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At https://www.britannica.com/science/fuzzy-logic , the Britannica article on Fuzzy Logic, there is a brief introduction to, and history of, fuzzy logic and fuzzy control.
At http://expertsystemdemos.net/shoe_sizes.php , there is a small expert system which I wrote to demonstrate fuzzy logic. It is based on a tutorial paper, and runs rules that predict a person's shoe size from their height. Your logistics rules work the same way, so I use this in some of my explanations. Please try the system by following the instructions on the linked page. When it gives a result, look at the explanatory graphs on the output page, and see how each rule contributes to the result via the input's membership in its fuzzy set. The rules I wrote for you are doing the same.
The paper "Animated Fuzzy Logic" by Gary Meehan and Mike Joy introduced the shoe-size rules to the literature and is the tutorial paper I cited above. You can download it from http://expertsystemdemos.net/phmb/Meehan_and_Joy_Animated_Fuzzy_Logic.pdf .
Unless you're a computer scientist, much of it — especially the stuff about functional programming — won't mean much. However, it's worth skimming to see what the authors say about why fuzzy logic is useful. The more you can make these ideas your own and internalise why they're useful for gas-supply logistics, the more you'll impress the government inspectors, and the easier it will be to talk to your programmers when you go on to commercialise. See what you can make of §4 and Figure 8, which show how the fuzzy sets used in the shoe-size system cover the advice space, and how each fuzzy set contributes to a result.
I have written a tutorial at http://expertsystemdemos.net/phmb/phmb_fes2.docx . The document doesn't assume any prior knowledge. At the least, see how fuzzy sets can be represented as XY graphs of sloping lines, triangles, and trapezoids, and how inputs to rules can be plotted on the X axis and a membership line derived from that. Note the difference between sets denoting the low end of an attribute, those denoting the high, and those in the middle. See how the weighting of output sets means that different rules can contribute different strengths to a result.
Bipin Reghunathan has written a LinkedIn tutorial on fuzzy logic for inventory optimisation, here. (I'm linking to a version I saved in the Wayback Machine, as it's more likely to endure.) It's an easy read, and includes a list of rules for optimising from demand variability and other attributes. I recommend reading it before you meet the inspectors.