Computer Modelling
The use of computer models to simulate different business activities and to assist in decision-making processes is almost as old as ibm itself. Business modelling was a central part of operational research (or), a fad of the 1950s and 1960s.
But it outgrew its or roots as the mainframe came to be replaced by the pc. Operational research was originally carried out by specialists in isolated research-style environments. But business modelling is now based on widely available software that allows non-technical general managers to try out lots of different options on (electronic) paper before deciding which one to use.
A retailer, for instance, might develop a model to help choose where to locate a new store. It would feed in data about the size of the catchment area, the local road networks, parking facilities, demographics and its local competitors. The model would then come up with the optimal location. Consultants kpmg say that “to take major decisions without first testing their consequences in a safe environment can be likened to training an airline pilot by having him fly a 747 without first having spent months in the simulator”. Business modelling also helps to democratise decision-making when it is diffused throughout the organisation. In Reengineering the Corporation, Michael Hammer wrote: When accessible data is combined with easy-to-use analysis and modelling tools, frontline workers – when properly trained – suddenly have sophisticated decision-making capabilities.
Decisions can be made more quickly and problems resolved as soon as they crop up. Among the biggest users of sophisticated business models are large airlines. They have to juggle with a multitude of different fare structures and to handle tricky things like stand-by tickets. Modelling such situations can save them millions of dollars a year. Other common uses of business modelling include the following.
- Financial planning, with the help of spreadsheets. This quantifies the impact of a business decision on the balance sheet and the profit-and-loss account.
- Forecasting. Analysing historical data and using it to predict future trends.
- Mapping processes in a visual representation of the resources required for a task and the steps to be taken to perform it.
- Data mining. Analysing vast quantities of data in order to dig out unpredictable relationships between variables.
- “Monte Carlo” simulation. Putting in random data to measure the impact of uncertainty on the outcome of a project.
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