project blog measure phase.docx

7/29/2019 Project blog Measure phase.docx

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Lean Six Sigma – Measure Phase As you will recall from the define phase of this project we have been tasked to reduce the notional

raw material loss associated with the output of Line 2 by 50%, but 50% of what?. This phase will

address this question and establish a process baseline. We looked at the high level metric used by

the organisation in the define phase which compared actual recorded output versus the expected

output, for the use of a given quantity of raw material. This reported an annual loss / opportunity

depending on your viewpoint of €100,000 when expressed in financial terms. I had and still have

some concerns around this calculation and it will be investigated further during the analysis phase.

That said, when we look at the output of the brainstorming sessions when this issue was put under

review by SME’s (subject matter experts) it became apparent that the process had numerous

opportunities for investigation and potential improvement. (We needed to sort the important few

from trivial many)

While we all intuitively understood that a relationship existed between the use of raw material and

output produced, operationally it really wasn’t addressed in a co-ordinated way, each function

operated in a silo. Engineering kept the lines running; the production operatives watched the

machines and Quality assurance made sure that the product met the quality specifications.

“To Be” Process Map development incorporating process measurement requirements.

We decided to conduct a session with a number of specific objectives:

Generate detail map of the “To Be” process

Identify the key Quality Characteristics for the issue under review (raw material usage)

Identify key process measurement / control points



The group identified two key areas for detailed investigation / improvement initiatives:

1. Operator training

2. Fill Volumes

Workflow map Filler Operations



While we all better understood the challenges, we needed to convert this understanding into

something actionable. It was agreed that a revised set of Standard Operating Procedures would be

developed for the Filler Operators; the rationale for this was two-fold:

- A number of the current operators had not been fully trained- The Filler Operator was best placed to monitor and modify fill volumes on an on-going basis

It was agreed during the session that Filler represented the biggest challenge / opportunity for

improvement. It was noted that the operator would adjust the fill heights if he observed rejects, on

line automated process. (Low fills, 1970mls) In many instances this resulted in 95 units being over

filled to address 1 position on a 96 position carousel. It was certainly felt that emphasis was placed

on producing output that met specification before raw material optimisation, which was an

afterthought. As one of the filler operators put it, “a reject unit equates to approximately 2000mls

whereas 95 x 10mls = 950mls, no brainer”. It should be noted that an (standard operating

procedure) exists for the reporting of this type of issue to engineering for investigation / corrective

action, normally in the form of a replacement head, which potentially involves downtime. Theengineering team are tasked with maintaining line efficiency / utilisation and are reluctant to stop

the operation to perform these adjustments, so they tend to be left to shift change or product

change over.

Another key driver in the over-all process is the legal requirements listed below for 2000mls product.

This metric is subject to external audit and inspection, failure to meet these specifications could

result in quarantined product and plant closure, so they are taken very seriously.

Packing Regulations:

T1 and T2's for 2000mls

Bottle Size T1 T2

2000mls 1970.00 1940.00

Calculations (Ref: weights and measures (packaging) Regs 2006)

Nominal Quantity – mls

(Qn)

Tolerable Negative Error

% of Qn g/ mls

1000 - 9999 1.5

Packer Rules:

Actual contents of packages must not be less, on average, than the nominal quantity

Not more than 2.5% of the packages may be non-standard – ie less than T1 but greater than T2

No package may be inadequate – ie less than T2

NB All conditions must be satisfied



As prescribed by the Six Sigma methodology we had identified the critical to quality characteristic,

fill volumes, consequently we needed to collect data and determine the statistical performance of

our process and additionally determine if the process was capable of producing output within the

specification limits.

Data Collection Plan:

While the system does not have an on-line data collection capability in respect of fill volumes, part

of the routine QA process is to take 5 random samples at 2 hour intervals to test for acidity / Brixs

etc. An additionally element was added to this process, the weight of each sample was recorded. It

was therefore possible to convert this data into net fills by subtracting the closure and pre-form

weight and multiplying by a product specific conversion formula. The initial data was collected over

a four week two shift cycle. This period was considered as adequate to represent all process

variation, particularly environmental as it is perceived that the filler performance is significantly

impacted by the ambient temperature.

1451291139781654933171

2020

2010

2000

1990

Sample

S a m p l e M e a n

_ _ X=2006.10

UCL=2016.78

LCL=1995.43

1451291139781654933171

60

40

20

0

Sample

S a m p l e R a n g e

_ R=18.51

UCL=39.14

LCL=0

11

1

1111

1

1

11

1

1

1

11

1

11

1

Line 2 Fill Volume Control Chart

5 samples taken at 2 hour interval 2 shift cycle for 4 weeks

Reference period w/b 14/01/13 - w/e 08/02/13

Line 2 Control Chart

On reviewing the control chart generated from this data it is evident that the process is not in

statistical control which is somewhat problematic but fundamental. It is interesting to note from the

control chart that there appears to be some improvement in the third and fourth weeks without any

interventions. We have discussed this and believe that it is most likely the result of greater vigilance

on behalf of the filler operator as a consequence of the project activity.

I did consider running an R&R gauge exercise to validate the data collection process but felt that it

was unnecessary at this point as the data was collected by two experienced Lab technicians. I may

well revisit this decision.



While there is little point in trying to determine the Cp and Cpk capabilities of the process while it

exhibits this level of special cause variation it is possible to get some sense of the process

performance using individual values and Pp and Ppk . For this exercise we used the lower

specification limit of 1970mls given the packing regulations and an upper specification of 2005mls as

any value above the nominal value of 2000mls represents a give-away or wastage.

20402030202020102000199019801970

LSL TargetUSL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.458

(Anderson-Darling)

Lower Spec 1970

Target 2000

Upper Spec 2005

Customer Requirements

Z.Bench -0.33

% Out of spec (observed) 65.63

% Out of spec (expected) 63.02

PPM (DPMO) (observed) 656250

PPM (DPMO) (expected) 630198

Total N 160

Mean 2008.2

Mean off target Yes

P-value 0.000

Standard deviation 9.6951

Capability statistics

Pp 0.60

Ppk -0.11

Process Characterization

Points should be close to line.

Capability Snapshot for Net Fill mls

Summary Report

Histogram

Are the data inside the limits and close to the target?

Normality Plot Comments

With a Pp value of 0.60 the process is clearly not capable and skewed on the high side, the

PPM(DPMO) would indicate that for every 1,000,000 units produced over 630,000 will be filled

beyond 2005mls, a very significant give-away.

While the outcome of the statistical process control exercise was not unexpected it presented a

problem, how do we establish a baseline for our process? The answer, rework the control chart /

data by removing the conflict points. While this is not ideal it does establish a baseline with which to

measure the process and should the special cause variation reoccur it will be readily identifiable.



1361211069176614631161

2016

2008

2000

Sample

S a m p l e M

e a n

_ _ X=2006.19

UCL=2016.37

LCL=1996.01

1361211069176614631161

30

20

10

0

Sample

S a m p l e R a n g e

_ R=17.65

UCL=37.32

LCL=0

Line 2 Fill Volume Control ChartRevised chart removing points of conflict

5 samples taken at 2 hour interval 2 shift cycle for 4 weeks

Reference period w/b 14/01/13 - w/e 08/02/13

Revised X-bar R control chart

Conclusions & Experience :

My experience to date has been very valuable and challenging in a number of respects. Operating as

an intern has proved to be challenge in terms of my ability to influencing the team, you have to bemore persuasive when you are not the boss and can’t rely on positional power. While I am not the

project champion, I am the Six Sigma champion and it has been difficult trying to keep the team

working within the methodology. The key mantra at the moment is “what data do we have to

support that decision”.

On a personal level I think would have been better prepared to occupy this position had I been 4 – 6

weeks further along with the Six Sigma 2 module as the techniques are very powerful and make a

compelling argument in most instances. Hence the old adage “the numbers don’t lie”, most people

can relate to this on some level, as the basis of a rational / logical data driven decision making

strategy. My most memorable moment thus far has been a discussion around the properties of normally distributed data and the benefit of the Central limit Theorem, I could see the glace

appearing on the eyes in front of me!

As we move into the Analyse phase I am a little more confident that we at least have identified a

number of opportunities to improve the process and deliver a tangible process improvement and

consequential benefit to the business. The challenge going forward is going to be interesting, it’s one

thing knowing you have a problem (or opportunity), it is another thing trying to develop and

implement realistic solutions. (We don’t have the budget to buy a new filler!)

project blog measure phase.docx

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