Supply Chain & ML

Smart inventory: the future of planning

From manual, guess-based replenishment to predictive AI that anticipates demand and optimizes invested capital.

Guesswork and stockouts

For many companies, materials replenishment still relies on operator intuition or manually updated Excel files. The result is a precarious balance between two equally damaging extremes.

On one side, unexpected stockouts that halt production, delay deliveries, and damage customer relationships. On the other, warehouses packed with slow-moving stock, with tied-up capital that generates no value and storage costs that erode margins.

The issue is not a lack of data: it is the inability to turn data into real-time operational decisions. Every day of delayed replenishment or every oversized purchase is a hidden cost that compounds, quarter after quarter, while no traditional report catches it.

Data quality first

Artificial intelligence does not work on dirty data. Before launching any predictive model, DataDeep runs a complete Exploratory Data Analysis (EDA) on company data.

The analysis starts from the foundations: are the bills of materials complete and up to date? Do vendor master records show realistic lead times or default values that were never verified? Do warehouse movements show time gaps or anomalous entries?

What the system corrects

Inconsistent lead times: vendors with declared delivery times of 7 days but historically never under 3 weeks. The system recalculates real lead times based on actual transactions.
Incomplete bills of materials: missing components or obsolete codes that distort net requirement calculations. The EDA flags every inconsistency before it can affect forecasts.
Unmapped seasonality: recurring peaks that Excel files miss. Historical analysis identifies cyclical patterns and embeds them in the forecasting model.

Only after data quality has been certified does the system move on to training the forecasting model. Garbage in, garbage out: without this step, any forecast would be unreliable.

AI forecasting

The core of the system is a machine learning algorithm that combines sales history with seasonality and market trends to generate accurate, dynamic demand forecasts.

Unlike traditional methods based on moving averages or static reorder points, the DataDeep model learns continuously: it tells an anomalous spike (a one-off large order) apart from real demand growth, avoiding excess purchases triggered by misleading data.

Dynamic replenishment proposals

What to buy: the system identifies the SKUs that will hit the reorder point within the forecast horizon, factoring in ABC classification and production criticality.
How much to buy: the suggested quantity balances purchase cost (economic order quantities, volume discounts) with carrying cost, optimizing working capital.
When to buy: order timing accounts for the vendor's real lead time (not the declared one), delivery windows, and warehouse logistics constraints.

The result is an always up-to-date procurement plan that adapts automatically to demand changes without manual intervention.

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