In high school chemistry, my excellent instructor, Mr. Gleich, drilled into us the concept of the limiting factor. It was so counter intuitive. Why wouldn’t speeding up any part of the reaction not speed up the overall reaction? It made no sense to me and many others, but he drilled it in. He used analogies, exercises and demonstrations… eventually, even though it’s counter-intuitve, it was clear: only the limiting factor matters.
As a newbie PM, I instantly recognized the same math applied in many situations I faced. I had a product or milestone, a chain of reactions turning raw materials into a final result, over time. I wanted to know how long the reaction would take, and where to apply the catalyst. And I still think this is the key to optimizing any process. Reducing pain, playing politics, and playing favorites has their place in deciding where to put your energy, but the only thing that will really make a process faster is ruthless focus on the limiting factor, enabled by a clear understanding of the rates at each step. You need to understand what the slowest, least efficient step is, but also keep an eye that optimizations to that step are not offset elsewhere in the process. Although a full model is helpful, action anywhere outside the limiting factor is not. Know the whole system, but act on one factor at a time.
To give a concrete example, say you have an enterprise product that has a per-client implementation process. Say you want to increase the number of customers you can onboard per month. If your can make the install process faster and the IT training smoother, but the real blocker is legal sign-offs on the contracts, your revenue will not really budge. If legal can only approve 4 deals a month, the number of trainings you can do is irrelevant to your goal. Of coarse, after you get legal to pick up the pace, you might find those trainings are limiting you after all, but that is what iteration is for.
That’s a simplistic example, but this applies to things you might not normally think of as “processes”, but can be modeled and attacked this way. For example, revenue is actually the residue of a reaction. Your product/marketing/sales meeting the market atmosphere. If there is a slow reaction, a low revenue per unit of time is the result. If revenue is slow, or inert, limiting factor analysis can identify what part of your hoped for, theoretical, market reaction is not happening. Marketing message not landing? Missing a key feature? Pricing wrong? Lay out your factors and their rates. Then act ruthlessly to improve the limiting one.