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Seeing tomorrow's demand
before it arrives
Level 1 · The Company
Meet Value AG
Leading in German real estate valuation
All asset classes
Independent partner to banks, insurers, investors
Valuation reports, inspections, market data
Teams on site across Germany
Level 2 · The Problem
Static plans meet a moving market
Operational teams planned staffing with manual estimates and static rules. Seasonality and real-time trends slipped through: over-staffed quiet weeks, bottlenecks at the peaks.
Valuation demand
Static staffing plan
Mismatch
Schematic illustration.
Level 3 · The Planning Desk
Plan a year of staffing
Drag across the chart to set staffing for each month, then lock in your plan.
Your staffing plan
Actual demand
Mismatch
ML forecast staffing
Schematic illustration, all figures fictional.
Run the forecast to unlock the results
Level 4 · The Results
Demand, predicted. Operations, right-sized.
The Outcome
90%
forecast accuracy
up to 50%
less over- and understaffing, across all regions
ML forecast models
Trends, seasonality, product mix, external factors
Built into planning
Lower cost, better service levels, right-sized teams
The stack behind it
Docker
Azure DevOps
Power BI
Docker
Azure DevOps
Power BI
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© Gemma Analytics · Case Study Value AG