Apply Design of Experiments (DOE) improvement techniques in food and drink operations
This standard is about the skills and knowledge needed to apply Design of Experiments (DOE) techniques as part of your organisation's drive to increase the effectiveness and productivity of food and drink operations. Design of Experiments are tools and techniques to achieve operational cost savings by minimising process variation and reducing rework, scrap, and the level of need for quality monitoring or inspection. This is important in the achievement of excellence and the success of manufacture, processing and supply across the food and drink supply chain.
You will need to apply and understand the principles of Design of Experiments to support improvement to meet the business objectives set out in your organisation's improvement plan. You will need to show and know how to present findings to relevant people within the organisation, including management colleagues. You will need to know how to comply with your company policy for improvement, take responsibility for your actions, and refer any issues outside of the limit of your authority to others.
This standard is for you if you work in food and drink operations including, manufacturing, processing, packing or supply chain activities. You may have responsibilities for aspects of organisational improvement in a team leadership or management role.
You must be able to:
Knowledge and Understanding
You need to know and understand:
- the organisation's improvement vision, strategy, objectives and the reasons for implementation of improvement programmes
- how the health, safety and hygiene requirements of a work area can influence the design of experiment improvement technique
- design of experiments as an improvement technique in food and drink operations
- why we use Design of Experiments and how this can benefit an improvement project
- the importance of determining the scope of an experiment
- how to apply and complete a Design of Experiments project
- the tools and techniques used in the Design of Experiments
- the data required to carry out the Design of Experiments
- how population and sample size is used in the Design of Experiments
- Alpha risk and Beta risk
- how to calculate Mean, Median, Mode, Standard Deviation, Range and Variance
- how graphical display can be used to show main effects and interactions
- the meaning of a population and a sample in terms of the Design of Experiments
- arrays design linked to the design of interactions
- levels of authority linked to problem resolution
- how to report findings and present improvements