Globachem adopted a Microsoft Fabric-based analtyics platform, set up to enhance sales insights and support future AI applications.
How can demand forecasting create value?
Every business with inventory faces the same challenge: How much should we stock? Too much inventory means high costs. Too little means lost sales. AI-powered demand forecasting helps you predict customer demand with greater accuracy, reducing stockouts, excess inventory, and unnecessary costs.
Curious about how demand forecasting can generate value for your company?
How does it work?
Steps in the forecasting process
AI projects like this should be tackled in different steps. This way, risk is gradually mitigated in your project. Each phase should be followed by a formal go/no-go decision. The final stage should be the seamless integration of the forecasts in your everyday processes.
Pre-project
Problem definition analysis
Proof of concept
1 product(group)
Scale up
Expansion to other product(group)s
Iterations
Iterative improvement in collaboration with the client
Production
Intergration in everyday workflow
Want to learn more about Demand Forecasting?
In this whitepaper, you’ll learn:
- What demand forecasting is
- Why it’s crucial for supply chain optimization
- The common forecasting pitfalls and how to avoid them
- The best practices for accurate demand forecasting
- What the investment for such a project looks like and how to maximize its ROI
Download the whitepaper here
Fill in the form and get your free copy.
InfoFarm Use Case References
AI-based demand forecasting – Manufacturing
Eduards Trailer Factory invests in a game-changing demand forecasting engine in order to maintain its competitive edge in the trailer market.
An AI-driven TMS – Logistics
Trans-IT, in partnership with Infofarm, is revolutionizing transport logistics with an AI-driven TMS that automates operations, enhances planning, and enables seamless communication—boosting efficiency, reducing costs, and increasing capacity by 30%.