Demand planning software that moves from forecast to decision

Demand planning is the process of predicting what customers will need, when, and where, so the rest of the supply chain can respond without overproducing or running short. It works at a more granular level than a sales forecast: instead of one revenue number, it looks at item-location combinations, which is what lets a manufacturer decide how many units of a specific SKU to build for a specific warehouse next month, not just what total revenue to expect. A sales forecast answers what the business will earn. Demand planning answers how many units, of what, and where, and that's the number production, procurement, and inventory actually plan against.

1. Data collection: pull sales, order, and shipment history from ERP or CRM systems.
2. Data cleaning: remove outliers and adjust for promotions, stockouts, or one-off events.
3. Portfolio management: factor in new product launches and manage phase-outs.
4. Forecast creation: apply statistical or machine-learning models at the right aggregation level.
5. Collaboration: align with sales, marketing, and finance to fold in market intelligence.
6. Performance tracking: measure accuracy and forecast value add, then refine the models.
7. Integration with S&OP or IBP: connect the demand plan to supply, financial, and business plans.

Most teams run this as a repeating monthly cycle, not a one-time project. Skipping the data cleaning step is the single most common reason a forecast drifts.

AI and machine learning improve demand planning by catching shifts that traditional statistical models miss, then updating the forecast automatically as new data comes in.

Traditional models assume the future looks like the past. AI catches the cases where it doesn't: a demand spike with no prior pattern, a shift tied to weather or a competitor's price move, or a signal buried in a sales rep's CRM notes. It flags those anomalies early, factors in external drivers like promotions or economic trends, and refreshes the forecast automatically 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.

Horizon improves accuracy by automating the baseline forecast, then adds things a spreadsheet can't:

Shared visibility: sales, marketing, and finance work off the same live plan instead of trading versions by email.
A full change log: every planner override is tracked back to who made it and why.
Connected data: the plan stays linked to ERP, inventory, and supply planning systems instead of living in an export.
Scenario testing: run multiple demand scenarios instantly to see how a change plays out before committing to it.
Proactive flags: forecast risks, variances, and adjustment opportunities surface on their own, instead of waiting for a planner to spot them at the next review.

Planners feel this mostly at the next S&OP review, when the numbers that shifted already have a name and a reason attached, instead of showing up as a surprise.

Yes. Horizon connects out of the box to all major ERPs, including SAP, Oracle, and Microsoft Dynamics, and also works with a simple Excel or CSV file if you'd rather not connect one. Missing or incorrect values get flagged automatically, so data issues get fixed at the source instead of silently breaking the forecast. See the full list of supported integrations.

Yes. Horizon tracks every new launch against forecast automatically, flagging under-forecast, over-forecast, scaling, or stalled products in real time. Planners decide whether to discontinue an item or transition it to a replacement, with an end date by item, location, and customer.