To boldly go

Ian Cox of JMP outlines the benefits to be gained from exploring opportunity spaces through experimental design.

Imagine you are in a rowing boat, and have been given the task to survey a lake by taking depth-soundings using a rope marked every meter and with a heavy weight on the end. You row to a spot, throw the weight over the side and then count the number of marks as you pull the weight back up to find out how deep the lake is at this point. But this takes time and energy, and so, given the task at hand, two questions soon arise: How many depth-soundings can you take? Where should you take them? You have some prior information – the lake has a depth of zero all round its edge – but answering these questions needs a bit more thought because it will depend on what you want to use the final survey for. For example, if you are interested in fishing, you might want to locate deep parts of the lake which you can cast to, so knowing the depth at the centre, which you can’t reach from the shore, might not be of much use.

In this example we have an 'opportunity space' that we need to probe to get useful information: The places we decide to row to constitute a 'design', and the opportunity space is two-dimensional because we need a longitude and latitude to specify each point in that design. When we measure the lake depth at a design point we get a 'response'. For those that know the topic already, this is, of course, a gentle introduction to experimental design, often shortened to DOE or DOX: DOE can provide an efficient way to probe an opportunity space to gather useful new knowledge, and this article is about some important advances in design.

There can be no doubt of the practical value of DOE: Whenever any product is in development, testing or manufacturing, DOE finds applications to assure that customers get what they want, and don't get what they don't want (and at the lowest cost to you). Industry journals are full of DOE examples and initiatives, such as Six Sigma, promoting usage. Market leading companies such as Proctor and Gamble, Unilever and Kraft Foods use DOE at all stages of the product lifecycle.

But there are some problems. For example, you may have noticed that not all lakes are square. DOE originally was developed with the requirement that designs be made 'by hand'. This heritage is still obvious in most software that DOE practitioners use today: These so-called 'classical designs' have been compiled into libraries that help set up how an experiment will be run. Although well tested and proven, these designs assume that the opportunity space is always square.

A more serious problem is that classical designs can be inflexible in relation to the new knowledge you are after, simply because they can't fit every case exactly. These problems lead to compromises like probing a smaller region of opportunity space than you want to, and having too many or too few design points, sometimes in the wrong places. Aside from the impact of having too many points (increased cost, cycle time and so on), the lake example makes clear that the design should be tied to your objectives and the usefulness of the new knowledge you hope to gain.

Fortunately, these problems can be avoided, and in such a way that you or your technologists, scientists and technicians can easily get to grips with. JMP can produce a design that is always customised to your specific situation. By also providing an interactive, graphical means to understand your experimental results, JMP can break down barriers to DOE usage within your company, allowing you to more quickly, easily and uniformly realise the benefits that DOE is famous for.

So, if you routinely have complex opportunity spaces you need to explore to make your customers happy, consider JMP. It tackles the design problem in a cohesive and powerful way, allowing you to 'boldly go' and explore, actively seeking out new, useful knowledge to drive innovation in your products and the way you make them.

Dr. Ian Cox works in the JMP Division of SAS as the European Marketing Manager for JMP. He has consulted internally and externally to various manufacturing and CPG companies for more than twenty years and is co-author of Visual Six Sigma - Making Data Analysis Lean.