SAG_Twitter_MEME_Chocolate_880x440_Nov19Tick, tick, tick, tick… The packaging machine in the chocolate factory rattles as it handles 550 bars per minute.

First a bar of chocolate is packaged in ultra-thin aluminum foil and then it is wrapped in the printed cardboard wrapper. “Lindt Excellence 70% Cacao” is printed on the packaging.

Occasionally a bar of chocolate falls into a basket. But it looks so normal that I have to ask: “A reject?”

“Not folded correctly,” says an employee.

Lindt has high quality standards. At the slightest deviation from the norm, the chocolate is sorted out and melted down again.

I’m standing in the middle of Lindt’s only factory in Germany, marveling at the cutting-edge production facility and breathing in the sweet smell of cocoa.

Some people envy me for my involvement in the EVAREST research project, funded by Germany’s Federal Ministry for Economic Affairs and Energy.

As part of the first EVAREST consortium meeting, our implementation partner Lindt invited us to tour this facility. Visiting the production halls was to help us project partners develop a sense for this use case - in the middle of all the “data theory.” Gruppenfoto

Why data? Huge amounts of data are generated in food production. In the case of chocolate, data comes from the cocoa plantation, during transport and in the production halls. 

Machines and systems count, measure and collect every possible value with data ranging from the quality of raw ingredients to the seasonal availability of ingredients to current market demand. How many cocoa beans are the farmers harvesting and where? What quality of beans? And which types of beans yield particularly good chocolate?

Until now, this data was only used by farmers, suppliers and the producers - in local data islands, each for themselves alone to keep an eye on their own little part of the chain.

Today, the food industry wants to create from these data streams an additional income source for growers and producers. But this type of data is also meaningful for others, especially if the information is networked.

For example, when a producer of fine chocolate knows about the quality and size of the cocoa harvest early on, the company can plan its procurement better—for instance, if certain quality beans might soon be in short supply. Financial experts would also benefit and could make early, solid predictions about price developments for raw materials. This knowledge is worth its weight in gold.

Project EVAREST was launched in January 2019 with an objective to use data to help manufacturers and producers to optimize production. The global service platform will be based on technology from the consortium, as well as from Software AG - connecting products, facilities, systems and machines to enable the analysis and evaluation of IoT data in a global ecosystem. To learn more about EVAREST and Software AG’s participation please click below.  

And by the way: For anyone who is jealous of my “chocolate day,” I must disappoint you: I don’t actually eat chocolate.

 

Project EVAREST

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