Demand Planning, the cornerstone of the supply chain
Demand Planning is the process of forecasting future demand for your products so you can adjust production, purchasing and inventory accordingly. It is the first building block of any high-performing supply chain.
For a midsize manufacturer or distributor, forecast quality directly drives customer service level, inventory levels and operating costs. A 10% forecast error can translate into millions of euros in excess inventory or stockouts.
The limits of Excel for Demand Planning
80% of French midsize companies still use Excel as their main forecasting tool. The spreadsheet may seem flexible, but it has critical limitations:
No built-in statistical models. Forecasts rely on moving averages or gut feeling, without using exponential smoothing, ARIMA or machine learning algorithms.
Fragmented data. Each demand planner works in their own file, with no automatic consolidation or centralized history.
No native collaboration. Sales adjustments (field intelligence) get lost in emails and file versions.
No accuracy measurement. Without an automated forecast accuracy KPI, there is no way to measure and improve forecast quality.
What a dedicated Demand Planning tool changes
A tool like Ganacos transforms demand planning by bringing:
Automatic forecasting models that select the best algorithm for each SKU based on its history and variability.
A collaborative workflow that lets sales reps enrich statistical forecasts with their field knowledge, in a way that is tracked and measurable.
Alerts and exceptions that focus the demand planner's attention on the SKUs that truly need human intervention.
Real-time KPIs: forecast accuracy, bias, value added by manual adjustments, MAPE by product family.
Where should you start?
The move away from Excel can be gradual. Start by centralizing your sales history, define your forecasting levels (product, customer, region) and measure your current forecast accuracy. That is your baseline for quantifying the gains from a dedicated tool. Ganacos can be deployed in 3 months, with measurable ROI from the very first cycles.
