EY Demand Forecasting and Inventory Optimization

avtor: EY Global

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
    1. Incorporate customer-specific demand forecasting with predictions at the location and product level
    2. 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

    1. Utilize best-fit machine learning models to increase forecasting accuracy and holding a more precise inventory
    2. Reduce human bias through automation and incorporating external/internal factors
  • Simulate inventory strategies before deploying at scale
    1. Prevent supply chain gaps by simulating seasonal demand and ordering variability
    2. Tweak inventory strategy to determine the optimal inventory approach to balance supply and demand
  • Leverage vast amount of data utilizing Microsoft Azure cloud technology
    1. 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
    1. Offer flexibility with solution add-ons and development
    2. Incorporate the best market tools specific to the client

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