Demand planning is demanding.
The questions of “when” and “how much to stock” are complex, persistent and dynamic. So much so that the perfect balance between supply and demand seems impossible.
While perfection in inventory management may be difficult to achieve, enhancements are material, as well as manageable. Through EY Demand Forecasting & Inventory Optimization, optimizing your inventory management is entirely attainable.
Better together. Better balance.
Working together with Databricks and Microsoft, we developed the EY Demand Forecasting & Inventory Optimization solution to apply machine learning (ML) forecasting, inventory simulation and scalable deployment. This data-driven approach informs your decision-making to better balance supply and demand.
EY Demand Forecasting & Inventory Optimization maintains precise inventory levels at granular product, location and customer levels through a broad ML forecasting system. By leveraging ML and distributed computing, it accentuates your ability to account for detailed supply and demand trends.
Market changes make inventory management markedly difficult. EY Demand Forecasting & Inventory Optimization adapts to, and effectively forecasts, market and economic shifts. Accounting for these variables improves decision-making, cuts unnecessary inventory and meets consumer demands.
Overstocked? Understocked? Data makes the difference.
EY Demand Forecasting & Inventory Optimization helps establish a trusted data pipeline powered by Microsoft Azure and Databricks, providing scalable and multicloud capabilities. The EY forecasting methodology applied through the solution incorporates customer history and demographics.
By applying predictive data analytics, the data and models are standardized, reused and scaled. This results in improved forecasting, scenario analyses and visualization – all leading to improved decision-making, no matter your industry.