Nittany Data Labs (NDL) is a student organization self-described as Penn State’s “analytic think tank…[existing]to teach students about the wonderful world of data.” The group formed last semester with seven members, and has since grown into the 28-person organization that it is today.
One of the group’s major projects works in conjunction with Penn State Food Services, and it may be a part of saving Food Services hundreds of thousands of dollars over the next few years. This project is a large undertaking for NDL, so it’s split its responsibilities into multiple phases.
For the first phase of the project, NDL used its data sciences division to create a database of temperature information on the shipments that Food Services receives from vendors. If food shipments are not held at the correct temperature during their commute to the warehouse then they are considered non-compliant, and cannot be accepted. This leaves Food Services scrambling to find other sources to make up for the non-compliant shipment.
Before the NDL project, the only information that Food Services office staff had access to was the number of non-compliant shipments per week. Now, the Food Services staff has access to a database dashboard, which includes information about each vendor and their history of non-compliant shipments.
“Our solution allows [Food Services staff] to understand which vendors are doing well, which vendors are doing poorly, and gives them the data to back it up,” said NDL Co-Director Chris Sharkey, explaining that the system allows Food Services to separate non-compliant shipments by vendor in order to see which vendors they may want to consider ending their contracts with.
The next phase of the collaboration with Food Services relates to its recent warehouse expansions. “Everything that [Food Services] buys for industrial use in the kitchens is purchased directly from the manufacturer, which makes it as cheap as possible,” Co-Director Vamshi Voruganti explained. “Things that [Food Services] sells in retail operations like The Mix are purchased from distributors so that they can buy custom quantities, even though it could be slightly more expensive.”
Because of the new warehouse capacities, Food Services now has the space to store additional products in quantities purchased directly from manufacturers. To identify the most effective cost-saving solutions, NDL is working to “structure [purchasing information]into a database to look at what [Food Services] is buying through third-party distributor that they could be buying directly from the manufacturer,” Voruganti said.
The third and final phase of the project involves scanning invoice data from hard copies and converting it to optimal computer character recognition so the data can be analyzed electronically. “From there, we can take the data into a dashboard and write code against it to identify cost savings,” Sharkey said. Processes like this are useful for forecasting future purchasing patterns, and replace the manual ordering system with an electronic system that makes the process repeatable and trainable for new Food Services employees.
“We want to get rid of that spreadsheet and calculator, want them to be able to forecast purchasing from the web portal, and want them to be able to see current and past inventories and know all vendor relationships,” Sharkey said. “This system would optimize everything, saving both time and money for Food Services.”
Sharkey and Voruganti explained that a common problem for companies comes when they want to integrate technology into their daily operations, but aren’t sure how to effectively manage the systems. NDL helps these companies identify what integrating technology would look like for them specifically.
“We’re all figuring this out together, and no one is an expert,” Voruganti said. “If you have a passion for data and you’re willing to learn, come join us.”
Students joining Nittany Data Labs do not need to have any prior experience; the leadership of the organization takes the time to train each new member on both technical and non-technical aspects of their projects through various weekly workshops and training sessions on one of their two distinct tracks: business intelligence or data science. Students who may want to join NDL should fill out an interest form here.