1. Understanding Lean and Six
sigma and my Green belt
Journey

2. Overview
I complied this Presentation to explain the differences
between lean and six sigma to my colleagues within my
organisation.
From there the presentation goes on to detail the project I
led to achieve my Green belt certification in March 2012 and
how this structured approach has changed my thought
process towards problem solving within technical processes
My main development has been within six sigma rather
than lean and for my next role I would ideally like utilise my
existing skills and develop into a competent Lean Six Sigma
Black Belt where I can drive sustainable improvement and
help develop others
2

3. Overview
Stability and Accuracy
Speed
2

4. 3

5. Lean Focus

6. House of Lean
9

7. What is 6 Sigma
Sigma is a letter
in the Greek Alphabet
Vision
Goal
Philosophy
Metric
Method
Tool
Symbol
Benchmark
Value
6
• A level of performance that reflects the
number of concerns in our products or
services
• A statistical measurement of our process
capability, as well as a benchmark for
comparison
• A set of basic statistical “tools” to help
us measure, analyse, improve, and
control our processes
• A commitment to Customer Satisfaction
and Shareholder Value to achieve an
acceptable level of performance

8. 6-Sigma and process capability
Sigma Level
% Good
Cpk
1
691462
30.9
0.33
2
308530
69.1
0.67
3
66807
93.3
1
4
6210
99.4
1.33
5
233
99.98
1.67
6
7
DPMO
3.4
99.99966
2

9. 6-Sigma Strategy
Know What’s Important to the
Customer
Center Around the Target
Minimise Variation
Reduce Concerns
8

10. The Six Sigma Methodology
•Determine
baseline of the
process
• look for clues
to understand
the root cause
of the process
(fill the funnel)
•What does your
data tell you?
•Narrow down
and verify the
root causes
•How will you fix
the problem?
•Move on to
solution through
hypothesis
testing
30 - 50
Project Progress
•What problem
would you like to
fix?
•create a Project
Charter,
•Create a highlevel view of the
process,
• Begin to
understand the
needs of the
customers of the
process.
10 - 15
8 - 10
Number of
Input variables
4-8
3-6
9
•How do you
sustain the newly
achieved
improvement?
•Document
exactly how you
will achieve
sustained
improvement
KPIV’s to optimise
and control

11. The Six Sigma Tools
• Establish a
Team
• Identify a
Sponsor
• Administer PreWork
• Pareto
• Project Charter
• VOC & CTQs
10
• Define current
state
• SIPOC
• Map the process
• Value Analysis
• MSA
• Collect data
• Check sheets
• Prioritization
using Pareto
Charts
• FMEA
• Control Charts
• Boxplot
• Time series
• Control Charts
• Histogram
Process Capability
Cause & Effect
Pareto
Scatter Diagram
Histogram
Boxplot
Multi-vari Studies
Interaction Plots
Regression
Analysis of
Variation
• Hypothesis
Testing
• FMEA
•
•
•
•
•
•
•
•
•
•
• Brainstorming
• Design of
Experiments
• Pareto
• Hypothesis Testing
• Boxplot
• FMEA
• Process Mapping
• Selection Matrix
• Pilot
•
•
•
•
••
•
Control plan
Specification
Control Charts
Error Proofing
SOPs
Training Plan

12. Overall Approach
Practical Problem
y
f ( x1 , x2 ,..., x k )
Practical Solution
26
Statistical Problem
Statistical Solution

13. D
M
A
I
C
S
My Practical example
Defining the Problem
Project Description: Realise £750,000 of raw material savings through rice pellet supplier
change
Problem Statement: New Supplier product gives poor quality product quality compared to
existing supplier
Project Objective: Technical match of product of new supplier to existing supplier
In Scope: Supplier manufacturing process and popping process at Skelmersdale
Area Out of Scope: Seasoning and Packaging processes
Primary Metric KPI: Appearance defect %
KPI Definition: Technical match for appearance and popped cake characteristics
Secondary Metric KPI: Area Weaklink %
KPI Definition: Site Weaklink standards
13

14. D
M
A
I
C
S
Product produced using new supplier rice
pellets were found to give very variable
product quality during trials. The quality
of product was not consistent and failed
to match existing supply. For this reason
the trial was cancelled before completion.
At this stage I was not aware if this quality
issue lay with the site processes or with
the proposed new suppliers process.
During the trial I obtained the suppliers
process data to help define were the
focus areas lay and found the following.
14
The Supplier „s Process
A probability plot showed the proposed
new supplier finished pellet process data
to be non normal with data stacked at the
specification limits and a capability analysis
gave a Pp 0.38 and a Ppk of 0.19 indicating
issues with process control.
When we measured pellet characteristics
such as 100 piece weight we could see on
the control chart that a lot of variation
was seen from bag to bag showing a lack
of consistency and control

15. D
M
A
I
C
S
Define actions
•Using the suppliers own data I could see that the process was non normal which, in this
circumstance, indicated that process data was manipulated. The capability showed that the
process was not capable within the defined specification limits. This suggest that the supplier
was having issues controlling he process which was in turn leading to quality issues at the
process.
•A quality assessment of the trial product and existing supply showed that some LT product
matched current Liven supply where as other product was significantly worse and failed to
meet on line quality standards.
•Through our process understanding we were not convinced that finished pellet moisture was
the critical variable that affected popped product quality.
Our sister plant in the USA pellets for an identical process.
Actions
•Proposed supplier process control needed to be improved through constructive feedback of
process analysis
•Compare Current supplier against proposed supplier
•Define critical variables within the process that affected quality
•Use current business knowledge to build our understanding of pellet manufacture and critical
control points of the process.
15

16. D
Comparing the current and proposed rice
supplies it showed there was no
difference in the variation and mean
average for the finished moistures .
When comparing bag to bag moisture of
the proposed suppliers product against
appearance defects results it was found
that the bag to bag moisture could be the
same for both good and bad product
Good
product
Bad product
16
M
A
I
C
To confirm the hypothesis that finished
pellet moisture was not the critical variable I
ran a fitted line plot to of Moisture against
defects and found that there was not
correlation
We created a SIPOC or process map to
define the scope and some areas for
review.
S

17. D
M
A
I
C
S
Measuring the Current Process
• The proposed supplier process was being controlled by finished pellet moisture as the
primary control measure which was measured at the end of the process. This was shown
to be very variable and could not meet specification limits. It was agreed that finished pellet
moisture was the best way to measure control throughout the process and could be used to
compare the process as improvements were made as the project progressed.
• On discussion with colleagues in the USA it was clear that moisture measurement
throughout the process was critical for good process control. The proposed supplier was
asked to measure moisture throughout the process and define specification limits for these
to allow these variables to be measured. The critical control points were also defined
through the process map of LT‟s process.
• Extruder control was also highlighted as critical to improve LT baseline performance and
was agreed as an area of focus and measurement area. LT were advised to start
measurement of this process area and feedback results for measurement.
17

18. D
Comparing the two extrusion lines at the
proposed supplier we could see that line 1
was showing less variability and was
statistically better in a number key areas
of control at the extruder. This was
highlighted to LT and they consulted the
extruder manufacturer to improve line 2
to the same standard
18
Although line 1 was less variable the
control charts for pellet characteristics of
weight and diameter showed the process
on both lines was not stable. More
control was needed at the extruder to
reduce the variation that was impacting
size and diameter
M
A
I
C
S
Moisture control needed to be improved.
Inconsistency in raw material in feed
moistures was not being controlled at the
pre mixing stage . Poor extruder control
was also leading to variable post extruder
moistures. Improvements in control in
these areas will lead to reduced variability
of the finished product.

19. D
M
A
I
C
S
Measuring the Current Process
•After we feedback the findings of the variability seen in finished pellet moisture and statistical
differences between the supplier processes they reported a number of issues that were
affecting consistent running of the line were previously no issues had been reported. To
rectify these they complied and completed the following improvement list.
• Replace flour feed hopper so that flour bridging is eliminated.
• Add level probe on flour feed hopper to permit auto feed from mixing hopper to feed hopper
• Add a water chiller to reduce cooling water used for cooling extruder to regulate zone
temperatures in the extruder.
• Improve cam design on vibratory conveyors from extruders to make it more robust.
• Replace electrical heaters in Vibratory dryers.
• Obtain uniform bed in apron dryer to obtain more uniform moisture across the bed and to
reduce clumping:
• Improve distribution using doctor blade in dryer feed hopper.
• Improve controllability of inlet temperature in dryer. Reduce temperature fluctuation to +/- 2
Deg C.
19

20. Analysing The Root Cause
D
M
A
I
C
S
Once the lack of a relationship between finished pellet moisture was proven we focused our
attention on gelatinisation of the pellet. This involved analysis of the pellets at a third party lab
to test for uncooked starch levels using DSC and also pellet analysis using RVA.
I also visited a reputable University to build an understanding of starch and how is reacts within
processes
The practical question was how does starch in the pellet affect the popped cake
quality?
20

21. D
M
A
I
C
S
Cell structure of poor product through
increased SME and/ or not enough
available water?
DSC testing shows the amount of
uncooked starch remaining as an
enthalpy value. This value was
compared to the appearance defects
observed on trial product. A moderate
negative correlation was observed
between the two. the hypothesis we
derived from this was that the high
enthalpy value = good product and low
enthalpy value = bad product
21
Desired granule structure
post LT extruder
As we analysed more trial material we
found this hypothesis to be untrue. We
found that we could get good product at
low or even 0 enthalpy values! although
we had not shown that high enthalpy
values gave bad product
The next hypothesis was around
extrusion and the energy used to work
the product. The specific mechanical
energy (SME) exerted on the product
could alter the state of the starch granule
causing quality issues (See picture above)

22. You need to be able to show this
D
M
A
I
C
S
A visit to the University was instrumental in building understanding of the starch granule and the
right characteristics required for the popping process
We took the findings and complied the following guidelines for the proposed supplier.
Good Product: Right amount of SME and available water to swell the starch granule without
deconstructing the granule. This allows good expansion at the popping machine and the ability
to hold its form when popped. This is because there is a strong cell wall intact to maintain the
popped shape
Bad Product: Too Much SME with enough available water will deconstruct the starch too much
giving good expansion but will not hold its form as the granule structure has a weak cell wall
that will not hold as well.
Bad Product: Too little SME will not swell the granule giving poor expansion as it will not take
up the available water
Bad Product: Incorrect amount of available water will give the same results as too much or too
little SME
To prove the hypothesis we asked LT to reduce the work that done in the extruder and to send
samples to the UK for popping
22

23. D
M
A
I
C
S
Normal Gelatinisation
Low Gelatinisation
Defect Rate = 2%
Defect Rate = 0%
•The hypothesis proved to be true and we observed very good expansion and retention of form on the
low gelatinisation (Or reduced mechanical energy) pellets .
• On the normal gelatinisation we saw less expansion or retention of form, but still good quality product
in line with the current supplier
• To achieve this the proposed supplier reduced SME in the extruder by reduction in heating zone
temperatures by 25oC on zone 2 and 15oC on Zone 3
• Reduced SME has maintained granule structure and increased post extruder moisture by 2%
23

24. Improving the Process
D
M
A
I
C
S
The improve phase was focused around defining critical control points for the proposed
suppliers process and implementing measures for these areas as follows:
• Measurement of rice flour moisture pre production is critical
• Correct addition of water to flour to maintain consistent 30% moisture within rice/ water mix is
critical. The supplier was currently adding the same water regardless of incoming flour
moisture as seen in gelatinisation trials
• Determine optimum process controls a measurement of SME within the extruder would be
very useful. SME combines all the factors within the extruder that will affect starch
gelatinisation and granule structure.
• Best product observed at 30% infeed moisture with 25.4% post extruder moisture at reduced
temperatures in the extruder. Define settings for process control.
• Checking of post extrusion moisture is a critical process control to ensure consistency in
extruded product.
• Determining optimum gelatinisation level as measured by KOH is critical and is a required
process control check. Best product observed (Low Gel) was KOH = 2, Normal was KOH =5
which gave very variable product
24

25. D
M
A
I
C
S
Illustrating The Process
By reviewing the process map, communication with colleagues in the USA and analysis of the process data the critical control points
of the process were identified and actions agreed and implemented that is driving both improved understanding of the process and
control
Defining the rice flour mix
moisture as a critical control
point gave consistency of
product into the process
Understanding the reactions within the
extruder and their affect on product quality
was key to the progression of the project and
was a step change in understanding for both
the proposed supplier and my organisation.
Key outputs were defined and measured
25
At the start of the project finished pellet
moisture was the primary measure used to
determine quality. This was proven to have
no correlation with popped cake quality .
Analysis enabled the project team to quickly
move on from this and re focus efforts.

26. D
As we defined the processes critical control points and
LT implemented process improvements over time we
could see the variability of the finished pellet moisture
was reducing . The Low Gel product was also seen to
give more consistent running at LT with less extruder
blockages as well as improved product after the
Skelmersdale popping process.
From the point of the failed 22T trial to the success of
the September trials there is a statistical difference in
the finished pellet moisture showing the greater control
and consistency of product now being achieved
26
M
A
I
C
The Capability graph shows this improvement also. The Pp had
moved form 0.38 to 0.68 and the Ppk had improved to 0.59
from 0.19. This meant the defect level had moved from 22.35%
to 2.45%. The data was non normal which LT had explained was
due to their scrapping policy of out of specification product.
S

27. Controlling the Improved Process
D
M
A
I
C
S
• In the control phase the key thing was to establish robust specifications that would ensure
consistently of product that would match successful trial material.
• A bulk density measure was added to the specification as this was seen as a more consistent
and reliable method than having separate weight and diameter checks which are difficult and
time consuming to complete.
• It was also an opportunity to review the specification and work with the supplier to reduce un
necessary restrictions on the process and increase process efficiencies by doing so.
• The process data of LT pellets has been agreed to be captured by tote during production and
this will be analysed and reviewed to ensure consistency as we switch to “normal” running.
• Physical popping checks by LT on their popping machines have been incorporated into
quality standards. This will ensure that at the point of manufacture any issues with expansion
and quality will be seen at the supplier who can root cause this and prevent poor product at the
popping process.
27

28. D
Bulk Density was seen to be variable
between bags which was in turn leading to
variable popped cakes weights at the
popping process. The popped cake weights
were lower than those seen with the
existing supplier. The bulk density
specification was set to both reduce the
variability seen and bring popped cakes
weights in line with the current process.
28
I also successfully changed the finished
specification moisture to achieve a
number of benefits.
As finished pellet moisture was proven
not to be critical to popped product
quality a reduction in the target moisture
and an increased specification limits
would
a) Improve yields – moving from a 12%
aim to 11% aim saves £26000 a year
b) Widening the specification by 0.5%
would remove an unnecessary
restriction on the LT process which
was at 5% waste trying to achieve this
measure
M
A
I
C
S
Key to good product and included in the
specification was the testing of the degree
of gelatinisation of extruded pellets
through KOH testing at LT (See above) .
This was set at 2 - 4 and in combination
with the popping test at LT gives a robust
visual check of quality prior to shipping to
the UK

29. Overall Approach
Practical Problem
y
f ( x1 , x2 ,..., x k )
Practical Solution
26
Statistical Problem
Statistical Solution

30. My Experience
Six Sigma training has changed my thought processes towards
problem solving and has made me much more questioning.
It has given me knowledge and tools to help me define and analyse
problems that previously I struggled to quantify.
Provided me with a proven structure that gives me clarity in my
approach.
Has helped my to influence the direction of projects and senior
managers within the business through clear and unambiguous data
presentation.
Helped me to understand process capability indices and how they are
applied within the business.
28

31. The change to six sigma thinking
Old Behaviours
1. We only use experience, not data.
2. We collect data, but just look at the numbers.
New Behaviours
1.We are driven by data in all aspects of our
business.
2.We make decisions based upon facts and not
instinct.
26

32. Lean 6 Sigma Green Belt
What is 6 Sigma