Business problems are complex. They have many variables – things that if changed, will cause change in business outcomes. And to make it even more complicated, each variable has many cause-and-effect links. That’s the problem with problems.
Cause and Effect
As an example of cause and effect (shown below), marketing spend causes customer conversion. And customer conversion causes increase in potential customers. Considering a separate cause and effect linkage, improved reputation causes increased customer conversion too. But poor staff competence degrades reputation. Poor reputation degrades customer conversion, acting against the effect of marketing spend.
So is it more effective to train staff or increase marketing spend?
The answer is that even in this simple five-variable problem, we know that there’s more too it. The problem is much more complex and needs to be captured completely and modelled before we can speculate the real cause or causes. We need more data on these dependencies before we can debate what to do to drive the customer conversion in our example.
No two firms are the same and consequently every business problem is different.
Most businesses also involve the development of assets: for example, stock assets, capability assets or financial assets. Asset levels significantly affect outcomes.
And many causal relationships have inherent delays. It takes time, for example, to gear up marketing campaigns and we might wait months for our marketing spend to have an effect on customer conversions.
These two effects of gearing up and using assets and delays in effects significantly complicate the scenario.
At this point most managers despair. It’s surely easier to manage by gut feel and guesswork than try to understand the various systems behaviours?
That despair comes in part from the way we’re taught to solve problems. Intuitively we break problems down. We try to isolate the parts, ignoring the complexity. In the above example we wouldn’t relate marketing spend with staff competence. We’d talk of the marketing spend influencing customer conversion – full stop! It’s only when we embrace the complexity of the problem that we understand that simplifying the problem loses clarity.
That loss of clarity, stemming from the way we’re taught to problem solve, makes a wrong decision likely.
On the one hand, managers know that their task is complex. But their natural approach to solving problems is reductionism – reducing the problem to simplify it. So gut feel, heuristics, wise sayings, business tips and tricks and advice from gurus prevail in search of simplicity. All are wrong, because every business is different and every business problem is unique. All business problems are complex and complex problems can’t be solved with tips and tricks.
So what to do?
The solution to the manager’s conundrum comes from engineering. If we consider the business as a system, with variables, dependencies, assets and delays, we can set up a model that shows how the firm works.
That model can then be exercised. We can ask questions like “what happens to stock if we improve the delivery times from sub-contractors”, or “to what extent will quality data in our CRM allow us to achieve our growth targets”. And we can for the first time include variables like staff knowledge, skills, beliefs and behaviour alongside the ‘hard’ assets like buildings and plant.
So to solve any business problem, particularly those involving people, business modelling is essential. It’s the only way to understand the complete picture. It’s therefore the only way to get a quality answer and hence to aid managers in making quality plans for solution.