Poster
Client Context
Inspire Brands is a multi-brand restaurant company whose current portfolio nearly 32,000 Arby’s, Baskin-Robbins, Buffalo Wild Wings (BWW), Dunkin’, Jimmy John’s, Rusty Taco, and SONIC Drive-In locations worldwide. The company was founded in 2018 and is headquartered in Atlanta, Georgia.
This project particularly focuses on the BWW's beer selection process. The brand sells beer in three ways: through bottles, cans, and taps, with the majority of beer sold from taps. When determining which beer brands to place on tap, the corporate locations’ product mixes across the nation are determined at the corporate level and consider a variety of factors, such as regionality, sales, and consumer preferences. Beer brands placed on tap range from nationally recognized brands to regionally recognized brands.
Project Objective
After the acquisition of Buffalo Wild Wings in 2018, the Inspire Brands management team identified a discrepancy in the brand’s proportion of total sales from beer compared to the industry standard for sports bars. The team stated that this difference stems from inconsistencies between beer supplied and demanded and a lack of data-driven insights within the beer selection process. The team expressed the need to move from a corporate-level selection process to a sports bar-level selection process to account for the growing shift in demand from national, well-recognized brands to regional, hyper-local brands. With these challenges in mind, two opportunities have been identified to address the design problems. First, create product demand forecasting on at the sports bar level, and second, streamline the beer selection process. Both opportunities are completed as a proof of concept based upon the sports bar locations in Georgia. By doing so, the proposed system will generate the most profitable product mixes on a sports bar-by-sports bar and month-by-month basis. The objective of this project is to increase profit in a two-fold process by 1) increasing revenue through capturing market potential and 2) decreasing costs through minimizing unused product.
Design Strategy
To address the challenges, the design strategy is divided into three distinct stages: the training, processing, and solving stage. The first stage is the training stage. This stage utilizes a product demand forecasting model trained on historical sales data to understand which beers worked well on tap and why. The next stage is the processing stage. This stage utilizes the product demand forecasting model’s predictions to calculate the marginal value (i.e. additional profit) of adding a beer style to a product mix. Lastly, the solving stage utilizes an integer program optimization model that evaluates the marginal values for each beer style and solves for the product mix of highest profit. Lastly, the final output/deliverable is the monthly selection of beer styles at each sports bar, constrained by the number of taps available at a particular bar. The deliverable also contains the projected volume demanded for a beer during a given month. Each stage possesses its own validation approach, ranging from performance plotting to statistical analysis to sensitivity analysis.
Deliverables
The deliverable provided to the client encapsulates the three stages of the design strategy into a user-friendly application, called the Product Mix Selection Application. Once implemented, this application can utilize any inputted data from a given time period and region in the country. The Product Mix Selection Application, a locally-run desktop application, enables the Buffalo Wild Wings team to generate sports bar-specific monthly product mixes. The application’s interface utilizes human-computer interaction design principles and is minimalistic and intuitive. In addition, an accessible Help window, regular feedback on calculation progress, and carefully designed affordances aid in user adoption. In a series of button clicks, users have the ability to upload point of sales data, cost data, and promotional data to train the model. The last step outputs a CSV file of sports bar-specific monthly product mixes for the desired time period, including a range of values for expected demand.
Project Information
Student Team
Diego Granizo
Ashwin Haritsa
Selin Karaoguz
Philip Murray
Katie Neil
Wilson Pu
Katie Wah
Andrew Yowell
Faculty Advisor
Faculty Evaluator
