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Practical Applications of Lean Six 
Sigma Towards Excellence in 
Food Safety 
Aizad Ahmad 
MSC, MBA, SSBB, PFPc 
Canada 
1
HACCP 
• Hazard Analysis and Critical Control Points (HACCP) is a prevention-based food 
safety system. It provides a syst...
Canadian Food Inspection Agency 
• As of March 2011, there were 3,502 field food safety inspectors & 4,898 
inspection sta...
4 
Conventional Strategy 
Conventional definitions of quality focused on 
conformance to standards. 
Requirement 
or 
LSL ...
5 
What is Six Sigma…as a Method? 
DMAIC provides the method for applying the Six Sigma 
philosophy in order to improve pr...
6 
What is Six Sigma…as a Benchmark? 
Yield 
99.9997% 
99.976% 
99.4% 
93% 
65% 
50% 
PPMO 
3.4 
233 
6,210 
66,807 
308,5...
Data Driven Decisions 
Y= f (X) 
To get results, should we focus our behavior on the Y or X ? 
• Y 
• Dependent 
• Output ...
8 
Six Sigma Strategy 
(X1) 
(X7) 
(X5) 
(X6) 
(X3) 
(X2) 
(X4) 
(X8) 
(X10) 
(X9) 
We use a variety of Six Sigma tools to...
9 
Six Sigma Roles and Responsibilities 
There are many roles and 
responsibilities for successful 
implementation of Six ...
MBB should be well versed with all aspects of Six Sigma, from technical 
applications to Project Management. MBBs need to ...
11 
Green Belt 
Green Belts are practitioners of Six Sigma Methodology and 
typically work within their functional areas o...
We go thru processes everyday. Below are some examples of processes. 
Can you think of other processes within your daily e...
13 
Process Maps 
• The purpose of Process Maps is to: 
– Identify the complexity of the process 
– Communicate the focus ...
14 
Process Map Example 
The Process Map below is for a call center. 
START 
LOGON TO PC & 
APPLICATIONS 
SCHEDULED 
PHONE...
15 
Types of Process Maps 
The Linear Flow Process Map 
Calls 
for 
Order 
Pizza 
Correct 
Customer 
Hungry 
Take 
Order 
...
16 
Cross Functional Process Map 
When multiple departments or functional groups are involved in a complex 
process it is ...
17 
What is a Customer? 
There are different types of customers which dictates how we interact with 
them in the process, ...
18 
What is a CTQ? 
• Critical to Quality (CTQ ’s) are measures that we use to capture VOC 
properly. (also referred to in...
19 
Cost of Poor Quality - Categories 
Internal COPQ 
External COPQ 
• Warranty 
• Customer Complaint Related 
Travel 
• C...
Time value of money 
20 
COPQ - Iceberg 
Rework, 
Client sues 
Inspection 
Warranty 
Rejects 
Govt fines 
Lost sales 
Late...
Waste does not add, subtract or otherwise modify the throughput 
21 
COPQ and Lean 
in a way that is perceived by the cust...
22 
COPQ – Hard and Soft Savings 
While hard savings are always more desirable because 
they are easier to quantify, it is...
23 
The Basic Six Sigma Metrics 
In any process improvement endeavor, the ultimate 
objective is to make the process: 
• B...
24 
Project Selection 
Understanding Six Sigma 
Six Sigma Fundamentals 
Selecting Projects 
Selecting Projects 
Refining &...
25 
Project Selection – Core Components 
Business Case – The Business Case is a high level 
articulation of the area of co...
26 
Business Case Example 
During FY 2005, the 1st Time Call Resolution 
Efficiency for New Customer Hardware Setup 
was 8...
27 
The Business Case Template 
Fill in the Blanks for Your Project: 
During ___________________________________ , the ___...
28 
What is a Project Charter? 
The Project Charter expands on the Business Case, it 
clarifies the projects focus and mea...
29 
Project Charter - Definitions 
• Problem Statement - Articulates the pain of the defect or error in the 
process. 
• O...
30 
What is the Financial Evaluation? 
The financial evaluation establishes the 
value of the project. 
The components are...
Whatever your organization’s protocol may be these aspects should be 
accounted for within any improvement project. 
I 
M ...
32 
Benefits Calculation Template 
The Benefits Calculation Template 
facilitates and aligns with the aspects 
discussed f...
33 
Pareto Analysis 
Pareto Analysis: 
• A bar graph used to arrange information in such a way that priorities for 
proces...
34 
Lean Six Sigma 
Lean Six Sigma combines the strengths of each system: 
• Lean 
– Guiding principles based 
operating s...
35 
Seven Components of Waste 
Muda is classified into seven 
components: 
– Overproduction 
– Correction (defects) 
– Inv...
36 
English Translation 
There have been many attempts to force five English “S” words to maintain 
the original intent of...
Overview of Brainstorming Techniques 
A commonly used tool to solicit ideas by using categories to stimulate cause and 
ef...
Cause and Effect Diagram 
A commonly used tool to 
solicit ideas by using 
categories to stimulate 
cause and effect 
rela...
39 
Chemical Purity Example 
Measurement 
Incoming QC (P) 
Measurement 
Method (P) 
Measurement 
Capability (C) 
Manpower ...
40 
Types of Process Maps 
The SIPOC “Supplier – Input – Process – Output – 
Customer” Process Map 
Process 
r See Below 
...
41 
The Vital Few 
A Six Sigma Belt does not just discover which X’s 
are important in a process (the vital few). 
– The t...
42 
Example 
Click the Demo button to see an example.
43 
Example 
Click the Summary Worksheet 
YX Diagram Summary 
Process: 
Date: 
laminating 
5/2/2006 
Output Variables Inpu...
44 
Purpose of FMEA 
Failure Mode and Effects Analysis (FMEA) : 
• Improve the quality, reliability, and safety of product...
45 
Why Create an FMEA? 
As a means to manage… 
RISK!!! 
We want to avoid causing failures in the Process as well as the 
...
46 
The FMEA… 
# Process 
Functio 
n 
(Step) 
Potential 
Failure 
Modes 
(process 
defects) 
Potential 
Failure 
Effects 
...
47 
Ranking Severity 
Effect Criteria: Severity of Effect Defined Ranking 
Hazardous: 
Without 
Warning 
May endanger the ...
48 
Sample Transactional Severities 
Effect Criteria: Impact of Effect Defined Ranking 
Critical Business 
Unit-wide 
May ...
49 
Ranking Occurrence 
Probability of Failure Possible Failure Rates Cpk Ranking 
Very High: Failure is almost 
inevitabl...
50 
FMEA Components…Current Process Controls 
Current Process Controls refers to the three types of controls that are in p...
51 
FMEA Components…Detection (DET) 
Detection is an assessment of the probability that the proposed type of control 
will...
52 
Ranking Detection 
Detection 
Almost Impossible 
Criteria: The likelihood that the existence of a defect will 
be dete...
53 
Risk Priority Number “RPN” 
The Risk Priority Number is a value that will be used to rank order the 
concerns from the...
54 
Statistical Notation – Cheat Sheet 
An individual value, an observation 
A particular (1st) individual value 
For each...
55 
Box Plot 
Box Plots summarize data about the shape, dispersion and center of the 
data and also help spot outliers. 
B...
56 
Box Plot Anatomy 
Median 
* Outlier 
Upper Limit: Q3+1.5(Q3-Q1) 
Upper Whisker 
Q3: 75th Percentile 
Q2: Median 50th P...
57 
Curve Fitting Time Series 
MINITAB™ allows you to add a smoothed line to your time series 
based on a smoothing techni...
58 
Introduction to MSA 
So far we have learned that the heart 
and soul of Six Sigma is that it is a 
data-driven methodo...
59 
Measurement System Analysis 
MSA is a mathematical procedure to quantify variation introduced to a 
process or product...
60 
Accurate but not precise - On 
average, the shots are in the 
center of the target but there is a 
lot of variability ...
61 
Components of Variation 
Whenever you measure anything, the variation that you observe can 
be segmented into the foll...
62 
Gage R & R Study 
Part Allocation From Any Population 
10 x 3 x 2 Crossed Design is shown 
A minimum of two measuremen...
63 
Excel Attribute R & R Template 
Attribute Gage R & R Effectiveness 
SCORING REPORT 
DATE: 5/10/2006 
Attribute Legend5...
64 
Capability Analysis 
Y1 
Y2 
Y3 
Y = f(X) (Process Function) 
Verified 
Op i Op i + 1 
? 
Analysis Scrap 
Off-Line 
Co...
65 
Capable and 
on target 
Average 
LSL USL 
Target 
Process Output Categories 
Off target 
LSL USL 
Average 
Target 
Inc...
66 
Problem Solving Options – Shift the Mean 
This involves finding the variables that will shift the process over to the ...
67 
Problem Solving Options – Reduce Variation 
This is typically not so easy to accomplish and occurs often in Six 
Sigma...
68 
Problem Solving Options – Shift Mean & Reduce Variation 
This occurs often in Six Sigma projects. 
LSL 
USL 
Shift & R...
69 
MINITAB™ Example 
LSL USL 
597.75 598.50 599.25 600.00 600.75 601.50 
Process Data 
LSL 598 
Target * 
USL 602 
Sample...
70 
MINITAB™ Example 
LSL USL 
597 598 599 600 601 602 603 
Process Data 
LSL 598 
Target * 
USL 602 
Sample Mean 600.061 ...
71 
Types of Error 
1.Error in sampling 
– Error due to differences among samples drawn at random from the 
population (lu...
72 
Population, Sample, Observation 
Population 
– EVERY data point that has ever been or ever will be generated from a 
g...
73 
The Mission 
Mean Shift 
Variation 
Reduction 
Both 
Your mission, which you have chosen to accept, is to reduce cycle...
74 
Test of Means (t-tests) 
t-tests are used: 
– To compare a Mean against a target. 
• i.e.; The team made improvements ...
75 
One-Sample T: Values 
Ho 
Test of mu = 5 vs not = 5 
Session Window 
Ha 
(X  
X) 
S 
 
Variable N Mean StDev SE Mea...
76 
Evaluating the Results 
Since the P-value of 0.034 is less than 0.05, reject the null hypothesis. 
Based on the sample...
77 
Hypothesis Testing Roadmap 
Normal 
Test of Equal Variance 1 Sample Variance 1 Sample t-test 
Variance Equal Variance ...
78 
2 Sample t-test 
A 2-sample t-test is used to compare two Means. 
MINITABTM performs an independent two-sample t-test ...
79 
Box Plot 
Boxplot of BTU.In by Damper 
1 2 
Damper 
BTU.In 
20 
15 
10 
5 
5. State Statistical Conclusions: Fail to r...
80 
Minitab Session Window 
Number of 
Samples 
Calculated 
Average 
 
(X  
X) 
 
 
n 
i 1 
i 
n 1 
s 
2 
S 
SE Mean...
81 
Paired t-test 
• A Paired t-test is used to compare the Means of two measurements from the 
same samples generally use...
82 
Box Plot 
MINITABTM Session Window 
Box Plot of AB Delta 
One-Sample T: AB Delta 
Test of mu = 0 vs not = 0 
Variable ...
83 
Analyze Phase - The Roadblocks 
Look for the potential roadblocks and plan to address them 
before they become problem...
84 
Types and Magnitude of Correlation 
40 50 60 70 80 90 100 110 120 
110 
100 
90 
80 
70 
60 
50 
40 
30 
Strong Positi...
85 
Regression (Prediction) Equation 
Regression Analysis: Payton yards versus Payton carries 
The regression equation is ...
86 
Regression (Prediction) Equation 
Compare to the Fitted Line. 
payton carries 
payton yards 
150 200 250 300 350 400 
...
87 
Example Regression Line 
Agitator RPM 
PGM concentrate (g/ton) 
10 15 20 25 30 35 40 45 
70 
60 
50 
40 
30 
20 
10 
S...
88 
Example Regression Line 
Agitator RPM 
PGM concentrate (g/ton) 
10 15 20 25 30 35 40 45 
70 
60 
50 
40 
30 
20 
10 
S...
89 
Confidence and Prediction Intervals 
% discount 
% response from mailing 
0 10 20 30 40 
80 
70 
60 
50 
40 
30 
20 
1...
90 
Six Sigma Strategy 
(X1) (X8) (X11) (X9) 
(X4) 
(X6) (X7) (X5) 
(X10) 
(X2) (X3) 
(X3) 
(X4) 
(X2) 
(X5) 
(X1) 
(X8) 
...
91 
One Factor at a Time is NOT a DOE 
One Factor at a Time (OFAT) is an experimental style 
but not a planned experiment ...
92 
Nomenclature for Factorial Experiments 
The general notation used to designate a full 
factorial design is given by: 
...
93 
Visualization of 2 Level Full Factorial 
Uncoded levels for factors 
T P T*P 
-1 
-1 
+1 
-1 
-1 
+1 
+1 
+1 
+1 
-1 
...
3.35 
94 
Graphical DOE Analysis - The Cube Plot 
Consider a 23 design on a catapult... 
Stop Angle 
8.2 4.55 
5.15 2.4 
2...
Kanban 
 We cannot sustain 
Kanban without Kaizen. 
The Vision of Lean Supporting Your Project 
 We cannot sustain Kaize...
96 
What is Waste (MUDA)? 
Waste is often the root of any Six Sigma project. The 7 basic elements 
of waste (muda in Japan...
97 
The Goal 
Don’t forget the goal -- Sustaining your Project which eliminates MUDA! 
With this in mind, we will introduc...
98 
5S Translation - Workplace Organization 
Step Japanese Literal Translation English 
Step 1: Seiri Clearing Up Sorting ...
99 
What is Standardized Work? 
If the items are organized and orderly, then 
standardized work can be accomplished. 
– Le...
100 
Prerequisites for Standardized Work 
Standardized work does not happen without the visual factory which can 
be furth...
What is Kaizen? 
• Definition*: The philosophy of continual 
101 
improvement, that every process can and 
should be conti...
102 
Prerequisites for Kaizen 
Kaizen’s need the following cultural elements: 
Management Support. Consider the corporate ...
103 
Two Types of Kanban 
Type 1: Finished goods Kanbans 
– Signal Kanban: Should be posted 
at the end of the processing ...
104 
s Level for Project Sustaining in Control 
5-6s: Six Sigma product and/or process design eliminates an error 
conditi...
105 
5 – 6 s Full Automation 
Full Automation: Systems that monitor the process and 
automatically adjust critical X’s to ...
106 
Traditional Quality vs. Mistake Proofing 
Traditional Inspection 
Result 
Worker or 
Machine Error 
Don’t Do 
Anythin...
Contact Method 
107 
Types of Mistake Proof Devices 
– Physical or energy contact 
with product 
• Limit switches 
• Photo...
108 
Advantages of Mistake Proofing as A Control Method 
Mistake Proofing advantages include: 
– Only simple training prog...
109 
SPC Overview: I-MR Chart 
• An I-MR chart combines a Control Chart of the average moving range with the Individual’s ...
110 
SPC Overview: Xbar-R Chart 
If each of your observations consists of a subgroup of data, rather than just individual ...
111 
SPC Overview: U Chart 
• C Charts and U Charts are for tracking defects. 
• A U Chart can do everything a C Chart can...
112 
SPC Overview: P Chart 
• NP Charts and P Charts are for tracking defectives. 
• A P Chart can do everything an NP Cha...
Type 1 Corrective Action = Countermeasure: improvement made to the process 
which will eliminate the error condition from ...
Control Charts were designed as a methodology for indicating change in 
performance, either variation or Mean/Median. 
Cha...
Focus of Six Sigma and the Use of SPC 
Y=F(x) 
To get results, should we focus our behavior on the Y or X? 
115 
Y 
Depend...
116 
Control Chart Anatomy 
Special Cause 
Variation 
Process is “Out 
of Control” 
Common Cause 
Variation 
Process is “I...
117 
Control and Out of Control 
Outlier 
Outlier 
3 99.7% 
95% 
68% 
2 
1 -1 
-2 
-3
Size of Subgroups 
Typical subgroup sizes are 3-12 for variable data: 
118 
– If difficulty of gathering sample or expense...
Sampling too little will not allow for sufficient detection of shifts in the 
process because of Special Causes. 
UCL=7.38...
120 
SPC Selection Process 
Choose Appropriate 
Control Chart 
type 
of data 
type of 
attribute 
data 
subgroup 
size 
AT...
121 
Understanding Attribute Control Chart Selection 
Type of Chart When do you need it? 
 Need to track the fraction of ...
122 
Special Cause Rule Default in MINITABTM 
If a Belt is using MINITABTM, you must be aware of what default settings 
fo...
Pre-Control Charts use limits relative to the specification limits. This is the 
first and ONLY chart you will see specifi...
124 
Responding to Out of Control Indications 
• The power of SPC is not to find out what the Center Line and Control Limi...
125 
Cost Considerations 
Cost to implement improvement: 
– Initial cost to implement improvement 
• Cost to train existin...
126 
Example of Completed Solution Selection Matrix 
OVERALL 
IMPACT 
RATING 
COST 
RATING 
TIME 
RATING 
OVERALL 
RATING ...
127 
Control Plan Information 
The team develops the Control Plan by utilizing all 
available information from the followi...
The Certified Lean Six Sigma Black Belt Assessment 
The Certified Lean Six Sigma Black Belt (CLSSBB) tests are 
useful for...
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Application of Lean Six Sigma In Food Processing Process Improvement

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A presentation given at the Canadian Institute of Food Science and Technology at Niagara Falls, Canada in Nov. 2011.

Published in: Leadership & Management

Application of Lean Six Sigma In Food Processing Process Improvement

  1. 1. Practical Applications of Lean Six Sigma Towards Excellence in Food Safety Aizad Ahmad MSC, MBA, SSBB, PFPc Canada 1
  2. 2. HACCP • Hazard Analysis and Critical Control Points (HACCP) is a prevention-based food safety system. It provides a systematic method for analyzing food processes, the possible hazards and designating the critical control points necessary to prevent unsafe food from reaching the consumer. HACCP is built around seven principles: 1. Analysis of food hazards: biological, chemical or physical 2 Identification of critical control points: raw materials, storage, processing, distribution and consumption 3. Establishment of critical control limits and preventive measures: for example, minimum cooking temperature and time. 4. Monitoring of these critical control points 5. Establishment of corrective actions 6. Keeping records 7. Systematic and regular auditing of the system in place by independent third party certification bodies. 2
  3. 3. Canadian Food Inspection Agency • As of March 2011, there were 3,502 field food safety inspectors & 4,898 inspection staff • Around 350 food recalls per year • Recalls & warnings are posted on the www.inspection.gc.ca , can also get email alerts • More than 869,000 tests done annually • Food Safety Enhancement Program (FSEP)-HACCP, website has information about it. Main focus: Dairy (HACCP generic model), Chicken, Fish & seafood • Business Development Canada (BDC) pprovides funding for HACCP implementation & Six Sigma Lean management http://www.bdc.ca/EN/solutions/consulting/Pages/cs_haccp.aspx 3
  4. 4. 4 Conventional Strategy Conventional definitions of quality focused on conformance to standards. Requirement or LSL Requirement or USL Target Bad Bad Good Conventional strategy was to create a product or service that met certain specifications. – Assumed that if products and services were of good quality. then their performance standards were correct. – Rework was required to ensure final quality. – Efforts were overlooked and unquantified (time, money, equipment usage, etc).
  5. 5. 5 What is Six Sigma…as a Method? DMAIC provides the method for applying the Six Sigma philosophy in order to improve processes.  Define - the business opportunity  Measure - the process current state  Analyze - determine root cause or Y= f (x)  Improve - eliminate waste and variation  Control - evidence of sustained results
  6. 6. 6 What is Six Sigma…as a Benchmark? Yield 99.9997% 99.976% 99.4% 93% 65% 50% PPMO 3.4 233 6,210 66,807 308,537 500,000 World Class Benchmarks Source: Journal for Quality and Participation, Strategy and Planning Analysis 10% GAP Industry Average 10% GAP Non Competitive COPQ <10% 10-15% 15-20% 20-30% 30- 40% >40% Sigma 6 5 4 3 2 1 What does 20 - 40% of Sales represent to your Organization?
  7. 7. Data Driven Decisions Y= f (X) To get results, should we focus our behavior on the Y or X ? • Y • Dependent • Output • Effect • e.g. Temperature • e.g. water activity • X1 . . . XN • Independent • Input-Process • Cause • Problem • e.g. heat adjustment, conveyor speed, raw material composition Why should we test or inspect Y, if we know this relationship? 7
  8. 8. 8 Six Sigma Strategy (X1) (X7) (X5) (X6) (X3) (X2) (X4) (X8) (X10) (X9) We use a variety of Six Sigma tools to help separate the “vital few” variables effecting our Y from the “trivial many.” Some processes contain many, many variables. However, our Y is not effected equally by all of them. By focusing on the vital few we instantly gain leverage. Archimedes said: “ Give me a lever big enough and fulcrum on which to place it, and I shall move the world.”
  9. 9. 9 Six Sigma Roles and Responsibilities There are many roles and responsibilities for successful implementation of Six Sigma. MBB Black Belts Green Belts Yellow Belts • Executive Leadership • Champion/Process Owner • Master Black Belt • Black Belt • Green Belt • Yellow Belt Eventually there should be a big base of support internal to the organization.
  10. 10. MBB should be well versed with all aspects of Six Sigma, from technical applications to Project Management. MBBs need to have the ability to 10 Master Black Belt influence change and motivate others. • Provide advice and counsel to Executive Staff • Provide training and support – In class training – On site mentoring • Develop sustainability for the business • Facilitate cultural change
  11. 11. 11 Green Belt Green Belts are practitioners of Six Sigma Methodology and typically work within their functional areas or support larger Black Belt Projects. • Well versed in the definition & measurement of critical processes – Creating Process Control Systems • Typically works project in existing functional area • Involved in identifying improvement opportunities, works part-time on Six Sigma • Involved in continuous improvement efforts – Applying basic tools and PDCA • Team members on DMAIC teams – Supporting projects with process knowledge & data collection
  12. 12. We go thru processes everyday. Below are some examples of processes. Can you think of other processes within your daily environment? 12 Examples of Processes • Injection molding • Decanting solutions • Filling vial/bottles • Crushing ore • Refining oil • Turning screws • Building custom homes • Paving roads • Changing a tire • Recruiting staff • Processing invoices • Conducting research • Opening accounts • Reconciling accounts • Filling out a timesheet • Distributing mail • Backing up files • Issuing purchase orders
  13. 13. 13 Process Maps • The purpose of Process Maps is to: – Identify the complexity of the process – Communicate the focus of problem solving • Process Maps are living documents and must be changed as the process is changed – They represent what is currently happening, not what you think is happening. – They should be created by the people who are closest to the process Process Map Start Step A Step B Step C Step D Finish
  14. 14. 14 Process Map Example The Process Map below is for a call center. START LOGON TO PC & APPLICATIONS SCHEDULED PHONE TIME? LOGON Y TO PHONE CALL or WALK-IN? PHONE DATA CAPTURE BEGINS DETERMINE WHO IS INQUIRING ACCESS CASE TOOL CASE TOOL RECORD? N A Z CALL WALK-IN DETERMINE NATURE OF CALL & CONFIRM UNDERSTANDING Y N C B D PHONE TIME Y N Z B C REVIEW CASE TOOL HISTORY & TAKE NOTES TRANSFER APPROPRIATE? IMMEDIATE RESPONSE AVAILABLE? PUT ON HOLD, REFER TO REFERENCES Y N Y N TRANSFER CALL ANSWER? Y N QUERY INTERNAL HRSC SME(S) ANSWER? Y N OFF HOLD AND ARRANGE CALL BACK PHONE DATA ENDS PROVIDE RESPONSE PHONE& NOTE DATA ENDS D ADD TO RESEARCH LIST Z LOGOFF PHONE, CHECK MAIL,E-MAIL,VOICE MAIL SCHEDULED PHONE TIME? N Y A E EXAMINE NEXT NOTE OR RESEARCH ITEM ACCESS CASE TOOL ENTER APPROPRIATE SSAN (#,9s,0s) IF EMP DATA NOT POPULATED, ENTER OLD CASE Y N UPDATE ENTRIES INCL OPEN DATE/TIME CREATE A CASE INCL CASE TYPE DATE/TIME, & NEEDED BY AUTO ROUTE Y ROUTE CASE CLOSED N Y N CLOSE CASE W/ DATE/TIME E TAKE ACTION or DO RESEARCH F GO TO F or E DEPENDING ON E NEXT CASE F
  15. 15. 15 Types of Process Maps The Linear Flow Process Map Calls for Order Pizza Correct Customer Hungry Take Order Make Pizza Cook Pizza Box Pizza Deliver Pizza Customer Eats As the name states, this diagram shows the process steps in a sequential flow, generally ordered from an upper left corner of the map towards the right side. The Deployment-Flow or Swim Lane Process Map Calls for Order Customer Hungry Take Order Make Pizza Cook Pizza Box Pizza Deliver Pizza Customer Eats Deliverer Cook Cashier Customer Pizza Correct The value of the Swim Lane map is that is shows you who or which department is responsible for the steps in a process. This can provide powerful insights in the way a process performs. A timeline can be added to show how long it takes each group to perform their work. Also each time work moves across a swim lane, there is a “Supplier – Customer” interaction. This is usually where bottlenecks and queues form.
  16. 16. 16 Cross Functional Process Map When multiple departments or functional groups are involved in a complex process it is often useful to use cross functional Process Maps. – Draw in either vertical or horizontal swim lanes and label the functional groups and draw the Process Map Vendor Department Accounting Bank Financial General Accounting Start Request transfer Sending Fund Transfers Attach ACH form to Invoice Produce an Invoice Fill out ACH enrollment form Receive payment End Vendor info in FRS? Input info into web interface Match against bank batch and daily cash batch Accepts transactions, transfer money, and provide batch total Review and Process transfer in FRS 3.0 Journey Entry Maintain database to balance ACH transfers 21.0 Bank Reconciliation ACH – Automated Clearing House. No Yes
  17. 17. 17 What is a Customer? There are different types of customers which dictates how we interact with them in the process, in order to identify customer and supplier requirements we must first define who the customers are: External – Direct: those who receive the output of your services, they generally are the source of your revenue – Indirect: those who do not receive or pay for the output of your services but have a vested interest in what you do (government agencies) Internal - those within your organization who receive the output of your work
  18. 18. 18 What is a CTQ? • Critical to Quality (CTQ ’s) are measures that we use to capture VOC properly. (also referred to in some literature as CTC’s – critical to customer) • CTQ ’s can be vague and difficult to define. – The customer may identify a requirement that is difficult to measure directly so it will be necessary to break down what is meant by the customer into identifiable and measurable terms Product: • Performance (shelf life) • Features (flavour/colour) • Conformance • Timeliness (cooking time) • Reliability • Serviceability • Durability • Aesthetics • Reputation • Completeness Service: • Competence • Reliability • Accuracy • Timeliness • Responsiveness • Access • Courtesy • Communication • Credibility • Security • Understanding
  19. 19. 19 Cost of Poor Quality - Categories Internal COPQ External COPQ • Warranty • Customer Complaint Related Travel • Customer Charge Back Costs • Etc… Prevention • Error Proofing Devices • Supplier Certification • Design for Six Sigma • Etc… Detection • Supplier Audits • Sorting Incoming Parts • Repaired Material • Etc… • Quality Control Department • Inspection • Quarantined Inventory • Etc…
  20. 20. Time value of money 20 COPQ - Iceberg Rework, Client sues Inspection Warranty Rejects Govt fines Lost sales Late delivery Engineering change orders Excess inventory Long cycle times Hidden Costs In food safety Visible Costs Lost Customer Loyalty More Set-ups Working Capital allocations Excessive Material Orders/Planning Recode (less obvious)
  21. 21. Waste does not add, subtract or otherwise modify the throughput 21 COPQ and Lean in a way that is perceived by the customer to add value. Lean Enterprise Seven Elements of Waste *  Correction  Processing  Conveyance  Motion  Waiting  Overproduction  Inventory • In some cases, waste may be necessary, but should be recognized and explored: – Inspection, Correction, Waiting in suspense – Decision diamonds, by definition, are non-value added • Often, waste can provide opportunities for additional defects to occur. • We will discuss Lean in more detail later in the course. *Womack, J. P., & Jones, D. T. (1996). Lean Thinking. New York, NY: Simon & Schuster
  22. 22. 22 COPQ – Hard and Soft Savings While hard savings are always more desirable because they are easier to quantify, it is also necessary to think • Labor Savings • Cycle Time Improvements • Scrap Reductions • Hidden Factory Costs • Inventory Carrying Cost COPQ – Soft Savings • Gaining Lost Sales • Missed Opportunities • Customer Loyalty • Strategic Savings • Preventing Regulatory Fines COPQ – Hard Savings about soft savings.
  23. 23. 23 The Basic Six Sigma Metrics In any process improvement endeavor, the ultimate objective is to make the process: • Better: DPU (defects per unit), DPMO (defects per million output), RTY (rolled throughput yeild) : there are others, but they derive from these basic three • Faster: Cycle Time reduction (e.g. less cooking time, less preparation time with food safety • Cheaper: COPQ If you make the process better by eliminating defects you will make it faster. If you choose to make the process faster, you will have to eliminate defects to be as fast as you can be. If you make the process better or faster, you will necessarily make it cheaper. The metrics for all Six Sigma projects fall into one of these three categories
  24. 24. 24 Project Selection Understanding Six Sigma Six Sigma Fundamentals Selecting Projects Selecting Projects Refining & Defining Financial Evaluation Elements of Waste Wrap Up & Action Items
  25. 25. 25 Project Selection – Core Components Business Case – The Business Case is a high level articulation of the area of concern. This case answers two primary questions; one, what is the business motivation for considering the project and two, what is our general area of focus for the improvement effort? Project Charter – The Project Charter is a more detailed version of the Business Case. This document further focuses the improvement effort. It can be characterized by two primary sections, one, basic project information and simple project performance metrics. Benefits Analysis – The Benefits Analysis is a comprehensive financial evaluation of the project. This analysis is concerned with the detail of the benefits in regard to cost & revenue impact that we are expecting to realize as a result of the project.
  26. 26. 26 Business Case Example During FY 2005, the 1st Time Call Resolution Efficiency for New Customer Hardware Setup was 89% . This represents a gap of 8% from the industry standard of 97% that amounts to US $2,000,000 of annualized cost impact.
  27. 27. 27 The Business Case Template Fill in the Blanks for Your Project: During ___________________________________ , the ____________________ for (Period of time for baseline performance) (Primary business measure) ________________________ was _________________ . (A key business process) (Baseline performance) This gap of ____________________________ (Business objective target vs. baseline) from ___________________ represents ____________________ of cost impact. (Business objective) (Cost impact of gap)
  28. 28. 28 What is a Project Charter? The Project Charter expands on the Business Case, it clarifies the projects focus and measures of project performance and is completed by the Six Sigma Belt. Components: • The Problem • Project Scope • Project Metrics • Primary & Secondary • Graphical Display of Project Metrics • Primary & Secondary • Standard project information • Project, Belt & Process Owner names • Start date & desired End date • Division or Business Unit • Supporting Master Black Belt (Mentor) • Team Members
  29. 29. 29 Project Charter - Definitions • Problem Statement - Articulates the pain of the defect or error in the process. • Objective Statement – States how much of an improvement is desired from the project. • Scope – Articulates the boundaries of the project. • Primary Metric – The actual measure of the defect or error in the process. • Secondary Metric(s) – Measures of potential consequences (+ / -) as a result of changes in the process. • Charts – Graphical displays of the Primary and Secondary Metrics over a period of time.
  30. 30. 30 What is the Financial Evaluation? The financial evaluation establishes the value of the project. The components are: – Impact • Sustainable • One-off – Allocations • Cost Codes / Accounting System – Forecast • Cash flow • Realization schedule OK, let’s add it up! Typically a financial representative is responsible for evaluating the financial impact of the project. The Belt works in coordination to facilitate the proper information.
  31. 31. Whatever your organization’s protocol may be these aspects should be accounted for within any improvement project. I M P A C T 31 Benefits Capture - Calculation “Template” Sustainable Impact “One-Off” Impact Reduced Costs Increased Revenue Costs Implemen-tation Capital C O S T C O D E S F O R E C A S T Realization Schedule (Cash Flow) By Period (i.e. Q1,Q2,Q3,Q4) There are two types of Impact, One Off & Sustainable Cost Codes allocate the impact to the appropriate area in the “Books” Forecasts allow for proper management of projects and resources
  32. 32. 32 Benefits Calculation Template The Benefits Calculation Template facilitates and aligns with the aspects discussed for Project Accounting.
  33. 33. 33 Pareto Analysis Pareto Analysis: • A bar graph used to arrange information in such a way that priorities for process improvement can be established. • The 80-20 theory was first developed in 1906, by Italian economist, Vilfredo Pareto, who observed an unequal distribution of wealth and power in a relatively small proportion of the total population. Joseph M. Juran is credited with adapting Pareto's economic observations to business applications.
  34. 34. 34 Lean Six Sigma Lean Six Sigma combines the strengths of each system: • Lean – Guiding principles based operating system – Relentless elimination of all waste – Creation of process flow and demand pull – Resource optimization – Simple and visual Strength: Efficiency • Six Sigma – Focus on voice of the customer – Data and fact based decision making – Variation reduction to near perfection levels – Analytical and statistical rigor Strength: Effectiveness An Extremely Powerful Combination!
  35. 35. 35 Seven Components of Waste Muda is classified into seven components: – Overproduction – Correction (defects) – Inventory – Motion – Overprocessing – Conveyance – Waiting Sometimes additional forms of muda are added: – Under use of talent – Lack of safety Being Lean means eliminating waste.
  36. 36. 36 English Translation There have been many attempts to force five English “S” words to maintain the original intent of 5S from Japanese. Listed below are typical English words used to translate: 1.) Sort (Seiri) 2.) Straighten or Systematically Arrange (Seiton) 3.) Shine or Spic and Span (Seiso) 4.) Standardize (Seiketsu) 5.) Sustain or Self-Discipline (Shitsuke) 5 S Sort Identify necessary items and remove unnecessary ones, use time management. Place things in such a way that they can be easily reached whenever they are needed. Shine Visual sweep of areas, eliminate dirt, dust and scrap. Make workplace shine. Straighten Self-Discipline Make 5S strong in habit. Make problems appear and solve them. Standardize Work to standards, maintain standards, wear safety equipment.
  37. 37. Overview of Brainstorming Techniques A commonly used tool to solicit ideas by using categories to stimulate cause and effect relationship with a problem. It uses verbal inputs in a team environment. 37 The Y The Problem or Condition The X’s (Causes) Cause and Effect Diagram People Machine Method Problem Matel rial Measurement Environment Categories
  38. 38. Cause and Effect Diagram A commonly used tool to solicit ideas by using categories to stimulate cause and effect relationship with a problem. It uses verbal inputs in a team environment. Cause and Effect Diagram People Machine Method Products – Measurement – People – Method – Materials – Equipment – Environment Transactional – People – Policy – Procedure – Place – Measurement – Environment The Y The Problem or Condition Problem Categories for the legs of the diagram can use templates for products or transactional symptoms. Or you can select the categories by process step or what you deem appropriate for the situation. The X’s (Causes) Materlial Measurement Environment Categories 38
  39. 39. 39 Chemical Purity Example Measurement Incoming QC (P) Measurement Method (P) Measurement Capability (C) Manpower Skill Level (P) Adherence to procedure (P) Work order variability (N) Materials Raw Materials (C) Multiple Vendors (C) Specifications (C) Startup inspection (P) Handling (P) Purification Method (P) Methods Room Humidity (N) RM Supply in Market (N) Shipping Methods (C) Mother Nature Column Capability (C) Nozzle type (C) Temp controller (C) Data collection/feedback (P) Equipment Chemical Purity Insufficient staff (C) Training on method (P)
  40. 40. 40 Types of Process Maps The SIPOC “Supplier – Input – Process – Output – Customer” Process Map Process r See Below Suppliers ATT Phones Office Depot TI Calculators NEC Cash Register Call for an Order Outputs r r r r r r r Price Order confirmation Bake order Data on cycle time Order rate data Order transaction Delivery info Customers r r Cook Accounting Requirements r r r r r r Inputs r r r r r r r r r r r r Pizza type Size Quantity Extra Toppings Special orders Drink types & quantities Other products Phone number Address Name Time, day and date Volume Level 1 Process Map for Customer Order Process Answer Phone Write Order Sets Price Confirm Order Address & Phone Complete call < 3 min Order to Cook < 1 minute Complete bake order Correct bake order Correct address Correct Price Order to Cook r r r r The SIPOC diagram is especially useful after you have been able to construct either a Level 1 or Level 2 Map because it facilitates your gathering of other pertinent data that is affecting the process in a systematic way.
  41. 41. 41 The Vital Few A Six Sigma Belt does not just discover which X’s are important in a process (the vital few). – The team considers all possible X’s that can contribute or cause the problem observed. – The team uses 3 primary sources of X identification: • Process Mapping • Fishbone Analysis • Basic Data Analysis – Graphical and Statistical – A List of X’s is established and compiled. – The team then prioritizes which X’s it will explore first, and eliminates the “obvious” low impact X’s from further consideration. The X-Y Matrix is this Prioritization Tool!
  42. 42. 42 Example Click the Demo button to see an example.
  43. 43. 43 Example Click the Summary Worksheet YX Diagram Summary Process: Date: laminating 5/2/2006 Output Variables Input Variables Description Weight Description Ranking Rank % broken 10 temperature 162 14.90% unbonded area 9 human handling 159 14.63% smears 8 material properties 130 11.96% thickness 7 washer 126 11.59% foreign material 6 pressure 120 11.04% 0 robot handling 120 11.04% 0 time 102 9.38% 0 clean room practices 90 8.28% 0 clean room cleanliness 78 7.18% 0 - 0.00% Input Matrix Results 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% temperature material properties pressure time clean room cleanliness Input (X's) Output (Y's) Input Summary
  44. 44. 44 Purpose of FMEA Failure Mode and Effects Analysis (FMEA) : • Improve the quality, reliability, and safety of products. • Increase customer satisfaction. • Reduce product development time and cost. • Document and track actions taken to reduce risk and improve the process. • Focus on continuous problem prevention not problem solving.
  45. 45. 45 Why Create an FMEA? As a means to manage… RISK!!! We want to avoid causing failures in the Process as well as the Primary & Secondary Metrics .
  46. 46. 46 The FMEA… # Process Functio n (Step) Potential Failure Modes (process defects) Potential Failure Effects (Y's) S E V C l a s s Potential Causes of Failure (X's) O C C Current Process Controls D E T R P N Recommen d Actions Responsibl e Person & Target Date Taken Action s S E V O C C D E T R P N 1 2 3 4 5 6 7 8 9
  47. 47. 47 Ranking Severity Effect Criteria: Severity of Effect Defined Ranking Hazardous: Without Warning May endanger the operator. Failure mode affects safe vehicle operation and/or involves non-compliance with government regulation. Failure will occur WITHOUT warning. 10 Hazardous: With Warning May endanger the operator. Failure mode affects safe vehicle operation and/or involves non-compliance with government regulation. Failure will occur WITH warning. 9 Very High Major disruption to the production line. 100% of the product may have to be scrapped. Vehicle/item inoperable, loss of primary function. Customers will be very dissatisfied. 8 High Minor disruption to the production line. The product may have to be sorted and a portion (less than 100%) scrapped. Vehicle operable, but at a reduced level of performance. Customers will be dissatisfied. 7 Moderate Minor disruption to the production line. A portion (less than 100%) may have to be scrapped (no sorting). Vehicle/item operable, but some comfort/convenience item(s) inoperable. Customers will experience discomfort. 6 Low Minor disruption to the production line. 100% of product may have to be re-worked. Vehicle/item operable, but some comfort/convenience item(s) operable at a reduced level of performance. Customers will experience some dissatisfaction. 5 Very Low Minor disruption to the production line. The product may have to be sorted and a portion (less than 100%) re-worked. Fit/finish/squeak/rattle item does not conform. Most customers will notice the defect. 4 Minor Minor disruption to the production line. A portion (less than 100%) of the product may have to be re-worked online but out-of-station. Fit/finish/squeak/rattle item does not conform. Average customers will notice the defect. 3 Very Minor Minor disruption to the production line. A portion (less than 100%) of the product may have to be re-worked online but in-station. Fit/finish/squeak/rattle item does not conform. Discriminating customers will notice the defect. 2 None No effect. 1 * Potential Failure Mode and Effects Analysis (FMEA), Reference Manual, 2002. Pgs 29-45. Chrysler Corporation, Ford Motor Company, General Motors Corporation.
  48. 48. 48 Sample Transactional Severities Effect Criteria: Impact of Effect Defined Ranking Critical Business Unit-wide May endanger company’s ability to do business. Failure mode affects process operation and / or involves noncompliance with government regulation. 10 Critical Loss - Customer Specific May endanger relationship with customer. Failure mode affects product delivered and/or customer relationship due to process failure and/or noncompliance with government regulation. 9 High Major disruption to process/production down situation. Results in near 100% rework or an inability to process. Customer very dissatisfied. 7 Moderate Moderate disruption to process. Results in some rework or an inability to process. Process is operable, but some work arounds are required. Customers experience dissatisfaction. 5 Low Minor disruption to process. Process can be completed with workarounds or rework at the back end. Results in reduced level of performance. Defect is noticed and commented upon by customers. 3 Minor Minor disruption to process. Process can be completed with workarounds or rework at the back end. Results in reduced level of performance. Defect noticed internally, but not externally. 2 None No effect. 1
  49. 49. 49 Ranking Occurrence Probability of Failure Possible Failure Rates Cpk Ranking Very High: Failure is almost inevitable. < 0.33 10 ³ 0.33 9 High: Generally associated with processes similar to previous processes that have often failed. ³ 0.51 8 ³ 0.67 7 Moderate: Generally associated with processes similar to previous processes that have experienced occasional failures but not in major proportions. ³ 0.83 6 ³ 1.00 5 ³ 1.17 4 Low: Isolated failures associated with similar processes. ³ 1.33 3 Very Low: Only isolated failures associated with almost identical processes. ³ 1.5 2 Remote: Failure is unlikely. No failures ever associated with almost identical processes.  1 in 2 1 in 3 1 in 8 1 in 20 1 in 80 1 in 400 1 in 2,000 1 in 15,000 1 in 150,000  1 in 1,500,000 ³ 1.67 1 Potential Failure Mode and Effects Analysis (FMEA), Reference Manual, 2002. Pg. 35.. Chrysler Corporation, Ford Motor Company, General Motors Corporation.
  50. 50. 50 FMEA Components…Current Process Controls Current Process Controls refers to the three types of controls that are in place to prevent a failure in with the X’s. The 3 types of controls are: •SPC (Statistical Process Control) •Poke-Yoke – (Mistake Proofing) •Detection after Failure # Process Function (Step) Potential Failure Modes (process defects) Potential Failure Effects (Y's) S E V C l a s s Potential Causes of Failure (X's) O C C Current Process Controls D E T R P N Recommen d Actions Responsibl e Person & Target Date Taken Action s S E V O C C D E T R P N Ask yourself “how do we control this defect?”
  51. 51. 51 FMEA Components…Detection (DET) Detection is an assessment of the probability that the proposed type of control will detect a subsequent failure mode. This information should be obtained from your Measurement System Analysis Studies and the Process Map. A rating should be assign in conjunction with the predetermined scale. # Process Functio n (Step) Potential Failure Modes (process defects) Potential Failure Effects (Y's) S E V C l a s s Potential Causes of Failure (X's) O C C Current Process Controls D E T R P N Recommen d Actions Responsibl e Person & Target Date Taken Action s S E V O C C D E T R P N
  52. 52. 52 Ranking Detection Detection Almost Impossible Criteria: The likelihood that the existence of a defect will be detected by the test content before the product advances to the next or subsequent process Ranking Test content must detect < 80% of failures 10 Very Remote Test content must detect 80% of failures 9 Remote Test content must detect 82.5% of failures 8 Very Low Test content must detect 85% of failures 7 Low Test content must detect 87.5% of failures 6 Moderate Test content must detect 90% of failures 5 Moderately High Test content must detect 92.5% of failures 4 High Test content must detect 95% of failures 3 Very High Test content must detect 97.5% of failures 2 Almost Certain Test content must detect 99.5% of failures 1 Potential Failure Mode and Effects Analysis (FMEA), AIAG Reference Manual, 2002 Pg. 35.. Chrysler Corporation, Ford Motor Company, General Motors Corporation.
  53. 53. 53 Risk Priority Number “RPN” The Risk Priority Number is a value that will be used to rank order the concerns from the process. The RPN is the product of, Severity, Occurrence and Detect ability as represented here… RPN = (SEV)*(OCC)*(DET) # Process Functio n (Step) Potential Failure Modes (process defects) Potential Failure Effects (Y's) S E V C l a s s Potential Causes of Failure (X's) O C C Current Process Controls D E T R P N Recomme nd Actions Responsibl e Person & Target Date Taken Action s S E V O C C D E T R P N
  54. 54. 54 Statistical Notation – Cheat Sheet An individual value, an observation A particular (1st) individual value For each, all, individual values The Mean, average of sample data The grand Mean, grand average The Mean of population data A proportion of sample data A proportion of population data Sample size Population size Summation The Standard Deviation of sample data The Standard Deviation of population data The variance of sample data The variance of population data The Range of data The average Range of data Multi-purpose notation, i.e. # of subgroups, # of classes The absolute value of some term Greater than, less than Greater than or equal to, less than or equal to
  55. 55. 55 Box Plot Box Plots summarize data about the shape, dispersion and center of the data and also help spot outliers. Box Plots require that one of the variables, X or Y, be categorical or Discrete and the other be Continuous. A minimum of 10 observations should be included in generating the box plot. Middle 50% of Data Maximum Value 75th Percentile 50th Percentile (Median) Mean 25th Percentile min(1.5 x Interquartile Range or minimum value) Outliers
  56. 56. 56 Box Plot Anatomy Median * Outlier Upper Limit: Q3+1.5(Q3-Q1) Upper Whisker Q3: 75th Percentile Q2: Median 50th Percentile Q1: 25th Percentile Lower Whisker Lower Limit: Q1+1.5(Q3-Q1) Box
  57. 57. 57 Curve Fitting Time Series MINITAB™ allows you to add a smoothed line to your time series based on a smoothing technique called Lowess. Lowess means Locally Weighted Scatterplot Smoother. Graph> Time Series Plot> Simple…(select variable Time 3)…Data View…Smoother…Lowess Index Time 3 1 10 20 30 40 50 60 70 80 90 100 605 604 603 602 601 600 599 598 597 596 Time Series Plot of Time 3
  58. 58. 58 Introduction to MSA So far we have learned that the heart and soul of Six Sigma is that it is a data-driven methodology. – How do you know that the data you have used is accurate and precise? – How do know if a measurement is a repeatable and reproducible? How good are these? Measurement System Analysis or MSA
  59. 59. 59 Measurement System Analysis MSA is a mathematical procedure to quantify variation introduced to a process or product by the act of measuring. Measurement Process Environment Measurement Item to be Measured Reference Equipment Procedure Operator The item to be measured can be a physical part, document or a scenario for customer service. Operator can refer to a person or can be different instruments measuring the same products. Reference is a standard that is used to calibrate the equipment. Procedure is the method used to perform the test. Equipment is the device used to measure the product. Environment is the surroundings where the measures are performed.
  60. 60. 60 Accurate but not precise - On average, the shots are in the center of the target but there is a lot of variability Accuracy and Precision Precise but not accurate - The average is not on the center, but the variability is small
  61. 61. 61 Components of Variation Whenever you measure anything, the variation that you observe can be segmented into the following components… Observed Variation Unit-to-unit (true) Variation Measurement System Error Precision Accuracy Repeatability Reproducibility Stability Bias Linearity All measurement systems have error. If you don’t know how much of the variation you observe is contributed by your measurement system, you cannot make confident decisions. If you were one speeding ticket away from losing your license, how fast would you be willing to drive in a school zone?
  62. 62. 62 Gage R & R Study Part Allocation From Any Population 10 x 3 x 2 Crossed Design is shown A minimum of two measurements/part/operator is required Three is better! 1 2 3 4 5 6 7 8 9 10 Operator 1 Operator 2 Operator 3 Trial 1 Trial 2 Trial 1 Trial 2 Trial 1 Trial 2 P a r t s
  63. 63. 63 Excel Attribute R & R Template Attribute Gage R & R Effectiveness SCORING REPORT DATE: 5/10/2006 Attribute Legend5 (used in computations) NAME: Joe Smith 1 pass PRODUCT: My Gadget All operators 2 fail BUSINESS: Unit 1 agree within and All Operators between each agree with Other standard Known Population Operator #1 Operator #2 Operator #3 Y/N Y/N Sample # Attribute Try #1 Try #2 Try #1 Try #2 Try #1 Try #2 Agree Agree 1 pass pass pass pass pass fail fail N N 2 pass pass pass pass pass fail fail N N 3 fail fail fail fail pass fail fail N N 4 fail fail fail fail fail fail fail Y Y 5 fail fail fail pass fail fail fail N N 6 pass pass pass pass pass pass pass Y Y 7 pass fail fail fail fail fail fail Y N 8 pass pass pass pass pass pass pass Y Y 9 fail pass pass pass pass pass pass Y N 10 fail pass pass fail fail fail fail N N 11 pass pass pass pass pass pass pass Y Y 12 pass pass pass pass pass pass pass Y Y
  64. 64. 64 Capability Analysis Y1 Y2 Y3 Y = f(X) (Process Function) Verified Op i Op i + 1 ? Analysis Scrap Off-Line Correction Correctable ? The X’s (Inputs) X1 X2 X3 X4 X5 The Y’s (Outputs) Data for Y1…Yn 10.16 10.11 10.05 10.33 10.44 9.86 10.07 10.29 10.36 10.16 10.11 10.05 10.33 10.44 9.86 10.07 10.29 10.36 9.87 9.99 10.12 10.43 10.21 10.01 10.15 10.44 10.03 10.33 10.15 9.87 9.99 10.12 10.43 10.21 10.01 10.15 10.44 10.03 10.33 10.15 10.16 10.11 10.05 10.33 10.44 9.86 10.07 10.29 10.36 Yes No Frequency Variation – “Voice of the Process” 9.80 9.9010.0 10.1 10.2 10.3 10.4 10.5 Critical X(s): Any variable(s) which exerts an undue influence on the important outputs (CTQ’s) of a process Requirements – “Voice of the Customer” LSL = 9.96 USL = 10.44 Defects Defects -6 -5 -4 -3 -2 -1 +1 +2 +3 +4 +5 +6 Data - VOP 9.70 9.80 9.90 10.0 10.1 10.2 10.3 10.4 10.5 10.6 10.16 10.11 10.05 10.33 10.44 9.86 10.07 10.29 10.36 9.87 9.99 10.12 10.43 10.21 10.01 10.15 10.44 10.03 10.33 10.15 10.16 10.11 10.05 10.33 10.44 9.86 10.07 10.29 10.36 Percent Composition Capability Analysis Numerically Compares the VOP to the VOC
  65. 65. 65 Capable and on target Average LSL USL Target Process Output Categories Off target LSL USL Average Target Incapable Average LSL USL Target
  66. 66. 66 Problem Solving Options – Shift the Mean This involves finding the variables that will shift the process over to the target. This is usually the easiest option. LSL USL Shift
  67. 67. 67 Problem Solving Options – Reduce Variation This is typically not so easy to accomplish and occurs often in Six Sigma projects. LSL USL
  68. 68. 68 Problem Solving Options – Shift Mean & Reduce Variation This occurs often in Six Sigma projects. LSL USL Shift & Reduce
  69. 69. 69 MINITAB™ Example LSL USL 597.75 598.50 599.25 600.00 600.75 601.50 Process Data LSL 598 Target * USL 602 Sample Mean 599.115 Sample N 100 StDev (Within) 0.559239 StDev (O v erall) 0.604106 Potential (Within) C apability C p 1.19 C PL 0.66 C PU 1.72 C pk 0.66 O v erall C apability Pp 1.10 PPL 0.62 PPU 1.59 Ppk 0.62 C pm * O bserv ed Performance PPM < LSL 30000.00 PPM > USL 0.00 PPM Total 30000.00 Exp. Within Performance PPM < LSL 23088.05 PPM > USL 0.12 PPM Total 23088.18 Exp. O v erall Performance PPM < LSL 32467.79 PPM > USL 0.90 PPM Total 32468.68 Within Overall Process Capability of Supplier 1
  70. 70. 70 MINITAB™ Example LSL USL 597 598 599 600 601 602 603 Process Data LSL 598 Target * USL 602 Sample Mean 600.061 Sample N 100 StDev (Within) 1.00606 StDev (O v erall) 1.14898 Potential (Within) C apability C p 0.66 C PL 0.68 C PU 0.64 C pk 0.64 O v erall C apability Pp 0.58 PPL 0.60 PPU 0.56 Ppk 0.56 C pm * O bserv ed Performance PPM < LSL 40000.00 PPM > USL 60000.00 PPM Total 100000.00 Exp. Within Performance PPM < LSL 20251.30 PPM > USL 26969.82 PPM Total 47221.11 Exp. O v erall Performance PPM < LSL 36425.88 PPM > USL 45746.17 PPM Total 82172.05 Within Overall Process Capability of Supplier 2
  71. 71. 71 Types of Error 1.Error in sampling – Error due to differences among samples drawn at random from the population (luck of the draw). Eg. Water sampling in a stream,lake – This is the only source of error that statistics can accommodate. 2.Bias in sampling – Error due to lack of independence among random samples or due to systematic sampling procedures (height of horse jockeys only). 3.Error in measurement – Error in the measurement of the samples (MSA). 4.Lack of measurement validity – Error in the measurement does not actually measure what it intends to measure (placing a probe in the wrong slot; measuring temperature with a thermometer that is just next to a furnace).
  72. 72. 72 Population, Sample, Observation Population – EVERY data point that has ever been or ever will be generated from a given characteristic. Sample – A portion (or subset) of the population, either at one time or over time. Observation – An individual measurement. X X X X X X
  73. 73. 73 The Mission Mean Shift Variation Reduction Both Your mission, which you have chosen to accept, is to reduce cycle time, reduce the error rate, reduce costs, reduce investment, improve service level, improve throughput, reduce lead time, increase productivity… change the output metric of some process, etc… In statistical terms, this translates to the need to move the process Mean and/or reduce the process Standard Deviation. You’ll be making decisions about how to adjust key process input variables based on sample data, not population data - that means you are taking some risks. How will you know your key process output variable really changed, and is not just an unlikely sample? The Central Limit Theorem helps us understand the risk we are taking and is the basis for using sampling to estimate population parameters.
  74. 74. 74 Test of Means (t-tests) t-tests are used: – To compare a Mean against a target. • i.e.; The team made improvements and wants to compare the Mean against a target to see if they met the target. – To compare Means from two different samples. • i.e.; Machine one to machine two. • i.e.; Supplier one quality to supplier two quality. – To compare paired data. • Comparing the same part before and after a given process. They don’t look the same to me!
  75. 75. 75 One-Sample T: Values Ho Test of mu = 5 vs not = 5 Session Window Ha (X  X) S  Variable N Mean StDev SE Mean 95% CI T P Values 9 4.78889 0.24721 0.08240 (4.59887, 4.97891) -2.56 0.034 N – sample size Mean – calculate mathematic average StDev – calculated individual Standard Deviation (classical method) SE Mean – calculated Standard Deviation of the distribution of the Means Confidence Interval that our population average will fall between 4.5989, 4.9789 n SE Mean    n i 1 i n 1 s 2 T-Calc = Observed – Expected over SE Mean T-Calc = X-bar – Target over Standard Error T-Calc = 4.7889 – 5 over .0824 = - 2.56
  76. 76. 76 Evaluating the Results Since the P-value of 0.034 is less than 0.05, reject the null hypothesis. Based on the samples given there is a difference between the average of the sample and the desired target. X Ho 6. State Practical Conclusions The new supplier’s claim that they can meet the target of 5 for the hardness is not correct.
  77. 77. 77 Hypothesis Testing Roadmap Normal Test of Equal Variance 1 Sample Variance 1 Sample t-test Variance Equal Variance Not Equal 2 Sample T One Way ANOVA 2 Sample T One Way ANOVA
  78. 78. 78 2 Sample t-test A 2-sample t-test is used to compare two Means. MINITABTM performs an independent two-sample t-test and generates a confidence interval. Use 2-Sample t to perform a Hypothesis Test and compute a confidence interval of the difference between two population Means when the population Standard Deviations, σ’s, are unknown. Two tailed test: – H0: μ1 = μ2 If P-value > 0.05 fail to reject Ho – Ha: μ1 ≠ μ2 If P-value < 0.05 reject Ho One tailed test: – H0: μ1 = μ2 – Ha: μ1 > or < μ2 Stat > Basic Statistics > 2-Sample t m1 m2
  79. 79. 79 Box Plot Boxplot of BTU.In by Damper 1 2 Damper BTU.In 20 15 10 5 5. State Statistical Conclusions: Fail to reject the null hypothesis. 6. State Practical Conclusions: There is no difference between the dampers for BTU’s in.
  80. 80. 80 Minitab Session Window Number of Samples Calculated Average  (X  X)   n i 1 i n 1 s 2 S SE Mean (N1 – 1) + (N2-1) n -1.450 0.980 T-Calc = Observed d – Expected d divided by s T-Calc = Estimate for difference – Target for distance over s T-Calc = (9.91 – 10.14) / T-Calc = -0.235 / s -0.38 Two- Sample T-Test (Variances Equal) Ho: μ1 = μ2 Ha: μ1≠ or < or > μ2
  81. 81. 81 Paired t-test • A Paired t-test is used to compare the Means of two measurements from the same samples generally used as a before and after test. Stat > Basic Statistics > Paired t • MINITABTM performs a paired t-test. This is appropriate for testing the difference between two Means when the data are paired and the paired differences follow a Normal Distribution. • Use the Paired t command to compute a confidence interval and perform a Hypothesis Test of the difference between population Means when observations are paired. A paired t-procedure matches responses that are dependent or related in a pair-wise delta manner. This matching allows you to account for (d) variability between the pairs usually resulting in a smaller error term, thus increasing the sensitivity of the Hypothesis Test or confidence interval. – H: μ= μoδ o – H: μ≠ μaδ o • Where μδ is the population Mean of the differences and μ0 is the hypothesized Mean of the differences, typically zero. mbefore mafter
  82. 82. 82 Box Plot MINITABTM Session Window Box Plot of AB Delta One-Sample T: AB Delta Test of mu = 0 vs not = 0 Variable N Mean StDev SE Mean AB Delta 10 0.410000 0.387155 0.122429 95% CI T P (0.133046, 0.686954) 3.35 0.009 5. State Statistical Conclusions: Reject the null hypothesis 6. State Practical Conclusions: We are 95% confident that there is a difference in wear between the two materials.
  83. 83. 83 Analyze Phase - The Roadblocks Look for the potential roadblocks and plan to address them before they become problems: – Lack of data – Data presented is the best guess by functional managers – Team members do not have the time to collect data – Process participants do not participate in the analysis planning – Lack of access to the process
  84. 84. 84 Types and Magnitude of Correlation 40 50 60 70 80 90 100 110 120 110 100 90 80 70 60 50 40 30 Strong Positive Correlation Output Input Moderate Positive Correlation 50 60 70 80 90 100 110 100 90 80 70 60 50 40 Output Input 40 50 60 70 80 90 85 75 65 55 Weak Positive Correlation Output Input Weak Negative Correlation 10 20 30 40 50 60 85 75 65 55 Output Input Moderate Negative Correlation 0 10 20 30 40 50 110 100 90 80 70 60 50 40 Output Input Output Strong Negative Correlation 0 10 20 30 40 50 60 70 80 110 100 90 80 70 60 50 40 30 Input
  85. 85. 85 Regression (Prediction) Equation Regression Analysis: Payton yards versus Payton carries The regression equation is Payton yards = -163.497 + 4.91622 Payton carries Constant Coefficient Level of X Payton yards  -163.497  4.91622 250 1,065.6 To predict how many yards Payton would run if he had 250 carries use the prediction equation above.
  86. 86. 86 Regression (Prediction) Equation Compare to the Fitted Line. payton carries payton yards 150 200 250 300 350 400 2000 1750 1500 1250 1000 750 500 S 153.985 R-Sq 87.3% R-Sq(adj) 86.2% Fitted Line Plot payton yards = - 163.5 + 4.916 payton carries ~1067 yds
  87. 87. 87 Example Regression Line Agitator RPM PGM concentrate (g/ton) 10 15 20 25 30 35 40 45 70 60 50 40 30 20 10 S 9.08220 R-Sq 71.8% R-Sq(adj) 69.0% Fitted Line Plot PGM concentrate (g/ton) = 1.119 + 1.333 Agitator RPM
  88. 88. 88 Example Regression Line Agitator RPM PGM concentrate (g/ton) 10 15 20 25 30 35 40 45 70 60 50 40 30 20 10 S 7.61499 R-Sq 82.2% R-Sq(adj) 78.2% Fitted Line Plot PGM concentrate (g/ton) = 30.53 - 1.460 Agitator RPM + 0.05586 Agitator RPM**2 Stat>Regression>Fitted Line Plot
  89. 89. 89 Confidence and Prediction Intervals % discount % response from mailing 0 10 20 30 40 80 70 60 50 40 30 20 10 0 -10 Regression 95% CI 95% PI S 2.91382 R-Sq 98.6% R-Sq(adj) 98.4% Fitted Line Plot % response from mailing = - 0.416 + 0.1526 % discount + 0.04166 % discount**2 The prediction interval is the range where a new observation is expected to fall. In this case, we are 95% confident an 18% discount will yield between 10% and 23% response from the mailing. The confidence interval is the range where the prediction equation is expected to fall. The true prediction equation could be different. However, given the data we are 95% confident that the true prediction equation falls within the confidence intervals.
  90. 90. 90 Six Sigma Strategy (X1) (X8) (X11) (X9) (X4) (X6) (X7) (X5) (X10) (X2) (X3) (X3) (X4) (X2) (X5) (X1) (X8) (X11) (X5) (X3) (X11) (X4) SIPOC VOC Project Scope P-Map, X-Y, FMEA Capability Box Plot, Scatter Plots, Regression Fractional Factorial Full Factorial Center Points
  91. 91. 91 One Factor at a Time is NOT a DOE One Factor at a Time (OFAT) is an experimental style but not a planned experiment or DOE. The graphic shows yield contours for a process that are unknown to the experimenter. Pressure (psi) 75 80 85 Unknown To Experimenter 90 Yield Contours Are 6 1 2 3 4 95 30 31 32 33 34 35 135 130 125 120 Temperature (C) Trial Temp Press Yield 1 125 30 74 2 125 31 80 3 125 32 85 4 125 33 92 5 125 34 86 6 130 33 85 7 120 33 90 7 5 Optimum identified with OFAT True Optimum available with DOE
  92. 92. 92 Nomenclature for Factorial Experiments The general notation used to designate a full factorial design is given by: – Where k is the number of input variables or factors. – 2 is the number of “levels” that will be used for each factor. • Quantitative or qualitative factors can be used.
  93. 93. 93 Visualization of 2 Level Full Factorial Uncoded levels for factors T P T*P -1 -1 +1 -1 -1 +1 +1 +1 +1 -1 -1 +1 Temp 300 350 Press 500 600 22 600 (-1,+1) (+1,+1) (-1,-1) (+1,-1) Temp Press 300F 350F 500 Four experimental runs: • Temp = 300, Press = 500 • Temp = 350, Press = 500 • Temp = 300, Press = 600 • Temp = 350, Press = 600 Coded levels for factors
  94. 94. 3.35 94 Graphical DOE Analysis - The Cube Plot Consider a 23 design on a catapult... Stop Angle 8.2 4.55 5.15 2.4 2.1 Start Angle 0.9 1.5 Fulcrum A B C Response Run Start Stop Meters Number Angle Angle Fulcrum Traveled 1 -1 -1 -1 2.10 2 1 -1 -1 0.90 3 -1 1 -1 3.35 4 1 1 -1 1.50 5 -1 -1 1 5.15 6 1 -1 1 2.40 7 -1 1 1 8.20 8 1 1 1 4.55 What are the inputs being manipulated in this design? How many runs are there in this experiment?
  95. 95. Kanban  We cannot sustain Kanban without Kaizen. The Vision of Lean Supporting Your Project  We cannot sustain Kaizen (Six Sigma) without Standardized Work. Kaizen  We cannot sustain Standardized 95 Work without a Visual Factory. Standardized Work  We cannot sustain a visual factory without 5S. The Continuous Goal… Sustaining Results Visual Factory 5S Workplace Organization Lean tools add discipline required to further sustain gains realized with Six Sigma Belt Projects.
  96. 96. 96 What is Waste (MUDA)? Waste is often the root of any Six Sigma project. The 7 basic elements of waste (muda in Japanese) include: – Muda of Correction – Muda of Overproduction – Muda of Processing – Muda of Conveyance – Muda of Inventory – Muda of Motion – Muda of Waiting Get that garbage outta here! The specifics of the MUDA were discussed in the Define Phase: – The reduction of MUDA can reduce your outliers and help with defect prevention. Outliers because of differing waste among procedures, machines, etc.
  97. 97. 97 The Goal Don’t forget the goal -- Sustaining your Project which eliminates MUDA! With this in mind, we will introduce and review some of the Lean tools used to sustain your project success.
  98. 98. 98 5S Translation - Workplace Organization Step Japanese Literal Translation English Step 1: Seiri Clearing Up Sorting Step 2: Seiton Organizing Straightening Step 3: Seiso Cleaning Shining Step 4: Seketsu Standardizing Standardizing Step 5: Shitsuke Training & Discipline Sustaining Focus on using the English words, much easier to remember.
  99. 99. 99 What is Standardized Work? If the items are organized and orderly, then standardized work can be accomplished. – Less Standard Deviation of results – Visual factory demands framework of standardized work. The “one best way” to perform each operation has been identified and agreed upon through general consensus (not majority rules) – This defines the “Standard” work procedure Visual Factory 5S - Workplace Organization We cannot sustain Standardized Work without 5S and the Visual Factory. Standardized Work
  100. 100. 100 Prerequisites for Standardized Work Standardized work does not happen without the visual factory which can be further described with: Availability of required tools (5S). Operators cannot be expected to maintain standard work if required to locate needed tools Consistent flow of raw material. Operators cannot be expected to maintain standard work if they are searching for needed parts Visual alert of variation in the process (visual factory). Operators, material handlers, office staff all need visual signals to keep “standard work” a standard Identified and labeled in-process stock (5S). As inventory levels of in-process stock decrease, a visual signal should be sent to the material handlers to replenish this stock
  101. 101. What is Kaizen? • Definition*: The philosophy of continual 101 improvement, that every process can and should be continually evaluated and improved in terms of time required, resources used, resultant quality and other aspects relevant to the process. • Kaikaku are breakthrough successes which are the first focus of Six Sigma projects. * Note: Kaizen Definition from: All I Needed To Know About Manufacturing I Learned in Joe’s Garage. Miller and Schenk, Bayrock Press, 1996. Page 75. Standardized Work Visual Factory 5S - Workplace Organization Kaizen
  102. 102. 102 Prerequisites for Kaizen Kaizen’s need the following cultural elements: Management Support. Consider the corporate support which is the reason why Six Sigma focus is a success in your organization Measurable Process. Without standardized work, we really wouldn’t have a consistent process to measure. Cycle times would vary, assembly methods would vary, batches of materials would be mixed, etc… Analysis Tools. There are improvement projects in each organization which cannot be solved by an operator. This is why we teach the analysis tools in the breakthrough strategy of Six Sigma. Operator Support. The organization needs to understand that its future lies in the success of the value-adding employees. Our roles as Belts are to convince operators that we are here for them--they will then be there for us.
  103. 103. 103 Two Types of Kanban Type 1: Finished goods Kanbans – Signal Kanban: Should be posted at the end of the processing area to signal for production to begin. – P.I.K Kanban: Used for a much more refined level of inventory control. Kanban is posted as inventory is depleted thus insuring only the minimum allowable level of product is maintained. Type 2: Incoming Material Kanbans – Used to purchase materials from a supplying department either internal or external to the organization. Regulates the amount of WIP inventory located at a particular process. Intra-process P.I.K. Production Instruction Kanban Signal Withdrawal Inter-Process Between two processes Supplier There are two main categories of Kanbans:
  104. 104. 104 s Level for Project Sustaining in Control 5-6s: Six Sigma product and/or process design eliminates an error condition OR an automated system monitors the process and automatically adjust critical X’s to correct settings without human intervention to sustain process improvements 4-5s: Automated mechanism shuts down the process and prevents further operation until a required action is performed 3-5s: Mistake proofing prevents a product/service from passing onto the next step 3-4s: SPC on X’s with the special causes are identified and acted upon by fully trained operators and staff who adhere to the rules 2-4s: SPC on Y’s 1-3s: Development of SOPs and process audits 0-1s: Training and awareness BEST WORST
  105. 105. 105 5 – 6 s Full Automation Full Automation: Systems that monitor the process and automatically adjust critical X’s to correct settings. • Automatic gauging and system adjustments • Automatic detection and system activation systems - landing gear extension based on aircraft speed and power setting • Systems that count cycles and automatically make adjustments based on an optimum number of cycles • Automated temperature controllers for controlling heating and cooling systems • Anti-Lock braking systems • Automatic welder control units for volts, amps and distance traveled on each weld cycle
  106. 106. 106 Traditional Quality vs. Mistake Proofing Traditional Inspection Result Worker or Machine Error Don’t Do Anything Defective Sort At Other Step Discover Error Take Action/ Feedback No Defect Source Inspection “KEEP ERRORS FROM TURNING INTO DEFECTS” Next Step
  107. 107. Contact Method 107 Types of Mistake Proof Devices – Physical or energy contact with product • Limit switches • Photo-electric beams Fixed Value Method – Number of parts to be attached/assembled etc. are constant – Number of steps done in operation • Limit switches Motion-step Method – Checks for correct sequencing – Checks for correct timing • Photo-electric switches and timers 1 Guide Pins of Different Sizes 2 Error Detection and Alarms 3 Limit Switches 4 Counters 5 Checklists
  108. 108. 108 Advantages of Mistake Proofing as A Control Method Mistake Proofing advantages include: – Only simple training programs are required – Inspection operations are eliminated and the process is simplified – Relieves operators from repetitive tasks of typical visual inspection – Promotes creativity and value adding activities – Results in defect free work – Requires immediate action when problems arise – Provides 100% inspection internal to the operation The best resource for pictorial examples of Mistake Proofing is: Poka-Yoke: Improving Product Quality by Preventing Defects. Overview by Hiroyuki Hirano. Productivity Press, 1988.)
  109. 109. 109 SPC Overview: I-MR Chart • An I-MR chart combines a Control Chart of the average moving range with the Individual’s Chart. • You can use individuals charts to track the process level and to detect the presence of Special Causes when the sample size is 1. • Seeing both charts together allows you to track both the process level and process variation at the same time, providing greater sensitivity that can help detect the presence of Special Causes. Individuals Chart 4 3 2 1 0 -1 -2 -3 -4 Observation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Measure Data LCL Xbar UCL MRbar Chart 5 4 3 2 1 0 Observation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Range Range LCL Rbar UCL
  110. 110. 110 SPC Overview: Xbar-R Chart If each of your observations consists of a subgroup of data, rather than just individual measurements, an Xbar-R chart providers greater sensitivity. Failure to form rational subgroups correctly will make your Xbar-R charts dangerously wrong. Xbar Chart 1.5 1 0.5 0 -0.5 -1 -1.5 -2 Subgroup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Xbar Xbar LCL Xbarbar UCL Rbar Chart 6 5 4 3 2 1 0 Subgroup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Rbar Rbar LCL Rbar UCL
  111. 111. 111 SPC Overview: U Chart • C Charts and U Charts are for tracking defects. • A U Chart can do everything a C Chart can, so we’ll just learn how to do a U Chart. This chart counts flaws or errors (defects). One “search area” can have more than one flaw or error. • Search area (unit) can be practically anything we wish to define. We can look for typographical errors per page, the number of paint blemishes on a truck door or the number of bricks a mason drops in a workday. • You supply the number of defects on each unit inspected. U Chart 1 0.8 0.6 0.4 0.2 0 Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 DPU DPU LCL Ubar UCL
  112. 112. 112 SPC Overview: P Chart • NP Charts and P Charts are for tracking defectives. • A P Chart can do everything an NP Chart can, so we’ll just learn how to do a P Chart! • Used for tracking defectives – the item is either good or bad, pass or fail, accept or reject. • Center Line is the proportion of “rejects” and is also your Process Capability. • Input to the P Chart is a series of integers — number bad, number rejected. In addition, you must supply the sample size. P Chart 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Proportion Defective (P) P LCL Pbar UCL
  113. 113. Type 1 Corrective Action = Countermeasure: improvement made to the process which will eliminate the error condition from occurring. The defect will never be created. This is also referred to as a long-term corrective action in the form of mistake proofing or design changes. Type 2 Corrective Action = Flag: improvement made to the process which will detect when the error condition has occurred. This flag will shut down the equipment so that the defect will not move forward. SPC on X’s or Y’s with fully trained operators and staff who respect the rules. Once a chart signals a problem everyone understands the rules of SPC and agrees to shut down for Special Cause identification. (Cpk > certain level). Type 3 Corrective Action = Inspection: implementation of a short-term containment which is likely to detect the defect caused by the error condition. Containments are typically audits or 100% inspection. SPC on X’s or Y’s with fully trained operators. The operators have been trained and understand the rules of SPC, but management will not empower them to stop for investigation. S.O.P. is implemented to attempt to detect the defects. This action is not sustainable short-term or long-term. SPC on X’s or Y’s without proper usage. = WALL PAPER. 113 SPC Overview: Control Methods/Effectiveness
  114. 114. Control Charts were designed as a methodology for indicating change in performance, either variation or Mean/Median. Charts have a Central Line and Control Limits to detect Special Cause variation. 114 Elements of Control Charts Developed by Dr Walter A. Shewhart of Bell Laboratories from 1924 Graphical and visual plot of changes in the data over time – This is necessary for visual management of your process. Observation Individual Value 1 4 7 10 13 16 19 22 25 28 60 50 40 30 20 10 0 UCL=55.24 _ X=29.06 LCL=2.87 1 Control Chart of Recycle Process Center (usually the Mean) Special Cause Variation Detected Control Limits
  115. 115. Focus of Six Sigma and the Use of SPC Y=F(x) To get results, should we focus our behavior on the Y or X? 115 Y Dependent Output Effect Symptom Monitor X1 . . . XN Independent Input Cause Problem Control If we find the “vital few” X’s, first consider using SPC on the X’s to achieve a desired Y?
  116. 116. 116 Control Chart Anatomy Special Cause Variation Process is “Out of Control” Common Cause Variation Process is “In Control” Special Cause Variation Process is “Out of Control” Run Chart of data points Process Sequence/Time Scale Lower Control Limit +/- 3 sigma Mean Upper Control Limit
  117. 117. 117 Control and Out of Control Outlier Outlier 3 99.7% 95% 68% 2 1 -1 -2 -3
  118. 118. Size of Subgroups Typical subgroup sizes are 3-12 for variable data: 118 – If difficulty of gathering sample or expense of testing exists the size, n, is smaller – 3, 5, and 10 are the most common size of subgroups because of ease of calculations when SPC is done without computers. Size of subgroups aid in detection of shifts of Mean indicating Special Cause exists. The larger the subgroup size, the greater chance of detecting a Special Cause. Subgroup size for Attribute Data is often 50 – 200. Lot 1 Lot 2 Lot 3 Lot 4 Lot 5 Short-term studies Long-term study
  119. 119. Sampling too little will not allow for sufficient detection of shifts in the process because of Special Causes. UCL=7.385 UCL=6.559 119 Frequency of Sampling Output 7.5 7 6.5 6 5.5 5 1 7 13 19 25 31 37 Sample every half hour Observation Individual Value 1 2 3 4 5 6 7 8 9 10 11 12 13 7.5 7.0 6.5 6.0 5.5 5.0 _ X=6.1 LCL=4.815 I Chart of Sample_3 Observation Individual Value 1 2 3 4 5 6 7 8 7 6 5 4 UCL=8.168 _ X=6.129 LCL=4.090 I Chart of Sample_6 Observation Individual Value 1 2 3 4 6.6 6.4 6.2 6.0 5.8 5.6 5.4 5.2 5.0 _ X=5.85 LCL=5.141 I Chart of Sample_12 All possible samples Sample every hour Sample 4x per shift
  120. 120. 120 SPC Selection Process Choose Appropriate Control Chart type of data type of attribute data subgroup size ATTRIBUTE CONTINUOUS I – MR Chart X – R Chart X – S Chart SPECIAL CASES CumSum Chart EWMA Chart DEFECTS DEFECTIVES C Chart U Chart NP Chart P Chart type of defect type of subgroups CONSTANT VARIABLE CONSTANT VARIABLE 1 2-5 10+ Number of Incidences Incidences per Unit Number of Defectives Proportion Defectives Individuals & Moving Range Mean & Range Mean & Std. Dev. Cumulative Sum Exponentially Weighted Moving Average Sample size
  121. 121. 121 Understanding Attribute Control Chart Selection Type of Chart When do you need it?  Need to track the fraction of defective units; sample size is variable and usually > 50  When you want to track the number of defective units per subgroup; sample size is usually constant and usually > 50  When you want to track the number of defects per subgroup of units produced; sample size is constant  When you want to track the number of defects per unit; sample size is variable P nP C U
  122. 122. 122 Special Cause Rule Default in MINITABTM If a Belt is using MINITABTM, you must be aware of what default settings for the rules. You can alter your program defaults with: Tools>Options>Control Charts and Quality Tools>Define Tests This would be changed to 8 if you prefer the Western Electric Rules. Many experts have commented on the appropriate tests and numbers to be used. Decide then be consistent when implementing.
  123. 123. Pre-Control Charts use limits relative to the specification limits. This is the first and ONLY chart you will see specification limits plotted for Statistical Process Control. This is the most basic type of chart and unsophisticated use of process control. 0.0 0.25 0.5 0.75 1.0 123 Pre-Control Charts Red Zones. Zone outside the specification limits. Signals the process is out-of-control and should be stopped RED Yellow GREEN Yellow Red LSL Target USL Yellow Zones. Zone between the PC Lines and the specification limits, indicates caution and the need to watch the process closely Green Zone. Zone lies between the PC Lines, signals the process is in control
  124. 124. 124 Responding to Out of Control Indications • The power of SPC is not to find out what the Center Line and Control Limits are. • The power is to react to the Out of Control (OOC) indications with your Out of Control Action Plans (OCAP) for the process involved. These actions are your corrective actions to correct the output or input to achieve proper conditions. Observation UCL=39.76 VIOLATION: Special Cause is indicated • SPC requires immediate response to a Special Cause indication. • SPC also requires no “sub optimizing” by those operating the process. – Variability will increase if operators always adjust on every point if not at the Center Line. ONLY respond when an Out of Control or Special Cause is detected. – Training is required to interpret the charts and response to the charts. Individual Value 1 4 7 10 13 16 19 22 25 28 31 40 30 20 10 0 _ X=18.38 LCL=-3.01 1 Individual SPC chart for Response Time OCAP If response time is too high, get additional person on phone bank
  125. 125. 125 Cost Considerations Cost to implement improvement: – Initial cost to implement improvement • Cost to train existing work force • Cost to purchase any new materials necessary for improvement • Cost of resources used to build improvement • Any capital investments required – On-going costs to sustain improvement • Future training, inspection, monitoring, and material costs It’s all about the cash!
  126. 126. 126 Example of Completed Solution Selection Matrix OVERALL IMPACT RATING COST RATING TIME RATING OVERALL RATING 86 7 7 4214 52 7 7 2548 63 3 6 1134 36 5 5 900 60 3 3 540 63 5 2 630 Outside noises do not interfer with speakers Coffee is hot and rich tasting Plenty of bottled water available Food choices include "healthy choices" Significance Rating 10 9 8 9 Potential Improvements Impact Rating Impact Rating Impact Rating Impact Rating 1 Hotel staff monitors room 2 2 6 0 2 Mgmt visits/leaves ph # 2 0 4 0 3 Replace old coffee makers/coffee 0 7 0 0 4 Menus provided with nutrition info 0 0 0 4 5 Comp. gen. "quiet time" scheduled 6 0 0 0 6 Dietician approves menus 0 0 0 7 Improvement Selection Matrix Output Improvements with the higher overall rating should be given first priority. Keep in mind that long time frame capital investments, etc. should have parallel efforts to keep delays from further occurring.
  127. 127. 127 Control Plan Information The team develops the Control Plan by utilizing all available information from the following: – Results from the Measure and Analyze Phases – Lessons learned from similar products and processes – Team’s knowledge of the process – Design FMEAs – Design reviews – Defect Prevention Methods selected Aligning Systems & Structures Documentation Plan Monitoring Plan Response Plan Training Plan
  128. 128. The Certified Lean Six Sigma Black Belt Assessment The Certified Lean Six Sigma Black Belt (CLSSBB) tests are useful for assessing Black Belt’s knowledge of Lean Six Sigma. The CLSSBB can be used in preparation for the ASQ Certified Six Sigma Black Belt (CSSBB) exam or for any number of other certifications, including private company certifications. The Lean Six Sigma Black Belt Course Manual Open Source Six Sigma Course Manuals are professionally designed and formatted manuals used by Belt’s during training and for reference guides afterwards. The OSSS manuals complement the OSSS Training Materials and consist of slide content, instructional notes data sets and templates. Get the latest products at… www.OpenSourceSixSigma.com 128 Advertisement 128

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