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Evidence Guide: MSS405052 - Design an experiment

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Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

MSS405052 - Design an experiment

What evidence can you provide to prove your understanding of each of the following citeria?

Choose an improvement project

  1. Review a process/value stream map.
  2. Identify areas in need of improvement.
  3. Select a process/value stream area for analysis and improvement.
  4. Determine the objective of the experiment in consultation with relevant stakeholders.
Review a process/value stream map.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Identify areas in need of improvement.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Select a process/value stream area for analysis and improvement.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine the objective of the experiment in consultation with relevant stakeholders.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Design the experiment

  1. Select appropriate factorial design.
  2. Estimate signal to noise ratio.
  3. Determine required number of runs and factorial fraction.
  4. Determine resolution.
  5. Design a sequential series of experiments.
  6. Calculate resource requirement for this design.
  7. Determine whether resource requirements are practical in consultation with relevant stakeholders.
  8. Modify experiment, if required, to match available resources.
  9. Determine/develop required metrics.
Select appropriate factorial design.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Estimate signal to noise ratio.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine required number of runs and factorial fraction.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine resolution.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Design a sequential series of experiments.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Calculate resource requirement for this design.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine whether resource requirements are practical in consultation with relevant stakeholders.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Modify experiment, if required, to match available resources.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine/develop required metrics.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Conduct the experiment

  1. Conduct first run of experiment.
  2. Replicate in random order for required number of runs.
  3. Block out known sources of variation.
  4. Conduct other experiments in series.
  5. Record data/have data recorded.
Conduct first run of experiment.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Replicate in random order for required number of runs.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Block out known sources of variation.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Conduct other experiments in series.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Record data/have data recorded.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Analyse and confirm the experimental results

  1. Identify aliases/confounding of variables/results.
  2. Analyse data using statistics pack or similar.
  3. Interpret analysed data in line with objectives.
  4. Identify confidence level of analysed data.
  5. Design experiment to confirm correlations identified.
  6. Conduct confirming experiment.
  7. Analyse data from confirming experiment.
  8. Confirm results (or conduct further experiments).
Identify aliases/confounding of variables/results.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Analyse data using statistics pack or similar.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Interpret analysed data in line with objectives.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Identify confidence level of analysed data.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Design experiment to confirm correlations identified.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Conduct confirming experiment.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Analyse data from confirming experiment.

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Confirm results (or conduct further experiments).

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Evidence Guide

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element.

1

Choose an improvement project

1.1

Review a process/value stream map.

1.2

Identify areas in need of improvement.

1.3

Select a process/value stream area for analysis and improvement.

1.4

Determine the objective of the experiment in consultation with relevant stakeholders.

2

Design the experiment

2.1

Select appropriate factorial design.

2.2

Estimate signal to noise ratio.

2.3

Determine required number of runs and factorial fraction.

2.4

Determine resolution.

2.5

Design a sequential series of experiments.

2.6

Calculate resource requirement for this design.

2.7

Determine whether resource requirements are practical in consultation with relevant stakeholders.

2.8

Modify experiment, if required, to match available resources.

2.9

Determine/develop required metrics.

3

Conduct the experiment

3.1

Conduct first run of experiment.

3.2

Replicate in random order for required number of runs.

3.3

Block out known sources of variation.

3.4

Conduct other experiments in series.

3.5

Record data/have data recorded.

4

Analyse and confirm the experimental results

4.1

Identify aliases/confounding of variables/results.

4.2

Analyse data using statistics pack or similar.

4.3

Interpret analysed data in line with objectives.

4.4

Identify confidence level of analysed data.

4.5

Design experiment to confirm correlations identified.

4.6

Conduct confirming experiment.

4.7

Analyse data from confirming experiment.

4.8

Confirm results (or conduct further experiments).

Required Skills and Knowledge

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element.

1

Choose an improvement project

1.1

Review a process/value stream map.

1.2

Identify areas in need of improvement.

1.3

Select a process/value stream area for analysis and improvement.

1.4

Determine the objective of the experiment in consultation with relevant stakeholders.

2

Design the experiment

2.1

Select appropriate factorial design.

2.2

Estimate signal to noise ratio.

2.3

Determine required number of runs and factorial fraction.

2.4

Determine resolution.

2.5

Design a sequential series of experiments.

2.6

Calculate resource requirement for this design.

2.7

Determine whether resource requirements are practical in consultation with relevant stakeholders.

2.8

Modify experiment, if required, to match available resources.

2.9

Determine/develop required metrics.

3

Conduct the experiment

3.1

Conduct first run of experiment.

3.2

Replicate in random order for required number of runs.

3.3

Block out known sources of variation.

3.4

Conduct other experiments in series.

3.5

Record data/have data recorded.

4

Analyse and confirm the experimental results

4.1

Identify aliases/confounding of variables/results.

4.2

Analyse data using statistics pack or similar.

4.3

Interpret analysed data in line with objectives.

4.4

Identify confidence level of analysed data.

4.5

Design experiment to confirm correlations identified.

4.6

Conduct confirming experiment.

4.7

Analyse data from confirming experiment.

4.8

Confirm results (or conduct further experiments).

Evidence required to demonstrate competence in this unit must be relevant to and satisfy the requirements of the elements and performance criteria and include the ability to design one (1) or more experiments and to:

choose an improvement project

design and conduct the experiment

analyse and confirm the results.

Must provide evidence that demonstrates sufficient knowledge to interact with relevant personnel and be able to design an experiment, including knowledge of:

charting, such as Pareto charts, main effects plots, scatter plots, interaction plots, contour plots, response surface plots

statistical principles and analysis, such as analysis of means (ANOM), prediction equations, analysis of variance (ANOVA)/one-way ANOVA, desirability function, hit a target, advanced graphical data analysis, multi-variate planning, variation trees and funnelling, hypothesis testing, central limit theorem, statistical analysis roadmap, analysis for means and t-test, correlation and regression

factorial analysis principles and methods, such as multi-variate analysis, Taguchi S/N ratios, 2/3 level factorial, Taguchi L8, 2/4-1 half fraction, Plackett-Burman 8-run, full factorial

acceptance criteria/confidence levels

appropriate statistics packs, which to choose and how to use.

Range Statement

This field allows for different work environments and conditions that may affect performance. Essential operating conditions that may be present (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) are included.

Competitive systems and practices include one or more of:

lean operations

agile operations

preventative and predictive maintenance approaches

statistical process control systems, including six sigma and three sigma

Just in Time (JIT), kanban and other pull-related operations control systems

supply, value, and demand chain monitoring and analysis

5S

continuous improvement (kaizen)

breakthrough improvement (kaizen blitz)

cause/effect diagrams

overall equipment effectiveness (OEE)

takt time

process mapping

problem solving

run charts

standard procedures

current reality tree.

Objective of the experiment includes one or more of:

screen factors to find the critical few

optimise a few critical factors

solve process problems

reduce waste

increase reliability.

Factorial design includes one or more of:

2/3 level factorial

Taguchi L8

2/4-1 half fraction

Plackett-Burman 8-run

full factorial.

Signal-to-noise ratio may be estimated by one or more of:

previous experiment design experience

previous process capability studies

statistical process control data

estimated from other sources.

Resolution includes one or more of:

Resolution III design: A design where main factor effects are confounded with two factor and higher order interactions

Resolution IV design: A design where main effects are confounded with three factor and higher order interactions and all two factor interactions are confounded with two factor interactions and higher order interactions

Resolution V design: A design where main effects are confounded with four factor and higher order interactions and two factor interactions are confounded with three factor interactions and higher order interactions.

Sequential series of experiments includes all of:

a screening design (fractional factorial) to identify the significant factors

a full factorial or response surface design to fully characterise or model the effects

confirmation runs to verify results.

Required metrics include one or more of:

quantitative measures normally associated with the process

other quantitative measures relevant to the experiment

ranking systems for normally qualitative measures, such as defectives.

Statistics pack include one or more of:

minitab

JMP

other specialist statistics packs

spreadsheets, such as Excel, particularly with specific add-ons, such as Sigma XL, Analyse It or other add-ons.