This copyrighted case* received Second Place for in the CSCMP 2015 Supply Chain Innovation Award™.Unlike most companies, which rely on over-simplified, top-down forecasts based on highly aggregated historical data to drive targets, this innovative approach to supply chain planning and demand modeling uses advanced analytical techniques to accurately model and forecast demand in an extremely complex and unpredictable business environment--using machine learning to adapt and automatically fine tune the analysis to better distinguish between ‘signal’ and ‘noise’. What’s more, this unique approach uses a single system and demand model both for intermittent finished goods and parts, and provides end-to-end visibility to the entire supply chain (from stores to the manufacturing plants and upstream suppliers) with all activities synchronized to a common demand signal--driven by detailed, bottom-up forecasts for every SKU at every location.Please note: Case study fees are nonrefundable.*© 2015 Council of Supply Chain Management Professionals (CSCMP). Permission is granted by CSCMP to duplicate case study materials for classroom use only to members of CSCMP who are designated by CSCMP as Educator Members. Case study materials may not be sold by any party other than CSCMP. Except as described in this paragraph, no part of the case study materials may be reproduced, stored in a retrieval system, or transmitted in any form by any means without written permission from CSCMP. The case materials may not be uploaded to any public server, online service, network, or bulletin board without prior permission from CSCMP.