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Make Sure You Measure Up: Optimize Labor Against Internal & External Benchmarking

What was the state of reporting in the restaurant business 20 years ago? Heck… what about 10 years ago? Think about it: how did you collect key metrics from your stores? You aggregated data and used it for simple projections, forecasts, financials and to do what you could to optimize labor? Ten years ago people […]

What was the state of reporting in the restaurant business 20 years ago? Heck… what about 10 years ago? Think about it: how did you collect key metrics from your stores? You aggregated data and used it for simple projections, forecasts, financials and to do what you could to optimize labor?

Ten years ago people were data hungry. They wanted more and more data squeezed onto giant eyesores, err … charts.

A LOT has changed over the years, hasn’t it? Today, we’re almost on the other end of the spectrum. Most systems allow you to collect massive amounts of data.

And therein lies one of the single biggest challenges in-store teams and their IT departments face: how to organize and make sense of the in-store, near-store and above-store data to drive the right decisions and business performance.

HotSchedules Product Manager Jeff Kingery, a 20-year restaurant technology veteran, set out to untangle the disconnect in all of the restaurant tech at the 2017 HotSchedules Spark Conference, during a session titled Make Sure You Measure Up: Optimize Your Labor Against Internal & External Benchmarking.

Here are our big takeaways from Kingery’s reporting presentation:

1. Advanced reporting and forecasting are the new normal.

If there was ever a competitive advantage (outside of great teams and culture), the ability to get more predictability around labor, inventory and the financial performance of the operation is it. What that requires is more advanced forecasting and reporting. While that’s a no-brainer for above-store leaders, it’s not always an easy concept to grasp for store-level managers. Which doesn’t always mix well with new managers.

2. Let the data do the thinking for you and your managers.

As an industry, we’ve been saying back office technology is going to get managers out of the back office and back on the floor … but the traditional back office hasn’t solved for that. The next wave of enterprise back-office technology is going to completely revolutionize the ways restaurant managers move through their day and how they make decisions. Managers will finally have the time to manage the margins, manage the people and control the operations. Restaurant managers are mini-CEOs of, in some cases, a multi-million dollar business but they aren’t always trained or skilled to manage it right out of the gate. Finding back office platforms that can marry internal and external data and serve it up in a way that not only meaningful for managers but actionable is going to dominate CIO and COO conversations. 

3. Managers need coaching to go from “guts” to glory to optimize labor.

In-store managers may not trust the forecast completely. They see that the forecast suggests an order of 10 cases of chicken and the manager says “where is that number coming from? My gut and experience tell me we need 15.” So they order 15 because they think they know better. The same goes when managers need to optimize labor. Their gut tells them they may only need 8 servers on staff for the dinner shift. But a change in the weather forecast could make that number too high or too low. It may take some time to get managers to “trust” the system, but once they see the impact data can have on their labor costs, they’ll start to come around.

4. Benchmarking and machine-learning drive better decisions.

How are we doing compared to the store across the street? All of our exit surveys say employees are leaving for money – but how much are our competitors paying? How do my comps compare to other similar stores within my franchise brand?

This line of questioning is really only the tip of the iceberg. In business, we benchmark against ourselves first and then as we get better rigor we start to look outward. In the restaurant industry, there a load of public data available that can be collected and then used in the context of your own operation’s performance.

As for machine learning – it’s not as “big brother”-y as it sounds. Machine learning looks at data from each store and then provides context and recommendations (i.e. predictability and visibility) related to that particular store’s unique characteristics.

5. Flexible reporting is necessary to navigate lots of data across an organization.

Ten years ago, the industry was on the cusp of the evolution toward dashboards and targeted, customizable reporting. The technology has finally caught up to the types of requests the various stakeholders have been asking IT to produce. Aggregated data that can be married to create Insights is possible if IT teams are willing to take the leap toward new back-office platforms that provide unified reporting and open-API call to third-party integrations. Even better, it’ll cost a whole lot less on the maintenance and deployment side.


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