The Wall Street Journal Spoke with our CIO, Laura Rea Dickey about our Advanced Analytics Solution, Smoke Stack
Published: 29 May 2015
Dickey’s Barbecue Pit, a fast-casual chain known for its hickory wood-burning pits, has dramatically improved its ability to assess the success of new menu items. What once took a month to evaluate now happens in just one day, thanks to a new data system that consolidates information from the company's 500 locations.
The barbecue chain has turned to cloud technology and Big Data to help boost efficiency and profitability as it competes with fast-casual giants like Chipotle. The system, called Smoke Stack, is a combination of cloud and Big Data tools, developed in partnership with iOLAP Inc., a consulting firm. The system integrates software from companies like Syncsort, Yellowfin, and Amazon Web Services (AWS), including data warehousing tools like Amazon Redshift. Smoke Stack pulls data from point-of-sale systems and combines it with other operational data, such as promotions, food purchasing patterns, media buys, and inventory levels.
Currently in use by 175 corporate users and beta testers, Smoke Stack is set to be rolled out to 350 restaurant operators in the coming week. One of the key components of the system is an agent that gathers data from each store’s point-of-sale system. These systems often have different software versions or data formats, so the challenge was ensuring the data could be pulled consistently and uploaded on a set schedule. To streamline this, each record is timestamped with location and date information.
Smoke Stack has also made it easier for Dickey’s to manage its inventory. The system allows the company to analyze varied datasets quickly, helping it keep stock-keeping units (SKUs) low. With a menu featuring just eight proteins served in two ways, along with side dishes and desserts, Dickey’s aims to limit SKUs to about 125. While exceptions are made for local favorites or test items, the company believes that tighter inventory translates to better efficiency and profitability.
The company evaluates each menu item based on five criteria: brand alignment, quality, profitability, guest response/sales, and preparation complexity. All five must be met for a dish to become a permanent menu item. For example, chili was once considered for the menu, but it didn’t meet the necessary criteria. It required too many ingredients and too much prep time, and it didn’t sell well, so it was eventually removed. Before the Smoke Stack system, gathering the data needed to evaluate chili’s performance would have taken weeks. Now, that analysis can be done in a single report, and decisions can be made quickly.
In addition to improving menu evaluations, Dickey’s has used Hadoop—a tool for handling large-scale data manipulations—for tasks like tracking when each point-of-sale system completes its end-of-day processes. Using AWS and Hadoop's MapReduce feature, the company was able to parse and analyze millions of records in just minutes, something that would have been much harder to achieve with traditional relational databases.
Sales data, which previously refreshed daily, now updates every 15 to 20 minutes. This real-time information can be cross-referenced with marketing data and other metrics, enabling Dickey’s to adjust operations and strategy quickly. The corporate team now reviews key performance indicators daily, and these findings shape the next day’s business decisions, including meetings, marketing initiatives, and training sessions.
In short, Dickey’s Barbecue Pit is using data and cloud technology to not only stay competitive but to make more agile, informed decisions that drive efficiency and profitability.
The barbecue chain has turned to cloud technology and Big Data to help boost efficiency and profitability as it competes with fast-casual giants like Chipotle. The system, called Smoke Stack, is a combination of cloud and Big Data tools, developed in partnership with iOLAP Inc., a consulting firm. The system integrates software from companies like Syncsort, Yellowfin, and Amazon Web Services (AWS), including data warehousing tools like Amazon Redshift. Smoke Stack pulls data from point-of-sale systems and combines it with other operational data, such as promotions, food purchasing patterns, media buys, and inventory levels.
Currently in use by 175 corporate users and beta testers, Smoke Stack is set to be rolled out to 350 restaurant operators in the coming week. One of the key components of the system is an agent that gathers data from each store’s point-of-sale system. These systems often have different software versions or data formats, so the challenge was ensuring the data could be pulled consistently and uploaded on a set schedule. To streamline this, each record is timestamped with location and date information.
Smoke Stack has also made it easier for Dickey’s to manage its inventory. The system allows the company to analyze varied datasets quickly, helping it keep stock-keeping units (SKUs) low. With a menu featuring just eight proteins served in two ways, along with side dishes and desserts, Dickey’s aims to limit SKUs to about 125. While exceptions are made for local favorites or test items, the company believes that tighter inventory translates to better efficiency and profitability.
The company evaluates each menu item based on five criteria: brand alignment, quality, profitability, guest response/sales, and preparation complexity. All five must be met for a dish to become a permanent menu item. For example, chili was once considered for the menu, but it didn’t meet the necessary criteria. It required too many ingredients and too much prep time, and it didn’t sell well, so it was eventually removed. Before the Smoke Stack system, gathering the data needed to evaluate chili’s performance would have taken weeks. Now, that analysis can be done in a single report, and decisions can be made quickly.
In addition to improving menu evaluations, Dickey’s has used Hadoop—a tool for handling large-scale data manipulations—for tasks like tracking when each point-of-sale system completes its end-of-day processes. Using AWS and Hadoop's MapReduce feature, the company was able to parse and analyze millions of records in just minutes, something that would have been much harder to achieve with traditional relational databases.
Sales data, which previously refreshed daily, now updates every 15 to 20 minutes. This real-time information can be cross-referenced with marketing data and other metrics, enabling Dickey’s to adjust operations and strategy quickly. The corporate team now reviews key performance indicators daily, and these findings shape the next day’s business decisions, including meetings, marketing initiatives, and training sessions.
In short, Dickey’s Barbecue Pit is using data and cloud technology to not only stay competitive but to make more agile, informed decisions that drive efficiency and profitability.