Factorial Designed Experiment Aim

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.

It is possible you have a case where just the cost of the experiment is very high but the loss of an unusable end result is low (this case is probably not very likely but is possible). In such a case then within spec targets would not matter much – the cost of being outside of the spec is low. It is also possible to have another situation a bit like this, if the end result was very costly but the loss function of being a certain amount outside of spec was low. So, for example, while your current customer would not accept the result, you could easily sell the product at 95% of the cost as long as it was somewhat near the spec.

Limiting your experiments to values that you felt confident would return results within the spec however, may well limit what can be learned. So the proper aim really depends on the hopes for the designed experiment and the associated costs and risks.

From my previous post on the use of factorial designed experiments:

This process of discovery is the key. If the costs of experiments (that produce out of spec results) are very high, then you likely need to attempt to experiments within bounds that are likely to have results remain in spec. Of course, if the rewards for a breakthrough are high enough then even high cost experiments can be wise.

Related: Articles on Multivariate Designed Experiments – Using Design of Experiments – 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments – YouTube Uses Multivariate Experiment To Improve Sign-ups 15%