Northstar
Workflow Cockpit
Entity bottleneck decomposition, decision execution, and close-readiness command tape.
Private briefing
Book a private briefing
This is not a generic capabilities pitch. We pick one decision workflow and pressure-test whether it is controllable.
Platform snapshots
Two production control surfaces
Northstar and CloseControl show how operators move from signal to accountable decision execution.
CloseControl
Control Tower
Unified decision inbox with escalations, approvals, due-window risk, and ownership context.
What you'll see in 20 minutes
- Inputs -> decision list -> human approval -> audit trail.
- How we quantify upside: pool -> conservative fixable share -> CFO-checkable math.
- What first validation needs: optional read-only check using one minimal export.
- How deployment works safely: read-only first, controlled write-backs only after comfort.
- What success looks like: single owner, single metric, weekly cadence.
Who should attend
- Day-to-day owner of the workflow.
- One finance partner who owns the metric.
What we need to prep
- Nothing for the first call.
- If it's worth validating, we request one minimal export (read-only) within 48-72h.
Example engines
Engines used in private briefings
Each briefing is anchored to one workflow, one owner, and one measurable operating metric.
Efficiency Engine
Inventory and Service Level Optimizer
Inventory policy and service level decisions tied to working capital.
Decision problem
Operating teams balance service level and working capital under demand volatility.
Objective
Show how inventory policy, lead time, and supplier reliability move cost, service, and cash outcomes.
Inputs
Demand profile
- Weekly demand mean by product class
- Demand variability by product class
- Replenishment cadence in weeks
Service and supply policy
- Target service level
- Lead time in weeks
- Supplier reliability factor
Cost structure
- Holding cost percent per year
- Stockout penalty per unit
First outputs
Reorder point and safety stock
Reorder point and safety stock by product class based on service level targets.
Fill rate and backorder volume
Expected fill rate and backorder units tied to demand variability.
Holding and stockout cost
Annual holding cost and stockout exposure by class and total.
Working capital impact
Working capital tied up and released versus the baseline policy.
Revenue Engine
Pricing and Demand Optimization Simulator
Pricing decisions tied to demand response, revenue, and margin.
Decision problem
Price moves often lack clear elasticity, win rate, or capacity impact.
Objective
Quantify how price, demand response, and competitive position shift revenue and margin.
Inputs
Pricing and cost
- Current list price and unit cost
- Discount floor for approvals
Demand response
- Baseline demand volume
- Price elasticity assumption
Market and capacity
- Competitor price index
- Win rate sensitivity
- Capacity constraint
First outputs
Recommended price
Price that maximizes contribution within capacity limits.
Demand and revenue
Expected demand, revenue, and gross margin at the recommended price.
Capacity utilization
Share of capacity consumed under the selected price.
Competitive sensitivity
Win rate response to competitor price shifts.
Strategic Engine
Strategic Investment Scenario Modeling
Capital allocation scenarios with probability ranges and downside risk.
Decision problem
Capital allocation decisions often rely on deterministic models that hide downside risk.
Objective
Quantify outcome distributions and compare downside protection versus upside capture.
Inputs
Portfolio allocation
- Initial capital and allocation mix
- Time horizon and scenario selection
Return assumptions
- Expected returns and volatility by asset class
- Correlation assumptions across asset classes
Simulation settings
- Simulation count and confidence percentiles
- Hurdle rate target and capital floor
First outputs
Outcome distribution
Probability range for portfolio outcomes with percentiles.
Downside risk metrics
Value at risk and conditional value at risk at selected confidence levels.
Risk adjusted return
Sharpe ratio and drawdown profile for each scenario.
Decision summary
Probability of meeting hurdle rates and expected downside exposure.
Capability Engine
Model Reliability and Drift Control Simulator
Model drift monitoring and retraining impact on value at risk.
Decision problem
Models decay in production without monitoring, creating value leakage and decision risk.
Objective
Show how monitoring cadence, retraining, and automation reduce drift exposure.
Inputs
Model health baseline
- Baseline model accuracy
- Drift rate per month
- Data quality score
Operations and monitoring
- Monitoring frequency in days
- Retraining cadence in weeks
- Automation coverage percent
Decision exposure
- Decision volume per month
- Cost per wrong decision
First outputs
Monthly value at risk
Expected value at risk from decision errors per month.
Detection and recovery time
Time to detect drift and restore baseline performance.
Reliability score
Score derived from accuracy stability and automation coverage.
Value protected
Estimated value preserved by improving monitoring and automation.
Move from briefing to validation
Briefings align scope and ownership, then the QOA validates measurable upside and delivery feasibility.