When strat firms talk about "modelling" what sort of complexity are they generally referring to? Can people please provide some examples? I'm not talking about firms that are very model focused (e.g. Lek) - just general strat firms.

that's bcoz lek is heavily into financial and commercial due diligence type of assignments

It really depends on the type of project..On a business DD you will build a business plan down to the FCF.. usually these model are highly complex, more than IB valuation modelsOn other projects you may build models that are somehow less complicated, usually called "business cases". Generally speaking, when you deal with PE funds or are in the process of running an Industrial plan for a client things get very complicated. Otherwise, there is not much complexity.

The fact is, the level of complexity depends entirely on budget.Most strategy projects are about comparing options, and don't need figures accurate to x decimal places - they need relative figures. So in deciding between two markets, an approximate modelling process will likely be used (the main inputs, but not worrying about factors that could have a 1%-2% impact but would take a lot of time, and money, to understand). The main thing to worry about is consistency - selecting inputs that can be obtained for each market / sector and compared side by side.Of course, sometimes those '1%-2%' factors will be considered, and will make a difference, but clients need to stump up the cash for it. More often than not, they are happy with the cruder option.I have seen some horribly crude, cringe-worthy models, including some in very big strategy projects where the figures didn't even stand up to the simplest of sense-checks (bravo Deloitte). The sad truth is that crude assumptions are often made and hidden in the (not very deep) depths of these models.

Remember that modelling doesn't just mean comparing market opportunities, though! In fact, usually it involves modelling something completely different (effect of cost reductions, etc).

The pressure to stay thin?

But if I could re-ask the original question: ignoring the level of complexity, what sort/type/kind of complexity is involved? In other words, what is it that makes the models complex?So does complexity normally arise from: - Stochastic analysis - Large number of inputs / factors - Mathematical complexity of the calculations - The use logic (IF/ELSE, Boolean, etc.) - Complexity of the business scenario itself that is being modelled