Your SlideShare is downloading. ×
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Six Sigma explained through SPC and Control Limits
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Six Sigma explained through SPC and Control Limits

9,890

Published on

Explaining Six Sigma to a layman in simple terms is always challenging. This presentation was prepared for non-process professionals to understand Six Sigma concept.

Explaining Six Sigma to a layman in simple terms is always challenging. This presentation was prepared for non-process professionals to understand Six Sigma concept.

Published in: Design, Business, Technology
0 Comments
11 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
9,890
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
276
Comments
0
Likes
11
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Every product or a service comes out of some delivery or a production system Every delivery system or production machine has its own specifications which result in process variations Process variations may result in variations in products If process variations result in product or service with variations out of acceptable specs, it causes quality issue Your processes must be predictable enough to deliver within customer specifications every time.
  • Every product or a service comes out of some delivery or a production system Every delivery system or production machine has its own specifications which result in process variations Process variations may result in variations in products If process variations result in product or service with variations out of acceptable specs, it causes quality issue Your processes must be predictable enough to deliver within customer specifications every time.
  • Every product or a service comes out of some delivery or a production system Every delivery system or production machine has its own specifications which result in process variations Process variations may result in variations in products If process variations result in product or service with variations out of acceptable specs, it causes quality issue Your processes must be predictable enough to deliver within customer specifications every time.
  • Every product or a service comes out of some delivery or a production system Every delivery system or production machine has its own specifications which result in process variations Process variations may result in variations in products If process variations result in product or service with variations out of acceptable specs, it causes quality issue Your processes must be predictable enough to deliver within customer specifications every time.
  • 1- σ : One standard deviation away from the mean in either direction on the horizontal axis (the blue area on the graph) accounts for somewhere around 68 percent of the people in this group. 2- σ : Two standard deviations away from the mean (the blue and brown areas) account for roughly 95 percent of the sample population. 3- σ : Three standard deviations (the blue, brown, green areas) account for about 99 percent of the sample population. 6- σ : Six Standard deviation (blue, brown, green, grey areas) account for 99.99% of sample population.
  • Product or a service variations results in poor quality. Quality control measures & fix-n-repairs means higher cost Cost on rejected pieces or cost of service beyond acceptable range Inconsistent product or service results in unhappy customers Unhappy customers may leave to competitors Hits the bottom line with reduced sales
  • This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation.
  • This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation.
  • This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation.
  • The more steps a process has, the more opportunities for errors. First-pass yields of each sub-process effect the first-pass yield of all downstream process steps, as well as the final output. If each sub-process runs at a 99% first-pass yield, then the entire process’ first-pass yield will total: 90% with 10 steps 82% with 20 steps 61% with 50 steps Note: Y RT stands for “Rolled Throughput Yield.”
  • Transcript

    • 1. R. Attri Engineering Management Series, Paper No. 3, April 2010Six Sigma ProcessSimplifiedexplained through SPC and ControllimitsRaman K. AttriApril 2010R. Attri Engineering Management Series, Paper No. 3, April 2010
    • 2. R. Attri Engineering Management Series, Paper No. 3, April 2010Six Sigma DefinedProcess characterization to optimize all manufacturingprocesses to achieve Cp and Cpk value equal to 2.0.Measure to define the capability of a processGoal for improvement that reaches near-perfectionSystem of Management approach to achieve highest qualityand performance
    • 3. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Variations vs QualityExample of a metal block of required length =10cmTolerance= +1%UCL= 10.1cmLCL= 9.9cm
    • 4. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Variations vs QualityPlot of Sample Data Over Time0204060801 5 9 13 17 21TimeSampleValueSampleValueUCLAverageLCLLengthincm9.910.1samplesAny block of length less than 9.9cm andmore than 10.1cm is said to have a“defect”.Two “opportunities” for defects – one inlength and one in breadth
    • 5. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Variations vs QualityPlot of Sample Data Over Time0204060801 5 9 13 17 21TimeSampleValueSampleValueUCLAverageLCLLengthincm9.910.1samplesAny block of length less than 9.9cm andmore than 10.1cm is said to have a“defect”.Two “opportunities” for defects – one inlength and one in breadth
    • 6. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Variations vs QualityPlot of Sample Data Over Time0204060801 5 9 13 17 21TimeSampleValueSampleValueUCLAverageLCLLengthincm9.910.1samplesAny block of length less than 9.9cm andmore than 10.1cm is said to have a“defect”.Two “opportunities” for defects – one inlength and one in breadth
    • 7. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Variations vs QualityPlot of Sample Data Over Time0204060801 5 9 13 17 21TimeSampleValueSampleValueUCLAverageLCLLengthincm9.910.1samplesAny block of length less than 9.9cm andmore than 10.1cm is said to have a“defect”.Two “opportunities” for defects – one inlength and one in breadth
    • 8. R. Attri Engineering Management Series, Paper No. 3, April 20103-Sigma vs 6-Sigma Process68.2%95.4%99.6%99.9%1 σ2 σ3 σ6 σ6-σ process target 99.9% of thesamples within UCL and LCL. A 0.1 %error margin (below specified quality)LSL USL
    • 9. R. Attri Engineering Management Series, Paper No. 3, April 2010σ-level in terms of defectsConcept of DPM coined by Bill Smith of MotorolaProcess capability is calculated using statistical toolsProcess capability in terms of σ is a symbol of quality forcustomer.
    • 10. R. Attri Engineering Management Series, Paper No. 3, April 2010Process variations hit bottom-line ofoperationsQuality issues in product variations may result in unhappycustomers and eventually lose of salesHigherCostsReducedSalesProductVariationPoorQualityUnhappyCustomers
    • 11. R. Attri Engineering Management Series, Paper No. 3, April 2010Process ControlFrequency(length)SizeLower control limit Upper control limit(a) In statisticalcontrol and capableof producing withincontrol limits, 6 Sigma
    • 12. R. Attri Engineering Management Series, Paper No. 3, April 2010Process ControlFrequency(length)SizeLower control limit Upper control limit(a) In statisticalcontrol and capableof producing withincontrol limits, 6 Sigma(b) In statistical controlbut not capable ofproducing withincontrol limits3-Sigma
    • 13. R. Attri Engineering Management Series, Paper No. 3, April 2010Process ControlFrequency(length)SizeLower control limit Upper control limit(a) In statisticalcontrol and capableof producing withincontrol limits, 6 Sigma(b) In statistical controlbut not capable ofproducing withincontrol limits3-Sigma(c) Out of control
    • 14. R. Attri Engineering Management Series, Paper No. 3, April 2010Measuring Process Capabilityσ6LSLUSLC p−=σμ3LSL−σμ3−USL=pkC Lesser of orProcess capability is a measure of therelationship between the natural variation ofthe process and the design specificationsA capable process must have a Cp or Cpkof at least 1.0A capable process is not necessarily in thecenter of the specification, but it falls withinthe specification limit at both extremesSix Sigma quality requires a Cp = 2.0
    • 15. R. Attri Engineering Management Series, Paper No. 3, April 2010Meanings of Cpk MeasuresCpk = negative numberCpk = zeroCpk = between 0 and 1Cpk = 1Cpk > 1
    • 16. R. Attri Engineering Management Series, Paper No. 3, April 2010Process Design Options3-Sigma process if off centre causes capability issue6-Sigma process allows for 1.5 Sigma Process ShiftAccounts for long term variability and complexity of process(Ranges from 1.4 to 1.6 sigma)3.4 DPMO = area under curve at 4.5 sigma level
    • 17. R. Attri Engineering Management Series, Paper No. 3, April 2010End-to-End Effectiveness is Key
    • 18. R. Attri Engineering Management Series, Paper No. 3, April 2010Goal of Six SigmaSigma DPMO Yield Cpk1.5 500,000 50% 0.503.00 66,800 93.320% 1.003.50 22,700 97.730% 1.174.00 6,210 99.3790% 1.334.50 1,350 99.8650% 1.505.00 230 99.9770% 1.676.00 3.4 99.99966% 2.00Goal of Six Sigma is to drive up Cp and Cpk to at least 2!
    • 19. R. Attri Engineering Management Series, Paper No. 3, April 2010Author’s contactFor any questions or training queries, contact the author:Raman K. Attrirkattri@rediffmail.comAuthor has over 15 years of project management, product development and quality management experience in leadingMNC product development corporations. He has earned numerous international certification awards - CertifiedManagement Consultant (MSI USA/ MRA USA), Certified Six Sigma Green Belt Professional (Six Sigma India),Certified Quality Director (ACI USA), Certified Engineering Manager (SME USA), Certified Project Director (IAPPMUSA), to name a few. His research and training interests are in learning, development, performance management,research management and product development. He holds MBA, Executive MBA, Masters in Technology and Bachelorin Technology. In addition to this, he has 60+ educational qualifications, credentials and certifications in his name..

    ×