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IBM and SAP to Offer New Co-Created Industry Solution for Retail and Consumer Packaged Goods Industries

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IBM (NYSE: IBM) and SAP SE (NYSE: SAP) have teamed up to provide a co-innovated solution for the retail and consumer packaged goods industries to help both increase profitability and improve the consumer experience.

The solution uses near real-time data to markedly improve planning and execution in the physical store. Unique data sources, such as IBM’s Metro Pulse, flow through SAP® Cloud Platform to provide insights that can be acted upon almost immediately. All of these transformative insights for better decision making are fueled by live business, unique near real-time market-demand signals and a digital core that helps make the execution possible.

This is the first plan for an industry focused solution resulting from the two companies’ digital transformation partnership announced last year. It is one of several industry-specific digital solutions currently in development. It also expands upon the investment IBM has made in retail and consumer packaged goods customers with SAP S/4HANA industry solutions. The two companies also intend to collaborate on SAP Model Company services, a prepackaged, ready-to-use, end-to-end reference solution that can be customized to meet specific line-of-business and industry needs to accelerate the time to value.

IBM Metro Pulse uses IBM’s cognitive services to provide hyperlocal insights around weather, events, traffic and demographics and helps address key industry challenges such as on-shelf availability and demand forecasting accuracy.  During trials of this cognitive technology across more than 100 stores in multiple American markets, the solution improved forecasting accuracy of volatile, hard-to-forecast products by 75 percent.

SAP’s expertise will bring together the various unique data sources, including IBM’s cognitive capabilities, on SAP Cloud Platform, allowing retailers to identify and take near-immediate action on these new insights to help improve business performance.

For example, a New York City sales operation manager for a beverage company may try to forecast sales during the New York Marathon at one of her team’s stores along the running route. Using this new solution, she could incorporate information on traffic flows at different points along the route, as well as the impact the weather forecast has on demand. This allows her to predict with much greater accuracy the spikes in demand at a store level.  Based on these insights, the sales operation manager can create tasks and suggested orders for sales representatives, who in turn can discuss with each individual store manager how to make the appropriate product adjustments expected to maximize incremental sales for both parties.

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