Demand planning

Demand planning with Deep Sensing.

The sharpest demand plans, in a fraction of the time. Horizon reads every signal, models every impact and ranks your priorities, so your planners only make the key decisions.

Demand forecast
Nationwide · SKU 10,482
HZN-1 live
Sensed: a key account pulling its order forward by 2 weeksThe order arrives before the plan catches up Today
Demand history
Baseline, forecast by HZN-1 on autopilot
Demand scenarios, modelled before the order moves
1 week earlier
2 weeks earlier
3 weeks earlier
Recommended action
Pull the order forwardby 2 weeks
Accept
18–45%

lower forecast error than traditional systems

Up to90%

of planning time back

Typically1–2%

of revenue added to EBITDA¹

¹€2–4M a year. Worked example for an illustrative €200M company. We run the same calculation with you, on your own numbers.

Deep Sensing

Every signal read. Every impact modelled.
Your priorities ranked.

Deep Sensing and Ori work as one system inside your demand plan.

1

Every signal read

CRM updates, sales chats, customer emails, POS data, weather and market data, read and linked to the right item, customer and location.

Weather · 06:00
Heatwave in Spain and Italy, weeks 27–28
Sales chat
Distributor asks for +20%
2

Every impact modelled

HZN-1 runs the baseline on autopilot: 16+ models compete for every item and the best combination wins by backtest. The Scenario engine models what each event does to demand.

Model
Backtest
Launches · causal
88.6%
Gradient boosting
87.2%
Neural networks
86.9%
3

Your priorities ranked

Ori hands your planners only the decisions that matter, ranked by impact. Approved decisions flow into the plan and the ERP, with owner and reasoning logged.

Move the Retailer North launch
75,000 units to weeks 12–13
Accept
Add the heatwave uplift
+18% ice cream, weeks 27–28
Accept

Building blocks

Everything a demand plan needs, in one place.

Demand forecasting →

Five model families per item, fed by every signal Deep Sensing reads. An explainable baseline across your hierarchy.

Collaboration →

Every input scored against what really happened, so the consensus plan learns whom to trust.

Portfolio management →

New items borrow a history from day one. Every launch tracked, every phase-out handed over cleanly.

Events, promotions and price →

Every event remembered with its measured effect, and the next one's impact modelled before it hits the plan.

Competitor and market signals →

Competitor moves, market data and industry news read, judged and linked to your items before they reach your orders.

Demand decisions and KPIs →

Signals read and ranked by impact. Only the decisions that matter reach an owner, with the impact modelled.

Process design →

Describe your process in plain language. Horizon turns it into a rhythm by role, with rules and tracked adoption.

Data transformation →

Your ERP, Excel or CSV mapped by AI into one planning model, kept every cycle and running in less than 24 hours.

HZN-1 · Horizon's proprietary AI engineLive
StatisticalMachine learningDeep learningFoundationCausalBest blend wins
Auto-tuning
Five model families tested
Winning blend · example item
Evaluating…Blend of 3 models

Five model families compete for every item. The best combination wins.

Five model families. HZN-1 picks the best blend per item by backtest, retrained weekly. Every input weighted, every event remembered, with full explainability on each number. Test it, or override it, always with a reason code.

See how the baseline is built →
Deep Sensing · one consensus planLive
SalesAdded value MarketingMade it worse FinanceNo effect ForecastConsensus plan Weighted by track record
Inputs inScoredWeightedOne number
Consensus loop
3 inputs in…One number
Role-based access
Each role, its own view

Every input on the record. The plan learns whom to trust.

One consensus number. Sales, marketing and finance feed the same plan. Every input scored against what really happened, so inputs with a track record carry more weight. Role-based access keeps each team to its own part of the plan.

Build consensus on evidence, not opinion →
Deep Sensing · product life cycleLive
Old SKU Linked: 3 earlier launches End date New SKUScaling
LinkedLaunchedTrackedHanded over
Launch status · example item
Tracking…Scaling
Phase-out end date
By item, location, customer

New items borrow a history. Old ones hand over cleanly.

A history from day one. New items borrow the pattern of similar products and earlier launches. Every launch tracked against forecast: under, over, scaling or stalled. Phase-outs hand over to the replacement, with end dates by item, location and customer.

Launch with a history, not a hunch →
Deep Sensing · event memoryLive
HolidayStock-outPromo Promo effect remembered: +12%+12% next promo, modelled
TaggedCleanedRememberedModelled
Removed from history
Holiday, stock-out, promo
Next promotion · example item
Modelling…+12% uplift

Every event remembered. Its next impact modelled first.

Clean history. Holidays, stock-outs and promotions are taken out, so one-off spikes don't distort the forecast. Every event remembered with its measured effect. Impact modelled before it happens, then pushed into the plan once approved, assumption visible.

Know the uplift before, not after →
Deep Sensing · market signalsLive
Competitor A: stock-out→ linked to your itemsMoves demand+11% demand
Market data: sell-out spikeIn the baseline
Industry news: promo launchScenario first
ReadJudgedLinkedQuantified
Read today
43 signals
Demand impact · example item
Judging…+11% demand risk

Competitor moves, read before they reach your orders.

Outside signals, read. Competitor stock-outs, market data and industry news in one feed instead of three. Each one judged: is it relevant, what does it touch, how much will it move demand? Only what matters reaches the plan, with the impact modelled.

Act on market signals, not hindsight →
Ori · the planner's assistantToday
43 signals read today · 40 already in the baseline
1Move the Retailer North launch75,000 unitsOwner setAccepted
2Plan first fills for 12 new stores11,520 unitsOwner set
3Add the heatwave uplift+18% ice creamOwner set
ReadRankedOwnedDone
Signals read today
43, ranked to 3
Decision 1 · example
Awaiting decisionAccepted, in the plan

Only the decisions that matter, ranked by impact.

Ranked by impact. Order changes, quotation shifts and other risks reach the right owner, with the impact already modelled. Every decision owned, with an approver, a due date and the evidence. Learned from: see which input helped or hurt accuracy.

Decide by impact, not by inbox →
Horizon · operating rhythmLive
“Weekly S&OP, sales input by Tuesday, finance sign-off monthly.”
DailyData pickupPlannerAuto ruleAlert reviewPlanner
WeeklySales inputSalesS&OP reviewPlanner
MonthlyConsensusS&OPSign-offFinance
DescribedBlueprintedAutomatedTracked
Blueprint
By role
Operating rhythm
Drafting…Weekly S&OP, tracked

Describe your process. Horizon turns it into a rhythm.

Plain language in, blueprint out. Daily, weekly and monthly steps by role, from data pickup to review. Every step owned: input, review, decision and approval. Rules let Horizon act on its own within agreed boundaries, and adoption is tracked.

Run the rhythm, not the reminders →
Horizon · AI data mappingLive
SAP S/4HANAMATNRKUNNRWERKSMEINSBUDAT
Planning modelItemValidCustomerValidLocationValidUnit2 missingFixedDateValid
Mapping kept, reused every cycle
ConnectMapValidateKeep
Mapping guide
AI-guided
Planning model
Mapping…Valid, ready to plan

Your data, mapped by AI. Running in less than 24 hours.

Connect your ERP, like SAP, Oracle or Dynamics 365, or a simple Excel or CSV. AI maps items, customers, locations, units and dates into one planning model and flags missing values. Mapped once, kept: reused every cycle, no IT project required.

Start with your data, not a data project →

Customers

Planners who plan with Horizon.

Learn more

Learn more about demand planning.

FAQ

Demand planning, answered.

What is demand planning, and why does it matter in supply chain management?

Demand planning predicts what customers will need, when and where, so production, procurement and inventory can respond without overproducing or running short. It works at item and location level, not as one revenue number: how many units of which SKU, for which warehouse, next month. Horizon adds Deep Sensing. It reads the signals that move demand, from ERP and CRM updates to sales chats and customer emails, models their impact on the plan, and brings planners only the decisions that matter, ranked by impact.

What is Deep Sensing, and what does it change for demand planning?

Deep Sensing combines all relevant information, structured and unstructured, with advanced reasoning and modelling. It turns that into the few decisions that matter, ranked by impact, with the impact already modelled. For demand planning, it means the plan responds to order changes, events and competitor moves as they appear, not at the next cycle. Within agreed boundaries it handles the routine on its own, and every approved decision is logged with its owner and reasoning. Language models read. Engines decide the numbers. People own the large calls.

What are the key steps in a strong demand planning process?

Collect sales, order and shipment history from your ERP or CRM. Clean it of promotions, stock-outs and one-off events. Manage launches and phase-outs. Build the forecast at the right level. Align sales, marketing and finance on one consensus plan. Track accuracy and forecast value added. Then connect the plan to S&OP or IBP. Most teams run this as a monthly cycle, not a one-time project, and skipping the cleaning step is the most common reason a forecast drifts. Horizon runs every step in one place, and Deep Sensing remembers every event with its measured effect, so its impact can be modelled before it happens again.

How do AI and machine learning improve demand planning and forecasting?

In two ways. First, HZN-1, Horizon’s proprietary AI engine, runs 16+ models for every item and picks the best combination by backtest, so a steady item and a new launch each get the right method. Second, Deep Sensing catches what history can’t show: a launch date moved in the CRM, a competitor stock-out or a signal in a customer email. It links each signal to the right item, customer and location and models its impact, instead of waiting for the next planning cycle. None of this replaces the planner. It shortens the distance between a signal appearing and a person deciding what to do about it.

How does Horizon improve demand planning and forecast accuracy?

Horizon automates the baseline, then adds what a spreadsheet can’t. Sales, marketing and finance work from one live consensus plan instead of trading versions by email. Every input and override is logged with who made it and why, then scored against what really happened, so inputs with a track record carry more weight. Scenarios can be tested before committing. And Deep Sensing brings risks and changes forward on its own, ranked by impact and with the impact modelled, so at the next S&OP review the numbers that moved already have a name and a reason attached.

Does Horizon integrate with my ERP system?

Yes. Horizon connects to all major ERPs, including SAP, Oracle and Microsoft Dynamics 365, or works from a simple Excel or CSV file. AI maps items, customers, locations, units and dates into one planning model and flags missing or incorrect values, so data issues are fixed at the source instead of silently breaking the forecast. The mapping is kept and reused every cycle, and with Horizon Instant you are up and running in less than 24 hours. See the full list of supported integrations.

Can Horizon manage new product launches and phase-outs?

Yes. New items borrow a history from similar products and earlier launches, so they are forecast from day one. Every launch is tracked against forecast automatically and flagged as under, over, scaling or stalled. Planners then decide whether to discontinue an item or hand it over to a replacement, with an end date by item, location and customer.

See it on your own data.

Horizon Instant: up and running in less than 24 hours.