On the third and final day of the 2018 HotSchedules Spark Conference, HotSchedules National Customer Success Manager Jesse Gutierrez and Senior Pilot Program Manager Dylan Wattecamps dove into the intricacies of Activity Based Forecasting (ABF).
HotSchedules ABF is part of our industry-leading, POS-integrated labor solution, helping restaurants optimize labor based on historical customer demand and labor rules. The magic word here is “optimize” — scheduling the exact number of employees (both front-of-house and back-of-house) needed to meet your typical customer flow based on the day of the week and the shift time. Some ABF implementations have resulted in lower labor costs, while others found that an increase in labor leads to an increase in top line sales.
How Does Activity Based Forecasting Work?
During their presentation, Gutierrez and Wattecamps provided an explanation of how HotSchedules ABF works. Here’s the breakdown:
- Select Data for Forecast: You select 5-8 weeks that you feel are most representative of the upcoming week at your restaurant. It could be the last 5-8 weeks or the last rolling 5-8 weeks a year ago or the first 5-8 weeks of the last four months — it’s up to you. In those weeks selected, ABF will see recurring events like daylight savings, weather patterns, holidays, and regularly occurring trends. Additionally, for one-off activities, you can program based on last week’s road construction or a high school football game.
- Establish Labor Rules: You give ABF all the logic in your head for deciding who works when and where in the restaurant. For example, you could set a ratio of servers as one per every $100 of sales, or one server for every five tables. You could also set two hosts for lunch, one bartender per bar, one cook per kitchen station, etc. Operators can set limits within the system on minor hours worked, ACA requirements, and predictive scheduling concerns. Furthermore, you can assign based on the revenue center such as bar vs. dining room vs. take-out, to ensure that you have optimal staffing for every spot in your restaurant where transactions occur.
- Generate Optimal Schedule: Once you’ve picked your data and established your labor rules, then ABF crunches all the numbers to give you the number of workers by position to meet the customer service expectations aligned to your sales forecast.
- Evaluate, Learn and Improve: You will see a graph of how your scheduled labor is performing against forecast and actual sales. The forecast will be as trustworthy as your inputs. If your inputs need tweaking, adjust the methodology going forward. ABF will get smarter over time and the more feedback you give, the smarter both of you become (more cash to the bottom line)!
Gutierrez and Wattecamps noted that the type of restaurant you operate also is part of the calculus. Transactions may be your primary labor driver while for many full-service customers, guests and entrees are more important to establishing optimal labor.
Landry’s: A Case Study in ABF Success
Landry’s is an ABF Power User and has seen a great deal of success with the soluion. Morgan Dufrene of the Landry’s IT team share his success deploying ABF across more than 300 Landry’s locations. In a sample of five Bubba Gump Shrimp Company locations, Dufrene was able to account for 47.2 weekly hours of labor cost saved over 7 months, yielding $211,000 in labor savings.
Dufrene said the key to this success was an organizational alignment that stems from the following factors:
- Buy-in at both the corporate and local store levels,
- Manager enthusiasm to try something new knowing the possibility of improving operations
- Communication across the organization
- Testing and piloting before deploying across all regions
- Tracking performance
Presenters all agreed that visibility into what is working encourages ABF adoption while giving above store managers concrete talking points to guide and coach managers to constantly improve operations. Below, you can view a short clip of the actual ABF tool in action.
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