Demand Forecasting and Manufacturing Procurement Optimization
The financial risk of planning manufacturing blind.
Lead times for designer eyewear range from 3 to 6 months. Planning production without data intelligence carries two major risks: if you produce too little, best-sellers sell out at the height of the season, causing irrecoverable lost sales; if you produce too much, the leftover glasses turn into dead stock that has to be cleared out, destroying the commercial margin.
Machine Learning based on Attribute Clustering.
We designed a predictive system that analyzes international B2B orders and breaks each model down into attributes (shape, material, color, size). Using Clusteringalgorithms, the model solves the problem of new launches (cold start) by inferring how they will perform based on similar models, delivering the optimal month-by-month manufacturing plan for the factory.
Measurable Impact
The engineering behind the impact
Unsupervised Machine Learning
An algorithmic architecture designed to solve the lack of history for new launches:
- K-Means / Hierarchical Clustering: Groups new models with historical references based on how closely their attributes match.
- Multivariate Regression: Volume projection based on global seasonality and the behavior of the assigned cluster.
Vectors and Attributes
- - B2B sales history.
- - Size, material and color.
- - Supplier *lead times*.
- - Launch calendar.
Analytics Pipeline and Python Orchestration
Data is extracted directly from theclient's ERP. The pipeline, developed in Python , runs anomaly-cleansing processes (abnormal sales spikes), trains the Machine Learning models and returns a clean raw-material purchase planning table.
Want to stop piling up dead stock and protect your profit margin?
Planning production on intuition is costing you thousands of euros in clearances and lost sales. We deploy predictive models connected to your data to tell you exactly what to manufacture, when and in what quantity.