Introduction to Six Sigma
Topics (Session 1) Understanding Six Sigma History of Six Sigma Six Sigma Methodologies & Tools Roles & Responsibilities
Six Sigma is. . . A performance goal, representing 3.4 defects for
every million opportunities to make one.
A series of tools and methods used to improve or
design products, processes, and/or services.
A statistical measure indicating the number of
standard deviations within customer expectations. business and its processes.
A disciplined, fact-based approach to managing a A means to promote greater awareness of customer
needs, performance measurement, and business improvement.
Whats in a name? Sigma is the Greek letter representing the standard
deviation of a population of data. Sigma is a measure
of variation (the data spread)
What does variation mean?20
Variation means that a
process does not produce the same result (the Y) every time. Some variation will exist in
all processes. Variation directly affects customer experiences.
Customers do not feel averages!
Measuring Process PerformanceThe pizza delivery example. . . Customers want their pizza
delivered fast!Guarantee = 30 minutes or less
What if we measured performance and found
an average delivery time of 23.5 minutes? On-time performance is great, right? Our customers must be happy with us, right?
How often are we delivering on time?Answer: Look at the variation!30 min. or less
Managing by the average doesnt tell the whole story. The 0 10 20 x 30 40 50
average and the variation together show whats happening.
Reduce Variation to Improve PerformanceHow many standard deviations can you fit within customer expectations?30 min. or less
x 30 40 50 Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).0 10 20
Managing Up the Sigma ScaleSigma 1 2 3 4 5 6 % Good % Bad30.9% 69.1% 93.3% 99.38% 99.977% 69.1% 30.9% 6.7% 0.62% 0.023%
DPMO691,462 308,538 66,807 6,210 233 3.4
Examples of the Sigma ScaleIn a world at 3 sigma. . . There are 964 U.S. flight
In a world at 6 sigma. . . 1 U.S. flight is cancelled
cancellations per day. The police make 7 false
every 3 weeks. There are fewer than 4 false
arrests every 4 minutes. In MA, 5,390 newborns are
arrests per month. 1 newborn is dropped every 4
dropped each year. In one hour, 47,283
years in MA. It would take more than
international long distance calls are accidentally disconnected.
2 years to see the same number of dropped international calls.
Topics Understanding Six Sigma History of Six Sigma Six Sigma Methodologies & Tools Roles & Responsibilities How YOU can use Six Sigma
The Six Sigma Evolutionary Timeline1818: Gauss uses the normal curve to explore the mathematics of error analysis for measurement, probability analysis, and hypothesis testing. 1736: French mathematician Abraham de Moivre publishes an article introducing the normal curve. 1896: Italian sociologist Vilfredo Alfredo Pareto introduces the 80/20 rule and the Pareto distribution in Cours dEconomie Politique. 1924: Walter A. Shewhart introduces the control chart and the distinction of special vs. common cause variation as contributors to process problems.
1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures
for Performing a Failure Mode Effects and Criticality Analysis.1941: Alex Osborn, head of BBDO Advertising, fathers a widely-adopted set of rules for brainstorming. 1986: Bill Smith, a senior engineer and scientist introduces the concept of Six Sigma at Motorola
1960: Kaoru Ishikawa introduces his now famous cause-and-effect diagram. 1970s: Dr. Noriaki Kano introduces his two-dimensional quality model and the three types of quality.
1995: Jack Welch launches Six Sigma at GE. 1994: Larry Bossidy launches Six Sigma at Allied Signal.
Six Sigma Companies
Six Sigma and Financial Services
Topics Understanding Six Sigma History of Six Sigma Six Sigma Methodologies & Tools Roles & Responsibilities
DMAIC The Improvement MethodologyDefineObjective: DEFINE the opportunity
Measure Analyze Improve ControlObjective: Objective: Objective: MEASURE current ANALYZE the root IMPROVE the performance causes of problems process to eliminate root causes Key Measure Tools: Critical to Quality Requirements (CTQs) Sample Plan Capability Analysis Failure Modes and Effect Analysis (FMEA) Key Analyze Tools: Histograms, Boxplots, MultiVari Charts, etc. Hypothesis Tests Regression Analysis Objective: CONTROL the process to sustain the gains.
Key Define Tools: Cost of Poor Quality (COPQ) Voice of the Stakeholder (VOS) Project Charter As-Is Process Map(s) Primary Metric (Y)
Key Improve Key Control Tools: Tools: Solution Selection Control Charts Matrix Contingency To-Be Process and/or Action Map(s) Plan(s)
Define DMAIC ProjectWhat is the project?$
Cost of Poor Quality
Voice of the Stakeholde r
Six Sigma What is the problem? The problem is the Output (a
Y in a math equation Y=f(x1,x2,x3) etc). What is the cost of this problem Who are the stake holders / decision makers Align resources and expectations
Define As-Is ProcessHow does our existing process work?Move-It! Courier Package Handling ProcessCourier Mail Clerk In-SortClerk In-SortSupervisor DistanceFeeClerk WeightFeeClerk Accounts ReceivableClerk Accounts Supervisor Out-SortClerk Out-SortSupervisorObserv e package weight (1 or 2) on back of package
Look up appropriate Weight Fee and write in top middle box on package back Take packages f rom Weight Fee Clerk Outbox to A/R Clerk Inbox. Add Distance & Weight Fees together and write in top right box on package back
Circle Total Fee and Draw Arrow f rom total to sender code
Take packages f rom A/R Clerk Outbox to Accounts Superv isorInbox. Take packages f rom Accounts Superv isor Outbox to OutSort Clerk Inbox.
Write Total Fee f rom package in appropriate Sender column on Accts. Supv .s log
Draw 5-point Star in upper right corner of package f ront
Sort packages in order of Sender Code bef ore placing in outbox
Take packages f rom Out-Sort Clerk Outbox to Out-Sort Superv isorInbox.
Add up Total # of Packages and Total Fees f rom log and create client inv oice
Observ e sender and receiv er codes and make entry in Out-Sort Superv isors log
Deliv erPackages to customers according to N, S, E, W route
Submit log to General Manager at end of round
Does EVERYONE agree how the current process works?Deliv er inv oiceto client Submit log to General Manager at conclusion of round. Submit log to General Manager at end of round
Define the Non Value Add steps
Define Customer RequirementsWhat are the CTQs? What motivates the customer?SECONDARY RESEARCHMarket Data
Voice of the Customer
Key Customer Issue
Critical to Quality
Listening Posts Industry Intel
PRIMARY RESEARCHSurvey s
Measure Baselines and CapabilityWhat is our current level of performance?Descriptive Statistics
Sample some data / not all dataCurrent Process actuals measured against the Customer expectation What is the chance that we will succeed at this level every time?0 10 20 30 40 50
Variable: 2003 OutputAnderson-Darling Normality Test A-Squared: P-Value: Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum 0.211 0.854 23.1692 10.2152 104.349 0.238483 0.240771 100 0.2156 16.4134 23.1475 29.6100 55.2907
95% Confidence Interval for Mu
95% Confidence Interval for Mu 21.142319.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5
95% Confidence Interval for Sigma 8.9690 11.8667
95% Confidence Interval for Median 95% Confidence Interval for Median 19.7313 26.0572
Analyze Potential Root CausesWhat affects our process?Ishikawa Diagram (Fishbone)
y = f (x1, x2, x3 . . . xn)
Analyze Validated Root CausesWhat are the key root causes?
Data Stratification Process Simulatio n
y = f (x1, x2, x3 . . . xn)Critical Xs
Improve Potential SolutionsHow can we address the root causes we identified? Address the causes, not the symptoms.
y = f (x1, x2, x3 . . . xn)Critical Xs
Divergent | Convergent
Improve Solution SelectionHow do we choose the best solution?Solution Selection MatrixQualit ySolution Sigma Time CBA Other Score
Six SigmaSolution Right Wrong Implementation Good Bad
Solution Implementatio n Plan
Control Sustainable BenefitsHow do we hold the gains of our new process? Some variation is normal and OK How High and Low can an X go yet not materially impact the Y Pre-plan approach for control exceptions35 UCL=33.48
Process Control System (Business Process Framework)Process Owner: Process Description: Direct Process Customer: CCR: Date:
FlowchartCustomer Sales Proce