No. 4, pp. 663-686. https://doi.org/10.1108/IJLSS-10-2018-0108