Validate the operating baseline
Reconcile intake, demonstrated output, yield loss, aging, and the control threshold with the people and data closest to the work.
Operations Lab · Recovery-risk instrument
Will the recovery plan survive contact with reality?
Average arithmetic can make a fragile plan look certain. Model the queue, add real operating variability, and see median timing, four-in-five timing, conservative timing, target-miss risk, and the throughput shortfall that stalls recovery entirely.
- Reproducible weekly trials
- 1,000
- Recovery timing
- P50–P95
- Data stored unless submitted
- 0
- No signup
- Sample scenarios
- Shareable assumptions
- Transparent model logic
Working dataThe calculator opens with sample inputs. Scenario values remain in the page and may be encoded in a share link; scenario and contact details leave the browser only when the visitor explicitly submits them.
Product boundaryThis is a transparent scenario distribution, not a fitted forecast or optimization engine. Validate the queue definition, operating assumptions, and high-utilization behavior before acting.
Test the promise—not just the arithmetic.
Replace the example with observed operating data, select how volatile the operation really is, and compare leadership’s target with the distribution of plausible recovery paths.
01 · Describe your queue
Works for any queue-shaped operating problem. Nothing you enter is stored or sent unless you submit the review form. Shared links contain assumptions, never contact details.
Start with an operating patternIllustrative starting points—not industry benchmarks.
Used to label every output and exported artifact.
Work sitting in queue now. The numeric field supports enterprise-scale queues.
The queue level leadership considers controlled.
New work entering the system each week.
Theoretical output before utilization and yield losses.
Share of rated capacity actually available for production.
Throughput consumed by correcting incomplete or defective work.
Share of backlog exposed to the model's fixed 8% aged-work handling penalty.
Usable output after losses from overtime, temporary labor, or weekend production.
Expected lift applied to rated capacity; validate through an operating pilot.
Leadership's desired time to reach the control threshold.
Optional. Use a defensible carrying cost, SLA penalty, churn exposure, or delay cost.
Assumptions active in this scenario
- Surge is treated as net usable output after operating losses.
02 · Modeled operating path
RecoveryNet backlog burn−3.5 units/ week
Effective output 11.5 units/wk against 8 units/wk inbound.
Recovery plan under moderate volatilityMedian recovery: 8 weeks. Four-in-five recovery odds arrive by week 9. 33% miss leadership's 8-week target.
Durable margin- Median · P50
- 8 wk
- Four-in-five · P80
- 9 wk
- Conservative · P95
- 12 wk
- Miss target window
- 33%
Durable margin. The plan absorbs roughly a 30.4% throughput shortfall before recovery stalls. Net burn amplification is 3.3×.
Recovery-risk fan1,000 weekly simulations · P10–P90 outcomes
0–12 weeksAverage-input recovery8 wk7.1 exact · this is not the risk-adjusted commitment
Required throughput11.1 units/wkTo reach 15 units in 8 weeks
Effective-output gap0 units/wkCurrent scenario supports the target path
Margin of safety30.4%30.4% throughput tolerance · 3.3× net-burn amplification
Model boundary: The average-input path is deterministic. The risk view runs 1,000 reproducible weekly trials with independent, zero-truncated variation around inbound and effective output; it is a scenario distribution, not a fitted forecast or confidence interval. It does not model seasonality, correlation, service-time distributions, congestion, or structural breaks. Validate every assumption with operating evidence before acting. No connection to client data.
What will be sentOnly this scenario and the contact fields you choose to submit.
- 40 units in queue; 15 units control threshold
- 8 units/wk inbound; 11.5 units/wk effective output
- Recovery; 7.1 modeled / 8 full weeks
- moderate volatility; P50 8 wk, P80 9 wk, 33% miss target
- Durable margin; 30.4% throughput tolerance
- No dollar assumption supplied
Evidence-aware, not evidence-substituting
Built from an operating engagement. Bounded by what public evidence can prove.
The model generalizes recovery mechanics used during Ryan Miller’s 2026 PSA advisory engagement. Public reporting documented a queue near 14 million units in mid-June and PSA’s official July 14 update reported 11 million. Those checkpoints demonstrate the operating context; they do not prove that Luna Sol alone produced PSA’s recovery.
01 · Source
Official operating checkpoint
PSA’s management-reviewed tracker is the primary source for its published backlog, throughput, quality, and capacity updates.
Open the PSA tracker ↗02 · Corroboration
Dated external reporting
Sports Illustrated reported the July 14 checkpoint of 11 million units and June output exceeding May by 10%.
Read the reporting ↗03 · Attribution
Operating results are collaborative
Published outcomes reflect PSA leadership and operating teams. This tool contains no confidential PSA data and makes no sole-attribution claim.
Review the evidence room →Methodology
Transparent math before executive judgment.
The calculator is designed to expose the few assumptions that determine whether a queue shrinks, stalls, or grows.
1. Establish effective weekly throughput
Rated capacity is adjusted by realized utilization and the planned productivity gain, then reduced for rework. The aging factor applies an 8% maximum drag in proportion to the aged-work mix: a 50% aged mix produces a 4% throughput drag. Any temporary surge capacity is added last. This creates an effective-throughput estimate rather than treating theoretical capacity as available output.
2. Compare output with incoming demand
Weekly inbound is subtracted from effective throughput. A positive difference is net backlog burn. A zero or negative result means there is no modeled recovery at the current assumptions, regardless of the target date.
3. Test the commitment
The model calculates the throughput required to move from the current queue to the control threshold within the selected window. Any difference between required and effective throughput becomes the capacity gap leadership must fund, remove, or renegotiate.
4. Stress the execution case
The sensitivity range varies effective throughput by ±12% before inbound demand is subtracted. The recovery-risk view then runs 1,000 reproducible weekly trials with independent variation around demand and output, reporting P50, P80, P95, and target-miss risk.
5. Read the fragility—not only the date
Margin of safety equals net burn divided by effective output. The inverse operating problem is the amplification factor: effective output divided by net burn. When the margin is thin, a routine percentage change in output creates a much larger percentage change in backlog burn.
6. Treat high utilization as a warning, not a free multiplier
The input model uses utilization as a transparent throughput multiplier, but real queues become nonlinear near saturation: variability and waiting time rise sharply as utilization approaches 100%. The model does not implement Kingman’s approximation or a service-time distribution, so high-utilization scenarios require direct queueing analysis before commitment.
Trust boundary
This is a transparent scenario distribution, not a fitted forecast or optimization engine. It does not learn from historical data or model correlation, seasonality, service times, congestion, or structural breaks. Validate those mechanisms before acting.
What the product produces
The date, the odds, and the point where the plan breaks.
01
Risk-adjusted timing
P50, P80, and P95 recovery timing plus the share of simulated paths that miss leadership’s target window.
02
Fragility
Margin of safety, net-burn amplification, and the throughput shortfall that stalls recovery completely.
03
Decision gap
The effective and rated capacity required to turn the target from a hope into a controlled operating path.
When the answer is “no recovery”
Do not negotiate the date before identifying the constraint.
Use the hypothesis map to determine which operating mechanism deserves evidence first. If the recovery path crosses functions, governance, or material financial risk, Ryan can translate the scenario into an implementation plan.
Fixed-scope entry engagement
Two-week Backlog Reality Check.
Move from a browser scenario to an evidence-backed operating decision. Ryan validates the queue definition, tests the recovery assumptions against operating evidence, and converts the result into an accountable action path.
Identify the three binding constraints
Separate symptoms from mechanisms and define the evidence, falsifier, owner, and decision attached to each leading constraint.
Sequence the recovery decision
Deliver a practical action plan with scenario ranges, leading indicators, governance cadence, and the first implementation gates.
Working windowTwo focused weeks
Delivery modelPrincipal-led
Commercial boundaryFixed fee agreed before kickoff
Request current scope and fee →See how evidence becomes a decision
The exact fee, acceptance criteria, access requirements, and exclusions are confirmed in writing. No performance outcome is implied by this page.
Model questions
How to interpret the result.
How does the backlog recovery model calculate effective throughput?
Rated capacity is adjusted for realized utilization, planned productivity gain, rework loss, and a disclosed aging-work drag, then surge capacity is added. Weekly inbound demand is subtracted from that effective throughput to calculate net backlog burn.
Why does the calculator show a sensitivity range?
The transparent range applies plus or minus 12 percent variation to effective throughput before inbound is subtracted. The separate recovery-risk view runs 1,000 reproducible weekly scenarios at low, moderate, or high operating volatility and reports P50, P80, and P95 timing plus target-miss risk. Neither is a fitted forecast or confidence interval.
What does 'No recovery' mean?
It means the assumptions provided do not create positive net backlog burn: effective weekly throughput does not exceed weekly inbound demand. The operating question then shifts from timing to constraint removal, capacity design, or demand control.
When should this model not be used on its own?
Do not use it as a substitute for direct observation, validated operating data, workforce planning, demand forecasting, financial approval, or safety and compliance review. Use it to make assumptions explicit and identify the next decision to validate.
The operator behind the model
Ryan Miller, EMBA
Founder and Principal of Luna Sol Group. Ryan’s work spans last-mile transformation, capacity planning, network operations, customer experience, and large-scale frontline execution. This calculator makes that operating lens inspectable before a conversation begins.
A specific exchange, not a generic lead form.
Send the scenario above and Ryan will reply with 2–3 operating observations within one business day—no deck and no pitch.
View Ryan’s LinkedIn profile ↗