Digital transformation in manufacturing has the potential to extend annual world financial worth by $4.5 trillion in line with the IDC MarketScape.i With a lot upside, producers are taking a look at how applied sciences like IoT, machine studying, and synthetic intelligence (AI) can be utilized to optimize provide chains, enhance manufacturing unit efficiency, speed up product innovation, and improve service choices.
Digital transformation begins by amassing knowledge from machines on the plant ground, belongings within the provide chain, or merchandise being utilized by clients. This knowledge will be mixed with different enterprise knowledge after which modeled and analyzed to achieve actionable insights.
Let’s check out three producers—Festo, Kao, and AkzoNobel—and see how every one is utilizing applied sciences like IoT, machine studying, and AI to speed up their digital transformation.
Offering predictive upkeep as a service
Primarily based in Germany, Festo sells electrical and pneumatic drive options to 300,000 clients in 176 nations. The corporate’s purpose is to extend uptime for purchasers by offering predictive upkeep choices as software program as a service (SaaS) choices. Festo’s technique is to attach machines to the cloud with Azure IoT after which allow clients to visualise knowledge alongside the complete worth chain.
One of many first SaaS choices is Festo Dashboards constructed on Azure. Festo Dashboards offers a transparent and intuitive standing of apparatus like sensor temperatures and valve switches. With Festo Dashboards, producers can extra simply monitor power consumption, shortly diagnose faults, and optimize manufacturing availability.
Anticipating shopper traits for higher manufacturing forecasting
Kao, considered one of Japan’s main shopper manufacturers, sees the buyer market evolving. In the present day, shoppers prioritize their product expertise over product high quality. Additionally they look to social media for buying steering. These behaviors result in forecasting challenges. To maintain up with these adjustments, Kao sought to higher perceive particular person clients and categorize traits into micro-segments. The corporate phrases this method “small mass advertising and marketing.” Kao designed a knowledge evaluation platform utilizing Microsoft Azure Synapse Analytics and Microsoft Energy BI to foretell shopper traits for his or her detergent, beauty, and toiletry merchandise. The Kao staff mixed knowledge from real-time purchases, social media, and historic gross sales. Kao competes extra successfully utilizing predictive fashions, and chain retailer staff are empowered with real-time info for promoting.
Lowering the event time of recent paint colours
Dutch paint and coatings chief, AkzoNobel, is energetic in additional than 100 nations. The corporate has honed the artwork of colour matching for 2 centuries for automobiles, buildings, and interiors. One of many firm’s companies is growing the paint to restore automobiles when drivers have an accident. Producers within the automotive and different industries always dream up new finishes to provide their fashions an edge on the competitors.
To maintain up with fast fee of change, AkzoNobel launched Azure Machine Studying into its colour prediction course of. Beforehand, scientists labored painstakingly in labs to regulate, recalibrate, and tweak a colour till it was excellent. The corporate labored with its scientist and technicians to combine machine mearning into their growth course of. The primary affect is seen within the lab, the place groups are actually capable of create extra colour recipes, extra precisely, in much less time. Beforehand, it might take as much as two years to get a automotive colour prepared. Now AkzoNobel is seeing new paint colours prepared in a single month.
For concepts on accelerating your digital transformation journey obtain, The Highway to Clever Manufacturing: Leveraging a Platform, co-authored by Microsoft and Capgemini.
i IDC MarketScape: Worldwide Industrial IoT Platforms in Manufacturing 2019 Vendor Evaluation