As one would expect I spend a lot of time describing how system modeling works as a problem solving approach. My usual description -- in fact the one that I wrote again this morning -- goes something like this:
"System Modeling works by explicitly mapping the causal drivers that link today's resource allocations (your management decisions) to future outcomes. The technique provides a fact-based, quantitative, and transparent basis for management policy development."
Since we're describing simulation models that run on a computer it's easy to assume that all of the "facts" in the simulation are quantitative as they appear to be in a spreadsheet. But in a systems model that's not really true. The "non-quantitative facts" identify people and things in the system and, crucially, the logical relationships between those things. They describe the "physics" of the system. Things like "We have to provide a quote to the prospect before they can buy it". Or that "I have to build a widget before I can put it in inventory". Or that "I have to ship it from inventory to get it to the customer". Sometimes the physics are about human behavior: "If supply is constrained I need to accelerate ordering" is an all time favorite of mine.
Maybe this seems simple and obvious. But the sum of all of these relationships is often complex and (this is important) can feed back onto itself in a feedback loop. Also, these facts may be well known to the players in the system but they are seldom written down anywhere and are therefore "implicit" knowledge.
Finally, I don't know of any other modeling or problem-solving approach that offers to capture these causal relationships, marry them to the quantitative data (eg: how long does that take? How many of those things are there?) so that potential management policies can be evaluated in light of "all of the facts".
