8D Training, Eight Disciplines Training : Tonex TrainingBryan Len
8D Training, Eight Disciplines Training
8D Training covers the eight disciplines (8D) model used in advanced problem solving. 8D training covers the approach typically employed by quality engineers to identify, correct, and eliminate recurring problems as part of product and process improvement:
8D approach establishes a permanent corrective action based on statistical analysis of the problem
8D focuses on the origin of the problem by determining its root causes
It is comprised by eight stages, or disciplines
8D is a structured corrective action process; 8D stands for the Eight Disciplines of Problem Solving
Root Cause Analysis(RCA) is an integral part of the 8D process
8D Training Objectives:
Upon completion of the 8D Training Course, the attendees are able to:
Define what 8D is
Demonstrate and apply 8D Problem Solving Process
Illustrate 8D Problem Solving Process by a diagram
Understand Root Cause Analysis and 5 Why’s applied to 8D
Apply 8D Eight Disciplines to problem solving
Become familiar with the 8D steps
Become more proficient at Root Cause Analysis using 5 Why’s
Learn about 8D checklist and best practices
Learn about How 8D aligns with SAE AS13000: Problem Solving Requirements for Suppliers
8D Training Course Outlines:
Learn and work with the 8 disciplines in 8D Training. Upon completion of the 8D Training Course, the attendees will learn about:
Introduction to 8D
D0: prepare and Plan
D1: Use a team
D2: Define and describe the problem
D3: Develop interim containment plan
D4: Determine, identify, and verify root causes
D5: Choose and verify permanent corrections
D6: Implement and validate corrective actions
D7: Take preventive measures
D8: Congratulate your team
Hands-on Activities
Request more information. Visit Tonex website link below and learn more about 8d training eight disciplines training
https://www.tonex.com/training-courses/8d-training-eight-disciplines-training/
8D Training, Eight Disciplines Training : Tonex TrainingBryan Len
8D Training, Eight Disciplines Training
8D Training covers the eight disciplines (8D) model used in advanced problem solving. 8D training covers the approach typically employed by quality engineers to identify, correct, and eliminate recurring problems as part of product and process improvement:
8D approach establishes a permanent corrective action based on statistical analysis of the problem
8D focuses on the origin of the problem by determining its root causes
It is comprised by eight stages, or disciplines
8D is a structured corrective action process; 8D stands for the Eight Disciplines of Problem Solving
Root Cause Analysis(RCA) is an integral part of the 8D process
8D Training Objectives:
Upon completion of the 8D Training Course, the attendees are able to:
Define what 8D is
Demonstrate and apply 8D Problem Solving Process
Illustrate 8D Problem Solving Process by a diagram
Understand Root Cause Analysis and 5 Why’s applied to 8D
Apply 8D Eight Disciplines to problem solving
Become familiar with the 8D steps
Become more proficient at Root Cause Analysis using 5 Why’s
Learn about 8D checklist and best practices
Learn about How 8D aligns with SAE AS13000: Problem Solving Requirements for Suppliers
8D Training Course Outlines:
Learn and work with the 8 disciplines in 8D Training. Upon completion of the 8D Training Course, the attendees will learn about:
Introduction to 8D
D0: prepare and Plan
D1: Use a team
D2: Define and describe the problem
D3: Develop interim containment plan
D4: Determine, identify, and verify root causes
D5: Choose and verify permanent corrections
D6: Implement and validate corrective actions
D7: Take preventive measures
D8: Congratulate your team
Hands-on Activities
Request more information. Visit Tonex website link below and learn more about 8d training eight disciplines training
https://www.tonex.com/training-courses/8d-training-eight-disciplines-training/
A presentation on the continuous improvement tool of total quality management, i.e. PDCA- Plan,Do,Check,Act. Covers the basics of PDCA to give an idea on it's need, use, methodology etc. The presentation will help the beginners gain knowledge about the PDCA cycle and will cover their basic needs on it.
PECB Webinar: Achieve business excellence through the power of Six SigmaPECB
We will cover:
• Why every company needs Six Sigma implementation
• How processes are improved by using Six Sigma
• Real benefits in profits, and reductions in defects
Presenter:
This webinar will be presented by M.Youssef.K, Executive Consultant & Trainer at Six Sigma Associates - SSA.
Anyone who has ever attempted to estimate software testing effort realizes just how difficult the task can be. The number of factors that can affect the estimate is virtually unlimited. The key to good estimates is to understand the primary variables, compare them to known standards, and normalize the estimates based on their differences. This is easy to say but difficult to accomplish because estimates are frequently required even when very little is known about the project and what is known is constantly changing. Throw in a healthy dose of politics and a bit of wishful thinking and estimation can become a nightmare. Rob Sabourin provides a foundation for anyone who must estimate software testing work effort. Learn about the test team’s and tester’s roles in estimation and measurement, and how to estimate in the face of uncertainty. Analysts, developers, leads, test managers, testers, and QA personnel can all benefit from this tutorial.
Peter Gray and Dave Slater of Royal Bolton Hospital shown at the 2nd Lean Healthcare Forum on 6th June 2006 ran by the Lean Enterprise Academy
www.leanuk.org
A presentation on the continuous improvement tool of total quality management, i.e. PDCA- Plan,Do,Check,Act. Covers the basics of PDCA to give an idea on it's need, use, methodology etc. The presentation will help the beginners gain knowledge about the PDCA cycle and will cover their basic needs on it.
PECB Webinar: Achieve business excellence through the power of Six SigmaPECB
We will cover:
• Why every company needs Six Sigma implementation
• How processes are improved by using Six Sigma
• Real benefits in profits, and reductions in defects
Presenter:
This webinar will be presented by M.Youssef.K, Executive Consultant & Trainer at Six Sigma Associates - SSA.
Anyone who has ever attempted to estimate software testing effort realizes just how difficult the task can be. The number of factors that can affect the estimate is virtually unlimited. The key to good estimates is to understand the primary variables, compare them to known standards, and normalize the estimates based on their differences. This is easy to say but difficult to accomplish because estimates are frequently required even when very little is known about the project and what is known is constantly changing. Throw in a healthy dose of politics and a bit of wishful thinking and estimation can become a nightmare. Rob Sabourin provides a foundation for anyone who must estimate software testing work effort. Learn about the test team’s and tester’s roles in estimation and measurement, and how to estimate in the face of uncertainty. Analysts, developers, leads, test managers, testers, and QA personnel can all benefit from this tutorial.
Peter Gray and Dave Slater of Royal Bolton Hospital shown at the 2nd Lean Healthcare Forum on 6th June 2006 ran by the Lean Enterprise Academy
www.leanuk.org
As of September 2012 a spool manufacturer was required to supply 23,300 Spools by Jun 2013
Based on the current run-rate of 197 spools per week the spools manufacturer is forecasted to complete the full order of 23,300 Spools in Apr 2014, an estimated 10 months behind schedule
As the spool manufacturer was on the critical path for the LNG construction project, the delayed spools were a critical risk to first-gas target date
5 - Day Kaizen Event focusing on AR Changeover to:
- Lead time reduction
- Cycle time reduction
- Improve workflow
- WIP reduction
- Improve ergonomics, housekeeping & safety
- Clear SOPs & training materials
Achieved
- 81% Lead time reduction
- 37% Cycle time reduction
- 80% WIP reduction
Purpose of the Call:
•Recap of aggregated MedRec audit month data that identifies potential opportunities for improvement
•Review quality improvement concepts as it relates to measuring for quality improvement
•Hear how Horizon Health team (NB) is using their data to improve MedRec processes
•Receive a tutorial on how to access your MedRec Quality Score run charts in Patient Safety Metrics.
WATCH: http://bit.ly/1EVcREL
3 - Day Kaizen Event focusing on AR Changeover to:
- Changeover time reduction
- Increased machine productivity, run time
- Improve cell organization
- Improve ergonomics & safety
- Clear SOPs & training materials
Achieved AUD$220,000 annual savings
Kaizen Blitz Paint shop layout - changing the layout to improve safety and workflow
Do we try to change people's behaviours, our should we change the situation? A change in the physical work environment can allow the desired behaviours to happen without resistance
Aiming to stimulate and change the nature of the improvement dialogue within NHS boards across Scotland, Quality Improvement leaders, Martin Barkley, Pedro Delgado, Eibhlin McHugh and Elaine Mead presented current QI Infrastructure work both locally and nationally.
Six Sigma Statistical Process Control (SPC) Training ModuleFrank-G. Adler
The Statistical Process Control (SPC) Training Module v4.0 includes:
1. MS PowerPoint Presentation including 129 slides covering Introduction to Process Control, Types of Histograms, Measures of Location & Variability, Process Control Charts, Process Control Limits, Out-of-Control Criteria, Sample Size & Frequency, Out-of-Control Action Plan, Process Control Plan, and 6 Workshop Exercises.
2. MS Excel Confidence Interval Analysis Calculator making it really easy to calculate Confidence Intervals (mean value, standard deviation, capability indices, defect rate, count) and perform a Comparison of two Statistics (mean values, standard deviations, defect rates, counts).
3. MS Excel Process Control Plan Template
ISO 9001 2015 ASQ Workshop by Colin GrayColin Gray
The current and expected development of ISO 9001 - Annex SL reviewed. Gain insight into the ISO 9001 Draft International Standard (DIS) of ISO 9001 which has recently been released.
How to Handle Guest with Complaints in HotelHotelCluster
The key to running a successful hotel is customer service. A big part of this is addressing customer complaints and ensuring that these complaints are resolved to the customer’s satisfaction. Successful resolution will have a positive effect on the customer, who will be more conducive to returning to the hotel in the future, as the way the complaint was handled and resolved makes the customer feel special and shows him that the hotel is genuinely interested in keeping its customers happy and satisfied.
Presentation for the 16th QMOD conference which details a novel approach of using the tools techniques and methods of Six Sigma to improve students learning of Six Sigma
Using ExamSoft Data to Prepare For and Ease the Accreditation ProcessExamSoft
Presented by Kimberly K. Daugherty, PharmD, BCPS, Professor and Assistant Dean of Academic Affairs and Assessment, Sullivan University College of Pharmacy, and Ashley Castleberry, PharmD, MAEd, Assistant Professor and Director of Assessment, University of Arkansas for Medical Sciences College of Pharmacy, and Leah Simpson, EdD, MPA, Director of Curriculum Development and Assessment, University of Kentucky
When preparing for the accreditation process, ExamSoft data can be used to document student mastery of accreditation standards, the student development of self-awareness, as well as provide data for course and curriculum improvement. Data from ExamSoft can also provide students with report cards providing autonomy supportive feedback (“feed up, feed back, feed forward”). In this model, the first step is to establish a clear goal for the students to obtain (feed up). Next, the teacher responds to the work being done (feed back). Finally, students are provided with ways to improve attainment of their goals (feed forward). This process can also be useful for the teacher as it allows for justifiable improvement in teaching and in the course. Student report cards are autonomy supportive, as they can provide students with the goal to obtain (achievement of the program outcomes) and allow the teacher or advisor to comment on things being done well or needing improvement. It is possible to collate student data so that a college can see which outcomes are being taught well in the curriculum and which may need improvement. Finally, report cards can be useful tools to help students develop and practice their metacognitive skills. The University of Arkansas, University of Kentucky, and Sullivan University Colleges of Pharmacy have been using this type of data to improve student learning and performance. The purpose of this session is to describe how ExamSoft longitudinal assessment data can be used to develop a report card for students showing their progression through a curriculum and how this data can be used for accreditation.
Based on Paper "Build Your Library a Six Sigma Way" presented at Malibnet International Conference at IIM Indore on “ International Conference on Creating Wisdom through shared learning Role of Librarian And Information Managers” held on 11Oct 2012- 13 Oct 2012
This is an introduction for six sigma green belt program , it briefly describes the its history, adoption , and various component and tools . This presentation also include the current ASQ examination pattern .
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This session presents a novel usage of the tools techniques and methods of Six Sigma to the vexing problem of mobile data overages. Learn about an individual's daily data usage collected over the span of one year and applies control charts, hypothesis testing, and process capability to determine the optimal monthly number of gigabytes of data to purchase. The case extensively uses nonparametric testing and simulation to predict the most appropriate data plan to purchase
A case study utilizing the Six Sigma data analysis toolkit to examine a 15.5-mile daily morning commute completed on bicycle. The case first explores the usage of control charts to examine the total completion time in addition to various waypoints along the route. It then utilizes hypothesis testing to attempt to prove if a statistically significant improvement has occurred. It then demonstrates a multifactor regression model to predict the time needed to traverse the route. Finally it does a cost comparison between cycling, taking the metro and driving to work.
Teaching tactical industrial engineering to high school studentsBrandon Theiss, PE
Case study of a group of high school students who worked with a replacement window manufacturer to apply six sigma to improve their manufacturing process
Terrorism is endemic to the modern world. It is impossible to board an airplane, attend a sporting event, or walk into a public building without experiencing its symptoms. However, is the incidence rate of such horrific events actually increasing? This paper draws data from The Global Terrorism Database, which collects information on terrorist events around the world (1970 through 2011), and attempts to answer this very question. This research applies G- Control Charts, most commonly used for monitoring of workplace accidents and various health care application, to determine if the time between incidences of terrorism has in fact decreased. Though not intended as a basis for policy decisions, the paper demonstrates a novel use of control charts and provides a basis for a better informed debate.
Data Collected from two Starbucks location in NJ for the purposes of modeling the time between a customer walks into the store and the beverage is ordered
A Strategic Approach: GenAI in EducationPeter Windle
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Teaching Six Sigma Using Six Sigma
1. Teaching Six Sigma Using Six Sigma a
DMAIC Approach
Brandon Theiss
Brandon.Theiss@gmail.com
2013 ASQ WCQI Session (M30)
2. About Me• Academics
– MS Industrial Engineering Rutgers University (hopefully)
– BS Electrical & Computer Engineering Rutgers University
– BA Physics Rutgers University
• Awards
– ASQ Top 40 Leader in Quality Under 40
– ASQ National Education Quality Excellence Award Finalist
– IIE Early Career Achievement Award Winner 2013
• Professional
– Principal Industrial Engineer -Medrtonic
– Master Black belt- American Standard Brands
– Systems Engineer- Johnson Scale Co
• Certifications
– ASQ Certified Manager of Quality/ Org Excellence Cert # 13788
– ASQ Certified Quality Auditor Cert # 41232
– ASQ Certified Quality Engineer Cert # 56176
– ASQ Certified Reliability Engineer Cert #7203
– ASQ Certified Six Sigma Green Belt Cert # 3962
– ASQ Certified Six Sigma Black Belt Cert # 9641
– ASQ Certified Software Quality Engineer Cert # 4941
– Licensed to practice before United States Patent and Trademark Office
• Publications
– Going with the Flow- The importance of collecting data without holding up your processes- Quality Progress March
2011
– "Numbers Are Not Enough: Improved Manufacturing Comes From Using Quality Data the Right Way" (cover story).
Industrial Engineering Magazine- Journal of the Institute of Industrial Engineers September (2011): 28-33. Print
3. Learning Objectives
• Apply Six Sigma to the Teaching of Six Sigma
• Create Practitioner Academic Partnerships
• Uniquely Apply SPC Charts
• Use Statistical Hypothesis testing to improve
learning outcomes
4. Motivation
• Teaching the tools, techniques and Methods of Lean Six
Sigma is inherently difficult in academic setting.
• When taught in a industrial setting students have a
common motivation (the improved welfare of the
company), similar levels of education and knowledge of
domain specific information. Students are encouraged to
learn by applying the material to their daily activities.
• This is not possible in an academic setting particularly in a
mixed environment that includes everything from
undergraduate juniors through senior PhD researchers.
• In addition undergraduate students tend either lack
professional or have experience in Fields that are not
traditionally thought of as benefiting or implementing Six
Sigma (waitressing, check out clerk etc.)
5. 5
Putting some numbers to the motivation
• Lean Six Sigma is a commonly adopted business
improvement technique which integrates, the scientific method,
statistics and defect reduction to obtain tangible results.
•Within 50 miles of Rutgers there are 2,249 active job listings
for the phrase “six sigma green belt”
•Non University Affiliated Classes are available however are
prohibitively expensive for most students ~$2,000.
•ASQ de facto industry standard for Greenbelt Certification
•Current Industrial Engineering Undergraduate and Graduate
programs do not prepare students to effectively implement the
Six Sigma toolkit.
•Salary Report indicates Certified Green belts earn $12,000
more per year
6. Class Demographics
• 71 Students Registered
– 57 At Student Tuition Rate ($296)
– 14 At Professional Tuition Rate ($495)
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Junior Year Senior
Year
BA/BS Some
Grdudate
MA/MS/JD PhD/PE
Highest Accademic Grade
Completed
24222018161412108642
20
15
10
5
0
Years Of Work Exprience
Frequency
3
Histogram of Years Of Work Exprience
7. Solution
• The beauty of the Six Sigma Methodology is that it can be applied
to any process.
• The definition of a process is quite broad and can be reduced to any
verb- noun combination.
• Therefore the collective process which the class studied and
improved was to
Pass [the]
ASQ
Certified Six Sigma Green
Belt Exam
• Therefore the foundational Six Sigma Concept of DMAIC (Define
Measure Analyze Improve Control) represents both the material
covered in the course as well as the pedagogical method used for
instruction
8. About the Course & Partnership
• Offered as a Non-Credit extracurricular course
at Rutgers University in Piscataway NJ
• Co-Sponsored by the Rutgers Student Chapter
of the Institute for Industrial Engineers (IIE) and
the Princeton NJ section of American Society for
Quality (ASQ)
• Open and advertised to all members of the
Rutgers Community (students, staff and faculty)
as well as the surrounding public
• Objective of the course was to train students to
pass the June 2nd 2012 administration of the
ASQ Certified Six Sigma Green Belt Exam
10. Pre Test
• On the first night of classes students were
given an introductory survey of Six Sigma
by means of a worked example applying
DMAIC to the Starbucks Experience from a
Customers Prospective.
• Students were then given a copy of the
Certified Six Sigma Green Belt Handbook by
Roderick A. Munro
• Then given a 50 Question Multiple Choice
Test representative of the ASQ CSSGB Exam
• The Test was administered on two
successive nights (Monday and Tuesday)
11. Measurement System
• An Apperson GradeMaster™ 600 Test
Scanner was utilized which enabled test to
be scored and returned immediately upon
student submission at the exam site.
• In addition all of each answer to every
question was downloaded to connected
computer enabling further detailed analysis
15. Is process in Control?
343128252219161310741
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Observation
IndividualValue
_
X=0.5589
UCL=0.9468
LCL=0.1709
I Chart of Test Score
16. Is the Process Capable?
0.840.720.600.480.36
LSL
LSL 0.78
Target *
USL *
Sample Mean 0.558857
Sample N 35
StDev (Within) 0.120985
StDev (O v erall) 0.117718
Process Data
C p *
C PL -0.61
C PU *
C pk -0.61
Pp *
PPL -0.63
PPU *
Ppk -0.63
C pm *
O v erall C apability
Potential (Within) C apability
PPM < LSL 971428.57
PPM > USL *
PPM Total 971428.57
O bserv ed Performance
PPM < LSL 966214.72
PPM > USL *
PPM Total 966214.72
Exp. Within Performance
PPM < LSL 969849.40
PPM > USL *
PPM Total 969849.40
Exp. O v erall Performance
Within
Overall
Process Capability of Test Scores
overall standard
deviation for the
entire study
overall standard deviation for
the entire study if special
cause eliminated
based on variation
within subgroups
17. Are there bad questions?
464136312621161161
1.0
0.8
0.6
0.4
0.2
0.0
Sample
Proportion
_
P=0.441
UCL=0.693
LCL=0.189
1
1
1
1
1
1
11
11
P Chart of Wrong
18. Does the order the exams are turned in effect the
score?
3330272421181512963
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Index
TestScore
MAPE 15.9381
MAD 0.0840
MSD 0.0124
Accuracy Measures
Actual
Fits
Variable
Trend Analysis Plot for Test Score
Linear Trend Model
Yt = 0.5018 + 0.00317*t
22. Is the process in Control?
28252219161310741
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
Observation
IndividualValue
_
X=55.93%
UCL=84.62%
LCL=27.25%
1
I Chart of Scores
29. Is there a difference Between Classes?
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Monday Tuesday
Boxplot of Monday, Tuesday
30. Is there a statistical Difference?
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Monday 35 19.56 0.558857 0.013857
Tuesday 29 16.22 0.55931 0.010357
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 3.26E-06 1 3.26E-06 0.000265 0.987056 3.995887
Within Groups 0.76114 62 0.012276
Total 0.761144 63
33. Brainstorming Techniques
• At the beginning of class students were asked as a group to
brainstorm ideas for why they failed the pre-test
– Only 4 ideas were proposed
• Students were taught the different brainstorming techniques
contained in the CSSGB Body of Knowledge
– Nominal Group Technique
– Multi-Voting
– Affinity Diagrams
– Force Field Analysis
– Tree Diagrams
– Cause and Effect Diagrams
• Students were then broken up into 6 different groups, assigned one
of the brainstorming techniques and given the task to brainstorm
why they failed the pre-test
38. Brainstorming Continued
• Students then told to return to their groups
and apply their “favorite” of the brainstorming
techniques to the task how can you Pass the
midterm exam
• Students Found the positive formulation of
the task much more challenging and most
groups stayed with the same technique they
used for the Negative version.
39. Team Dynamics
• The 3rd weeks lesson began with an
introduction of the Tuckman cycle of team
dynamics
• Students were asked
to reflect upon their
experience in the
brainstorming activity
to see if their
experiences paralleled
those predicted by the
model
40. Process Mapping
• The second portion of the 3rd Class was spent
introducing the process mapping strategies in
the CSSGB BoK
– SIPOC (Suppliers Inputs Process Outputs Customers)
– Process Mapping
– Value Stream
Mapping
41. Process Mapping Continued
• Students were again divided into 6 groups. Each group was assigned a map type and
told to Map the Exam Taking Process at either a Micro or Macro Level
• Micro Level Groups Handled the Physical steps of taking the exam such as reading
the question, locating the answer and filling in the bubbles
• Macro Groups Handled the all of the preparation leading up to taking the exam
• The point was to emphasize that the same tools techniques and methods can be
used on the very micro level (an operator tightening a bolt) to the very macro level
(the operations of a fortune 500 company)
42. 42
SIPOC at a even higher level
Input
• Students
• Body of
Knowledge
• Instructor
• Textbook
• Facilities
Supplier
•ASQ Princeton
•ASQ Corporate
•Rutgers University
Output
• Knowledge
• Certification
Customers
• Future Employers
• Current Employers
• Students
• Rutgers University
• ASQ Princeton
• Rutgers IIE
Educate
Students in Six
Sigma
Process
Identify Educational
Shortcoming
Create Course
Develop
Methodology
Locate Students
Teach Students
Administer Test
43. Control Charts
• Class 4 Introduced Students to the Control Charts Covered in the
CSSGB BoK
– I-MR
– X Bar-R
– X Bar- S
– P
– NP
– U
– C
• Students were emailed prior to class a Microsoft Excel Workbook
containing the test results and told to bring their laptops to class
• Students were asked to do the following by hand (with Excel
helping for the calculations):
– I-MR Chart for Test Scores
– P Chart testing for “Bad Questions”
– NP Chart testing for “Bad Questions”
– C Chart for the number of wrong responses per exam
– U Chart for the number of wrong responses per exam
49. Does a T-Test Indicate there was improvement?
t-Test: Two-Sample Assuming Unequal Variances
Mid Pre
Mean 0.607234 0.561702
Variance 0.014373 0.01111
Observations 47 47
Hypothesized Mean Difference 0
df 91
t Stat 1.955429
P(T<=t) one-tail 0.0268
t Critical one-tail 1.661771
P(T<=t) two-tail 0.0536
t Critical two-tail 1.986377
50. Does ANOVA Indicate there was
Improvement?
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Pre Total 64 35.78 0.559063 0.012082
Mid Total 53 31.72 0.598491 0.013705
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 0.045069 1 0.045069 3.516685 0.06329 3.923599
Within Groups 1.473823 115 0.012816
Total 1.518892 116
52. Is the Change in Control?
-15
-10
-5
0
5
10
15
C Chart of Change in # of Correct Responses
UCL = 8.29
LCL = -3.74
Mid= 2.28
53. Is the change in Scores Significant?
t-Test: Paired Two Sample for Means
Mid Pre
Mean 0.607234043 0.561702
Variance 0.014372618 0.01111
Observations 47 47
Pearson Correlation 0.689206844
Hypothesized Mean Difference 0
df 46
t Stat 3.475995635
P(T<=t) one-tail 0.000560995
t Critical one-tail 1.678660414
P(T<=t) two-tail 0.00112199
t Critical two-tail 2.012895599
57. Comparison of Results for Material
that has been Covered
Mid CoveredPre Covered
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Subscripts
CoveredScores
Boxplot of Covered Scores
58. Comparison of Covered Material
0.90.80.70.60.50.40.3
12
10
8
6
4
2
0
0.90.80.70.60.50.40.3
Pre Covered
Frequency
Mid Covered
Mean 0.5785
StDev 0.1252
N 64
Pre Covered
Mean 0.6516
StDev 0.1174
N 53
Mid Covered
Histogram of Pre Covered, Mid Covered
Normal
59. Does ANOVA Indicate there was
improvement?
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Pre Covered 64 37.02632 0.578536 0.015686
Mid Covered 53 34.53333 0.651572 0.013785
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 0.154648 1 0.154648 10.43065 0.001616 3.923599
Within Groups 1.70503 115 0.014826
Total 1.859678 116
60. Comparison of Results for Material
that has not been Covered
Mid Not CoveredPre Not Covered
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Subscripts
Scores
Boxplot of Scores
62. Does ANOVA indicate the Exam was
harder?
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Pre Not Covered 64 31.83333 0.497396 0.01785
Mid Not Covered 53 27.5 0.518868 0.024926
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 0.013367 1 0.013367 0.635003 0.427168 3.923599
Within Groups 2.420698 115 0.02105
Total 2.434065 116
64. Control Charts with Minitab
• Students were emailed a Microsoft Excel Workbook with the Mid-
Term data set
• It was heavily suggested that students purchase the Minitab
academic license and bring their laptops to class.
• Students then divided themselves into groups around those who
purchased the software and created the analysis control charts on
the preceding slides.
65. Hypothesis Testing Exercises
• In week 8 students were introduced to the hypothesis tests covered
in CSSGB BoK
– Z Test
– Student T
– Two Sample T (known variance)
– Two Sample T (unknown variance)
– Paired T Test
– ANOVA
– Chi Squared T
– F Test
• Students were emailed a data set containing both the Pre-Test and
Mid-Term data and asked to perform each of the listed test using
either Minitab or Microsoft Excel. The emphasis was placed on the
conclusions from the data
66. Confidence Intervals
• Not all students took the Mid-Term that took the
pre-test.
• This enabled students to utilize inferential
statistics to draw conclusions about the
population parameters (mean and variance
particularly)
• By using the class data set provided students
were able to calculate their confidence in the
overall population parameters for the average
test score as well as the standard deviation of the
entire class
67. Improve-Control
• Improve and Control are not an emphasis in the CSSGB BoK. For the
coverage of the material and extended example of the Starbucks
Experience from a customers perspective is presented.
• When introducing Lean and the types of Waste the process of making
various beverages are presented. Students then proposed improvement
strategies to minimize the ‘Muda’
Triple Tall Half Hot Half Cold Americano
(Current State)
Triple Tall Half Hot Half Cold Americano
(Future State)
73. Was The Difference Significant?
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Pre 64 35.78 0.559063 0.012082
Mid 47 28.54 0.607234 0.014373
Final 40 30.43 0.76075 0.020084
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 1.029282 2 0.514641 34.534 4.91E-13 3.057197
Within Groups 2.205562 148 0.014902
Total 3.234844 150
75. Was the Individual Improvement
Significant?
t-Test: Paired Two Sample for Means
Final Pre
Mean 0.750556 0.556667
Variance 0.019743 0.010023
Observations 36 36
Pearson Correlation 0.342582
Hypothesized Mean Difference 0
df 35
t Stat 8.199954
P(T<=t) one-tail 5.8E-10
t Critical one-tail 1.689572
P(T<=t) two-tail 1.16E-09
t Critical two-tail 2.030108
80. Results
• Ruba Amarin
• Margit Barot
• Miriam Bicej
• Matthew Brown
• Salem El-Nimri
• William Ewart
• Elizabeth Fuschetti
• Robert Gaglione
• Thomas Hansen
• Tarun Jada
• Javier Jaramillo
• Michael Kagan
• Anoop Krishnamurthy
• Timothy Lin
• Helen Liou
• Rebecca Marzec
• Charles Ott
• Sneha Patil
• Eugene Reshetov
• Matthew Rodis
• Thomas Schleicher
• Dante Triana
• Albert Tseng
• Bond Wann
• Paul White
• Sun Wong
• Shih Yen
• Jacob Ziegler
28 out of 37 Students that took the June 2nd Exam Passed the June 2nd Exam
Nationally 788 out of 1160 individuals passed the exam
82. Results
• Students test scores improved on average
19.4%
• 76% of Students Passed the exam compared
to 68% National Average
• Increased ASQ Princeton Membership by 62
members
• Largest Ever Fund Raiser for the Rutgers IIE
83. 83
Added Benefit
• From the funds generated by the course Rutgers was
able to send 21 Students to the national IIE
Conference in Orlando (shown above)
84. 84
It took a team
• Nate Manco
– ASQ Princeton Education Chair
• Richard Herczeg
– ASQ Princeton Section President
• Jeff Metzler
– Rutgers IIE President
• Dr. James Luxhoj
– Rutgers Industrial and Systems Eng
• Brandon Theiss
– Instructor
• Cindy Ielmini
– Rutgers Industrial and Systems Eng
85. Lessons Learned
• Using the passing the exam process as a class example
for the implementation of the tools and techniques of
Six Sigma is an effective methodology
• There is demand for teaching Six Sigma in an academic
setting
• The joint venture between Rutgers and ASQ is feasible
and mutually beneficial.
• Having a diverse student population increases the
overall performance of the group.
• Students need to be adequately qualified to sit for ASQ
exam prior to taking the course.
86. 86
We are sharing the Results
• Presented results at Institute of Industrial Engineers Lean and Six Sigma
Conference
• Will be presented at the ASQ International Conference on Quality
87. 87
Progress continues onward
• Course Scheduled to Run again in the Spring through Official Continuing
Education Office
• First of its kind joint meeting with ASQ Princeton and Rutgers IIE in
which the course results were presented.
Six Sigma is a problem solving tool kit that seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes.Six Sigma Green Belts are the tactical leads on improving functions within a job function that are able to apply the Lean Sigma Concepts to their daily work.The methods are universally applicable to anything where a customer is being serviced.
The cost of the course for students included the textbook and ASQ student membershipThe professional rate only included the text.The ASQ Certified Six Sigma Green Belt Requires 3 or more years of work experience in one of more areas of the Body of Knowledge. There was a very long and at times heated exchange with the ASQ certification committee about what constitutes work experience. A compromise was ultimately reached however there were still a large number of qualified students that were denied the right to sit for the exam
This is a unique pedagogical approach and from philosophically is quite “meta”. The objective under examination is in fact the actor performing the examination.The most brilliant of teacher can write the most profound equation on a chalkboard, and the most diligent of students can take pristine notes. However learning only occurs when the student is able to apply the material. Johann Wolfgang von Goethe was correct when he said “Knowing is not enough; we must apply.”Given the diversity of the composition of the students in terms of education, life experience, income and industry finding a common task in which to apply the LSS would have been impossible. The only true commonality between the group was that they were all humans and wanted to earn their greenbelt. We were able to leverage this fact in developing the instructional roadmap for course.Also the utilization of Shewhart Control Charts which are used to differentiate between common cause and special cause variation, is fairly novel in academic settings.
The instructor for the course, Brandon Theiss, is a Senior Member of ASQ and a Graduate student at Rutgers University. Currently there is not a course offered in the undergraduate Industrial and Systems Engineering Program at Rutgers. This course provided an opportunity for students to not only be exposed to the material but also to earn a nationally recognized certification in the tools techniques and methods of Six Sigma. It represented a first of its kind partnership between the student chapter of the IIE and ASQ Princeton section. Part of the proceeds for the course were used to fund the IIE trip to their national conference in Orlando.
The course met once per week over an 11 week period from 6:30 to 9:30PM. There were two sessions per week and students were free to attend either the Monday or Tuesday class based upon which ever was more convenient for their schedule
Students were notified via email prior to the first night of the course that an exam would be administered on the first night.This provided both a baseline for the future improvement as well as showing students directly the level of mastery they would need to obtain to become certified.
Feedback in any system is critically important. With a course that only meets once per week, having students wait a week would be to long. By providing students immediate feedback they were able to best utilize their time to study as well as not mis-learn material thinking that they had been correct on a question when in fact they were not.
A simple histogram of the exam results from the Monday section with a normal distribution fit. It does appear to be normal but has a very large standard deviation 11.8%
The probability plot indicates that there is insufficient data to reject the null hypothesis that the data is normally distributed. This is indicated by the P value which indicates the probability that the difference between the measured data and the model occurred by pure chance. The null hypothesis of normality would have been rejected if the value had been less than alpha (5%) representing a 95% confidence level.
It is technically debatable if the test scores are continuous or discrete variable and if a I chart is appropriate. However the point is to introduce students to control charts and an Individuals chart.Since no point lies about the Upper or Lower Control Limit, the process is in a state of “statistical control”. However common sense shows that this is nonsensical as the range of the limits is between 17% and 95%. This was caused by the large standard deviation observed.This was used as an opportunity to discuss the difference between statistical significance and actual significance. This reinforces the concept that the math does not know where the numbers came from and can at best direct teams to derive the true underlying meaning.
Again there is a technical point if the test scores are discrete or continuous. The above Process Capability study requires that the data be considered continuous. Process capability is essentially the probability of producing a product that will meet your customers specification. In this case the passing score (78%) sets that limit. As you can see in the above chart for every 1,000,000 students from the Monday population that took the pre-test exam ~970,000 students will fail.
Everyone has taken a test where the test taker believes there was a question that either had the wrong answer or was too difficult. By using a NP (or P) control chart, one can easily distinguish if a question was statistically significantly too difficult above the UCL or too easy below the LCL
There were several students who handed in their exams very quickly. We wanted to see if the amount of time a student spent on the exam effected their scores. And for the Monday data set it appears it did.
A histogram of the Tuesday data set
Again the data is normal as indicated by a P value greater than 5%. It is however notable in the above plot that there is a clear outlier.
Again we can see that there is clearly an outlier in the data set.
The Tuesday process is very similar in its inability to produce a unit meeting customers expectations and again will generate ~970,000 failures for every million students from the population that take the exam
In the above graph it does appear that there were questions that a statistically significant number of students got wrong.
Interestingly, the order in which a student turned in their exam did not have an effect on the Tuesday data set.
Combined Histogram of the results
Both distributions look somewhat similar.
The above shows a box plot comparing the two classes. The median appears to be higher in the Tuesday class. However is the difference significant?
An ANOVA analysis was performed which results in a very high p value which means that there is not a statistically significant difference between the two population means.
A Pareto Chart of the topic involved for each of the Out of Control Data points from the combined P Chart
Nominal Group -> when individuals over power a groupMulti-Voting -> Reduce a large list of items to a workable number quicklyAffinity Diagram -> Group solutionsForce Field Analysis -> Overcome Resistance to ChangeTree Diagram -> Breaks complex into simpleCause- Effect Diagram -> identify root causes
Most Common Model of group Development was proposed by Bruce Tuckman in 1965.In order for the team to grow, to face up to challenges, to tackle problems, to find solutions, to plan work, and to deliver results. They must go through the cycleFormingTeam members getting to know each otherTrying to please each otherMay tend to agree too much on initial discussion topicsNot much work accomplishedMembers orientation on the team goalsGroup is going through “honeymoon period”StormingVoice their ideaUnderstand project scope and responsibilitiesIdeas and understanding cause conflictNot much work gets accomplishedDisagreement slows down the teamNormingResolve own conflictsCome to mutually agreed planSome work gets doneStart to trust each otherPerformingLarge amount of work gets doneSynergy realized Competent and autonomous decisions are madeAdjourningTeam is disbanded, restructured or project re-scoped.Regression to Forming stage
Control Charts are used to differentiate between common cause (normal) and special cause (abnormal) variation.
There does not appear to be a large change between the Pre Test and the Mid Term
A T-Test indicates that there is significant improvement, as indicated by the one tail P value.
ANOVA on the other hand indicates that there is not a difference between the two means.
Displays a histogram of the changes in scores, about 40% of the students went down and 60% increased their score.
This is a somewhat novel adaptation of a C chart that allows for negative values. However there appear to be students that did much better and much worse than the other students.
Looking at a Paired-T test there was absolutely a statistically significant improvement.
Why did the test scores not improve more dramatically? Well the exams cover all of the material in the CSSGB BoK the course was only half complete. When we looked at the material covered up to the midterm on both the pre-test and the mid term the above pie charts show the percentage of the covered material on each exam.
Not surprisingly students performed better on the material that was covered as compared to the material that was not covered.
However the students also scored better on that same material on the pre test.
So was there actual improvement?
The change in the means indicates a ~8% improvement. However is that statistically significant?
ANOVA does indicates that there is a difference in the means. The students did in fact learn the material that was covered.
There does not appear to be a difference in the scores in the material that was not covered yet in the course.
There was a small increase in the means ~2% is that significant?
No. There is not a statistically significant difference between the pre-test and mid-term scores on the material that was not covered. As a result it would indicate that the exams were roughly the same difficulty.
The process is still incapable of generating a passing score on the test.
Minitab is the de facto industry standard for statistical process control. Unfortunately the undergraduate program at Rutgers does not include any training in the software suite. It is fairly intuitive however students needed additional instruction.
Unfortunately, as this courses primary purpose was to act preparation for the Greenbelt Exam a larger focus could not placed on this material. However in an industrial setting most projects fail in the control phase. Regression to the mean is the natural trend. Anyone that has ever tried to lose weight or quit smoking knows that the trouble is always in sustaining the improvement.
The above histogram does not quite look normal and has a very large standard deviation 14%.
A dot plot again shows a strange pattern.
The distribution is in fact bimodal. Unfortunately due to ASQ’s interpretation of the meaning of work, a large number of qualified application were unable to sit for the actual Greenbelt exam and became disenchanted with the course and represent the lower distribution. This assumption was supported by a post hoc online survey.
However the test scores did appear to approve (even with the lower distribution)
And the improvement was very significant as indicated P value of 4.91 x 10^-13
On average the students improved 19.4% only a few students scores decreased,
The Paired T Test Results also confirm that the students test scores improved!
A P Chart was again used to detect difficult questions.
A Pareto Chart above shows the topics that generated that special cause variation in the prior P chart.
The initial process capability was quite poor, producing defects ~970,000 failures per 1,000,0000
The final process capability though still not best in class, is much better, producing 475,000 failures per million (the observed is used since the data was already proven to be non normal as it is bimodal)
*Actual data has not yet been released for the national average yetAs Confucius says “I hear and I forget. I see and I remember. I do and I understand.”