Results
The issues were already in the building data. LeanFM found the patterns, helped prioritize action, and the corrected faults produced measurable savings.
Building data to findings
Existing data
Hidden faults
Clear action
Case study signal
Many building system problems do not appear as obvious alarms. LeanFM looks for patterns across system data to help teams identify what is wasting energy, affecting comfort, or creating unnecessary system strain.
The goal is not more data. The goal is clearer action.
Primary proof point
The seven-floor mixed-use museum required stable temperature and humidity control for sensitive artwork, visitor comfort, and staff comfort.
Owner
Carnegie Museums of Pittsburgh
Location
Pittsburgh, PA
Size
88,000 sq ft
Duration
2023-ongoing
After a new BAS was installed in 2021, the museum still needed to reduce HVAC energy waste, improve comfort, protect artwork, support sustainability, and extend equipment life. LeanFM analysis in 2022 found BAS logic faults that were corrected.
The museum had already invested in a BAS. The opportunity was not more hardware. It was finding the hidden logic faults and operating patterns the system was not surfacing clearly enough.
$56,386
Reported first-year savings
$101,383
Reported second-year savings
$100K+
Ongoing annual savings shown in the case study
The important point is not just the savings—it is that correctable logic faults were already visible in the data.
Results are from this specific case study. Actual outcomes depend on building conditions, available data, and corrective actions taken.
Trend-style view
The Warhol had a newer BAS, but LeanFM still found logic faults affecting performance.
The value was already in the building data. LeanFM helped surface what mattered.
Correcting hidden faults helped reduce waste and improve operational performance.
Museums need consistency, not just reactive alarms.
Across complex buildings, LeanFM has identified recurring patterns that traditional alarm workflows can miss.
Additional historical examples from complex facilities include:
Historical example
Hospital buildings with Trane BAS
Historical example
1.4M sq ft across 4 buildings with JCI BAS
Historical example
270K sq ft hospitality facility with Siemens BAS
Historical example
550K sq ft office facility with Honeywell BAS
Sensor drift
Simultaneous heating and cooling
Overcooling and reheat
BAS logic faults
Equipment running unnecessarily
Control sequence issues
Underused sensors
Unnecessary equipment strain
LeanFM does not promise a fixed savings percentage because every building is different. Results depend on system configuration, available data, operating conditions, and which corrective actions are taken.
But the pattern is consistent: hidden issues often exist in the data before they are obvious in the building.
Share existing building system data
LeanFM analyzes for hidden issues
Review prioritized findings
Decide what to address first
Findings tied to actual system behavior
Prioritized issues facilities teams can review
Evidence-based recommendations, not generic advice
Send the data you already have. LeanFM will help identify hidden issues affecting energy, comfort, and system performance.
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