The reason to normalise our variables before their use in a technical analysis is to ensure that their importance appears similar in our context-agnostic procedure. An optimisation procedure does not know if Sales from channel
A have a higher return-on-investement than Sales from channel
Therefore normalising our KPIs wholly depends on how are these KPIs evaluated internally. If
KPI_3 are three different revenue streams, measured using the same monetary unit, then normalising them is probably unnecessary and potentially misleading. If
KPI_3 account for number of clients, number of purchases, and average cost per purchase respectively, then these KPIs are incompatible and makes little sense to simply sum them together; normalising them (by dividing by their max as you describe) or scaling them (such that they have mean $0$ and variance $1$), it probably more relevant.
In the page linked, the worked example actually scaled the KPIs by their average value. It a slightly non-standard way to make sure that the KPIs scale independent (min-max scaling and normalisation to Gaussian are more common) but the idea is similar.