Predictive analytics
Demand, workload, anomalies: we turn historical data into decision-support tools. We start with data quality and test how useful a forecast is within your process.
Tell us about your processForecasting can support inventory, sales and capacity planning. Seasonality, time horizon and available data determine what can be assessed.
Trend and anomaly analysis helps identify changes worth investigating. A signal is not a diagnosis: people who know the process must interpret it in context.
Indicators and thresholds can help prioritise checks on supply, operations and maintenance. These are application examples, not guaranteed results: feasibility and performance must be tested on company data.
We define the decision to support, check available data and compare the model with a simple baseline. We evaluate it on periods not used for training, agreeing on metrics, limitations and error handling.
Only after validation do we integrate the output into business tools. Monitoring and review help identify changes in context. People remain in control of decisions.
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