INNOVATION


the owner can spend time on hiring, training and operational matters, rather than looking at inventory levels.” That functionality is useful, but it represents only the most basic use of data. “Predictive use of data is evolving for more sophisticated operators, though not for independents,” says Bender. “It is focused on data analysis, and while the data gathering is good, the analysis is not there yet. It needs the technology to evolve to become more user-friendly. That is where artificial intelligence (AI) will come in.” “Foodservice has been a slow adopter of tech for the last 50 years but needs leadership and management to know how to execute and monitor these things,” he adds. “We are still in


a people business, and we need to know what you want from the data, otherwise you just get lost in it.”


“HUMANS ARE GOOD AT HOSPITALITY, BUT COMPUTERS ARE BETTER AT MANY THINGS. THEY DON’T SLEEP AND THEY WORK 24/7, PLUS THEY UNDERSTAND WHAT HUMANS DON’T NEED TO, SUCH AS ORDERING, PREDICTIVE ANALYSIS, INVENTORY, MUSIC, LIGHTING AND SCENTS”


Pricing and the power of prediction The history of data gathering in foodservice is littered with examples of businesses that had all the data but gained little insight. Back in 2008, for example, Merrychef


had an estate of more than 3,500 connected ovens, each collecting data on many parameters. It had the largest connected estate of ovens but was simply generating an overflow of data the company did not know how to use. “Every client wants every piece of equipment connected, but the challenge they face is how to turn that data


into information,” says Phil Radford, senior vice president at equipment manufacturer Welbilt. Step by step that challenge is being


met. McDonald’s, for example, uses POS systems to inform ordering processes, with a growing element of smart forecasting. A system that can connect to local events can predict spikes in demand, as passing traffic will increase. The England team playing at Wembley Stadium will inevitably increase demand at local fast-food restaurants. Connecting to weather data can, for example, help predict more ice cream sales in hot weather. This is the realm of Big Data. For equipment manufacturers,


data is starting to have a major impact on how they generate revenue. “The best examples are pay-per-use models for coffee machines,” says Radford. “Some customers have not bought the equipment but pay a standard fee each month and then pay per use. It can be per cook for an oven, or per hour of steam for a steamer. This reduces the barriers to entry and is all based on the monitoring of data, which can be pulled from cloud services and turned into a fee model.” Six years ago, Visacrem launched a revolutionary machine-to-machine (M2M) system that remotely collects and provides real-time operations data from its espresso machines. The data goes to companies that own or manage the machines, enabling them to improve service, analyse their markets and fine-tune strategy. It gathers data on the number of coffee cycles performed and informs


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