Description
Optimise supply chain including logistics, procurement timing and inventory distribution across warehouses and stores
Benefits & ROI
1.1
Linked Case Studies
Case Study
Hitachi
Hitachi conducted an on-site demonstration of a warehouse management system equipped with its AI technology where results showed efficiency improvement in logistics tasks
Case Study
Otto
Otto reduces the rate of product returns by predicting sales for the next three months with 90% accuracy using machine learning to understand consumer preferences
Case Study
Lineage Logistics
Lineage is boosting warehouse efficiency by 20% with AI powered smart placement
Case Study
Eco Marine Power
Eco Marine Power unveils automated control and monitoring systems for ships
Case Study
UPS
UPS saves 10 million gallons of fuel and $100 million per year by optimising driver delivery routes that consider real-time traffic and weather information obtained from social media and machine learning
Case Study
UPS
UPS aims to avoid trouble spots in its global network by rerouting packages away from snow with machine learning
Case Study
Shell
Shell saves over a million dollars annually by doing inventory analysis 32 times faster using machine learning
Case Study
Instacart
Instacart predicts availability of 200 million grocery items every 30 minutes using machine learning
Case Study
Stitch Fix
StitchFix keeps track of customer behavioural and purchase patterns to predict demand and manage inventory using state transition matrices and Markov chain models
Case Study
Orient Overseas Container Line Limited (OOCL)
Orient Overseas Container Line Limited (OOCL) looking to optimise shipping operations with deep and reinforcement learning
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Source: mckinsey.com · Editor: original-sdg