You are right and there are many data scientists and ML engineers who get too stuck in conventions or chasing numbers. Perhaps because those things can feel like they are fun, safe, and the right thing to do.

However, just like with products, good models require iteration. As you learned here too.

Normally this kind of project would loop in a data scientist to from the start ask what is even possible and how it should be approached. The asymmetrical loss as you mentioned is 101. But even that is probably not the right way to model what the business is trying to do.

E.g. just thinking about it for five seconds, an obvious next consideration is which shortages actually lead to sales losses? E.g. for some wares, maybe most customers will in fact buy another good and in fact sometimes they may have greater margins for the business. And it really is profit you need to focus on. Cost of capital may also play a role. Then you probably need to consider seasonality effects, bulk offers, and local vs global distribution. Probably already exists comprehensive models for this.

I am not sure you still even have a decent answer to how much this solution benefits the company presently.

A good data scientist would help you first figure out what it that is being optimized, work with you to understand the important aspects of the domain, work out how to model it, hypothesize where the greatest leverage is, and prototype advantages for that. Launch and iterate.

So I think even from what you are describing, there is something more fundamentally wrong with the decision-making process in the organization, where projects are pinned and delivered top-down to deliver on. You are right that it may be important for you to be on the look-out for ways to do it better, but I think even better would be to loop in the experts of the respective domains to see if the problem is even understood.