Pradeep Singha, profile picture
Uploaded byPradeep Singha
PPT, PDF1,118 views

Six sigma

The document provides an introduction to Six Sigma, including its history, methodologies, roles and how individuals can use it. Six Sigma aims to reduce variation and defects through statistical analysis and process improvement. It originated in the 1980s and has been widely adopted by many companies. The DMAIC methodology is described, which stands for Define, Measure, Analyze, Improve, and Control project phases. Key tools for each phase like process mapping and control charts are also mentioned.

Embed presentation

Downloaded 44 times
Introduction to Six Sigma
Topics (Session 1) ♦ Understanding Six Sigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
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. ♦ A disciplined, fact-based approach to managing a business and its processes.
What’s 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) σ μ
What does variation mean? 20 ♦ Variation means that a 15 process does not produce 10 the same result (the “Y”) 5 every time. 0 -5 ♦ Some variation will exist in -10 all processes. ♦ Variation directly affects customer experiences. Customers do not feel averages!
Measuring Process Performance The 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 30 min. or less the variation! s 0 10 20 x 30 40 50 ♦ Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.
Reduce Variation to Improve Performance How many standard 30 min. or less deviations can you “fit” within s customer expectations? 0 10 20 x 30 40 50 ♦ Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).
Managing Up the Sigma Scale Sigma % Good % Bad DPMO 1 30.9% 69.1% 691,462 2 69.1% 30.9% 308,538 3 93.3% 6.7% 66,807 4 99.38% 0.62% 6,210 5 99.977% 0.023% 233 6 99.9997% 0.00034% 3.4
Examples of the Sigma Scale In a world at 3 sigma. . . In a world at 6 sigma. . . ♦ There are 964 U.S. flight ♦ 1 U.S. flight is cancelled every cancellations per day. 3 weeks. ♦ The police make 7 false arrests ♦ There are fewer than 4 false every 4 minutes. arrests per month. ♦ In MA, 5,390 newborns are ♦ 1 newborn is dropped every 4 dropped each year. years in MA. ♦ In one hour, 47,283 ♦ It would take more than international long distance calls 2 years to see the same number are accidentally disconnected. 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 Timeline 1818: Gauss uses the normal curve 1924: Walter A. Shewhart introduces to explore the mathematics of error the control chart and the distinction of analysis for measurement, probability special vs. common cause variation as analysis, and hypothesis testing. contributors to process problems. 1736: French 1896: Italian sociologist Vilfredo mathematician Alfredo Pareto introduces the 80/20 Abraham de rule and the Pareto distribution in Moivre publishes Cours d’Economie Politique. an article introducing the normal curve. 1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures 1960: Kaoru Ishikawa for Performing a Failure Mode Effects introduces his now famous and Criticality Analysis. cause-and-effect diagram. 1941: Alex Osborn, head of 1970s: Dr. Noriaki Kano BBDO Advertising, fathers a introduces his two-dimensional widely-adopted set of rules for quality model and the three “brainstorming”. types of quality. 1986: Bill Smith, a senior engineer and scientist introduces the 1995: Jack Welch concept of Six Sigma at Motorola 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 ♦ How YOU can use Six Sigma
DMAIC – The Improvement Methodology Define Measure Analyze Improve Control Objective: Objective: Objective: Objective: Objective: DEFINE the MEASURE current ANALYZE the IMPROVE the CONTROL the opportunity performance root causes of process to process problems eliminate root to sustain the gains. causes Key Define Tools: Key Measure Key Analyze Key Improve Key Control • Cost of Poor Tools: Tools: Tools: Tools: Quality (COPQ) • Critical to Quality • Histograms, • Solution • Control Charts • Voice of the Requirements Boxplots, Multi- Selection Matrix • Contingency Stakeholder (CTQs) Vari Charts, etc. • To-Be Process and/or Action (VOS) • Sample Plan • Hypothesis Tests Map(s) Plan(s) • Project Charter • Capability • Regression • As-Is Process Analysis Analysis Map(s) • Failure Modes • Primary Metric and Effect (Y) Analysis (FMEA)
Define – DMAIC Project What is the project? $ Project Cost of Charter Poor Voice of Quality S ta k e h o ld e r s 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 Process How does our existing process work? Move-It! Courier Package Handling Process Accounts Accounts Courier Mail Clerk In-SortClerk In-SortSupervisor DistanceFeeClerk WeightFeeClerk Out-SortClerk Out-SortSupervisor ReceivableClerk Supervisor Observ e package weight (1 or 2) on back of package Look up appropriate Weight Fee and write in top middle box on package back Add Distance & Take packages Weight Fees f rom Weight Fee together and write Clerk Outbox to in top right box on A/R Clerk Inbox. package back Circle Total Fee Does EVERYONE and Draw Arrow f rom total to sender code agree how the current Accounting Take packages Write Total Fee f rom A/R Clerk f rom package in process works? Outbox to appropriate Accounts Sender column on Superv isorInbox. Accts. Supv .’s log Take packages Draw 5-point Star f rom Accounts in upper right Superv isor corner of package Outbox to Out- f ront Sort Clerk Inbox. Define the Non Value Sort packages in order of Sender Code bef ore placing in outbox Take packages Add steps Add up Total # of Observ e sender Finalizing f rom Out-Sort Packages and and receiv er Clerk Outbox to Total Fees f rom codes and make Out-Sort log and create entry in Out-Sort Superv isorInbox. client inv oice Superv isor’s log Deliv erPackages Delivery to customers according to N, S, E, W route Deliv er inv oiceto client Submit log to Submit log to Submit log to General Manager General Manager General Manager at conclusion of at end of round at end of round round.
Define – Customer Requirements What are the CTQs? What motivates the customer? Voice of the Customer Key Customer Issue Critical to Quality SECONDARY RESEARCH Market Data Industry l e n yrt s udn I Benchmarking Customer t I Correspondence Customer Service s s o P gn ne s L i t i PRIMARY RESEARCH t Surve Surve ys ys OTM Obser- Focus Groups vations
Measure – Baselines and Capability What is our current level of performance? Descriptive Statistics ♦ Sample some data / not all data Variable: 2003 Output ♦ Current Process actuals measured against Anderson-Darling Normality Test A-Squared: 0.211 P-Value: 0.854 the Customer expectation Mean StDev 23.1692 10.2152 ♦ Variance 104.349 What is the chance that we will succeed Skewness Kurtosis N 0.238483 0.240771 100 0 10 20 30 40 50 at this level every time? Minimum 1st Quartile 0.2156 16.4134 Median 23.1475 3rd Quartile 29.6100 Maximum 55.2907 Pareto Chart for Txfr Defects 95% Confidence Interval for Mu 95% Confidence Interval for Mu 21.1423 25.1961 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 95% Confidence Interval for Sigma 100 100 8.9690 11.8667 95% Confidence Interval for Median 95% Confidence Interval for Median 80 19.7313 26.0572 60 Percent Count 50 40 20 0 0 t un er s Defect La te A mo Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0
Measure – Failures and Risks Where does our process fail and why? Subjective opinion mapped into an “objective” risk profile number Failure Modes and Effects Analysis (FMEA) Process/Product Process or Prepared by: Page ____ of ____ Product Name: Responsible: FMEA Date (Orig) ______________ (Rev) _____________ Process S O D R S O D R Step/Part E C E P Actions E C E P Number Potential Failure Mode Potential Failure Effects V Potential Causes C Current Controls T N Recommended Resp. Actions Taken V C T N X1 0 0 0 0 X2 0 0 0 0 X3 0 0 0 0 X4 0 0 0 0 0 0 etc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Analyze – Potential Root Causes What affects our process? Ishikawa Diagram (Fishbone) Six Sigma y = f (x1, x2, x3 . . . xn)
Analyze – Validated Root Causes What are the key root causes? Pareto Chart for Txfr Defects 100 100 80 Percent 60 Count 50 40 20 0 0 nt er s E x p e r im e n t a l D e s ig n Defect La te Am ou Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0 Data Regression Stratification Analysis Pareto Chart for Amt Defects 15 100 80 Process Simulatio Percent 60 Count 10 40 5 20 n 0 0 cy al er Defect Cu rren Cle ric Oth Count 12 3 2 Percent 70.6 17.6 11.8 Cum % 70.6 88.2 100.0 Six Sigma y = f (x1, x2, x3 . . . xn) Critical Xs
Improve – Potential Solutions How can we address the root causes we identified? ♦ Address the causes, not the symptoms. Generate Evaluate Clarify Decision y = f (x1, x2, x3 . . . xn) Critical Xs Divergent | Convergent
Improve – Solution Selection How do we choose the best solution? Solution Selection Matrix Qualit Solution Sigma Time CBA Other Score y Time Cost Right Six Sigma Solution Wrong Nice ☺ Try Solution Implementatio Nice n Plan X doo G da B not a ne ml p m e I Idea i t
Control – Sustainable Benefits How 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 exceptions Process Control System (Business Process Framework) Process Owner: Direct Process Customer: Date: Process Description: CCR: Flowchart Measuring and Monitoring Measures Key Specs (Tools) Responsibility Contingency Measure &/or Remarks Where & (Who) (Quick Fix) ments Targets Frequency P1 - activity 35 duration, min. UCL=33.48 P2 - # of incomplete Individual Value loan applications 25 Mean=24.35 15 LCL=15.21 0 10 20 30 Observation Number
DFSS – The Design Methodology Design for Six Sigma Define Measure Analyze Develop Verify ♦ Uses – Design new processes, products, and/or services from scratch – Replace old processes where improvement will not suffice ♦ Differences between DFSS and DMAIC – Projects typically longer than 4-6 months – Extensive definition of Customer Requirements (CTQs) – Heavy emphasis on benchmarking and simulation; less emphasis on baselining ♦ Key Tools – Multi-Generational Planning (MGP) – Quality Function Deployment (QFD)
Topics ♦ Understanding Six Sigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
Champions ♦ Promote awareness and execution of Six Sigma within lines of business and/or functions ♦ Identify potential Six Sigma projects to be executed by Black Belts and Green Belts ♦ Identify, select, and support Black Belt and Green Belt candidates ♦ Participate in 2-3 days of workshop training
Black Belts ♦ Use Six Sigma methodologies and advanced tools (to execute business improvement projects ♦ Are dedicated full-time (100%) to Six Sigma ♦ Serve as Six Sigma knowledge leaders within Business Unit(s) ♦ Undergo 5 weeks of training over 5-10 months
Green Belts ♦ Use Six Sigma DMAIC methodology and basic tools to execute improvements within their existing job function(s) ♦ May lead smaller improvement projects within Business Unit(s) ♦ Bring knowledge of Six Sigma concepts & tools to their respective job function(s) ♦ Undergo 8-11 days of training over 3-6 months
Other Roles ♦ Subject Matter Experts – Provide specific process knowledge to Six Sigma teams – Ad hoc members of Six Sigma project teams ♦ Financial Controllers – Ensure validity and reliability of financial figures used by Six Sigma project teams – Assist in development of financial components of initial business case and final cost-benefit analysis
Topics ♦ Understanding Six Sigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
Questions?
Topics for Detailed Discussion ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Problem Identification “If it ain’t broke, why fix it “This is the way we’ve always done it…”
Problem Identification • First Pass Yield • Roll Throughput Yield • Histogram • Pareto
Problem Identification First Pass Yield (FPY): The probability that 100 Units any given unit can go Step 1 Outputs / Inputs through a system defect-free without 100 / 100 = 1 rework. 100 Scrap 10 Units Step 2 90 / 100 = .90 90 Scrap 3 Units Step 3 87 / 90 = .96 87 Scrap 2 Units Step 4 85 / 87 = .97 At first glance, the yield would seem to be When in fact the FPY is (1 x .90 x .96 x .97 = . 85% (85/100 but….) 838) 85
Problem Identification Rolled 100 Units Outputs / Inputs Throughput Yield (RTY): Step 1 90 / 100 = .90 The yield of individual Re-Work process steps 10 Units 100 Units multiplied Step 2 97 / 100 = .97 together. Reflects the Re-Work hidden factory 3 Units 100 Units rework issues associated with Step 3 98 / 100 = .98 a process. Re-Work 2 Units 100 Units Step 4 .90 x .97 x .98 = .855 100 Units
Problem Identification RTY Examples - Widgets 50 Roll Throughput Yield Function 1 50/50 = 1 (50-5)/50 = .90 50 (50-10)/50 = .80 Function 2 5 (50-5)/50 = .90 50 Function 3 10 1 x .90 x .80 x .90 = .65 50 Function 4 5 Put another way, this process is operating 50 a 65% efficiency
Problem Identification RTY Example - Loan Underwriting 50 Roll Throughput Yield Application 50/50 = 1 (50-7-2)/50 = .82 2 50 7 Fails (43-6)/43 = .86 Underwrite Underwriting (43-1-2)/43 = .93 6 43 Complete Full Paperwork 1 x .82 x .86 x .93 = .66 2 1 43 Decide not to Close borrow 42 Put another way, this process is operating a 66% efficiency
Problem Identification Histogram – A histogram is a basic graphing tool that displays the relative frequency or occurrence of continuous data values showing which values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whether there are any outliers. Histogram of Cycle Time 40 30 Frequency 20 10 0 0 100 200 300 400 500 C8
Problem Identification Histogram – Can also help us graphically understand the data Descriptive Statistics Variable: CT Anderson-Darling Normality Test A-Squared: 6.261 P-Value: 0.000 Mean 80.1824 StDev 67.6003 Variance 4569.81 Skewness 2.31712 Kurtosis 8.26356 N 170 25 100 175 250 325 400 Minimum 1.000 1st Quartile 31.000 Median 66.000 3rd Quartile 105.000 95% Confidence Interval for Mu Maximum 444.000 95% Confidence Interval for Mu 69.947 90.417 54 64 74 84 94 95% Confidence Interval for Sigma 61.098 75.664 95% Confidence Interval for Median 95% Confidence Interval for Median 55.753 84.494
Problem Identification Pareto – The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects. Pareto Chart for WEB 100 100 80 Percent 60 Count 50 40 20 0 0 EB ers Defect No n-W Oth eb) (W Count 96 15 Percent 86.5 13.5 Cum % 86.5 100.0
Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Cost of Poor Quality COPQ - The cost involved in fulfilling the gap between the desired and actual product/service quality. It also includes the cost of lost opportunity due to the loss of resources used in rectifying the defect. Hard Savings - Six Sigma project benefits that allow you to do the same amount of business with less employees (cost savings) or handle more business without adding people (cost avoidance). Soft Savings - Six Sigma project benefits such as reduced time to market, cost avoidance, lost profit avoidance, improved employee morale, enhanced image for the organization and other intangibles may result in additional savings to your organization, but are harder to quantify. Examples / Buckets– Roll Throughput Yield Inefficiencies (GAP between desired result and current result multiplied by direct costs AND indirect costs in the process). Cycle Time GAP (stated as a percentage between current results and desired results) multiplied by direct and indirect costs in the process. Square Footage opportunity cost, advertising costs, overhead costs, etc…
Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Problem Refinement Multi Level Pareto – Logically Break down initial Pareto data into sub- sets (to help refine area of focus) Pareto Chart for WEB 100 100 80 Percent 60 Count 50 40 20 0 0 Pareto Chart for Type B WE ers Defect No n- Oth eb) (W 100 Count 96 15 100 Percent 86.5 13.5 80 Cum % 86.5 100.0 Percent 60 Count 50 40 20 0 0 g oi n nG al ime dO ers Defect An nu On eT im e an Oth eT On Count 45 35 13 16 Percent 41.3 32.1 11.9 14.7 Cum % 41.3 73.4 85.3 100.0
Problem Refinement Problem Statement – A crisp description of what we are trying to solve. Primary Metric – An objective measurement of what we are attempting to solve (the “y” in the y = f(x1, x2, x3….) calculation). Secondary Metric – An objective measurement that ensures that a Six Sigma Project does not create a new problem as it fixes the primary problem. For example, a quality metric would be a good secondary metric for an improve cycle time primary metric.
Problem Refinement Fish Bone Diagram - A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. Also called the Cause & Effect diagram and Ishikawa diagram Provides tool for exploring cause / effect and 5 whys
Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Process Understanding SIPOC – Suppliers, Inputs, Process, Outputs, Customers You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.
Process Understanding Process Map – should allow people unfamiliar with the process to understand the interaction of causes during the work-flow. Should outline Value Added (VA) steps and non-value add (NVA) steps. Full Form Control Open Start Size Sorts Pull & Sort Receipt / Docs Extract Ck / Vouch Verify Perfection Requal Group No Yes Prep cks, Remit Rulrs route Prep cks Ship to IP Pass 1 Pass 2 vouch Vouchers Key from Balance Data Cap image No Vouch OK Inventory Yes Prep Folders / Full Form Ship to Box QCReview Cust
Process Understanding Create daily peak Action staff need plan Plan No Yes Can they Call employee Add 30% to To Floor the required make it? (3x) no. Operations No Need OJT Yes Make No Compare to OJT Re-Tng it? Check off original Billet rpt desired Manually Review returnee Yes Update HR Staff staff & "need No Yes Billet Request Billet Need re to retrain" To Floor -train list Add 40% to Call (3x) Stop! staff needed Create Update Staff No IPS No Billet Rev Do they original Do they No Send Letters Yes Yes Have we No Yes Have we No Yes Interview / Meet Fleet Do they want to billet & want to Call Wait Rank as to desired hired hired New hiring respond? work this call work this List pre-hire "1 2 3" staff enough? enough? criteria peak? uncheck peak? ed What if the HR sends Hire in 1- Yes returnee is Yes Yes req for No No 2 order Start already staffing (3's are HR / working here show up No nos. not Place into Call Recruit on another Do they Do they orienta No No placed) dept 3X program? want to want to tion Stop! Stop! Currently stay on the stay on the send the ltr list list anyways Wait List Yes Yes Yes New & Other Take off Set 14 Take off Set 14 People IPS month IPS month call in system flag (on system flag (on IPS?) IPS?) schedule Yes No Gen Event Roster for Reach rpt in IPS training Show No Call Notify up? 1X HR Yes Training Gen rpt for Ops Kronos Recruit Train No Yes Update Pass? IPS
Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Potential X to Critical X “Y” is the dependent output of a variable process. In other words, output is a function of input variables (Y=f(x1, x2, x3…). Through hypothesis testing, Six Sigma allows one to determine which attributes (basic descriptor (generally limited or binary in nature) for data we gather – ie. day of the week, shift, supervisor, site location, machine type, work type, affect the output. For example, statistically, does one shift make more errors or have a longer cycle time than another? Do we make more errors on Fridays than on Mondays? Is one site faster than another? Once we determine which attributes affect our output, we determine the degree of impact using Design of Experiment (DOE).
Potential X to Critical X A Design of Experiment (DOE) is a structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Not only is the direct affect of an X1 gauged against Y but also the affect of X1 on X2 against Y is also gauged. In other words, DOE allows us to determine - does one input (x1) affect another input (x2) as well as Output (Y).
Potential X to Critical X DOE Example Main Effects Plot (data means) for Elapsed Main Effects Plot – 1.4 Lo w Hi g h Lo w Hig h Lo w Hig h Lo w Hig h Direct impact to Y 1.3 Elapsed 1.2 1.1 1.0 Jams DCDEL SK P2Jam Interaction Plot (data means) for Elapsed 1 3 1 3 1 3 1 3 1.50 Jams 1 1.25 3 1.00 1.50 DCDEL 3 1.25 Interaction Plot – 1 1.00 1.50 SK Impacts of X’s on 3 1 1.25 1.00 each other P2Jam 1.50 3 1.25 1 1.00
Potential X to Critical X DOE Optimizer – Allows us to statistically predict the Output (Y) based on optimizing the inputs (X) from the Design of experiment data.
Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
Improvement Once we know the degree to which inputs (X) affect our output (Y), we can explore improvement ideas, focusing on the cost benefit of a given improvement as it relates to the degree it will affect the output. In other words, we generally will not attempt to fix every X, only those that give us the greatest impact and are financially or customer justified.
Control Once improvements are made, the question becomes, are the improvement consistent with predicted Design of Experiment results (ie – are they what we expected) and, are they statistically different than pre-improvement results. Process Capability Analysis for Sept LSL USL Process Data USL 0.23000 Within Target * LSL -1.00000 Overall Mean -0.02391 Sample N 23 StDev (Within) 0.166425 StDev (Overall) 0.221880 Potential (Within) Capability Z.Bench 1.53 Z.USL 1.53 Z.LSL 5.87 Cpk 0.51 -1.0 -0.5 0.0 0.5 1.0 Cpm * Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance Z.Bench 1.14 % < LSL 0.00 % < LSL 0.00 % < LSL 0.00 Z.USL 1.14 % > USL 13.04 % > USL 6.35 % > USL 12.62 Z.LSL 4.40 % Total 13.04 % Total 6.35 % Total 12.62 Ppk 0.38
Control Control Chart - A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process(common cause) from variation that yields a change to the process(special cause). This change may be a single point or a series of points in time - each is a signal that something is different from what was previously observed and measured. I and MR Chart for Sept 1 Individual Value 0.5 UCL=0.5293 0.0 Mean=0.03 2 -0.5 LCL=-0.4693 Subgroup Sept 13 Sept 20 Date 9/13 9/25 0.7 1 0.6 UCL=0.6134 Moving Range 0.5 0.4 0.3 0.2 R=0.1877 0.1 0.0 LCL=0

More Related Content

PDF
SCP Six Sigma presentation 6.30.2010
byThe Hackett Group
 
PDF
Six Sigma
byahmad bassiouny
 
PPTX
Six sigma
bySwapnil Pawar
 
PPTX
Six sigma
byPraveen Sidola
 
PPTX
Six sigma
byMohammed Muzibur Rahman
 
PDF
Is 09 six-sigma
byMohamed Eissa
 
PPTX
Intro to six sigma s depoe.ppt
byStephen Depoe
 
PPT
Six sigma a contemporary issue
byBala Narendra Kiran
 
SCP Six Sigma presentation 6.30.2010
byThe Hackett Group
 
Six Sigma
byahmad bassiouny
 
Six sigma
bySwapnil Pawar
 
Six sigma
byPraveen Sidola
 
Six sigma
byMohammed Muzibur Rahman
 
Is 09 six-sigma
byMohamed Eissa
 
Intro to six sigma s depoe.ppt
byStephen Depoe
 
Six sigma a contemporary issue
byBala Narendra Kiran
 

What's hot

PDF
6 sigma basic best ppt
byJayesh Sarode
 
PPTX
Presentation on six sigma
byBECHAN KUMAR JHA
 
PDF
Apply Six sigma to sales and marketing
bytheairliner
 
PPTX
An introduction to lean six sigma
byRahul Singh
 
PDF
Lean six sigma - practical body of knowledge
byXuan Tran Phu
 
PPTX
Introduction to six sigma
byPROF. PUTTU GURU PRASAD
 
PPT
Improving Project Management Skills with Lean Six Sigma
byInternational Institute for Learning
 
PPSX
Introduction Of Six Sigma for IT & BPO by Vision Raval
byVision Raval
 
PPT
Six sigma overview
byVIJAYAKUMAR SAKTHIVEL
 
PPT
Process Improvement Through Use Of Lean Six Sigma Methods 110609
bymaedog
 
PPT
Effective Cost Measurement through DMAIC.
byKaustav Lahiri
 
PPTX
LEAN SIX SIGMA PROJECT - FINAL
byJihad S. Maksad - PMP, RMP, LEED AP
 
PPT
Six sigma in customer satisfaction
bysaravanan
 
PPT
Lean six sigma and process excellence ppt
byAnexas Consultancy
 
PPT
Six Sigma Asse
bytpuyleart
 
PPT
Six sigma
byAlaa Youssef
 
PDF
History Of Six Sigma By Praveen Gupta
byPraveen Gupta
 
PPTX
Six Sigma: Levels of Certification
byMonarch Metal
 
PPTX
Improve performance through Lean - Six Sigma management
byGhinea Rodica
 
PPTX
Six Sigma the best ppt
byRabia Shaikh
 
6 sigma basic best ppt
byJayesh Sarode
 
Presentation on six sigma
byBECHAN KUMAR JHA
 
Apply Six sigma to sales and marketing
bytheairliner
 
An introduction to lean six sigma
byRahul Singh
 
Lean six sigma - practical body of knowledge
byXuan Tran Phu
 
Introduction to six sigma
byPROF. PUTTU GURU PRASAD
 
Improving Project Management Skills with Lean Six Sigma
byInternational Institute for Learning
 
Introduction Of Six Sigma for IT & BPO by Vision Raval
byVision Raval
 
Six sigma overview
byVIJAYAKUMAR SAKTHIVEL
 
Process Improvement Through Use Of Lean Six Sigma Methods 110609
bymaedog
 
Effective Cost Measurement through DMAIC.
byKaustav Lahiri
 
LEAN SIX SIGMA PROJECT - FINAL
byJihad S. Maksad - PMP, RMP, LEED AP
 
Six sigma in customer satisfaction
bysaravanan
 
Lean six sigma and process excellence ppt
byAnexas Consultancy
 
Six Sigma Asse
bytpuyleart
 
Six sigma
byAlaa Youssef
 
History Of Six Sigma By Praveen Gupta
byPraveen Gupta
 
Six Sigma: Levels of Certification
byMonarch Metal
 
Improve performance through Lean - Six Sigma management
byGhinea Rodica
 
Six Sigma the best ppt
byRabia Shaikh
 

Viewers also liked

PPTX
Six sigma
byHumma Rashid
 
PPTX
Lean six sigma introduction
byThe Apprentiice
 
PPT
QM-032-SQC tool application
byhandbook
 
PDF
Session 5
bygrowthstreet
 
PDF
CSR 10 India Index 2012
byanuptiwari
 
PPTX
Proping of rupee
byPradeep Singha
 
PPTX
Day2day a akash
byPradeep Singha
 
PPT
Digital Six Sigma integration with Directed Innovation for Generation of High...
byMaria Thompson
 
PPTX
Total quality management
byPradeep Singha
 
PDF
Six Sigma Methodologies
byJerry Bridgewater
 
PPTX
Consumer protection act1956
byPradeep Singha
 
PPTX
Six Sigma v. PQMI (process improvement methodologies)
byJanet Gallo, PMP
 
PPTX
traning and development
byPradeep Singha
 
PDF
Lean six sigma black belt project by iftakhar
byMohammad IFTAKHAR
 
PPTX
CSR Reborn- a presentation on book on Corporate Social Responsibility
byanuptiwari
 
PDF
Donor Management Agenda And Speakers
byanuptiwari
 
PDF
Individuals donate, but do you know how to ask?
byanuptiwari
 
PDF
Getting Ready For Upcoming Act on CSR in India
byanuptiwari
 
PPTX
Dollar vs rupee
byPradeep Singha
 
PPTX
Six sigma an overview | Online Mini MBA (Free)
bymybskool-online-courses
 
Six sigma
byHumma Rashid
 
Lean six sigma introduction
byThe Apprentiice
 
QM-032-SQC tool application
byhandbook
 
Session 5
bygrowthstreet
 
CSR 10 India Index 2012
byanuptiwari
 
Proping of rupee
byPradeep Singha
 
Day2day a akash
byPradeep Singha
 
Digital Six Sigma integration with Directed Innovation for Generation of High...
byMaria Thompson
 
Total quality management
byPradeep Singha
 
Six Sigma Methodologies
byJerry Bridgewater
 
Consumer protection act1956
byPradeep Singha
 
Six Sigma v. PQMI (process improvement methodologies)
byJanet Gallo, PMP
 
traning and development
byPradeep Singha
 
Lean six sigma black belt project by iftakhar
byMohammad IFTAKHAR
 
CSR Reborn- a presentation on book on Corporate Social Responsibility
byanuptiwari
 
Donor Management Agenda And Speakers
byanuptiwari
 
Individuals donate, but do you know how to ask?
byanuptiwari
 
Getting Ready For Upcoming Act on CSR in India
byanuptiwari
 
Dollar vs rupee
byPradeep Singha
 
Six sigma an overview | Online Mini MBA (Free)
bymybskool-online-courses
 

Similar to Six sigma

PPT
Six sigma
bySuraj Satpathy
 
PPTX
Final Six Sigma
bySiddik Fruitwala
 
PPTX
Understanding six sigma
byOmar Aijaz
 
PPT
U20
bygreg_eisenbach
 
PPT
Basic Six Sigma Presentation
byvivekissar
 
PPTX
Six Sigma seminar by Nehall
byNehal Dixit
 
PPT
SIX SIGMA
byKumar Kunal
 
PPT
Six sigma
byKumar Kunal
 
PPT
Six Sigma and Its Implementation
byAnsar Lawi
 
PPT
Six Sigma Introduction
byAbhishek Kumar
 
PPT
What Is 6 Sigma
byVishwanath Ramdas
 
PPTX
Rutgers Governor School - Six Sigma
byBrandon Theiss, PE
 
PPT
Sixsigmaintrorerevisedii 13198308548378-phpapp02-111028144148-phpapp02
bysatyajit pandey
 
PPTX
Six sigma
byMukul Singh
 
PDF
Aqm Programme Six Sigma
byAjay Kumar Singh
 
PPTX
Six Sigma
byTushar Makvana
 
PPT
Six Sigma For Managers
byajjulazer
 
PPT
Six Sigma For Managers 185
byAnjoum .
 
PDF
Integrating Six Sigma and Lean Manufacturing the Challenges & Benefits
byAdvent Design Corporation
 
PDF
Integrating Six Sigma and Lean Manufacturing the Challenges & Benefits
byAdvent Design Corporation
 
Six sigma
bySuraj Satpathy
 
Final Six Sigma
bySiddik Fruitwala
 
Understanding six sigma
byOmar Aijaz
 
U20
bygreg_eisenbach
 
Basic Six Sigma Presentation
byvivekissar
 
Six Sigma seminar by Nehall
byNehal Dixit
 
SIX SIGMA
byKumar Kunal
 
Six sigma
byKumar Kunal
 
Six Sigma and Its Implementation
byAnsar Lawi
 
Six Sigma Introduction
byAbhishek Kumar
 
What Is 6 Sigma
byVishwanath Ramdas
 
Rutgers Governor School - Six Sigma
byBrandon Theiss, PE
 
Sixsigmaintrorerevisedii 13198308548378-phpapp02-111028144148-phpapp02
bysatyajit pandey
 
Six sigma
byMukul Singh
 
Aqm Programme Six Sigma
byAjay Kumar Singh
 
Six Sigma
byTushar Makvana
 
Six Sigma For Managers
byajjulazer
 
Six Sigma For Managers 185
byAnjoum .
 
Integrating Six Sigma and Lean Manufacturing the Challenges & Benefits
byAdvent Design Corporation
 
Integrating Six Sigma and Lean Manufacturing the Challenges & Benefits
byAdvent Design Corporation
 

More from Pradeep Singha

PDF
Ngo project
byPradeep Singha
 
PPTX
Home Loan
byPradeep Singha
 
DOC
Pradeep synopsis
byPradeep Singha
 
PPTX
Auto Expo
byPradeep Singha
 
PPT
Acceptance case
byPradeep Singha
 
PPT
2 sale of goods act1930
byPradeep Singha
 
PPTX
Job design
byPradeep Singha
 
Ngo project
byPradeep Singha
 
Home Loan
byPradeep Singha
 
Pradeep synopsis
byPradeep Singha
 
Auto Expo
byPradeep Singha
 
Acceptance case
byPradeep Singha
 
2 sale of goods act1930
byPradeep Singha
 
Job design
byPradeep Singha
 

Six sigma

  • 1.
  • 2.
    Topics (Session 1) ♦Understanding Six Sigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
  • 3.
    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. ♦ A disciplined, fact-based approach to managing a business and its processes.
  • 4.
    What’s in aname? ♦ Sigma is the Greek letter representing the standard deviation of a population of data. ♦ Sigma is a measure of variation (the data spread) σ μ
  • 5.
    What does variationmean? 20 ♦ Variation means that a 15 process does not produce 10 the same result (the “Y”) 5 every time. 0 -5 ♦ Some variation will exist in -10 all processes. ♦ Variation directly affects customer experiences. Customers do not feel averages!
  • 6.
    Measuring Process Performance Thepizza 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?
  • 7.
    How often arewe delivering on time? Answer: Look at 30 min. or less the variation! s 0 10 20 x 30 40 50 ♦ Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.
  • 8.
    Reduce Variation toImprove Performance How many standard 30 min. or less deviations can you “fit” within s customer expectations? 0 10 20 x 30 40 50 ♦ Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).
  • 9.
    Managing Up theSigma Scale Sigma % Good % Bad DPMO 1 30.9% 69.1% 691,462 2 69.1% 30.9% 308,538 3 93.3% 6.7% 66,807 4 99.38% 0.62% 6,210 5 99.977% 0.023% 233 6 99.9997% 0.00034% 3.4
  • 10.
    Examples of theSigma Scale In a world at 3 sigma. . . In a world at 6 sigma. . . ♦ There are 964 U.S. flight ♦ 1 U.S. flight is cancelled every cancellations per day. 3 weeks. ♦ The police make 7 false arrests ♦ There are fewer than 4 false every 4 minutes. arrests per month. ♦ In MA, 5,390 newborns are ♦ 1 newborn is dropped every 4 dropped each year. years in MA. ♦ In one hour, 47,283 ♦ It would take more than international long distance calls 2 years to see the same number are accidentally disconnected. of dropped international calls.
  • 11.
    Topics ♦ Understanding SixSigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
  • 12.
    The Six SigmaEvolutionary Timeline 1818: Gauss uses the normal curve 1924: Walter A. Shewhart introduces to explore the mathematics of error the control chart and the distinction of analysis for measurement, probability special vs. common cause variation as analysis, and hypothesis testing. contributors to process problems. 1736: French 1896: Italian sociologist Vilfredo mathematician Alfredo Pareto introduces the 80/20 Abraham de rule and the Pareto distribution in Moivre publishes Cours d’Economie Politique. an article introducing the normal curve. 1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures 1960: Kaoru Ishikawa for Performing a Failure Mode Effects introduces his now famous and Criticality Analysis. cause-and-effect diagram. 1941: Alex Osborn, head of 1970s: Dr. Noriaki Kano BBDO Advertising, fathers a introduces his two-dimensional widely-adopted set of rules for quality model and the three “brainstorming”. types of quality. 1986: Bill Smith, a senior engineer and scientist introduces the 1995: Jack Welch concept of Six Sigma at Motorola launches Six Sigma at GE. 1994: Larry Bossidy launches Six Sigma at Allied Signal.
  • 13.
  • 14.
    Six Sigma andFinancial Services
  • 15.
    Topics ♦ Understanding SixSigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
  • 16.
    DMAIC – TheImprovement Methodology Define Measure Analyze Improve Control Objective: Objective: Objective: Objective: Objective: DEFINE the MEASURE current ANALYZE the IMPROVE the CONTROL the opportunity performance root causes of process to process problems eliminate root to sustain the gains. causes Key Define Tools: Key Measure Key Analyze Key Improve Key Control • Cost of Poor Tools: Tools: Tools: Tools: Quality (COPQ) • Critical to Quality • Histograms, • Solution • Control Charts • Voice of the Requirements Boxplots, Multi- Selection Matrix • Contingency Stakeholder (CTQs) Vari Charts, etc. • To-Be Process and/or Action (VOS) • Sample Plan • Hypothesis Tests Map(s) Plan(s) • Project Charter • Capability • Regression • As-Is Process Analysis Analysis Map(s) • Failure Modes • Primary Metric and Effect (Y) Analysis (FMEA)
  • 17.
    Define – DMAICProject What is the project? $ Project Cost of Charter Poor Voice of Quality S ta k e h o ld e r s 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
  • 18.
    Define – As-IsProcess How does our existing process work? Move-It! Courier Package Handling Process Accounts Accounts Courier Mail Clerk In-SortClerk In-SortSupervisor DistanceFeeClerk WeightFeeClerk Out-SortClerk Out-SortSupervisor ReceivableClerk Supervisor Observ e package weight (1 or 2) on back of package Look up appropriate Weight Fee and write in top middle box on package back Add Distance & Take packages Weight Fees f rom Weight Fee together and write Clerk Outbox to in top right box on A/R Clerk Inbox. package back Circle Total Fee Does EVERYONE and Draw Arrow f rom total to sender code agree how the current Accounting Take packages Write Total Fee f rom A/R Clerk f rom package in process works? Outbox to appropriate Accounts Sender column on Superv isorInbox. Accts. Supv .’s log Take packages Draw 5-point Star f rom Accounts in upper right Superv isor corner of package Outbox to Out- f ront Sort Clerk Inbox. Define the Non Value Sort packages in order of Sender Code bef ore placing in outbox Take packages Add steps Add up Total # of Observ e sender Finalizing f rom Out-Sort Packages and and receiv er Clerk Outbox to Total Fees f rom codes and make Out-Sort log and create entry in Out-Sort Superv isorInbox. client inv oice Superv isor’s log Deliv erPackages Delivery to customers according to N, S, E, W route Deliv er inv oiceto client Submit log to Submit log to Submit log to General Manager General Manager General Manager at conclusion of at end of round at end of round round.
  • 19.
    Define – CustomerRequirements What are the CTQs? What motivates the customer? Voice of the Customer Key Customer Issue Critical to Quality SECONDARY RESEARCH Market Data Industry l e n yrt s udn I Benchmarking Customer t I Correspondence Customer Service s s o P gn ne s L i t i PRIMARY RESEARCH t Surve Surve ys ys OTM Obser- Focus Groups vations
  • 20.
    Measure – Baselinesand Capability What is our current level of performance? Descriptive Statistics ♦ Sample some data / not all data Variable: 2003 Output ♦ Current Process actuals measured against Anderson-Darling Normality Test A-Squared: 0.211 P-Value: 0.854 the Customer expectation Mean StDev 23.1692 10.2152 ♦ Variance 104.349 What is the chance that we will succeed Skewness Kurtosis N 0.238483 0.240771 100 0 10 20 30 40 50 at this level every time? Minimum 1st Quartile 0.2156 16.4134 Median 23.1475 3rd Quartile 29.6100 Maximum 55.2907 Pareto Chart for Txfr Defects 95% Confidence Interval for Mu 95% Confidence Interval for Mu 21.1423 25.1961 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 95% Confidence Interval for Sigma 100 100 8.9690 11.8667 95% Confidence Interval for Median 95% Confidence Interval for Median 80 19.7313 26.0572 60 Percent Count 50 40 20 0 0 t un er s Defect La te A mo Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0
  • 21.
    Measure – Failuresand Risks Where does our process fail and why? Subjective opinion mapped into an “objective” risk profile number Failure Modes and Effects Analysis (FMEA) Process/Product Process or Prepared by: Page ____ of ____ Product Name: Responsible: FMEA Date (Orig) ______________ (Rev) _____________ Process S O D R S O D R Step/Part E C E P Actions E C E P Number Potential Failure Mode Potential Failure Effects V Potential Causes C Current Controls T N Recommended Resp. Actions Taken V C T N X1 0 0 0 0 X2 0 0 0 0 X3 0 0 0 0 X4 0 0 0 0 0 0 etc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  • 22.
    Analyze – PotentialRoot Causes What affects our process? Ishikawa Diagram (Fishbone) Six Sigma y = f (x1, x2, x3 . . . xn)
  • 23.
    Analyze – ValidatedRoot Causes What are the key root causes? Pareto Chart for Txfr Defects 100 100 80 Percent 60 Count 50 40 20 0 0 nt er s E x p e r im e n t a l D e s ig n Defect La te Am ou Oth Count 79 17 4 Percent 79.0 17.0 4.0 Cum % 79.0 96.0 100.0 Data Regression Stratification Analysis Pareto Chart for Amt Defects 15 100 80 Process Simulatio Percent 60 Count 10 40 5 20 n 0 0 cy al er Defect Cu rren Cle ric Oth Count 12 3 2 Percent 70.6 17.6 11.8 Cum % 70.6 88.2 100.0 Six Sigma y = f (x1, x2, x3 . . . xn) Critical Xs
  • 24.
    Improve – PotentialSolutions How can we address the root causes we identified? ♦ Address the causes, not the symptoms. Generate Evaluate Clarify Decision y = f (x1, x2, x3 . . . xn) Critical Xs Divergent | Convergent
  • 25.
    Improve – SolutionSelection How do we choose the best solution? Solution Selection Matrix Qualit Solution Sigma Time CBA Other Score y Time Cost Right Six Sigma Solution Wrong Nice ☺ Try Solution Implementatio Nice n Plan X doo G da B not a ne ml p m e I Idea i t
  • 26.
    Control – SustainableBenefits How 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 exceptions Process Control System (Business Process Framework) Process Owner: Direct Process Customer: Date: Process Description: CCR: Flowchart Measuring and Monitoring Measures Key Specs (Tools) Responsibility Contingency Measure &/or Remarks Where & (Who) (Quick Fix) ments Targets Frequency P1 - activity 35 duration, min. UCL=33.48 P2 - # of incomplete Individual Value loan applications 25 Mean=24.35 15 LCL=15.21 0 10 20 30 Observation Number
  • 27.
    DFSS – TheDesign Methodology Design for Six Sigma Define Measure Analyze Develop Verify ♦ Uses – Design new processes, products, and/or services from scratch – Replace old processes where improvement will not suffice ♦ Differences between DFSS and DMAIC – Projects typically longer than 4-6 months – Extensive definition of Customer Requirements (CTQs) – Heavy emphasis on benchmarking and simulation; less emphasis on baselining ♦ Key Tools – Multi-Generational Planning (MGP) – Quality Function Deployment (QFD)
  • 28.
    Topics ♦ Understanding SixSigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
  • 29.
    Champions ♦ Promote awarenessand execution of Six Sigma within lines of business and/or functions ♦ Identify potential Six Sigma projects to be executed by Black Belts and Green Belts ♦ Identify, select, and support Black Belt and Green Belt candidates ♦ Participate in 2-3 days of workshop training
  • 30.
    Black Belts ♦ UseSix Sigma methodologies and advanced tools (to execute business improvement projects ♦ Are dedicated full-time (100%) to Six Sigma ♦ Serve as Six Sigma knowledge leaders within Business Unit(s) ♦ Undergo 5 weeks of training over 5-10 months
  • 31.
    Green Belts ♦ UseSix Sigma DMAIC methodology and basic tools to execute improvements within their existing job function(s) ♦ May lead smaller improvement projects within Business Unit(s) ♦ Bring knowledge of Six Sigma concepts & tools to their respective job function(s) ♦ Undergo 8-11 days of training over 3-6 months
  • 32.
    Other Roles ♦ SubjectMatter Experts – Provide specific process knowledge to Six Sigma teams – Ad hoc members of Six Sigma project teams ♦ Financial Controllers – Ensure validity and reliability of financial figures used by Six Sigma project teams – Assist in development of financial components of initial business case and final cost-benefit analysis
  • 33.
    Topics ♦ Understanding SixSigma ♦ History of Six Sigma ♦ Six Sigma Methodologies & Tools ♦ Roles & Responsibilities ♦ How YOU can use Six Sigma
  • 34.
  • 35.
    Topics for DetailedDiscussion ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 36.
    Problem Identification “If itain’t broke, why fix it “This is the way we’ve always done it…”
  • 37.
    Problem Identification • First Pass Yield • Roll Throughput Yield • Histogram • Pareto
  • 38.
    Problem Identification First Pass Yield (FPY): The probability that 100 Units any given unit can go Step 1 Outputs / Inputs through a system defect-free without 100 / 100 = 1 rework. 100 Scrap 10 Units Step 2 90 / 100 = .90 90 Scrap 3 Units Step 3 87 / 90 = .96 87 Scrap 2 Units Step 4 85 / 87 = .97 At first glance, the yield would seem to be When in fact the FPY is (1 x .90 x .96 x .97 = . 85% (85/100 but….) 838) 85
  • 39.
    Problem Identification Rolled 100 Units Outputs / Inputs Throughput Yield (RTY): Step 1 90 / 100 = .90 The yield of individual Re-Work process steps 10 Units 100 Units multiplied Step 2 97 / 100 = .97 together. Reflects the Re-Work hidden factory 3 Units 100 Units rework issues associated with Step 3 98 / 100 = .98 a process. Re-Work 2 Units 100 Units Step 4 .90 x .97 x .98 = .855 100 Units
  • 40.
    Problem Identification RTY Examples - Widgets 50 Roll Throughput Yield Function 1 50/50 = 1 (50-5)/50 = .90 50 (50-10)/50 = .80 Function 2 5 (50-5)/50 = .90 50 Function 3 10 1 x .90 x .80 x .90 = .65 50 Function 4 5 Put another way, this process is operating 50 a 65% efficiency
  • 41.
    Problem Identification RTY Example - Loan Underwriting 50 Roll Throughput Yield Application 50/50 = 1 (50-7-2)/50 = .82 2 50 7 Fails (43-6)/43 = .86 Underwrite Underwriting (43-1-2)/43 = .93 6 43 Complete Full Paperwork 1 x .82 x .86 x .93 = .66 2 1 43 Decide not to Close borrow 42 Put another way, this process is operating a 66% efficiency
  • 42.
    Problem Identification Histogram– A histogram is a basic graphing tool that displays the relative frequency or occurrence of continuous data values showing which values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whether there are any outliers. Histogram of Cycle Time 40 30 Frequency 20 10 0 0 100 200 300 400 500 C8
  • 43.
    Problem Identification Histogram– Can also help us graphically understand the data Descriptive Statistics Variable: CT Anderson-Darling Normality Test A-Squared: 6.261 P-Value: 0.000 Mean 80.1824 StDev 67.6003 Variance 4569.81 Skewness 2.31712 Kurtosis 8.26356 N 170 25 100 175 250 325 400 Minimum 1.000 1st Quartile 31.000 Median 66.000 3rd Quartile 105.000 95% Confidence Interval for Mu Maximum 444.000 95% Confidence Interval for Mu 69.947 90.417 54 64 74 84 94 95% Confidence Interval for Sigma 61.098 75.664 95% Confidence Interval for Median 95% Confidence Interval for Median 55.753 84.494
  • 44.
    Problem Identification Pareto– The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects. Pareto Chart for WEB 100 100 80 Percent 60 Count 50 40 20 0 0 EB ers Defect No n-W Oth eb) (W Count 96 15 Percent 86.5 13.5 Cum % 86.5 100.0
  • 45.
    Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 46.
    Cost of PoorQuality COPQ - The cost involved in fulfilling the gap between the desired and actual product/service quality. It also includes the cost of lost opportunity due to the loss of resources used in rectifying the defect. Hard Savings - Six Sigma project benefits that allow you to do the same amount of business with less employees (cost savings) or handle more business without adding people (cost avoidance). Soft Savings - Six Sigma project benefits such as reduced time to market, cost avoidance, lost profit avoidance, improved employee morale, enhanced image for the organization and other intangibles may result in additional savings to your organization, but are harder to quantify. Examples / Buckets– Roll Throughput Yield Inefficiencies (GAP between desired result and current result multiplied by direct costs AND indirect costs in the process). Cycle Time GAP (stated as a percentage between current results and desired results) multiplied by direct and indirect costs in the process. Square Footage opportunity cost, advertising costs, overhead costs, etc…
  • 47.
    Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 48.
    Problem Refinement Multi Level Pareto – Logically Break down initial Pareto data into sub- sets (to help refine area of focus) Pareto Chart for WEB 100 100 80 Percent 60 Count 50 40 20 0 0 Pareto Chart for Type B WE ers Defect No n- Oth eb) (W 100 Count 96 15 100 Percent 86.5 13.5 80 Cum % 86.5 100.0 Percent 60 Count 50 40 20 0 0 g oi n nG al ime dO ers Defect An nu On eT im e an Oth eT On Count 45 35 13 16 Percent 41.3 32.1 11.9 14.7 Cum % 41.3 73.4 85.3 100.0
  • 49.
    Problem Refinement ProblemStatement – A crisp description of what we are trying to solve. Primary Metric – An objective measurement of what we are attempting to solve (the “y” in the y = f(x1, x2, x3….) calculation). Secondary Metric – An objective measurement that ensures that a Six Sigma Project does not create a new problem as it fixes the primary problem. For example, a quality metric would be a good secondary metric for an improve cycle time primary metric.
  • 50.
    Problem Refinement FishBone Diagram - A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. Also called the Cause & Effect diagram and Ishikawa diagram Provides tool for exploring cause / effect and 5 whys
  • 51.
    Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 52.
    Process Understanding SIPOC –Suppliers, Inputs, Process, Outputs, Customers You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.
  • 53.
    Process Understanding Process Map– should allow people unfamiliar with the process to understand the interaction of causes during the work-flow. Should outline Value Added (VA) steps and non-value add (NVA) steps. Full Form Control Open Start Size Sorts Pull & Sort Receipt / Docs Extract Ck / Vouch Verify Perfection Requal Group No Yes Prep cks, Remit Rulrs route Prep cks Ship to IP Pass 1 Pass 2 vouch Vouchers Key from Balance Data Cap image No Vouch OK Inventory Yes Prep Folders / Full Form Ship to Box QCReview Cust
  • 54.
    Process Understanding Create daily peak Action staff need plan Plan No Yes Can they Call employee Add 30% to To Floor the required make it? (3x) no. Operations No Need OJT Yes Make No Compare to OJT Re-Tng it? Check off original Billet rpt desired Manually Review returnee Yes Update HR Staff staff & "need No Yes Billet Request Billet Need re to retrain" To Floor -train list Add 40% to Call (3x) Stop! staff needed Create Update Staff No IPS No Billet Rev Do they original Do they No Send Letters Yes Yes Have we No Yes Have we No Yes Interview / Meet Fleet Do they want to billet & want to Call Wait Rank as to desired hired hired New hiring respond? work this call work this List pre-hire "1 2 3" staff enough? enough? criteria peak? uncheck peak? ed What if the HR sends Hire in 1- Yes returnee is Yes Yes req for No No 2 order Start already staffing (3's are HR / working here show up No nos. not Place into Call Recruit on another Do they Do they orienta No No placed) dept 3X program? want to want to tion Stop! Stop! Currently stay on the stay on the send the ltr list list anyways Wait List Yes Yes Yes New & Other Take off Set 14 Take off Set 14 People IPS month IPS month call in system flag (on system flag (on IPS?) IPS?) schedule Yes No Gen Event Roster for Reach rpt in IPS training Show No Call Notify up? 1X HR Yes Training Gen rpt for Ops Kronos Recruit Train No Yes Update Pass? IPS
  • 55.
    Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 56.
    Potential X toCritical X “Y” is the dependent output of a variable process. In other words, output is a function of input variables (Y=f(x1, x2, x3…). Through hypothesis testing, Six Sigma allows one to determine which attributes (basic descriptor (generally limited or binary in nature) for data we gather – ie. day of the week, shift, supervisor, site location, machine type, work type, affect the output. For example, statistically, does one shift make more errors or have a longer cycle time than another? Do we make more errors on Fridays than on Mondays? Is one site faster than another? Once we determine which attributes affect our output, we determine the degree of impact using Design of Experiment (DOE).
  • 57.
    Potential X toCritical X A Design of Experiment (DOE) is a structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Not only is the direct affect of an X1 gauged against Y but also the affect of X1 on X2 against Y is also gauged. In other words, DOE allows us to determine - does one input (x1) affect another input (x2) as well as Output (Y).
  • 58.
    Potential X toCritical X DOE Example Main Effects Plot (data means) for Elapsed Main Effects Plot – 1.4 Lo w Hi g h Lo w Hig h Lo w Hig h Lo w Hig h Direct impact to Y 1.3 Elapsed 1.2 1.1 1.0 Jams DCDEL SK P2Jam Interaction Plot (data means) for Elapsed 1 3 1 3 1 3 1 3 1.50 Jams 1 1.25 3 1.00 1.50 DCDEL 3 1.25 Interaction Plot – 1 1.00 1.50 SK Impacts of X’s on 3 1 1.25 1.00 each other P2Jam 1.50 3 1.25 1 1.00
  • 59.
    Potential X toCritical X DOE Optimizer – Allows us to statistically predict the Output (Y) based on optimizing the inputs (X) from the Design of experiment data.
  • 60.
    Topics (Session 2) ♦ Problem Identification ♦ Cost of Poor Quality ♦ Problem Refinement ♦ Process Understanding ♦ Potential X to Critical X ♦ Improvement
  • 61.
    Improvement Once we knowthe degree to which inputs (X) affect our output (Y), we can explore improvement ideas, focusing on the cost benefit of a given improvement as it relates to the degree it will affect the output. In other words, we generally will not attempt to fix every X, only those that give us the greatest impact and are financially or customer justified.
  • 62.
    Control Once improvements aremade, the question becomes, are the improvement consistent with predicted Design of Experiment results (ie – are they what we expected) and, are they statistically different than pre-improvement results. Process Capability Analysis for Sept LSL USL Process Data USL 0.23000 Within Target * LSL -1.00000 Overall Mean -0.02391 Sample N 23 StDev (Within) 0.166425 StDev (Overall) 0.221880 Potential (Within) Capability Z.Bench 1.53 Z.USL 1.53 Z.LSL 5.87 Cpk 0.51 -1.0 -0.5 0.0 0.5 1.0 Cpm * Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance Z.Bench 1.14 % < LSL 0.00 % < LSL 0.00 % < LSL 0.00 Z.USL 1.14 % > USL 13.04 % > USL 6.35 % > USL 12.62 Z.LSL 4.40 % Total 13.04 % Total 6.35 % Total 12.62 Ppk 0.38
  • 63.
    Control Control Chart -A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process(common cause) from variation that yields a change to the process(special cause). This change may be a single point or a series of points in time - each is a signal that something is different from what was previously observed and measured. I and MR Chart for Sept 1 Individual Value 0.5 UCL=0.5293 0.0 Mean=0.03 2 -0.5 LCL=-0.4693 Subgroup Sept 13 Sept 20 Date 9/13 9/25 0.7 1 0.6 UCL=0.6134 Moving Range 0.5 0.4 0.3 0.2 R=0.1877 0.1 0.0 LCL=0

Editor's Notes

  • #2 © 2003 FleetBoston Financial Page
  • #3 © 2003 FleetBoston Financial Page
  • #12 © 2003 FleetBoston Financial Page
  • #16 © 2003 FleetBoston Financial Page
  • #29 © 2003 FleetBoston Financial Page
  • #34 © 2003 FleetBoston Financial Page