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By: Nick Blank March 1, 2012. Six Sigma Definitions Goals History Methods Roles Benefits Criticism Software Development.

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Presentation on theme: "By: Nick Blank March 1, 2012. Six Sigma Definitions Goals History Methods Roles Benefits Criticism Software Development."— Presentation transcript:

1 By: Nick Blank March 1, 2012

2 Six Sigma Definitions Goals History Methods Roles Benefits Criticism Software Development

3 Business management strategy. Seeks to improve quality of process outputs. o Identifying and removing the causes of defects. o Minimizing variability. Set of quality management methods. (statistical methods) Creates a special infrastructure of people. Follows a defined sequence of steps.

4 Critical to Quality: internal critical quality parameters that relate to the wants and needs of the customer. Critical to Customer: the input to the Quality Function Deployment activity. Defect: Any type of undesired result. Opportunity: area within a product, process, service, or other system where a defect could be produced or where you fail to achieve the ideal product in the eyes of the customer

5 To achieve no more than 3.4 defects per million opportunities. (99.99966%) 6 standard deviations between the mean and the nearest specification limit. Graph from http://en.wikipedia.org/wiki/Six_Sigmahttp://en.wikipedia.org/wiki/Six_Sigma

6 "Born" at Motorola from Art Sundry's criticism of the company's quality. As a result Motorola found out that better quality is cheaper. Bill Smith developed the particulars in 1986.

7 DMAIC o Improvement system for existing processes DMADV o Improvement system for developing new processes or products

8 Define - the problem, the voice of the customer, and the project goals. o Identify the Critical To Quality characteristics o Create a map of the process to be improved with defined and measurable, deliverables, and goals o Tools: Benchmark, Baseline, Voice of the Customer, Process Flow Map

9 Measure - key aspects of the current process and collect relevant data. o Establish valid and reliable metrics to monitor the progress of the project  Input, process, and output indicators are identified o Determine the impact of defects from each input on the CTQs o Once reasons for input failure are determined, preventative actions are put into place o Tools: Defect Metrics, Data Collection, Sampling Techniques

10 Defects Per Million Opportunities (DPMO) = (Total Defects / Total Opportunities) * 1,000,000 Defects (%) = (Total Defects / Total Opportunities)* 100% Yield (%) = 100 - %Defects http://www.isixsigma.com/process-sigma-calculator/

11 Analyze - the data to investigate and verify cause and effect relationships. o Identify the gap between existing performance and desired performance o Root Cause Analysis – finding the causes of defects o Process Improvement Scenarios o Tools: Cause and Effect diagrams, Decision and Risk Analysis, Control Charts

12 Improve - the current process based upon data analysis techniques. o Create new improvement solutions for each root cause o Cost/Benefit Analysis  What happens if improvements are not made or improvements take too long to implement o Process experimentation and simulation o Implement and adapt to these solutions and the results from these changes

13 Control - the future state process to ensure that any deviations are corrected. o A monitoring plan with proper change management methods o Implement the lesson learned o Put tools in place to maintain process improvement gains o Training o Document the project  New procedures and lessons learned are maintained and give a solid example o Identify future Six Sigma improvement opportunities

14 Define – the problem, the voice of the customer, and the project goals. Measure – key aspects of the current process and collect relevant data. Analyze – the data to investigate and verify cause and effect relationships.

15 Design - optimize the design and plan for design verification. o Specification Limits o Simulation model o Measurement and Control Plan

16 Verify - the design, set up pilot runs, implement the production process. o Pilot runs o Training of personnel that will maintain process o Implementing the processes o Document the processes

17 o Professional who works on project. o Awareness of Six Sigma, but no training. o Anyone

18 o Operate in support or direction of Black Belts. o Trained to analyze and solve quality problems. o Two weeks of training in methods and basic statistical tools.

19 o Team Leader on Six Sigma projects. o Capable in the explanation of six sigma philosophies and the principles. o Training in team leadership. o Knowledge of Lean. o Four to Five weeks of training.

20 o Expert in Six Sigma methods and tools. o Responsible for training others to the Green and Black belt levels. o Responsible for maintaining the integrity. o Highest certification. o Assists the Champion with deployment.

21 o Translate the company's vision, mission, goals and metrics. o Make resources available and remove road blocks. o Resolves cross-functional issues.

22 o Establish strategic focus. o Ensure Six Sigma within the context of the organization's culture and vision.

23 o Motorola o General Electric o Allied Signal o Citibank o Microsoft o Lockheed

24 General Electric produces annual benefits of over 2.5 billion. Less customer service costs. Raise customer satisfaction.

25 Need aggressive performance tracking and accountability for results. Employees need to be willing to use statistical tools. Pay more attention to steps than the actual result. Six Sigma is over sold. Too many consulting firms claiming mastery. Cost of infrastructure. Cost of training for "belts." (MBB at least $17,000 alone) A pacemaker needs higher standards than six sigma.

26 Process variation can never be eliminated or even reduced below a moderate level. No two modules are alike. There are very large differences in skills and experience from one developer to another. Specifications are not based around tolerances. Systems don't fail because the are assembled from many loosely toleranced components. A single well-placed defect in a low level component can be catastrophic.

27 Time - The amount of time required to perform a task. Size - The size of the work product. Defects - the number and type of defect, removal time, point of injection, and point of removal.

28 Works best with incremental and evolutionary product development. Instrumented to produce high quality metric data. Normally requires changes to the software development process and project management process. As quality is improved, more defects are found earlier in life cycle and more predictable. Has significant impact in scheduling tasks and project tracking. Can even predict how many defects in module with relative accuracy.

29 Standard Six Sigma not directly related to software development. Developers can't bridge gap between on their own. Too many competing initiatives - CMMI, ISO9000. Not enough time to analyze quality. One size fits all generates resistance.

30 The software development companies that are claiming success with Six Sigma are using it to improve the processes within the standards of CMMI and ISO. Individual ProcessOrganizational infrastructure Six SigmaIdentifies how the activities might be improved. Identifies what activities are used for improvement (DMAIC). CMMIIdentifies what activities are expected in the process. Identifies how those activities might be implemented.

31 Six Sigma Definitions Goals History Methods Roles Benefits Criticism Software Development

32  [1] Ideal Meadia LLC. i Six Sigma. Retrieved 2/18/2010. http://www.isixsigma.com/  [2] Huesing, Tina. “Six Sigma Through The Years”. Motorola, 2008. Retrieved 2/18/2012. http://6sigmaexperts.com/presentations/Six_Sigma_Through_the_Years. pdf  [3] Functional Methods. (2004). DMADV (Define, Measure, Analyze, Design, Verify) Roadmap. Retrieved 2/18/2012 http://www.functionalmethods.com/DMADV%20Roadmap.pdf  [4] Motorola, Inc. (2007). Motorola Univerisity, Six Sigma in Action. Retrieved 2/18/2012 http://www.motorola.com/motorolauniversity.jsp  [5] Al-Qutaish, R. E., & Al-Sarayreh, K. T. (2008). Applying six-sigma concepts to the software engineering: myths and facts. Proceedings of the 7th International Conference on Software Engineering Parallel and Distributed Systems SEPADS08, 178-183. Retrieved from http://portal.acm.org/citation.cfm?id=1416502.1416536


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