The question: where are projects waiting?
The case examined how shared functions could constrain delivery as R&D activity grew. Project dates alone can reveal that work takes longer, but do not explain whether the delay comes from capacity, task dependencies, review decisions or priority changes.
Polaris brought project schedules and certification records into the same investigation. The analysis covered 139 projects and 312 certification records, then examined the relationships among R&D, certification and shared platform functions. All business materials in this case were constructed for the sandbox.
Keep the comparison cohort fixed.
The reported duration comparison uses the same 15 projects launched in 2026. Their mean time from project approval to launch was 18.39 months in the constructed source dataset. This is the reference for the modeled alternative.
The broader dataset supported investigation, while the 15-project cohort defined the reported comparison. Keeping those scopes distinct avoids presenting a result for one subset as if it covered every project in the analysis.
| Measure | Scope | Value |
|---|---|---|
| Project records examined | Investigation dataset | 139 projects |
| Certification records examined | Investigation dataset | 312 records |
| Mean approval-to-launch duration | Same 15 projects launched in 2026 | 18.39 months |
| Duration after adding certification capacity | Offline model; same 15-project cohort | 17.47 months |
Replay one capacity change.
The investigation identified shared certification capacity as a potential source of waiting. An offline queue model then estimated how supplementing that capacity could change the approval-to-launch duration for the fixed cohort.
The replay produced an estimated mean of 17.47 months, a modeled difference of 0.92 months from the source baseline. Retained code, execution output and review records support this calculation. The difference describes the model under its assumptions; it is not a duration observed after a real organizational change.
Check the assumptions behind the estimate.
The queue model does not fully reproduce the waiting times in the constructed records. It also assumes that certification completion constrains the launch date. That critical-path assumption needs confirmation for each project; earlier certification may not change launch timing if another activity remains the limiting factor.
Other shared functions, including platform capacity, were not included in this capacity replay. The run was a research diagnosis with recorded review, not a test backed by independent executable frozen acceptance. Its result supports a scoped hypothesis for further evaluation.
What an operating team would validate next.
Before applying the proposal, the team would need to verify the critical path, available certification capacity and dependencies for its own projects. It would also need to establish whether the proposed capacity could be added in practice and whether bottlenecks would move elsewhere.
A business pilot should define the projects covered, the operating change, the observation window and the quality checks. Any duration or cost improvement would then be assessed from actual records. The source for this case is the Polaris internal R&D sandbox replay and review material from October 2026.
Questions before an evaluation
Was the 0.92-month difference achieved by a customer?
No. It is the difference between the constructed-data baseline and an offline queue-model estimate for the same 15 projects.
What does this case demonstrate?
It demonstrates a documented investigation, a comparable-cohort calculation and an explicit capacity hypothesis. It does not establish production impact or general simulation accuracy.