Retail supply chain professionals were stuck using outdated data and guesswork to try and forecast demand and how stock should be allocated as a result — something that was virtually impossible to get right. Those days are now gone thanks to AI.
Grocers must create a holistic view of consumers by orchestrating data-driven insights to feed demand-planning analytics that increase relevant recommendations, promote in-stock merchandise, and improve customer satisfaction. These are the steps grocers can take.
Reinventing the supply chain is underway — RIS explores how artificial intelligence, machine learning, and automation are sculpting new supply chain models in this interactive report.
Using “lift-and-learn” technology, consumers can obtain information about Unilever products by touching or lifting them off the end cap — without having to scan a QR code, touch a screen, or download an app.
FLO aimed to increase forecast accuracy, maximize availability, and optimize inventory smartly to improve supply chain efficiency. Learn how it's efforts helped the footwear retailer to reduce lost sales by 12% and increase availability 23%.
Benchmark your organization’s tech maturity against more than 80 distinct retail solutions. Discover where your peers are making significant tech investments and what you need to do to keep pace.
Dick’s Sporting Goods is leveraging its store investments to communicate product demand levels with suppliers in a new way, as well as investing in machine learning models to predict store sales down to the SKU level.
With AI-powered inventory solutions, Migros has achieved an 11% reduction in inventory days, alongside a 1.7% increase in inventory availability, across its entire retail and supply footprint.
To meet shopper expectations and deliver a seamless customer experience across all advertising channels — including in-person experiences — retailers should consider Audio-Out-Of-Home (Audio OOH) to complement their retail media strategy. Here are 3 reasons why.
When the COVID-19 pandemic hit the world, retail supply chain executives came to the painful realization that traditional forecasting models based on historical sales data were insufficient in predicting sales and demand.