Every year, in December, I embark on an epic quest to find mince pies. I have an addiction to these pastries, but also a particular dietary requirement which is not explicitly provided for by suppliers. I usually find a supply (and promptly buy a full gross before they can get away), but in the process I encounter the effects of what is, in the scheme of things, a trivial aspect of statistics in manufacturing to which I shall return below. When I last wrote[1] about this topic in Scientific Computing World, I was mostly concerned with the central rôle of data analysis in quality control. It plays a much wider and more diverse range of parts than that, however.
The fashion industry, for instance, is driven by statistical compromise. A dress maker uses over thirty metric descriptors, from height or waist circumference to the distance from neck to shoulder or armpit to hip. Most dresses, on the other hand, are bought on the basis of a single descriptor: a size, which in theory will always mean the same thing but in practice varies widely. In making this data reduction, a manufacturer needs to ensure that the best possible balance is struck between different customer shapes and perceptions. Most customers are going to find that a dress fits in one place, is too loose in another, and too tight in a third, but will only accept this within certain limits. Moving to a larger or smaller size will alleviate one problem while exacerbating another, and will also carry a psychological message about body shape. Getting the compromise wrong for a particular market will result in an exodus of customers to another manufacturer who has made shrewder decisions. These choices are heavily influenced by intuition and experience, but modelling on the basis of data analysis plays a large part, too. I know of one high street fashion supplier who regularly rents consulting time from a university department which maintains dedicated analytic software for the purpose. There are others which use in house statisticians running desktop software... [more]
