The use of computer models to simulate different business activities and to assist in decision-making processes is almost as old as IBM itself. Most business modelling nowadays is based on widely available software that allows non-technical general managers to try out different options on (electronic) paper before deciding which one to follow. A retailer, for instance, might have a model to help it choose where to locate a new store. Based on data about the size of the catchment area, the local road networks, parking facilities, demographics and local competitors, the model would come up with the optimal location.
Consultants KPMG say that “to take major [business] 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.
Coincidentally, large airlines are among the biggest users of sophisticated business models. They have to juggle a multitude of different fare structures and handle tricky things like stand-by tickets. Modelling such variables saves 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 income statement.
- 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.
The idea of using computer models to support decision-making was given a boost by a popular book published in 1990. “The Fifth Discipline”, written by MIT academic Peter Senge, argued that the ability to use models to experiment with corporate structure and behaviour would be a key skill in the future. Senge described computer simulation as “a tool for creating”.
Senge also promoted the idea of using modelling to create what he called “Microworlds”. These are simplified simulation models packaged as management games. They allow managers to “play” with an issue in safety rather than playing with it first in the real world.