Prioritizing a building portfolio for investment, ROI and payback analysis for controls upgrades, phased implementation from no-cost sequence fixes through hardware to analytics, model predictive control as the optimization frontier, and the organizational change management that determines whether any of it sticks.
An organization managing more than a handful of buildings faces a portfolio-level problem no single sequence of operation can answer: with a fixed budget, which buildings and which upgrades deserve investment first? This module works through building-normalized prioritization data, honest ROI and payback analysis that accounts for savings persistence rather than assuming it, and the phased implementation sequence — no-cost and low-cost sequence fixes first, then hardware, then analytics — that keeps a portfolio from buying a diagnostic platform before doing the free diagnosis it would have immediately recommended.
By the end of this module you should be able to explain what model predictive control actually does differently from the rule-based sequences covered throughout this program, and why the single most common reason a well-commissioned optimization sequence fails to deliver savings is a trust problem with facilities staff, not a technical one — the same trust failure Module 14's commissioning discipline is built to catch before occupants ever experience it.