A wallcoverings company having interest in paint, wallpapers and other decorative materials was anxious to see how the long term development of the market was likely to shape demand for its products.

We set about developing simultaneously, a market research programme to investigate the choices people made for decoration of their homes and a mathematical model (simulation) of the corresponding market to link the expected market research data to a quantified explanation of behaviour. The intention was then to use it to extrapolate trends into the future on the basis of rational causal mechanisms rather than statistical data trend tracking. The market research turned out to be difficult as we found how little people could remember about the changes they made in their homes over relatively short periods. Nevertheless, we did have a fairly good basis of data about what the current sales levels were in the market as a whole and what the current distribution of wallcoverings on the standing walls of the nation were. We also knew what sort of cheap paint the construction industry applied in new houses. Along with this essentially static data we had access to the collective experience of some articulate painters and decorators as well as DIY enthusiasts.

With these components and some reasonable estimates of product lifetimes in actual use, we made a model that showed the aging process (wall demographics) and a transition process(rather like a Markov process transition matrix) for walls coming up for redecoration and being assigned to their new coverings.

From the model, and some of the market survey data, we were able to see the way the market would shift over the years. We saw a move towards a particular class of product and over the next few years it was borne out. Of course, that might have been luck, but I like to think it was a good model of the compelling force of reality.

Other Experience
McCann experience with problems over these time scales is that people rarely expand their time horizons far enough to see the whole picture. Even if they try, they tend to miss interaction effects and think more in terms of extrapolating, sometimes with statistical models, from the recent past. My (MJMcC) observation is that the demographics and the compelling force of reality can be quantified to make a big improvement in perception.

Your problem?
If you have a problem with the behaviour of a business, market sector, process or item of equipment and would like to get a quantitative handle on it to improve yield or optimise performance, then contact McCann Science, in confidence, of course. There's no charge for finding out that McCann can help.

About McSc
Summary
Principal (CV)
Contacting us
Contact in UK
Location USA
Philosophy
Software Tools
Fees
CAD service

Applications

Business & Commerce
TV Advertising
Drugs Competition
Housing Demand
Automobiles Demand
Wallcoverings Battle
Cash Flow
Human Resources
Project Management
Patent Analysis

Chemical Industry
Cooling Tower
Fermentation
Polymer Process
Heat exchange
Supercritical Fluid
Distributed parameter
Toxic gas allocation

Electrical
Control systems
Initiation systems
Ferrite Filters
Microstrip Antennas
Lightning

Production & Process
Automated manufacture
Crimp and Press
Glass making
Glass molding
Glass to metal seals
Heat exchanger
Helium Leak Testing
RF Soldering
Vacuum web coating

Applications.