Description
Predict product demand trends to inform production decisions
Benefits & ROI
0.7
Linked Case Studies
Case Study
SteadyServ
SteadyServ manages inventory in real-time using a smart draft beer management system which monitors in-store inventory and analyses sales data to predict demand accurately
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
H&M
H&M improve single store sales by improving inventory planning through machine learning to discover trends on social media
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
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Source: mckinsey.com · Editor: original-sdg