An AI-enabled planning system that helps teams understand what is changing, test what to do next, and make better pricing, promotion, distribution, and SKU-level product decisions.
A guided walkthrough of how planners move from an emerging demand signal to diagnosis, simulation, and a controlled commercial decision.
Video preview placeholder — final walkthrough coming soon.
AI co-piloting helps planners monitor change, diagnose drivers and barriers, simulate scenarios, and decide what to do next.
The AI co-pilot continuously watches sell-out, distribution, and promotion signals to flag deviations from baseline before they compound.
The AI co-pilot decomposes the variance into likely drivers and barriers and points to the products, customers, and regions where the deviation is concentrated.
Planners simulate pricing, promotion, and distribution options and compare projected impact on demand, revenue, and margin before committing.
| Scenario | Demand(Δ) | Revenue(Δ) | Margin(Δ) |
|---|---|---|---|
| Current plan | — | — | — |
| Extend promotion | +4.2% | +$310K | +$82K |
| Targeted distribution | +3.1% | +$220K | +$58K |
The AI co-pilot proposes a recommendation with supporting evidence and expected impact. The planner reviews, adjusts, and approves.
Restore availability in affected clusters and extend the promotion by 1 week.
Demand planning teams need to make fast decisions while reconciling changing signals across pricing, promotions, availability, distribution, local events, weather, and competitive activity. At the same time, shoppers increasingly move between in-store and digital channels — buying from the shelf, ordering through retailer websites and apps, or combining both. These changing shopping patterns can materially affect where, when, and how demand appears.
The problem is also one of visibility. Retailer- and account-level averages can mask important differences at the SKU, store, channel, and local-market level, hiding both emerging risks and commercial opportunities.
The real challenge is not producing another forecast. It is understanding what is driving demand, what barriers are holding it back, and how shopper behavior is shifting across physical and digital channels — then deciding how to respond while keeping Sales, Finance, Marketing, and Operations aligned with the same shared source-of-truth view.
Sales, pricing, promotions, distribution, inventory, and external signals are collected from separate systems and spreadsheets.
Planners create raw baseline forecasts and manually incorporate local knowledge, commercial assumptions, and expected market events.
Sales, Finance, Marketing, and Operations review different assumptions through meetings, emails, and disconnected files.
When actual sales differ from plan, teams analyze products, customers, regions, promotions, and possible external causes.
Alternatives are evaluated individually, while decisions, results, and business context are rarely preserved for the next planning cycle.
Built on a shared source of truth, the workflow brings demand signals, assumptions, scenarios, and decisions into one current planning view across the organization.
Connect the core commercial and operational signals that shape demand planning, including POS or sell-out data, promotion and trade calendars, price and distribution, retailer / store / SKU hierarchies, and supply, availability, and seasonality signals.
Establish the analytical foundation for better planning decisions. This layer combines baseline demand, forecasting, scenario planning, promotion and distribution intelligence, and SKU-level incrementality and cannibalization analysis inside one trusted planning core built on reusable models, business rules, and pipelines.
Use AI to accelerate planning, explanation, and decision support. The AI layer helps teams explain results, diagnose changes, simulate alternatives, and generate recommendations based on the planning intelligence core.
Deliver the capability through a practical planning workflow. Teams can ask questions, plan and simulate scenarios, review results on a weekly basis, respond to alerts and exceptions, and work through dedicated scenario workspaces.
Turn planning intelligence into measurable business value. The result is faster and more confident decisions, better visibility into baseline demand and its drivers, clearer understanding of true incremental growth versus demand shifting across the portfolio, stronger scenario planning across price, promotion, distribution, and product mix, and earlier detection of risks and opportunities.
Builds baselines, forecasts demand, measures uplift, evaluates scenarios, and tracks accuracy and business impact.
Continuously monitors change, explains drivers and barriers, investigates anomalies, compares scenarios, and surfaces relevant evidence and prior learnings — reducing the manual work required to understand what is happening.
Adds market and customer context, tests assumptions, challenges recommendations, and makes the final decision — with more time to focus on judgment, action, and commercial opportunities.
The value is not only a more accurate forecast. Planners gain a faster way to understand changing demand, test commercial options, and align the business around a clear course of action.
Detect meaningful demand changes sooner and move more quickly from signal to explanation and action.
Continuously evaluate forecast performance and improve confidence in the assumptions, models, and signals supporting planning decisions.
Compare pricing, promotion, distribution, and product-mix scenarios while considering demand, revenue, margin, incrementality, and cannibalization.
Reduce time spent gathering data, reconciling assumptions, and manually investigating deviations so planners can focus on judgment, opportunities, and action.
Bring better evidence into conversations with retailers and other channel partners, supporting more informed decisions around promotions, assortment, distribution, and trade investment.
Start with the appropriate product, category, retailer, region, or planning decision. Connect and harmonize the required data and build the workflow for monitoring, diagnosis, simulation, and decision support.
A focused pilot can be used where appropriate, but Qubit Nexus also delivers complete planning capabilities that extend beyond a pilot.
Start where it makes sense for your organization and scale at the pace that creates value.
Qubit Nexus helps CPG teams build AI-enabled demand planning and revenue management capabilities inside their own environment — connected to their data, models, planning workflows, business constraints, and existing technology stack.