Goals & Guardrails
Keep objectives, targets, budgets and constraints explicit and separate from expected demand.
- Goal-attainment probability
- Decision rights and approvals
- Hard and soft constraints
RabbitHawk Forecasting & Optimization
See what is likely. Decide what to do next.
See the chance of hitting the business goal if nothing changes. Then compare the actions still available and choose a plan that respects service, cash, capacity and other limits. Keep the planning systems you already use.
Modelled planning walkthrough
One decision from goal to outcome
Start with the business goal. See the chance of hitting it if nothing changes. Then test how each available action changes the plan, check the rules and trade-offs, approve the decision and learn from what happened. Need deeper handling of late information? See Forecast Interventions.
Verified client outcome · separate deployment
From a three-week planning cycle to one day
In a separate deployment, RabbitHawk extended a multinational apparel and department-store retailer's existing ERP with probabilistic forecasting and goal-driven inventory decisions.
- Reduction in new inventory
- 52%observed sales revenue maintained during the evaluated period
- Reduction in MASE
- 96%versus saved legacy forecasts at style × colour × store × brand
- Reduction in manual workload
- 90%from weeks to hours
- Decision cycle
- 3 wks → 1 dayfrom debate to coordinated action
Four capability families
Choose one capability or connect the loop
Deploy each family independently, or connect them from goal to outcome.
Forecasting & Scenarios
Represent uncertainty in beliefs and possible outcomes, then produce hierarchy-coherent forecasts and compare scenarios without replacing the official forecast.
- Calibrated distributions
- Hierarchy reconciliation
- Context-conditioned scenarios
Decision Optimization
Choose actions under uncertainty within budgets, lead times, capacity and service limits.
- Single or multi-objective models
- Human review and amendment
- Decision consequence analysis
Outcomes & Learning
Preserve forecasts and decisions, compare them with outcomes and govern what changes next.
- Decision and outcome lineage
- Calibration and performance
- Governed model updates
Operational applications
Start with the decision problem
Existing-product demand
Forecast demand across item, channel, store, region and time with coherent uncertainty.
New-product and buying decisions
Combine attributes, imagery, analogues and sparse early sales to support cold-start forecasts and initial buys.
Inventory & replenishment
Balance service, working capital, lead time, minimum order quantities and stock risk.
Store traffic & workforce
Forecast traffic and align rosters with service, labour rules and local operating conditions.
Price, promotion & markdown
Estimate response and optimize timing and depth against margin, inventory and brand guardrails.
Logistics
Optimize routes, loads, transfers and capacity under uncertain demand and delivery conditions.
Production & capacity
Coordinate production, changeovers and capacity with demand risk, cost and service requirements.
Configured decision domains
Apply the same forecasting and optimization principles to energy, finance and other defined decisions.
Three modelled scenario snapshots
A shared decision grammar across revenue, inventory and logistics
The workflow above follows one governed decision end to end. These focused snapshots show how the same goal-and-scenario logic carries into three planning domains. No forecast or plan changes until review.
Approval, exception handling and outcome learning remain in the governed workflow above.
Three ways to begin
Modular by design
Keep the systems and models that already work. Add only the capability or connection required for the chosen outcome.
One capability
Fix the missing part
Add a forecast, optimizer, goals and guardrails, or outcome learning while retaining the rest of the stack.
Example: new-product forecast only
Deployment pattern 1One operational loop
Own one decision end to end
Connect the capabilities needed for one bounded problem, from forecast through recommendation and outcome.
Example: demand → replenishment → outcome
Deployment pattern 2Connected decisions
Model interactions that matter
Share information or jointly solve decisions when their objectives and constraints genuinely interact.
Example: promotion + inventory + workforce
Deployment pattern 3One model, many decision levels
Coherent at every decision level
Item, store, region and enterprise forecasts stay coherent while each role sees the detail it needs.
- Product, location, channel and time hierarchies reconcile.
- Forecast distributions remain visible, not collapsed into false precision.
- Optimization consumes the selected distribution, goals and constraints.
- Product
- Style, colour and size
- Location
- Store, region and enterprise
- Channel
- Retail, wholesale and digital
- Time
- Week, month and planning horizon
The selected forecast distribution stays coherent across every level used by the decision.
Start with the decision that matters
Tell us which forecast, optimizer or operational loop you need to improve. We will scope the smallest useful deployment.
Discuss your decision problem