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Follow the links below to find material targeted to the unit's elements, performance criteria, required skills and knowledge

Elements and Performance Criteria

  1. Choose an improvement project
  2. Design the experiment
  3. Conduct the experiment
  4. Analyse and confirm the experimental results

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.

Knowledge Evidence

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.