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Itradenetwork Unveils Machine Learning File Monitoring: A New Approach To A Foodservice Data Problem

Food and Beverages | Wednesday, March 16, 2022

Operators can now spend less time monitoring and correcting data and more time making crucial choices and uncovering savings and development potential.

FREMONT, CA: Small and independent restaurants should not be concerned about big data because they are unlikely to work with several software vendors. In a nutshell, they probably don't generate enough data. Restaurants of all sizes generate more data so, making data analytics an intriguing area to investigate. iTradeNetwork (ITN), the industry's largest perishables network, is pleased to unveil its new Machine Learning File Monitoring system for Spend Insights, a unique, automated method of providing foodservice operators with the most comprehensive distributor data available.

"This is the first time that the industry has seen something this proactive, complete, and accurate. We are giving you full visibility into your data, so you can quickly and seamlessly identify the files and data that need your attention. We are excited to help our foodservice operator customers redirect their efforts from chasing down data to more productive and value-added activities and our customers are excited too." says Wills McMahon, Director of Product Management, iTradeNetwork.

A finance or operations team can manually monitor thousands of incoming files from numerous distribution centers for hundreds of hours to collect data from all of an operator's units. This procedure might be unpredictable and prone to errors. Operators can't be sure they have the correct information at the right moment unless they have total visibility into the state and accuracy of their data. 

• It analyzes the number of files predicted from individual distribution hubs each week.

• The predicted date of file reception from individual distribution centers is provided.

• Confirms that distributor data files are processed or yet to process but were not received.

• The machine learning model assigns a confidence rating to each distribution center to provide decision-making context.

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