Demand moves, supply holds
Conditions lift or suppress category demand while the network can still serve it.
A merchandising decision
Activate, dial down or suppress local offers to match.
Weather-Driven Control Tower
Weather is a decision signal, not a forecast input.
A governed workflow that connects weather with store, inventory, shipment and promotion data on a common model, then routes scored, confidence-flagged recommendations to planning, transportation and merchandising teams.
What the workflow runs on
Feeds you already have
WeatherStore & SKUInventoryShipmentPromotion
Governed decision logic
Common modelStress scoresThreshold rulesConfidence
What reaches the team
Staging unitsLane actionsReforecast queue
The challenge
Everyone sees it. Nobody shares the read.
Weather is no longer just a forecasting input; it has become a major driver of lane disruption, late shipments, inventory imbalance and service risk across the network. Most planning processes cannot respond quickly or consistently when conditions change, which produces shortages in some regions and excess in others at the same time — alongside higher freight spend and sustained pressure on on-time, in-full performance.
Simultaneous shortage and excess
The same disruption drives out-of-stocks in one region and overstock in another, and no single view holds both at once.
No shared risk picture
Planning and freight teams work from different weather reads, so inventory and lane decisions are made against different assumptions.
Weather treated as an input, not a signal
It adjusts a forecast somewhere upstream instead of driving a coordinated, cross-functional decision.
Actions stay manual
Responses depend on who is paying attention that week rather than on consistent scores and repeatable playbooks.
Teams see the impact every week. They react late, inconsistently, and usually at a higher cost than the situation required.
The leadership question
One question, asked in every review
“How do we turn weather into a transparent, cross-functional decision engine for inventory, freight and merchandising — without rebuilding every planning process at once?”
The split
Demand and supply break in the same storm
The same weather event can raise demand in one place and cut off the lane serving it in another. Scoring them separately, on one shared model, is what lets merchandising and transportation act at the same time without working against each other.
Supply breaks, demand holds
Demand is unchanged, but exposure, congestion or capacity puts the lane at risk.
A freight decision
Pre-book capacity, reroute, shift mode or defer.
How the two axes resolve
Demand impact against lane risk
The two scores come off one normalized signal, so a planner and a transportation lead are reading the same event rather than two interpretations of it.
Demand impact →
Low risk · High impactForward-stage
Demand is coming and the network can serve it. Position inventory.
High risk · High impactProtect the lane first
Demand and disruption together. Secure capacity, then stage.
Low risk · Low impactMonitor
No action warranted. Keep it off the exception queue.
High risk · Low impactReroute or defer
No demand upside to protect. Take the cheaper intervention.
Lane risk →
What is included
Six capabilities, six deliverables
Each one is a packaged capability — the data model, the scoring logic and the deliverable already designed around the decision it serves. They are built to run together, but each produces an artefact that stands on its own.
01
Shared Weather Decision Model
Signal Backbone
A shared dimensional model and weather event layer that joins forecasts and climatology to stores, SKUs, DCs and lanes at operational grain, producing reusable weather stress scores and demand impact indices that power multiple decisions from one normalized signal.
02
Demand & Coverage Engine
Weather-Adjusted Demand & Inventory
Transparent formulas that adjust the baseline forecast using elasticity, category sensitivity and location exposure, then translate the result into recommended days of supply by store and SKU — converting coverage lift into forward-stage units that explicitly account for on-hand, in-transit and upstream constraints.
03
Lane Risk & Intervention Framework
Freight Lane Risk & Intervention
A lane risk score combining weather exposure, event severity, congestion risk, capacity tightness and transit volatility, with embedded rules that recommend pre-booking capacity, rerouting, shifting mode or deferring — whichever the risk threshold and remaining lead time indicate is the highest-value intervention.
04
Local Demand Impact Index
Merchandising & Offer Activation
A demand impact index blending weather stress, category elasticity, regional need, inventory readiness and margin risk, linked to localized recommendations on which categories or offers to activate, dial down or suppress — so local commercial action only fires where demand, supply and margin conditions actually align.
05
Weather Control Tower Decision Layer
Decision Outputs & Control Tower
Mapped inputs, calculations and outputs for four core questions: where to forward-stage inventory, which lanes and modes to adjust, which stores and SKUs to reforecast first, and which offers to activate locally — surfaced as exception-based queues of ranked actions with confidence flags.
06
Weather Decision Governance Playbook
Data Governance & Quality
A data confidence score reflecting completeness, timeliness, join coverage and provider reliability, tagging every recommendation high, medium or low — with named business owners, data stewards and a defined cadence for back-testing and recalibrating thresholds and sensitivities.
The artefacts
What actually arrives, and who it arrives for
Six named deliverables rather than access to a tool. Each is versioned, traceable to the model that produced it, and routed to the team that acts on it.
Deliverable 01 · Signal BackboneShared Weather Decision Model
A shared dimensional model and weather event layer that joins forecasts and climatology to stores, SKUs, DCs and lanes at operational grain, producing reusable weather stress scores and demand impact indices that power multiple decisions from one normalized signal.
Deliverable 02 · Weather-Adjusted Demand & InventoryDemand & Coverage Engine
Transparent formulas that adjust the baseline forecast using elasticity, category sensitivity and location exposure, then translate the result into recommended days of supply by store and SKU — converting coverage lift into forward-stage units that explicitly account for on-hand, in-transit and upstream constraints.
Deliverable 03 · Freight Lane Risk & InterventionLane Risk & Intervention Framework
A lane risk score combining weather exposure, event severity, congestion risk, capacity tightness and transit volatility, with embedded rules that recommend pre-booking capacity, rerouting, shifting mode or deferring — whichever the risk threshold and remaining lead time indicate is the highest-value intervention.
Deliverable 04 · Merchandising & Offer ActivationLocal Demand Impact Index
A demand impact index blending weather stress, category elasticity, regional need, inventory readiness and margin risk, linked to localized recommendations on which categories or offers to activate, dial down or suppress — so local commercial action only fires where demand, supply and margin conditions actually align.
Deliverable 05 · Decision Outputs & Control TowerWeather Control Tower Decision Layer
Mapped inputs, calculations and outputs for four core questions: where to forward-stage inventory, which lanes and modes to adjust, which stores and SKUs to reforecast first, and which offers to activate locally — surfaced as exception-based queues of ranked actions with confidence flags.
Deliverable 06 · Data Governance & QualityWeather Decision Governance Playbook
A data confidence score reflecting completeness, timeliness, join coverage and provider reliability, tagging every recommendation high, medium or low — with named business owners, data stewards and a defined cadence for back-testing and recalibrating thresholds and sensitivities.
Previews are schematic. They carry no figures, because the numbers on them would be ours rather than yours.
The output
Every exception lands in one of four bands
Scores exist to be acted on. Each band maps to one intervention and one owner, so a weather event produces a queue rather than a conversation.
TIER 1Forward-stage
Demand is coming and the network can serve it. Position inventory ahead of it.
TIER 2Protect the lane
Exposure, congestion or capacity puts delivery at risk. Secure it first.
TIER 3Activate locally
Conditions, inventory and margin align. Turn the local offer on.
TIER 4Monitor
Below threshold on both axes. Keep it off the exception queue entirely.
The workflow
From a forecast to an action someone owns
The point of the sequence is that it ends at operational grain — the store, the SKU, the lane — because that is where the action is actually taken.
1
JoinForecasts and climatology joined to stores, SKUs, DCs and lanes
2
ScoreReusable weather stress scores and demand impact indices
3
AdjustBaseline forecast adjusted by elasticity and location exposure
4
TranslateCoverage converted to forward-stage units against on-hand and in-transit
5
AssessLane risk from exposure, severity, congestion, capacity and transit volatility
6
RouteRanked, confidence-flagged actions to planning, freight and merchandising
One normalized signal powers every downstream decision, so weather stress does not get recalculated three different ways by three different teams.
What changes
Four things that stop being true
The workflow does not add another report. It replaces four sentences that get said in every review with something specific enough to act on.
“We reacted to the storm.”
Staging, capacity and offers are decided before conditions land, on a scored threshold.
“Planning and freight disagree on the risk.”
Both read the same normalized signal at the same grain, so the disagreement disappears.
“We expedited to protect service.”
The rules recommend the cheapest sufficient intervention, not the fastest available one.
“Weather is in the forecast somewhere.”
It is an explicit, inspectable score attached to a store, a SKU and a lane.
Who it is for
One workflow, four seats
The same governed output, presented for the decision each team actually makes. Nobody is asked to interpret somebody else’s view.
Forecasting & Replenishment
Clear, ranked exceptions at store-SKU level, so limited analyst capacity concentrates on the highest weather-driven impact rather than the whole portfolio.
Transportation
Lane risk scores that support earlier pre-booking, smarter reroutes and targeted mode shifts to protect on-time, in-full performance and cost.
Inventory Planning
The ability to forward-stage where weather-adjusted demand and service risk are highest, reducing out-of-stocks and markdown exposure at the same time.
Merchandising
Offers activated or suppressed only where demand, supply and margin conditions align, improving the return on local commercial action.
How it is governed
What makes it repeatable
Anyone can produce this read once. What makes it a capability is that the grain, the logic and the model version are fixed and stored, so the next cycle is measured the same way as the last one.
Grain
Store, SKU, DC and lane. Scores are produced at the grain where the action is actually taken, not rolled up to a region nobody operates.
Interpretability
The scores are simple and inspectable by design. A planner who cannot explain a recommendation will not act on it.
Confidence
Every recommendation is tagged high, medium or low on completeness, timeliness, join coverage and provider reliability.
Recalibration
Named owners and stewards, with a defined cadence for back-testing and retuning thresholds as the network changes.
Start with one region.
Bring one region and one season. We’ll join weather to your stores, SKUs and lanes, score the exposure and show you the ranked actions it produces — before you commit to anything wider.