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.

Adjustable operating inputs: 10
Decision outputs: 6
Data stored: 0

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Sanitized public-case baseline
No operating data stored

Working dataThe model opens with a sanitized baseline. Input changes remain in page memory and are neither stored nor transmitted.

Product boundaryA 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.

Current backlog11mActive units in queueControl threshold5mTarget operating positionWeekly inbound350kNew demand entering the systemRated weekly capacity650kTheoretical output before lossesCapacity utilization92%Share of rated capacity realizedRework / defect load0.6%Throughput consumed by quality lossAging-work mix35%Older work carrying added handling dragSurge capacity60kOvertime or weekend outputPlanned productivity gain8%Technology and process improvementTarget window26 wkDesired time to control threshold

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

W1W7W13

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.

How leadership uses the model

Pressure-test the target

See whether current effective throughput can reach the operating threshold inside the desired window.

Expose the capacity gap

Separate theoretical capacity from output after utilization, quality, aging-work, and demand effects.

Choose the mechanism

Compare intake controls, surge labor, process gains, and durable capacity before committing resources.

Read the evidence-backed PSA case study

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