One-number forecasts hide the range a planner must absorb.
Review model-generated demand ranges, changing cadence and the evidence behind the selected route.
See uncertainty before committing.Decision intelligence for industrial planning
Turn uncertainty into decision evidence.
For industrial manufacturers and distributors facing volatile demand, excess stock or constrained capacity, Phaneon connects probabilistic forecasting with operational simulation—so planners can compare a response before committing stock, capacity or cash.
Demand exposureSee model-generated futures and service risk
Inventory choiceCompare policies under the same assumptions
Production responseTest capacity and bottleneck effects
Polaris → RigelForecast-to-decision workspace
Implemented no-write workflowScientific intelligence for complex systems.
Recognizable planning pressure
Make consequential planning choices with uncertainty intact.
Phaneon is designed for planning teams that need to understand demand risk and its operational consequences before changing the plan.
Industrial manufacturingAftermarket & service partsWholesale distribution
Service, backlog and working capital move together.
Compare bounded replenishment choices under the same demand, lead-time and operating assumptions.
Understand the trade-off before ordering.A feasible demand plan can still meet a bottleneck.
Test how shifts, queues, routings and finite resources reshape the operational response.
Investigate constraints before changing flow.One connected workflow
From demand signal to an operational choice.
The forecast, its assumptions and the resulting decision evidence stay attached from model review to bounded simulation.
01 · Research core inside Polaris
Aurora
Model demand as a set of possible futures.Aurora separates whether demand may occur from how much may follow, preserves calendar meaning and can use supplied availability context when sales may be constrained.
Probabilistic Forecasting Core · Research program · Not sold separately
02 · Planner workspacePolaris
Review uncertainty, exceptions and the retained baseline.Polaris checks data and calendar context, explains the selected forecast route and keeps alternatives, overrides and lineage visible to the planner.
Implemented forecasting evaluation software · Guided demonstrations available
03 · Decision simulationRigel
Compare inventory and production responses without write-back.Rigel runs bounded, matched experiments across service, stock and backlog—and cost when governed inputs are available—plus production flow, capacity and bottleneck evidence.
In Active Development · implemented non-writing evaluation software · Production P2One bounded caseAgreed baselineNo ERP replacementNo live-system write-back
Representative planning case
See the decision artifact—not just the model.
A service-parts planner needs to respond to rising demand while packing capacity is constrained. The example shows how Phaneon keeps uncertainty, alternatives and remaining assumptions in one review.
Service-parts family · 12-week horizonHow should replenishment change without overloading packing?
Representative dataThe range widened after constrained-sales periods.
Polaris retains the current baseline while the model-generated range and supplied stockout context are reviewed.
Three responses face the same evidence.
Higher backlog exposure if demand follows the upper range.
Higher inventory and packing-capacity exposure.
Phase the order and protect packing capacity before commitment.
What moves forward—and what still needs confirmation.
Review the staged response. Confirm supplier lead time and the packing-shift assumption before changing the operating plan.
- Compared
- Service · stock · backlog · capacity
- Protected
- Current policy and forecast lineage
- Still open
- Lead time and packing availability
Illustrative decision process · Representative data · Not a customer result or automatic recommendation
Explore the implemented flow
AI that shows its route, boundaries and next decision.
Aurora produces model-generated demand futures inside Polaris. Polaris checks the evidence, labels safeguards and keeps the retained route visible. Eligible evidence can then support matched, no-write Rigel experiments after the required gates clear.
See what may happen.Test what it means. Decide with context.
AI decision traceInteractive planning journey
Aurora core + Polaris workspace + Rigel simulationSelect a planning situation
Aurora maps model-generated demand. Polaris governs the evidence. Rigel tests the operational response.
Showing Demand accelerates
Forecast intelligence
Representative dataHow should the plan respond when demand begins to rise?
Observed signalModel-generated forecast
01Read
Demand signal detected
Polaris checks cadence, history quality, seasonality and recent movement before modeling.
Evidence ready02Forecast
Aurora maps model-generated futures
Occurrence, quantity and uncertainty are modeled across the planning horizon rather than reduced to one number.
Model output produced03Verify
Polaris keeps guardrails visible
Route diagnostics and a guarded fallback stay visible before forecast evidence moves forward.
Route reviewed04Test
A matched response is prepared
Once the required gates clear, replenishment options can face the same forecast and operating assumptions in Rigel.
Comparison scopedPlanner briefReview a staged replenishment response
The rising central path, remaining uncertainty and lead-time exposure are carried into a matched policy comparison.
Forecast rangeRoute diagnosticsPolicy trade-offs
The AI proposes evidence. The planner keeps the decision.Current software workflow · Representative demonstration dataExplore the evidence behind the AI →
The Phaneon method
Scientific discipline, shaped for planning work.
Forecasting and simulation become useful only when the baseline, assumptions and decision boundary remain inspectable.
Start with the decision
Define the planning choice, current baseline and success measure before comparing methods.
Keep uncertainty visible
Model-generated ranges, assumptions and evidence quality remain available for planner review.
Protect the baseline
A more complex method does not replace the current approach unless it earns that decision.
Keep people in control
Scenarios, overrides and comparisons stay reviewable, attributable and reversible.
Evidence posture
Know what works, what has been measured and what remains to prove.
Product capability, engineering checks and customer outcomes are deliberately kept separate. That makes the next evaluation easier to interpret—and harder to oversell.
Explore current evidence and limitationsForecast review, guarded routing and bounded no-write simulation.
Focused mechanism checks and a retrospective matched comparison with its negative result retained.
Service, stock, planning time and process value require an agreed design-partner evaluation.
Beyond the active core
Portfolio direction, clearly separated from current availability.
Polaris and Rigel remain the implemented evaluation focus. Regulus, Mira, Altair and Sirius describe the intended product constellation; they are not presented as products available today.
Regulus
Planning and schedulingExplainable coordination across constrained planning decisions.
Mira
Scenario intelligenceDisciplined comparison across changing operating assumptions.
Altair
Enterprise integrationVersioned movement of operational evidence between systems.
Sirius
Monitoring and explainabilityModel lineage, behaviour and decision accountability over time.
Design-partner scoping
Bring one difficult planning decision.
In the first conversation, we define the decision, current baseline, required data and success measure. No system connection or data upload is needed to assess fit.
Request a planning-case review01
Define the decision and current baseline.
02
Agree the evidence, security boundary and success measure.
03
Decide together whether a bounded evaluation is justified.
Current availabilityGuided demonstrations available · Design-partner scoping open