ttmg 5103 module techniques and tools for problem diagnosis and improvement prior to...

TTMG 5103 Module Techniques and Tools for problem diagnosis and

improvement prior to commercialization

Shiva Biradar

TIM Program, Carleton University

Agenda Process Behaviour charts

Monitor process performance to keep the new solution in control

Cause and effect diagram

Investigate the root causes of performance problems

Cause and Effect Matrix

Identify the key input-output relationships in need of attention

Control Plan

Ensure that your new solution becomes commercialized as

planned

Process Behaviour charts

Process behaviour charts are used to monitor

performance of a process, product, service or solution at

the out (Y) and input (X) levels to ensure whether the

process is executed as planned

Charts can be used to monitor the performance of new

innovation as it goes into production or commercialization

after its design

Process Behaviour charts

Charts help create visibility that is necessary to ensure

new innovation has successfully made the transition from

the drawing board into the real world

Basic charts are explored in this presentation

For sophisticated performance evaluation, one may need

process expert or statistician, then suggested to use

process behaviour charts software for calculations and

drawing charts

Process Behaviour Charts

General steps for constructing charts are

based on type of data involved:

Attribute data

Data that you cannot count

Variable data

Data on a scale

Process behavior charts

Steps to construct attribute data charts

1. Gather and plot data

2. Calculate control limits

3. Interpret the chart according to an established rules

Process behavior charts – Attribute data charts


1. Determine the frequency of data collection

2. Record the defect counts

3. Plot the defect data on a time series chart



1. Calculate the process average and this to the chart

2. Calculate the upper and lower limits (UCL and LCL)

Common cause variation - When process is in control, the

control limits show the ordinary amount of variation

Special cause variation - When the measurement falls outside

of the limits, the variation is extraordinary


3. Interpret the chart according to established rules

Rule 1 violation – if there is a large shift in the process

that should be investigated immediately (larger than UCL)



Rule 2 violation – when the process operates above or

below its performance for an extended period of time,

specially for nine or more cycles, it should be investigated

to improve permanent process improvement and defect

reduction



Rule 3 violation – when the process drifts in one direction

or the other for a duration of at least six measurement

cycles, it must be investigated to find the cause and fix

the process

Process behavior charts - Variable data charts

Many process have characteristics that are measured in

variable scale than counted on discrete scale

More information is available in variable data than in

count data

Variable charts yield more information than their

attribute counter parts

Process behavior charts – Variable data charts

Variable charts

Xbar/R Chart or average and range chart

Variable data charts construction



3. Interpret the chart according to an established rules



1. Determine the frequency of data collection and size of the

sub group

Subgroup is defined as a few measurements gathered from the

dame logical grouping

Data from the same machine on same shift in short period of

time



2. Record the raw variable data

3. Compute the average and range of each subgroup

4. Plot the subgroup averages and ranges



1. Calculate the process average and the average range,

and add them to charts

2. Calculate the upper and lower limits (UCL and LCL) for

the average chart and range chart, and add control limits

to charts



Rule 1 violation – if there is a large shift in the process

that should be investigated immediately (larger than UCL)



Rule 2 violation – when the process operates above or

below its performance for an extended period of time,

specially for nine or more cycles, it should be investigated

to improve permanent process improvement and defect

reduction



Rule 3 violation – when the process drifts in one direction

or the other for a duration of at least six measurement

cycles, it must be investigated to find the cause and fix

the process



Rule 4 violation – occurs when two of any three data

points reside more than two standard deviation from the

process mean. This indicates that process has

unnecessarily shifted higher or lower and the out of the

control state should be addressed



Rule 5 violation occurs when the process has shifted

higher or lower to a smaller degree than a rule of four

pattern. The fourth point of any five points that resides

more than one standard deviation beyond the mean

indicates that the process has shifted

Cause and Effect diagram

When out of control conditions are identified, to figure out what

happened and hot to figure it out from happening again,

following techniques can be used:

Cause and effect diagram

Design of experiments

Conjoint analysis

Measurement system analysis

Cause and Effect Diagram

Enables to brainstorm and categorize the variables that might

be causing poor performances in new innovation process

Use C&E diagram before going to production to reduce defects

Make sure the team is aware of the system and are open to

getting to root cause of any defect


Using C&E diagram, one can systematically identify all

the potential causes that may be contributing to low

customer satisfaction


Steps to construct C&E Diagram

State of the effect

Choose cause categories

Identify inputs

Ask why

Discover root causes


A C&E matrix helps to determine which critical process inputs have

the most impact on process outputs

A C&E matrix allows to qualitatively determine the importance of

cause-and-effect relationships between process inputs and outputs

Beneficial especially when enough quantitative data is not available

to understand relationship between inputs and outputs, or figure out

which factor has critical influence


Steps to construct C&E matrix

Identify and rank process outputs

Identify process steps and inputs

Rank process inputs

Calculate cumulative effect

Control Plan

Critical to ensuring the innovation will be produced and

delivered according to design regardless of location,

personal, environment or other variables

Helps to mitigate risk when moving from a controlled

environment( such as research lab) into an operational

environment (like the factory floor)

Control Plan

Enables any organizations to replicate the customer experience by

clearly documenting

how to keep the process in control

what to do if it goes out of control

who is responsible for putting it back in control

Results in reproducible process that delights customers and

maximizes profits

Control Plan

Steps to prepare control plan

1. Identify process Step

2. Identify inputs

3. Identify outputs

4. Identify Specification limit

5. Identify process capability

6. Identify measurement system

7. Identify current control

method

8. Identify who – roles

9. Identify when and where

10. Identify reaction plan

11. Identify transition plan

Conclusion

Charts can be used to monitor the performance of

new innovation as it goes into production or

commercialization after its design

A C&E diagram enables to brainstorm and categorize

the variables that might be causing poor

performances in new innovation process

Conclusion

A C&E matrix allows to qualitatively determine the

importance of cause-and-effect relationships between

process inputs and outputs

Control plan ensures the innovation will be produced

and delivered according to design regardless of

location, personal, environment or other variables

References

www.innovatorstoolkit.com

ttmg 5103 module techniques and tools for problem diagnosis and improvement prior to...

Documents

defect data

process average

process drifts

process expert

type of data

permanent process improvement

attribute datadata

count datavariable charts