The evidence guide provides advice on assessment and must be read in conjunction with the performance criteria, required skills and knowledge, range statement and the Assessment Guidelines for the Training Package.

Critical aspects for assessment and evidence required to demonstrate competency in this unit

A person who demonstrates competency in this unit must be able to provide evidence of their ability to:

design an experiment relevant to improvement strategies and targets of the organisation

conduct an experiment

confirm results, including conduct of confirming experiments.

Context of and specific resources for assessment

Assessment of performance must be undertaken in a workplace using or implementing one or more competitive systems and practices.

Access may be required to:

workplace procedures and plans relevant to work area

specifications and documentation relating to planned, currently being implemented, or implemented changes to work processes and procedures relevant to the assessee

documentation and information in relation to production, waste, overheads and hazard control/management

reports from supervisors/managers

case studies and scenarios to assess responses to contingencies.

Method of assessment

A holistic approach should be taken to the assessment.

Competence in this unit may be assessed by using a combination of the following to generate evidence:

demonstration in the workplace

workplace projects

suitable simulation

case studies/scenarios (particularly for assessment of contingencies, improvement scenarios, and so on)

targeted questioning

reports from supervisors, peers and colleagues (third-party reports)

portfolio of evidence.

In all cases it is expected that practical assessment will be combined with targeted questioning to assess underpinning knowledge.

Where applicable, reasonable adjustment must be made to work environments and training situations to accommodate ethnicity, age, gender, demographics and disability.

Guidance information for assessment

Assessment processes and techniques must be culturally appropriate and appropriate to the oracy, language and literacy capacity of the candidate and the work being performed.

Required skills

Required skills include:

analysing existing statistics and other data for relevance to the experiment

determining where additional data is required and developing strategies for acquisition

undertaking self-directed problem solving and decision-making

solving problems

communicating complex issues and techniques to stakeholders

documenting procedures and results

producing a range of charts and generating and validating required data for inclusion

using statistics packs

Required knowledge

Required knowledge includes:

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 funneling

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

The range statement relates to the unit of competency as a whole. It allows for different work environments and situations that may affect performance. Bold italicised wording, if used in the performance criteria, is detailed below. Essential operating conditions that may be present with training and assessment (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) may also be included.

Competitive systems and practices

Competitive systems and practices may include, but are not limited to:

lean operations

agile operations

preventative and predictive maintenance approaches

monitoring and data gathering systems, such as Systems Control and Data Acquisition (SCADA) software, Enterprise Resource Planning (ERP) systems, Materials Resource Planning (MRP) and proprietary systems

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

Competitive systems and practices should be interpreted so as to take into account:

the stage of implementation of competitive systems and practices

the size of the enterprise

the work organisation, culture, regulatory environment and the industry sector

Improvement

Improvement includes:

an improvement in performance of an area/section or the whole enterprise as measured in terms of customer features/benefits

Objective of the experiment

Objective of the experiment may include:

screen factors to find the critical few

optimise a few critical factors

solve process problems

reduce waste

increase reliability

Factorial design

Factorial design may include:

2/3 level factorial

Taguchi L8

2/4-1 half fraction

Plackett-Burman 8-run

full factorial

Signal-to-noise ratio

Signal-to-noise ratio may be estimated from:

previous experiment design experience

previous process capability studies

statistical process control data

estimated from other sources

Resolution

Resolution is typically:

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

A typical series of experiments consists 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

Required metrics may include:

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

Typical statistics packs include:

minitab

JMP

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