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Control Charts as a Transformational Quality Tool for a State Agency


Harsh Zadoo, Operational Excellence Consultant, Texas Department of Transportation, Austin, TX, USA


Transformation, Control Charts, Tools





All organizations are swimming in the parametric data they collect. Usually this data is displayed using Run Charts which provide limited inferences to aid decision making for their business. All Run Charts display values of a parameter (something an organization measures) along a vertical Y-Axis, over a period of time noted in regular time intervals on the horizontal X-Axis, e.g. a month. A Case Study from State Transportation Agency covers how the use of Run Charts to display monthly safety incidents had historically provided no diagnostic capabilities due to inherent nature of Run Charts. The resulting outcome of not getting these safety incidents under control presented a challenge and therefore an opportunity to the Agency. State Legislature was now asking that safety incidents be brought under control, immediately. Yet, two core questions asked by the Safety Division Director remained unanswered from the Run Charts in use – 1) When should I deploy my limited resources across the Districts to improve the safety incidents; and 2) Where should I improve safety incidents.

This was the right point in time to leverage the data in the Run Charts used by the Agency, to create Control Charts for the same metric (monthly safety incidents), for all Districts and the State. The Safety Division realized that the same database and data could be easily used to create the Control Charts for each District. When the Control Charts for one Pilot District were shared with the Section Director there was an immediate recognition of the fact that District Control Charts were created from the data in the past Run Charts for the district’s monthly incidents. With minimal statistical operations done on the mean value of the parameter’s (monthly safety incidents) historical record, the resulting control chart showed transformational diagnostic capabilities. The Control Chart of District’s Monthly Safety Incidents was instrumental in pointing out the District when the value was “Out of Control”. For these “Out of Control” data points the Safety Division Director (armed with the underlying data) for that month’s safety incidents started District level dialogue for months that were “Out of Control” for their Safety incident performance improvement. The District conducted root cause analysis using the Incident details provided. Only the months that showed statistically valid “Out of Control” condition warranted Root cause analysis for their Safety Incidents per month performance metric degradation. This analysis was followed by Closed Loop Corrective actions for their investigated root causes. Safety Incidents per month stabilized and then started to improve.

The Safety Division Director having closed one District’s metric improvement activity successfully felt confident to expand the learning from one District Pilot to the Top 10 Worst performing Districts. When the Control Charts for these Top 10 Worst performing Districts were shared with the Safety Division Director, he was able to immediately spot the performance levels and also the points in time when these Districts’ performance was “out of Control”. Not all Districts were sent off to investigate. Only the Districts that showed statistically valid “Out of Control” condition for a month warranted Root cause analysis for their Safety Incidents per month performance metric degradation. Each quarter a Control Chart review was driven by the Safety Division Director, with the 10 District Leaders conducting Root Cause (RC) analysis of underlying data and then Closed Loop Corrective Action (CLCA) to eliminate root causes behind the “Out of Control” data points. The year on year graph over three years has shown the results of the entire State Agency’s reduction of Incidents per month. The count of Safety incidents improved more than 50% over three years. The radical improvement made to this Agency wide Safety incident metric has saved lives and avoided injuries. It has also reduced related expenses for the State Agency by $5.2M annually.

Participating Organizations at the Lean & Six Sigma  World Conference

Government Agencies

  • Department of Commerce
  • Department of Defense
  • Department of Energy
  • Department of Health & Human Svcs.
  • Department of Homeland Security

  • Department of Justice
  • Department of State
  • Department of the Treasury
  • Department of Transportation
  • Department of Veterans Affairs
  • Environmental Protection Agency
  • NASA
  • Naval Surface Warfare Center
  • Pentagon
  • U.S. Air Force

  • U.S. Army
  • U.S. Marine Corps
  • U.S. Navy
  • U.S. Veterans Affairs
  • United States Army Corps of Engineers


  • AIG
  • Alcoa
  • AT&T
  • Bank of America Corp
  • BASF Corporation
  • Bayer Corporation
  • BMW
  • The Boeing Company
  • Bose Corporation
  • Bristol-Myers Squibb
  • Campbell Soup Company
  • Cardinal Health
  • Caterpillar
  • Chrysler Corporation
  • Chevron
  • Cisco Systems
  • Coca-Cola
  • Comcast
  • Daimler Chrysler
  • Disney
  • Dow Chemical

  • Dr Pepper 
  • Duracell
  • Dupont
  • Eastman Kodak
  • Facebook
  • Google
  • Exxon Mobil
  • Fedex
  • Ford Motor
  • General Electric 
  • General Motors
  • Gillette
  • Goodyear Tire
  • Hewlett Packard
  • Honeywell
  • Humana
  • IBM
  • Kohler
  • Lockheed Martin
  • Macy’s
  • M&M/Mars
  • ManpowerGroup
  • Maytag Appliances
  • Mercedes
  • Merck
  • Mitsubishi
  • Mobil Chemical
  • Motorola
  • NASA
  • Nestle 
  • Northrop Grumman
  • PepsiCo
  • Philip Morris International
  • PNC Financial Services Group
  • Pfizer
  • Pratt & Whitney
  • Procter & Gamble
  • Prudential
  • Raytheon
  • Rolls Royce Allison
  • Target
  • Johnson & Johnson 
  • Schindler Elevator Corporation
  • Schneider Electric
  • Shell
  • Siemens
  • Southwest Airlines
  • Staples
  • Tesla
  • Tiffany & Co.
  • Qualcomm
  • Underwriter Laboratories
  • UnitedHealth Group
  • United Technologies
  • Union Pacific
  • UPS
  • USAA
  • Verizon
  • Walmart
  • Wells Fargo
  • Westinghouse
  • Whirlpool
  • Xerox


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