Miele & Cie. KG, a leading manufacturer of high-end domestic appliances and commercial equipment, is announcing a proof-of-concept study based on Microsoft Azure Internet of Things (IoT) services that will help usher in the next generation of smart appliances and home cooking. The concept will allow home users to program their ovens to ensure their meals will come out perfectly cooked, providing an exceptional customer experience.
The new technology comes from a collaboration between Miele and Microsoft to identify new Internet-enabled customer experiences. Based on the partnership, Miele developed this concept, which allows users to browse recipes on Miele’s website and choose from various meals. With the selection of a recipe, the necessary food preparation stages are downloaded to the user’s smartphone or tablet and the matching program is loaded onto the oven through Azure. The oven is programed to cook the specific meal using the proper operating mode, temperature, cooking time, humidity and other factors, taking out guesswork and promising great results.
“This assistance system incorporates temperature charts, times and the machine’s special features, such as adding steam, to create the optimum roasting, cooking or baking results,” said Dr. Eduard Sailer, executive director of Technical Affairs at Miele. “This allows people to get the best results out of their cooking appliances.”
“This is just one example of how the Internet of Things and cloud technology are moving from enterprise experiences to personal experiences,” said Caglayan Arkan, general manager, Worldwide Manufacturing and Resources at Microsoft. “Miele is driving a truly innovative home solution that we are looking forward to experiencing in our own kitchens.”
The use of Azure IoT services offers additional benefits, as it can scale to be made available to Miele customers worldwide. Currently, the joint project is classified as a study, but other applications are conceivable on the Microsoft platform, such as status report, remote diagnostics and predictive maintenance.