Predictive intelligence
Predictive forecasting combines historical data, machine learning (ML), and statistical algorithms to anticipate future trends, behaviour, and financial outcomes.
The global predictive analytics market is projected to exceed USD 82.3 billion to USD 116.4 billion by the early 2030s.
Where it works
Replace backward-looking assumptions with live signals from your customers, operations, and assets.
Match production cycles with actual market appetite. AI models analyse real-time variables such as changing weather, economic shifts, and consumer behaviour.
Maximum value: prevent costly overstocks, remove bottlenecks, and reduce out-of-stock scenarios.
Process real-time Internet of Things (IoT) sensor data to forecast exactly when a machine component will fail.
Maximum value: move from reactive break-fix models to proactive scheduling, reducing downtime by up to 35%.
Track consumer lifecycles to forecast churn, predict customer lifetime value (LTV), and dynamically score customer leads.
Maximum value: optimize pricing, focus ad spend on high-intent cohorts, and retain customers proactively.
Customer case studies
From warehouse-level demand to battery anomaly detection, these projects show how data becomes operational clarity.
Demand forecasting
Tarang is working with a large retailer and doing demand forecasting for their retail business.
We use historical data to train the algorithms and make the forecast. The goal is to improve their forecasting accuracy, which directly improves their sales. Tarang is measured on the outcome of our demand forecasting platform.
Thus, Tarang has taken a leadership position in the IT industry.
Anomaly detection
An EV vehicle manufacturer needed to predict anomalies in 800 Volt battery modules. Each module produced 1 to 7 GB of data, and the goal was to detect anomalies and make corrections before they affected production.
End of Line Test: Final electrical, mechanical, and functional checks before products are packaged and shipped.
Method: DBSCAN, or density-based spatial clustering of applications with noise, with regression-based analysis.
Example anomaly: During charging, a module can spike from 12 V to 500 V. In an autonomous factory, one robot applying the wrong torque can create this failure.
Demand planning
A toy manufacturer wanted to improve demand forecasting accuracy by moving from an Excel method to an algorithm-based method.
127 SKUs
2 years Historical Shopify sales data
3 months Forecast horizon