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Retail AI at work: How leading retailers are using AI to optimize pricing and inventory to deliver increased revenues


Published on

NRF 2019: Retail's Big Show
Michael Feindt, Founder and Chief Scientific Officer, Blue Yonder
Desikan Madhavanur, EVP and Chief Product Development Officer, JDA Software

Published in: Retail
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Retail AI at work: How leading retailers are using AI to optimize pricing and inventory to deliver increased revenues

  1. 1. Retail AI at Work: How Leading Retailers are Using AI to Optimize Pricing & Inventory to Deliver Increased Revenues Desikan Madhavanur & Professor Dr. Michael Feindt
  2. 2. JDA’s Focus is on Delivering Exponential Customer Success Through AI/ML & SaaS-led Innovations Innovative History 400+ patents and THE market-leading end-to-end solution portfolio Innovative SaaS and AI/ML Roadmap JDA LuminateTM and BlueYonder Innovative Vision Powering the Autonomous Supply ChainTM Innovative Investments JDA Experience Centers 100+ data scientists
  3. 3. Cognitive Demand Digital Control Tower AI/ML-led Lifecycle Pricing Personalized Assortments Workforce Management Dynamic Tasking Empowered Workforce Satisfied Customers Optimized Inventory Transformed Product Delivery …Delivering A Seamless, Reimagined Customer Experience
  4. 4. JDA and Blue Yonder Cognitive solutions across ALL segments, solutions & verticals + Granular Plan Store – SKU level Consumer Data POS – Zip level Fresh Focus Shelf life, Availability Replenishment Availability, Daily Intraday Delivery Load Building Markdowns Localized Plans
  5. 5. Within Enterprise-AI Happening in Supply Chain Potential economic value creation from AI in the next 20 years McKinsey estimates that firms will derive between $1.3trn and $2trn a year in economic value from using AI in supply chains and manufacturing
  6. 6. Vision: AI-Based Automation and Optimization from Consumer to Manufacturing
  7. 7. Many predictions / optimizations / recipes —> automate!
  8. 8. AI in Retail Supply Chain 99% Automation Write-offs / waste Freshness Capital Out-of-stock Turnover Efficiency
  9. 9. AI in Retail Pricing Automation Market Share Turnovers Raw Profit Customers/New Customers Returns Complaints Rests at End of Season 99% 0
  10. 10. Morrisons Use Case AI-Enabled Business Results “… our customer satisfaction improved year-on-year by 5%. This independent survey scores Morrison’s especially high on service availability and the quality of our fresh food. Our new automated stock ordering system is also improving availability and freeing up time for our colleagues to serve customers better.” - David Potts, CEO 99% 30% 2-3 Store Replenishment Automation Rate Reduction of Shelf Gaps Days Reduction of Inventory
  11. 11. Ernsting’s Use Case Markdown Optimization at 1,850 Stores “With Blue Yonder, we’ve found a partner that enables us to implement our digital strategy for pricing: Our margins on selected items have already increased within our five-month test. As a result, we decided to enter into a long-term partnership with Blue Yonder.” - Horst Beeck, Chief Financial Officer, Ernsting’s Short collection cycles Increase of relative margins Sell new collections within determined timeframe High pressure to sell items quickly 90+% of an item sold within a defined period of time Challenges Goals Results
  12. 12. bonprix Use Case AI-based Price Optimization Optimization through AI technology Starting position Too high prices for many products in the highly-competitive Russian market AI-based solution A machine learning solution that delivers optimized pricing decisions for every product Result Increased demand and higher gross profits
  13. 13. Actionable Takeaways from this session: Retail AI at Work! Massive customer benefits: 1. personalized merchandising > increases customer satisfaction and brand loyalty 2. complete lifecycle pricing > drives customer demand, satisfaction and revenue 3. optimized inventory > increases margins, sales and profits, reduces waste