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EY Demand Forecasting and Inventory Optimization
avtor: EY Global
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Automating decision-making for inventory management using machine learning, simulation& optimization
Solution Overview
EY Demand Forecasting and Inventory Optimization enabled by Microsoft’s Cloud for Retail, builds a trusted data pipeline to standardize, reuse and scale data and models by using predictive data analytics to improve decision-making for inventory optimization. This allows for accurate forecasting, scenario analyses, visualization and incorporation of the following attributes:
- Individual customer predictions incorporate customer history, stock keeping unit (SKU) information, and customer/location demographics to better predict demand and optimize target inventory levels through machine learning capabilities
- Forecasting models utilize eight different forecasting techniques to find the best-fit model at the location-SKU level. A blended approach of repeat orders and new orders cuts out unnecessary inventory while maintaining service to customers
- Simulation engine that estimates inventory improvement, prevents unexpected supply chain gaps and performs a driver analysis to optimize ordering
- Scaled computation and storage efficiently distributes artificial intelligence (AI) models and data over multiple clusters. It allows for the calculation of hundreds of thousands of SKUs and locations
Solution Benefits
- Improve customer experience with the right inventory
- Incorporate customer-specific demand forecasting with predictions at the location and product level
- Capitalize on previously unmet customer demands and untapped sales, and improve customer satisfaction by maintaining sufficient supply
- Order the right amount of inventory at the right time
- Utilize best-fit machine learning models to increase forecasting accuracy and holding a more precise inventory
- Reduce human bias through automation and incorporating external/internal factors
- Simulate inventory strategies before deploying at scale
- Prevent supply chain gaps by simulating seasonal demand and ordering variability
- Tweak inventory strategy to determine the optimal inventory approach to balance supply and demand
- Leverage vast amount of data utilizing Microsoft Azure cloud technology
- Quickly analyze and incorporate large volumes of customer and inventory data in machine learning models and augment existing inventory management systems
- Integrate with current inventory system
- Offer flexibility with solution add-ons and development
- Incorporate the best market tools specific to the client
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