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Ruling Out One Site Early Gave Amazon's FDD Pilot Room to Hit 18% Energy Savings
Amazon moved forward with scaling fault detection and diagnostics (FDD) after a three-month pilot showed the program could deliver significant energy savings. Getting there required dropping one of the test sites first.
The pilot, run by JLL's smart building team supporting Amazon, was designed to test whether analytics could deliver roughly 10% savings in both energy and maintenance across a diverse portfolio of more than 500 buildings.
The pilot was given a three-month timeline, and the team selected three representative sites to run the trial. One fell out of the analysis within weeks.
That site had a severely degraded daisy-chain BMS network: the kind of legacy wiring that produces unreliable control data and makes analytics outputs meaningless. Rather than rebuild infrastructure during the short pilot window, JLL documented the issue and moved forward with the two remaining sites.
Cutting the site with unreliable data gave the other two a fair shot at proving the model.
Across them, the program generated 219 resolved faults and roughly 18% energy savings, well above the original energy target.
Maintenance savings proved more difficult.
The pilot produced only about 1.5% maintenance savings, largely because site teams were still operating under existing service contracts. The three-month window was too short to produce transformational changes that could ripple through maintenance contracts, further underscoring that maintenance savings may be a lagging indicator in a strong FDD program.
Even with those limitations, the energy results were strong enough to justify expansion. NPV modeling suggested a roughly two-year breakeven if the program scaled across Amazon-owned facilities.
Leadership approved the rollout.
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Amazon moved forward with scaling fault detection and diagnostics (FDD) after a three-month pilot showed the program could deliver significant energy savings. Getting there required dropping one of the test sites first.
The pilot, run by JLL's smart building team supporting Amazon, was designed to test whether analytics could deliver roughly 10% savings in both energy and maintenance across a diverse portfolio of more than 500 buildings.
The pilot was given a three-month timeline, and the team selected three representative sites to run the trial. One fell out of the analysis within weeks.
That site had a severely degraded daisy-chain BMS network: the kind of legacy wiring that produces unreliable control data and makes analytics outputs meaningless. Rather than rebuild infrastructure during the short pilot window, JLL documented the issue and moved forward with the two remaining sites.
Cutting the site with unreliable data gave the other two a fair shot at proving the model.
Across them, the program generated 219 resolved faults and roughly 18% energy savings, well above the original energy target.
Maintenance savings proved more difficult.
The pilot produced only about 1.5% maintenance savings, largely because site teams were still operating under existing service contracts. The three-month window was too short to produce transformational changes that could ripple through maintenance contracts, further underscoring that maintenance savings may be a lagging indicator in a strong FDD program.
Even with those limitations, the energy results were strong enough to justify expansion. NPV modeling suggested a roughly two-year breakeven if the program scaled across Amazon-owned facilities.
Leadership approved the rollout.
ā
Register for the next Nexus Labs event.
Sign up for the newsletter to get 5 stories like this per week:
This is a great piece!
I agree.