Discover how Nihilent helped a global energy management leader improve demand forecasting accuracy through an advanced analytics PoC. By applying multiple forecasting models and integrating diverse data sources, the solution improved accuracy and supported more responsive, data-driven supply chain decisions.
Service
Advanced Analytics, Demand Forecasting
Vertical
Energy Management & Automation
Region
Global
Tech Stack
Resense Analytics Platform, 40+ Forecasting Algorithms, Internal & External Data Models
The client is a global leader in energy management and automation, with operations in over 100 countries. While they had an existing supply chain planning system in place, ongoing inaccuracies in demand forecasting were creating operational inefficiencies and financial strain.
Inaccurate demand forecasting led to stockouts, excess inventory, and inefficient supply chain planning.
Existing systems struggled to accurately predict demand across diverse markets.
Poor forecasting led to lost sales due to material unavailability.
Slow-moving inventory accumulated, increasing holding costs.
Internal and external demand drivers were not being fully leveraged in planning.
Nihilent conducted a Proof of Concept (PoC) using its Resense analytics platform to evaluate and improve forecasting performance.
Multi-Algorithm Forecasting
Used a library of over 40 forecasting models to identify the most effective outputs
Multi-Level Granularity
Enabled predictions at multiple levels, including SKU, product line, and geography.
Data Integration
Combined internal data (pricing, inventory) with external factors (macroeconomic indicators) for richer insights.
Large-Scale Validation
Tested across 45,000 SKUs, 18 product lines, and 58 countries to ensure it could scale effectively.
improvement in accuracy compared to existing systems
stockouts and improved product availability
accumulation of slow-moving inventory
data-driven decision-making across supply chain operations
scalability for enterprise-wide implementation
This PoC demonstrated that improving forecasting is as much about using the right data as it is about choosing the right models. By taking a more comprehensive and practical approach, the organization was able to strengthen planning, reduce inefficiencies, and build a more responsive supply chain.
