Models

by Tony Gill

Introduction
Most systems thinking approaches result in the production of a model. The word model takes on different meanings depending on the discipline in which it is used. It can be a single or series of mathematical equations; a sequence of logical statements; a graphical representation; an architect’s scale model; a toy train set etc. Depending on the purpose of the model it can be ‘a representation of someone’s reality’ or a device to language or articulate a personal mental construct. Here, I am taking a pragmatic view of the word model rather than developing a philosophical treatise around the word.

In a business environment, the set of accounting books (leading to the Profit & Loss Statement and Balance Sheet) represents a model of the Business. The practice of double entry book keeping is supported by the equation: Assets = Liabilities plus Equity and/or Capital. A whole set of financial/stock-market measures is available to the multiple stakeholders in the business. However, it is increasingly recognised that this focus on ‘the bottom line’ is an insufficient way of capturing or measuring the complexity inherent in a dynamic business environment. The increasing use of performance measurement systems such as the Balanced Scorecard developed by Kaplan and Norton recognises this need to take a more balanced view of the organization.

Coming from a quality and process management perspective, more holistic ways of viewing the organization are also emerging. One needs only to reflect on the models or frameworks for the EFQM - European Foundation for Quality Management - and the Baldridge Award.

Uses
Key uses of models include:

  • facilitating communication and eventually sharing mental models;
  • capturing complex ideas into a framework that helps to delay the onset of data overload which results in the inability to make sense of it all;
  • sharing knowledge and learning about how the organization ‘works’;
  • providing a framework for a business performance management system;
  • providing a framework for a business improvement system;
  • diagnosing the organizational system eg, the Viable System Model;
  • informing information systems development;
  • managing change - modelling the "as is" and the "to be" positions.

It is worth drawing attention to the Conant-Ashby Theorem which states: every regulator of a system needs a good model of the system. This highlights the problem of modelling social systems - the typical organization. How do you know you have a good model of the system? Recognising this we must therefore be prepared to improve our models based on our ongoing experiences. Taking the other extreme, of course any useful model, however simple, is better than no model. One hopes that the various industry regulators use good systemic models.

A key strength within Phrontis is our abilty to apply different modelling approaches to the same problem situation thus gaining more insights into the challenges facing our clients. We use the principles of multimethodology to ensure intellectual rigour.

Simulation
Most of us have heard the expression: a picture tells a thousand words. In similar vein, simulation yields a thousand pictures. In November 1996, the UK Institute of Management journal Professional Manager reported on the ‘tools’ in use by the global manager. With the release of more user friendly simulation software packages, such as iThink, Powersim, Vensim, MyStrategy, Witness, etc., it was not surprising that simulation featured in the top 15 tools.

A core feature of simulation is its ability to ‘run’ the model of the system. This helps to test how good a representation of our ‘world view’ the model is. If the results of the simulation support our experiences then the likelihood is that we have a reasonably good model of the system. However, as soon as the simulation begins to deviate from our experience this is indicative of the need to improve our model.

A vital element of simulation is to be able to interactively explore possible futures or scenarios - scenario planning. Often these simulations are based on models that have significantly more variables in them than the human brain is able to process ie, cognitive overload. Armed with the knowledge of how the future may unfold, we are better placed to take decisions today. Without the ability to simulate our models we tend to rely on intuition. This may have served us well up to now but when dealing with increasing complexity, intuition needs to be supplemented by simulation which can reveal counter-intuitive behaviour inherent in the organizational system.

I am keen to dispel the thought that we can forecast the future interactively through simulation. If we could we would all make fortunes on the stock market; national treasury models would not yield such poor outcomes so consistently. Simulated models of the future, do help to increase our understanding of potential risk due to uncertainty but they do not remove risk.

 

 

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