Scaling AI Demand Planning

DemandSense AI

Spreadsheets and traditional forecasting methods cannot keep up with volatile markets. valantic’s DemandSense AI combines machine learning, multi-modal product data, and stockout-aware modeling to turn demand planning into a competitive advantage.

Ready to turn forecasting into an advantage?
Woman interacting with a futuristic holographic touch display at night. valantic AI Offering: DemandSense AI.

Highly accurate forecasts for inventory and revenue.

Modern demand management cannot rely on spreadsheets, gut feeling, and disconnected planning tools. Volatile markets, fragile supply chains, and rapidly shifting consumer behavior require forecasting that is faster, more granular, and more accurate than traditional approaches can deliver.

Most organizations know this. The problem is not awareness but architecture. Demand forecasting is spread across legacy tools and siloed teams, external signals are ignored, and stockouts are underestimated because the data only captures what was sold, not what customers actually wanted.

DemandSense AI is valantic’s demand forecasting and planning offering for CxOs and supply chain leaders. We design and implement AI-based forecasting models that combine historical and contextual data into a single forecasting engine, and we deploy them within an integrated planning platform that replaces the patchwork of spreadsheets with one consistent source of truth.

Where most demand planning falls short

The same four patterns come up in almost every organization that has tried to improve demand planning:

Manual and fragmented planning processes

Planning spread across spreadsheets, legacy tools, and siloed teams produces inconsistent numbers, slow decision cycles, and limited transparency for management.

Inaccurate or missing demand forecasts

Traditional methods struggle with volatility and complexity. The result is excess inventory that erodes margins and stockouts that damage revenue and customer experience simultaneously.

High inventory costs and tied-up working capital

Inadequate forecasts lead to overbuying, safety stock inflation, elevated working capital requirements, and higher storage, handling, and write-off costs.

External drivers are ignored

Traditional models rarely incorporate weather, seasonality, promotions, price changes, or competitive dynamics. Significant forecast potential stays untapped.

How we help: three steps, one forecasting capability

We address demand planning from data to deployment. Each step builds on the previous one to produce a forecasting capability that learns and adapts with your business.

01 · AI-Based Forecasting Models

Machine learning methods combining transactional sales and inventory data with external signals to generate forecasts traditional methods cannot match.

02 · Feature Engineering and Model Optimization

Forecasting features developed, model approaches compared, and performance validated through rigorous backtesting before deployment.

03 · Deployment and Forecast Monitoring

Forecasting models deployed within an integrated planning platform connecting demand signals directly to inventory, replenishment, and buying decisions.

AI-Based Forecasting Models

Machine learning methods combine transactional sales and inventory data with external signals to generate demand forecasts that traditional methods cannot match.

  • Multi-modal product understanding: we build product embeddings combining master data, pricing and discount structures, stock levels, replenishment cycles, and lifecycle information, allowing models to learn similar behaviors across products and improve accuracy for new and long-tail items
  • Stockout-aware demand estimation: statistical methods reconstruct true demand during stockout periods, so planning is based on what customers actually wanted, not just what was available
  • Granular, data-driven forecasting: models operate at single-SKU and store or region level, capturing local dynamics that aggregate forecasts systematically miss
Business team discussing performance metrics around a meeting table

Feature Engineering and Model Optimization

We develop forecasting features, compare model approaches, and validate performance through rigorous backtesting before deployment. 

  • Feature development: identify hidden trends in historical sales, cross-effects between products, locations, and channels, and demand signals at the granularity your planning requires
  • Model selection and optimization: compare machine learning approaches, tune hyperparameters, and validate accuracy using hold-out periods that reflect real planning horizons
  • Continuous performance validation: track accuracy metrics such as MAPE (Mean Absolute Percentage Error) and bias across products, locations, and time horizons; monitor for model drift and structural breaks; recalibrate based on realized outcomes
Businesswoman holding a tablet with data charts near a neon lamp

Deployment and Forecast Monitoring

We deploy forecasting models within an integrated planning platform that connects demand signals directly to inventory, replenishment, and buying decisions.

  • Unified planning platform: integrates demand forecasting, inventory and replenishment planning, budgeting, and buying execution in a single application, replacing disconnected spreadsheets with one consistent source of truth
  • Real-time Open-to-Buy steering: automatically calculate and visualize Open-to-Buy at item, buyer, and category level based on live sales, orders, stock, and budget targets
  • Interactive dashboards: give stakeholders a shared, fact-based view of demand, inventory, and buying decisions that supports faster and more confident planning
Businesswoman viewing global supply chain forecasts on a tablet

What you take away

Five concrete results your organization gains from DemandSense AI: 

  1. 1

    15 to 30% lower inventory levels

    More precise demand and replenishment planning reduces safety stock and working capital without compromising service levels.

  2. 2

    10 to 20% fewer forecast errors

    Machine learning models capture patterns and external drivers that traditional forecasting methods systematically miss.

  3. 3

    Lower operational costs

    Reduced storage, fewer emergency procurements, and more efficient planning processes translate directly to margin improvement.

  4. 4

    More data-driven planning

    Transparent forecasting dashboards give stakeholders a shared, fact-based view of demand, inventory, and buying decisions.

  5. 5

    A forecasting capability that learns

    Continuous model monitoring and recalibration mean accuracy improves over time rather than degrading.

Proven in practice

A leading Nordic fashion retailer with 20+ brands and 1.5 million SKUs went from idea to live AI-powered demand planning in just 4 months, with full ROI achieved by month 8. Merchandisers can now predict trends, meet demand, and avoid stock issues 10 times faster, while the supply chain became measurably more sustainable and less wasteful.

valantic transformed A1 Telekom Austria Group’s financial planning by cutting the effort for monthly forecasting from one full week to just 1.5 hours, enabling faster data-driven decisions and significantly improving operational efficiency and business agility.

Read more

DK Company’s planning team went from making decisions on stale data to acting on live signals across sales, returns, inventory, and margins, enabling sharper replenishment, better campaign timing, and more confident collection planning across every phase of the season.

See all valantic case studies for more examples across industries.

First step: the DemandSense AI Starter Workshop

The right starting point is a focused one-week engagement. We evaluate your demand planning landscape, identify improvement areas, quantify the business case, and define a roadmap for AI-powered demand planning.

Pre-workshop questionnaire

Gather insights on current data sources, systems, planning processes, and pain points.

Workshop session

Stakeholder interviews, data landscape mapping, use case identification, and value quantification (onsite or remote).

Findings report and roadmap

Business case with quantified value potential, data readiness assessment, and phased roadmap with expected ROI.

Format: One week including preparation and follow-up

Investment: EUR 10,000

Start your AI journey with valantic

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Ready to turn forecasting into a strategic advantage?

Demand planning is either a source of competitive edge or a drag on working capital and margins. DemandSense AI is for supply chain and commercial leaders who want to close the gap between the forecasts they have and the accuracy their business requires.

Lars Schultz Hansen, valantic

Lars Schultz Hansen

Director Business Analytics

valantic

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Rasmus Korsager Ørtoft

Senior Partner, Advisory and Solutions

VENZO – a valantic company

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