Why are Dynamic Business Models (DBMs) so powerful?

So what do these and other DBMs do that's so special and that enables them to solve real-world challenges so quickly and reliably?

REAL-WORLD COMPLICATIONS

Accumulating "Stocks". See What is a Stock and why should I care? 

 A Stock is a collection, group, population, mass or volume of things or material that accumulates or depletes over time. 

Key business Stocks include customers, staff, product-range, cash, capacity, inventory and various intangible items (more on these below). The managerial problem Stocks generate is that cause-and-effect can be widely separated in time, so what seemed like a good decision at the time you took it turns into a bad decision when its impact arises, because other things changed in the meantime.

Interdependence. Many items in a business system depend on multiple other factors. We can handle simple cases with 'pyramid' approaches - how A depends on B and C, then how C depends on D, E and F, and so on. But many items - especially those critical things fill up or lose those Stocks - can depend on many several different kinds of other factor in other parts of the business system. Winning customers, for example, depends on our price, product range and product performance, sales effort, and so on.

Feedback. An unavoidable consequence of inter-dependence is that it causes feedback effects. Some feedback can be helpful, by reinforcing favourable changes. Rising production, for example, drives out costs and enables lower prices that in turn lead to higher sales and production rates. Other feedback can be problematic, as when customer-growth raises pressure on service capacity, lowering service quality and resulting in the slow-down or reversal of that growth. We can even find feedback that reinforces bad things, such as loss of staff creating overload for remaining staff, who then leave faster.

Thresholds. Many causal mechanisms feature tipping points – nothing much happens until a threshold is reached, at which point things change very fast.

There can be negative thresholds - service quality, for example, is fine when service demand is well within capacity limits, but fall catastrophically when overload arises. Positive thresholds cause good outcomes if crossed. For example, demand for a new technology product typically surges when performance passes an acceptability threshold and when its price falls to an affordable level (watch this happen for electric vehicles!) Then you can get the same threshold working differently multiple times - that affordability threshold, for example, will be vary for different customer groups. 

Non-mathematical relationships. Those thresholds are a particular case of non-linear relationships that cannot be easily written down in a mathematical formula, but there are plenty of others - how customer-demand changes with price, for example, rarely follows the nice formulae that economists love.  

Intangible factors. Most real-world systems feature intangible factors (though ‘intangible’ does not mean ‘unmeasurable’!). Intangibles come in three broad categories ... quality-related factors clearly influence outcomes such as product-support and customer satisfaction; information-based factors like data and knowledge affect how many parts of a business work; state-of-mind factors, such as staff motivation and market-reputation have important impacts. 

In theory (!!) all such items and mechanisms could be included and calculated in a spreadsheet - they are just numbers and calculations, after all - but in practice it is impossible. We humans simply can't get our heads around all the relationships and formulations laid out flat in rows and columns of numbers. 

DBMs make it possible to handle all of these issues: 

... you can see the causality

... you can capture all that interdependence and feedback

... you can express those non-math relationships and thresholds 

... you can represent those intangible factors and their impact