iTradeNetwork Introduces Machine Learning File Monitoring A Fresh Take on a Foodservice Data Problem

iTradeNetwork (ITN), the industry’s largest perishables network, proudly announces its new Machine Learning File Monitoring system for Spend Insights— a unique, automated way to provide foodservice operators with the most complete distributor data in the industry. Operators can now devote less time to monitoring and correcting their data, and more time to making key decisions faster and unlocking savings and growth opportunities.

Before making any decisions about sourcing, contracting, or rebates, operators need to be confident those choices make financial sense for their business. As a result, foodservice operators face some key questions: “Do I have the best spend data I can get?” and “Can I rely on what I have to make strategic decisions?”

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Given their size and scope, operators sometimes devote entire teams to answering these questions. A finance or operations team can spend hundreds of hours manually monitoring thousands of incoming files from various distribution centers in order to aggregate data from all of an operator’s units. This process can be unpredictable and error-prone. Without complete visibility into the state and accuracy of their data, operators can’t be sure they have the right information at the right time.

With iTrade’s Machine Learning File Monitoring, operators can quickly identify the typical 2-3% of their missing spend —and in a fast-moving industry, the completeness of your data reaps far-reaching financial benefits for your organization. iTrade’s machine learning-based file monitoring goes beyond just determining whether or not a distributor data file has been received. It:

• Analyzes the number of files expected weekly from individual distribution centers
• Provides the expected date of file receipt from individual distribution centers
• Confirms distributor data files have been processed or should have been processed but have not been received
• Assigns a confidence level to each distribution center based on the machine learning model to give context to decision making

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In short, it analyzes and interprets incoming distributor data, detects anomalies based on volume and history, flags files requiring additional follow-up, and provides a scorecard for all of the distribution centers providing data to foodservice operators.

“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,” comments Wills McMahon, Director of Product Management, iTradeNetwork.

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