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Adjustable operating inputs
PSA recovery digital twin · Sanitized portfolio reconstruction
Test the mechanics behind a controlled recovery.
Model how inbound demand, rated capacity, utilization, rework, aging work, surge capacity, and productivity gains change the path from an 11 million-unit public checkpoint toward a five million-unit control threshold.
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Decision outputs
0
Data stored
No signup
Sanitized public-case baseline
No operating data stored
Working data
The model opens with a sanitized baseline. Input changes remain in page memory and are neither stored nor transmitted.
Decision boundary
A sanitized portfolio reconstruction using normalized inputs and public context. It contains no PSA-confidential data and is not a forecast or representation of PSA’s internal planning.
Interactive recovery scenario
Adjust the assumptions that determine the recovery path.
Change demand, capacity, quality, aging, and surge inputs to see how each operating mechanism affects timing, throughput, and the remaining capacity gap.
01 · Scenario inputs
Adjust normalized operating assumptions. The model recalculates locally in your browser.
02 · Modeled operating pathRecovery
Net backlog burn−334k/ week
Effective output 684k against 350k inbound.
Estimated thresholdNov 202618 modeled weeks
Required throughput581kTo reach 5m in 26 weeks
Capacity gap0kCurrent scenario clears the target
+20% demand shock−264kWeekly burn preserved
13-week backlog path6.7m projected
Illustrative sensitivity range17–21 weeksRange applies ±12% variation to modeled net throughput.
Model boundary: A sanitized portfolio reconstruction using normalized inputs and public context. It contains no PSA-confidential data and is not a forecast or representation of PSA’s internal planning.
01 · Scenario inputs
Sanitized baseline and published control ranges.
The static snapshot below preserves every source input, default, range, step, and operating hint.
Current backlog
11m
5–18m · step 0.5m
Active units in queue
Control threshold
5m
2–10m · step 0.5m
Target operating position
Weekly inbound
350k
200–700k · step 10k
New demand entering the system
Rated weekly capacity
650k
350–900k · step 10k
Theoretical output before losses
Capacity utilization
92%
65–100% · step 1%
Share of rated capacity realized
Rework / defect load
0.6%
0–5% · step 0.1%
Throughput consumed by quality loss
Aging-work mix
35%
0–80% · step 5%
Older work carrying added handling drag
Surge capacity
60k
0–180k · step 10k
Overtime or weekend output
Planned productivity gain
8%
0–20% · step 1%
Technology and process improvement
Target window
26 wk
8–52 weeks · step 1 week
Desired time to control threshold
02 · Modeled operating path
The baseline opens in recovery.
The values below are the deterministic source result at the published baseline. Changing any input in the working model recalculates every output locally.
Net backlog burn
−334k / week
Effective output 684k against 350k inbound.
Estimated threshold
Nov 2026
18 modeled weeks
Required throughput
581k
To reach 5m in 26 weeks
Capacity gap
0k
Current baseline clears the target
+20% demand shock
−264k
Weekly burn preserved
Illustrative sensitivity
17–21 weeks
Range applies ±12% variation to modeled net throughput.
Model boundary
A sanitized portfolio reconstruction using normalized inputs and public context. It contains no PSA-confidential data and is not a forecast or representation of PSA’s internal planning.
How leadership uses the model
Use the mechanics to frame the decision.
01
Pressure-test the target
See whether current effective throughput can reach the operating threshold inside the desired window.
02
Expose the capacity gap
Separate theoretical capacity from output after utilization, quality, aging-work, and demand effects.
03
Choose the mechanism
Compare intake controls, surge labor, process gains, and durable capacity before committing resources.