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Ticket Accuracy Six Sigma Case Study

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Kevin Egan
Kevin Egan

The pharmaceutical company was experiencing a 40% error rate in ticket logging into their help desk, costing 80 person-hours per month to correct. Measurement found the existing sigma level was 0.5, with a 31% defect rate. Root causes were identified as a lack of operational definitions, inconsistent interpretation between agents, and too many options for classifying tickets. Improvements such as correcting databases, reducing classification options, and clarifying definitions increased the sigma level to 2.8 and reduced defects by 69%, saving the company over $26,000 annually by reducing correction time.

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Six Sigma in Action Ticket Accuracy Customer Profile – 41,000 employee pharmaceutical research company Business Problem & Impact Process Capability –fo r C 3 P C ha rt Before The customer estimated error rate of 40% in the accuracy of 0.7 tickets logged into the client’s in-house help desk was causing 0.6 3.0S L 80 person-hours of time per month to correct. 0.5 Measure & Analyze Proportion 0.4 Data Collection: Accuracy of logged calls was measured - defects 0.3 P = 0.3 existing sigma (long term) was found to be 0.5 (31% defects) Root Causes: Lack of operational definitions of the calls, 0.2 differences in agent interpretation, too many options for 0.1 classifying tickets and the accuracy of supporting databases 0.0 -3.0S were identified as root causes. 0 10 20 Improve & Control S am p le Num b e r Existing databases that feed the ticket system were corrected and maintenance controls were established. The number of fields that client help desk staff had to select from was reduced, and operation definitions were clarified and published. Sigma was increased to 2.8 and defects were reduced by 69%. Results/Benefits As a result of the project, the customer saved over US$26K in productivity since less time was needed to correct ticket errors within their process. A savings of US$26K annual to the customer!

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Ticket Accuracy Six Sigma Case Study

  • 1. Six Sigma in Action Ticket Accuracy Customer Profile – 41,000 employee pharmaceutical research company Business Problem & Impact Process Capability –fo r C 3 P C ha rt Before The customer estimated error rate of 40% in the accuracy of 0.7 tickets logged into the client’s in-house help desk was causing 0.6 3.0S L 80 person-hours of time per month to correct. 0.5 Measure & Analyze Proportion 0.4 Data Collection: Accuracy of logged calls was measured - defects 0.3 P = 0.3 existing sigma (long term) was found to be 0.5 (31% defects) Root Causes: Lack of operational definitions of the calls, 0.2 differences in agent interpretation, too many options for 0.1 classifying tickets and the accuracy of supporting databases 0.0 -3.0S were identified as root causes. 0 10 20 Improve & Control S am p le Num b e r Existing databases that feed the ticket system were corrected and maintenance controls were established. The number of fields that client help desk staff had to select from was reduced, and operation definitions were clarified and published. Sigma was increased to 2.8 and defects were reduced by 69%. Results/Benefits As a result of the project, the customer saved over US$26K in productivity since less time was needed to correct ticket errors within their process. A savings of US$26K annual to the customer!