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 facility efficiency, speed up product innovation, and improve service choices.
Digital transformation begins by gathering knowledge from machines on the plant flooring, belongings within the provide chain, or merchandise being utilized by prospects. This knowledge could be mixed with different enterprise knowledge after which modeled and analyzed to realize 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 prospects in 176 international locations. The corporate’s objective is to extend uptime for patrons 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 prospects 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 kit like sensor temperatures and valve switches. With Festo Dashboards, producers can extra simply monitor vitality consumption, shortly diagnose faults, and optimize manufacturing availability.
Anticipating client developments for higher manufacturing forecasting
Kao, one among Japan’s main client manufacturers, sees the patron market evolving. At present, 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 modifications, Kao sought to raised perceive particular person prospects and categorize developments into micro-segments. The corporate phrases this strategy “small mass advertising and marketing.” Kao designed a knowledge evaluation platform utilizing Microsoft Azure Synapse Analytics and Microsoft Energy BI to foretell client developments 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 workers are empowered with real-time data for promoting.
Lowering the event time of recent paint colours
Dutch paint and coatings chief, AkzoNobel, is energetic in additional than 100 international locations. The corporate has honed the artwork of shade matching for 2 centuries for vehicles, buildings, and interiors. One of many firm’s companies is creating the paint to restore vehicles when drivers have an accident. Producers within the automobile and different industries consistently dream up new finishes to offer their fashions an edge on the competitors.
To maintain up with fast price of change, AkzoNobel launched Azure Machine Studying into its shade prediction course of. Beforehand, scientists labored painstakingly in labs to regulate, recalibrate, and tweak a shade 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 in a position to create extra shade recipes, extra precisely, in much less time. Beforehand, it may take as much as two years to get a automobile shade prepared. Now AkzoNobel is seeing new paint colours prepared in a single month.
For concepts on accelerating your digital transformation journey obtain, The Street to Clever Manufacturing: Leveraging a Platform, co-authored by Microsoft and Capgemini.
i IDC MarketScape: Worldwide Industrial IoT Platforms in Manufacturing 2019 Vendor Evaluation