Supply chain data connects to in-store analytics through shared, real-time data flows: the same transaction, shipment and selling records that drive replenishment also describe what is happening in the store 1. Retailers can now use centralized supply chain analysis that provides real-time insights to optimize inventory and logistics 2, and real-time insights of this kind support both supply chain optimization and prediction of customer behaviour 3.
Where the two sets of data meet
- Big data can inform store design and inventory management while increasing supply chain efficiency, with retailers combining datasets such as sales histories, weather predictions and seasonal sales cycles so the right goods are in stock at the right time 4.
- Real-time selling data lets merchandising teams build assortments for each location, allocating more of a certain colour, fit or size to a specific store's inventory 1.
- Attribute level data captured at point of sale can feed analytics and machine learning models that improve demand forecasting, replenishment accuracy, shelf-life optimisation and loss prevention 5.
- Operational analytics helps optimize inventory management and supply chain operations, while predictive analytics forecasts demand 1.
What blocks the connection
Retail generates many petabytes of data every hour, yet customer touchpoints are often not linked and systems and data sit siloed and disconnected 6. Real-time data has to be connected to be valuable, which means building an intelligent supply chain that lets all partners track products and forecast how long delivery to specific customers will take 7. Once connected, the payoff is measurable: one retailer applying data analytics shrank inventory holdings by 25% 8, and analytics further refines inventory management by minimizing waste and improving responsiveness to demand fluctuations 9.