Did you know that approximately 15-20% of food items are wasted during NFL games at the stadium food counters? Assuming 50,000 consumers with an average spending of $8, that amounts to $60,000 to $80,000 worth of sandwiches, nachos and bratwurst getting wasted. That’s a lot of money!
In order to reduce this wastage and at the same time not have a shortfall of food items, Cygnet was assigned the task of developing a predictive model of food orders placed during the games. The model is supposed to analyze the past history of food orders during NFL games and then develop an algorithm to predict orders in the next game.
Cygnet’s client is a provider of analytical services to point of sale solutions in the sports and entertainment industry. These services aim to provide real time insight and automated decision making to game and event organizers in order to strengthen their logistics and operations planning.
Essentially, the task is to predict the inventory based on historical sales figures. In the POC stage, Cygnet is working with limited number of outlet locations and limited number of food items. However, once the algorithm is tested, the full-fledged analytics solution will be developed. The technology used is AI (Artificial Intelligence) algorithms such as ID3 (Iterative Dichotomizer 3) along with CAIM and feedback based statistical forecasting built on Microsoft .NET and SQL server.
Cygnet’s team has been up to the challenge so far with client showing appreciation in the following words - “You, and your team, are definitely headed in the right direction as far as development is concerned, and I am very pleased with the progress up to this point”
Cygnet has always assisted its clients with innovative solutions to business challenges and is always ready to accept the next challenge. Read some of our case studies on the Microsoft .NET technology.