Learn how a global energy management and industrial automation leader partnered with Nihilent to deploy Resense, an AI/ML-driven forecasting engine that improved demand planning accuracy, reduced inventory imbalance, and scaled monthly forecasting across more than two million SKU-country combinations.
Service
AI/ML Demand Forecasting and Inventory Optimization
Vertical
Manufacturing B2B (Energy Management and Industrial Automation)
Region
Global
Tech Stack
Resense Forecasting Engine, Feature Engineering Module, Performance Tracking Dashboards, Macroeconomic Signal Integration
The client is a global energy technology leader managing a large and complex product portfolio across multiple countries and demand environments. Its supply chain planning operation needed a more accurate and scalable forecasting model to support global inventory decisions.
01
Low forecast accuracy in the legacy enterprise planning solution created severe inventory imbalances globally.
02
Excess stock tied up capital in some product categories while stockouts hurt sales in others.
03
Manual planner interventions were reactive, biased, and inefficient.
04
Demand patterns included highly lumpy and intermittent classes that traditional models struggled to capture.
05
The business needed to process forecasts at scale across 2,000,000+ SKU-country combinations while incorporating macroeconomic drivers, seasonality, bias, and trend variation.
Nihilent used a consulting-led approach to understand the client’s forecasting issues, validate the right approach through a targeted proof of concept, and then operationalize the solution at scale.
Segmented forecasting framework
Resense aligned AI/ML models to specific demand classes, including intermittent demand patterns, to improve forecast fit by segment.
External variable enrichment
The solution ingested and weighted macroeconomic variables to strengthen baseline forecasts.
Planner collaboration
Nihilent worked with regional planners to validate trends and improve confidence in model outputs.
Automated forecasting engine
Resense generated live monthly forecasts across 2,000,000+ SKU-country combinations.
Performance tracking dashboards
Dashboards continuously monitored forecast bias, error levels, trend variance, and seasonality profiles.
in forecast accuracy was achieved over the legacy enterprise supply chain planning solution during the proof of concept and live rollout.
SKU-country combinations are now forecast monthly through a live, automated forecasting engine.
events helped protect revenue and improve service levels for critical product categories.
tracking of bias, trends, and seasonality enabled more accurate consensus forecasting by planners.
For global manufacturers with complex demand signals, legacy planning tools often fail to capture intermittent demand, external drivers, and large-scale forecasting complexity. By deploying Resense, the client replaced rigid planning logic with a scalable, AI/ML-based forecasting engine that improves forecast quality, strengthens capital efficiency, and gives planners better visibility for decision-making.
