Machine Learning & Forecasting

The problem

Your company reacts. It always reacts. Stock runs out and you rush to reorder. Equipment fails and production stops. Demand shifts and you find out after you’ve already made too much or too little. Every operational decision is made looking in the rearview mirror, using historical data that describes what happened, not what’s going to happen.

The forecasting models you use today, if you have any, are Excel sheets with moving averages and the intuition of someone who’s been in the role for years. They work until they don’t. And when they fail, the cost is dead inventory, unhappy customers or idle machines.

The problem is not that you lack data. You have years of history in your ERP. What you lack is a system that learns from that data and tells you what’s coming before it arrives.

Our solution

We train machine learning models on your own data to solve specific operational problems. We’re not talking about generic AI. We’re talking about models that adapt to your business, your seasonality, your patterns.

Each model is deployed to production with an automated retraining pipeline. When new data arrives, the model updates itself. No manual intervention. No progressive degradation.

The most common use cases:

• Demand forecasting: anticipate orders by product, customer or region weeks in advance
• Anomaly detection: identify unusual patterns in transactions, consumption or operational metrics
• Predictive maintenance: predict equipment failures before they happen, reducing unplanned downtime
• Inventory optimization: calculate optimal stock per SKU by combining demand forecasts with lead times

What’s included

Phase 1: Data assessment & feature engineering (Week 1-3)

• Audit of available data: volume, quality, temporal granularity
• Exploratory analysis and pattern detection in historical data
• Prediction problem definition and success metrics
• Feature engineering: creating predictive variables from raw data
• Feasibility assessment: is there enough signal in the data to predict?

Phase 2: Model development & validation (Week 4-8)

• Training of multiple algorithms (XGBoost, Prophet, neural networks) depending on the case
• Time-series cross-validation: the model is tested on data it has never seen
• Feature importance analysis: which factors drive predictions
• Performance comparison vs. current methods (Excel, intuition)
• Final model selection with clear metrics: MAE, RMSE, accuracy
• Prediction visualization in interactive dashboards

Phase 3: Production deployment with monitoring (Week 9-10)

• Model containerization and deployment to cloud or on-premise infrastructure
• REST API for integration with ERP, dashboards or alert systems
• Real-time model performance monitoring (drift detection)
• Alert system when predictions exceed critical thresholds
• Monitoring dashboard for the technical team

Phase 4: Retraining pipeline & handover (Week 11-12)

• Automated retraining pipeline with new data
• Model versioning with MLflow: full traceability
• Technical and business documentation
• Internal team training to interpret and act on predictions
• Evolution plan: new models and use cases identified

Proven results

In our ML projects:

• Demand forecast accuracy above 85% at weekly SKU level
• 40% reduction in stockouts with optimal inventory models
• Anomaly detection in transactions 48 hours before operational impact
• Models in production that retrain automatically without manual intervention
• Positive ROI within the first quarter of operation