I think a simpler model is enough if it gives the same decision or result as the more complicated model. If the extra details do not change the decision, then using the simpler model saves time and effort.

You can know that a more complicated OR model is unnecessary when a simpler model produces the same decision and objective value under the same important assumptions and constraints. Compare the solutions, check whether the added variables or constraints in the complex model actually change the optimal decision, and perform a sensitivity or scenario analysis to see if the result remains the same under reasonable changes. If the additional complexity does not provide meaningful improvement or information, the simpler model may be sufficient and easier to interpret and implement.