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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.

PurposeThe paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.Design/methodology/approachTo optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.FindingsThis paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.Originality/valueVery few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

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