Running a chicken wing restaurant is not rocket science.
But it’s more complicated than one might think.
If, say, you’re managing supplies for hundreds of chicken wing franchises nationally, analyzing income against expenditures requires the parsing of many thousands of factors, variables and scenarios. Consider: wings are bought by the pound and sold by the piece, so cost by the pound must be tracked against sales by the piece. While that’s fairly straightforward, the problem is that chickens bred in the South are larger than those bred in the Northeast, so the calculation of income against expenditure must be adjusted on a regional basis. Then there’s the matter of serving sizes – three, five or seven pieces, for example, with a discount applied to larger portions. Here, you need to track consumption patterns and their impact on profit margins. And don’t forget about managing inventories of six or seven varieties of sauces and monitoring which ones move and which ones sit on the shelves.
Compiling lots of data is relatively easy; parsing it to know what to do with it is the tough part.
Hawk-like oversight of expenditures against sales at a granular level is necessary to ensure that waste – whether it’s chicken, sauces, spices, plates or napkins – is minimized throughout the supply chain. And any restaurant, whether it’s serving low-end fast food or high-end foi gras, has to minimize waste in order to survive.
The challenge restaurant chains face when collecting and analyzing data is to translate the micro detail of individual orders and sales to a bigger picture of trends that yield actionable insight. Put differently, compiling lots of data is relatively easy; parsing it to know what to do with it is the tough part. Moreover, at the individual restaurant level, managers focused on creating good food and keeping tables full are unlikely to specialize in the nuances of data science. Rather, they seek straightforward and specific direction on what’s moving, what isn’t and how to use that information to optimize their purchasing and consumption of supplies.
Today, a number of restaurant chains are exploring the potential of Artificial Intelligence (AI) and cognitive tools that automate data collection and analysis to enable a better understanding of restaurant performance. In terms of analytic capabilities, adopting smart tools is analogous to moving from a handheld calculator to an Excel spreadsheet. Through more granular tracking of multiple metrics, restaurants can, for example, more closely gauge the impact of increased sales on profitability. This is critical, since restaurants focused on increasing sales often find that the growth in revenue is offset by a corresponding increase in expenses – resulting, in other words, in a wash.
Smart tools can allow restaurants to hone their purchasing strategies to better manage inventory, particularly around perishables.
In addition, cognitive applications use pattern recognition capabilities to discern trends and correlations between seemingly random data sets, allowing restaurants to do cause/effect analyses of multiple and often confounding variables. For example, was the spike in chicken wing sales last weekend the result of a discount coupon campaign? Radio ads? Or a major sporting event?
By combining detailed analytics of sales and expenditures at the micro-level, along with fact-based insight into the multiple factors impacting demand and sales volume, smart tools can allow restaurants to hone their purchasing strategies to better manage inventory, particularly around perishables. And for an industry built on razor-thin margins, reducing waste even by even minimal amounts can be significant. Moreover, if pennies saved from reduced waste are reinvested into proven marketing initiatives, businesses can create a virtuous circle of increased profitability.
Today, a variety of service providers offer cognitive tools and platforms that can be configured to a restaurant chain’s requirements. While interest in the industry is growing, adoption to date has been relatively slow, with cost being the primary obstacle. A platform that costs $25,000 a year in implementation and licensing fees is a significant investment for a franchise operator that might be making $100,000 a month in sales.
Some smaller chains are pursuing models whereby a provider implements a solution at a discount in exchange for advertising and testimonials. Others are considering a cooperative approach, where cost and usage is shared. Ultimately, however, larger chains with the scale and resources to cost-effectively roll out smart solutions will likely be the first to reap the benefits and gain a competitive edge.