Sub-optimization, also known as “operating in silos” is a massive and common problem in organisations and that’s Bad News.
The Good News is that you can use almost any business game to let your leaders, managers and practitioners forcefully experience the consequences of sub-optimization behaviours.
First of all it is called sub-optimization because the organisation ends up with less than optimal performance because its people have fixated on lower level goals. It is important to note that not all sub-optimization is bad: you can have “conscious strategic sub-optimization”, for example in a multiple market scenario you might have market specific goals such as “grow market share in Brazil” which reduce the financial performance of the whole enterprise but for a good strategic reason. So the stuff that is really damaging is the “unconscious sub-optimization” where people do not realise the consequences of their decisions on the whole organisation.
You can sub-optimize in two different ways: by Business Units or by Functional Unit.
Business unit sub-optimization is prioritising one business unit over another on criteria which reduce the overall financial performance of the whole - like the Brazil example. Business Unit sub-optimization, is not always a problem as it is often (but not always) done with good strategic rationale e.g. to build position in an important growing future market.
Unconscious functional sub-optimization is the real killer! This leads to the classic and pervasive silo thinking and behaviours. For example, Sales sell without any regard for manufacturing capacity and good old manufacturing always try to operate their production at 90% capacity to minimise production costs and quality issues, irrespective of whether that leaves the company with too much or too little stock based on customer demand. That’s a real classic sub-optimization conundrum!
So what can you do about it? In a business game you can set things up so that in the first round the players start with a sub-optimization constraint in place and see the results. In the second round you then remove the constraint and the players see the results. Then in the third round you let the players choose whether to have the constraint in place or not. The trick is to explain the sub-optimisation constraint in a perfectly natural way which appears to make perfect sense to the players – a real no-brainer! You can put sub-optimization constraints in place in simulation game a number of ways – targets, penalties, rewards, rules or budgets.
The simplest method, without having to change the inner working of the game, is to identify a couple of measures which must be optimized each round (e.g. Sales Forecast and Manufacturing Quality).
If you are able to change the game itself you can have rules which kick-in for round 1 and then switch-off in round 2. For example, you could have separately managed budgets for sales and manufacturing in round 1 but combined budgets in round 2. An interesting consequence of doing this is it may also reveal that a main cause of the sub-optimization behaviours is because people are being measured and rewarded around a bad set of measures.
If you don’t change this measurement system then any attempt to change the individual behaviour will be futile.