Representative End User Clients
Representative Automation Clients
Representative Software Clients
Airports, commercial office buildings, and retail shopping malls are some the leaders in digital transformation. The urban landscape is undergoing one of the most significant upheavals in decades, as commercially available technologies for the Industrial Internet of Things (IIoT) are making the deployment of these new service models even easier. At the Hanover Fair this year, both Thyssen Krupp and Otis Elevator had a presence in the Microsoft booth. ThyssenKrupp Elevator has long embraced digital transformation as a top priority in the company, and used this event to display their launch of a connected elevator service. This was a great opportunity to get past the high level messaging and get a view into the details of the deployment. Unfortunately, I didn't get a chance to visit with Otis Elevator.
Pain Free Integration
In a partnership with Microsoft, ThyssenKrupp has deployed an intelligent connected monitoring system to provide predictive maintenance services for both the installed base and new elevator installations. The predictive maintenance service uses Microsoft’s Azure Cloud Machine Learning to implement the predictive maintenance algorithms. According to both Microsoft and Thyssen Krupp employees involved with the development, integration was virtually pain free. As you can imagine, over the course of several decades Thyssen Krupp has continued to advance the technology for elevator controls. The challenge for Thyssen engineers was to developed a single embedded solution which integrates into the cabinet of both new and older models of elevators. But, this connection is vital to provide a broad deployment of predictive maintenance services for the customer base.
Services Across Brands
Based on discussions with the Thyssen personnel, the embedded solution can also be integrated into the elevators of their competitors. To achieve this, Thyssen Krupp has reverse engineered the elevator controls of their competitors such that buildings that have a variety of brand of elevators can use a single predictive maintenance service. This was interesting as a similar strategy is being deployed by Rolls Royce in aircraft engine applications whereby the rollout of a predictive maintenance services for jet engines would be extended to support their competitors’ products. In the aerospace industry this is facilitated by the benefits of SAE standards for diagnostic interfaces to aircraft engines. Owner operators are better served by using a single point of contact for multiple brands of equipment. However, it will be fascinating to see how this evolves with more complex equipment whether it is jet engines or tubomachinery.
Adoption Across the Industry is Moving Rapidly
Predictive maintenance services from companies such as Thyssen suggest that the industry is well beyond the early adopter stage of deployments. The primary reason that adoption is increasing is that the infrastructure for the Cloud is in place and the development suites for analytics are extremely easy to use. In speaking with a MicroSoft data scientist the development of predictive learning algorithms in Cortana took under a few months, not years. In a separate conversation with Rolls Royce regarding the port their in house proprietary analytics, it was only a matter of weeks to migrate to Cortana. Companies no longer want to invest in proprietary infrastructure of algorithmic development. The widespread availability of proven technology for IIoT is making inroads because it lowers the cost of providing services. Rolls Royce is putting to rest 30 years of development in a predictive maintenance service solution and replacing it with Microsoft’s Azure Suite. The business justification is obvious as these companies move toward a rental model for these deployments. It is nearly impossible to compete with the scale of companies such as Microsoft with an in house development. Deployments will become increasingly numerous as companies recognize that outsourcing infrastructure reduces the business risk associated with adding new services.
Pain Free Integration
In a partnership with Microsoft, ThyssenKrupp has deployed an intelligent connected monitoring system to provide predictive maintenance services for both the installed base and new elevator installations. The predictive maintenance service uses Microsoft’s Azure Cloud Machine Learning to implement the predictive maintenance algorithms. According to both Microsoft and Thyssen Krupp employees involved with the development, integration was virtually pain free. As you can imagine, over the course of several decades Thyssen Krupp has continued to advance the technology for elevator controls. The challenge for Thyssen engineers was to developed a single embedded solution which integrates into the cabinet of both new and older models of elevators. But, this connection is vital to provide a broad deployment of predictive maintenance services for the customer base.
Services Across Brands
Based on discussions with the Thyssen personnel, the embedded solution can also be integrated into the elevators of their competitors. To achieve this, Thyssen Krupp has reverse engineered the elevator controls of their competitors such that buildings that have a variety of brand of elevators can use a single predictive maintenance service. This was interesting as a similar strategy is being deployed by Rolls Royce in aircraft engine applications whereby the rollout of a predictive maintenance services for jet engines would be extended to support their competitors’ products. In the aerospace industry this is facilitated by the benefits of SAE standards for diagnostic interfaces to aircraft engines. Owner operators are better served by using a single point of contact for multiple brands of equipment. However, it will be fascinating to see how this evolves with more complex equipment whether it is jet engines or tubomachinery.
Adoption Across the Industry is Moving Rapidly
Predictive maintenance services from companies such as Thyssen suggest that the industry is well beyond the early adopter stage of deployments. The primary reason that adoption is increasing is that the infrastructure for the Cloud is in place and the development suites for analytics are extremely easy to use. In speaking with a MicroSoft data scientist the development of predictive learning algorithms in Cortana took under a few months, not years. In a separate conversation with Rolls Royce regarding the port their in house proprietary analytics, it was only a matter of weeks to migrate to Cortana. Companies no longer want to invest in proprietary infrastructure of algorithmic development. The widespread availability of proven technology for IIoT is making inroads because it lowers the cost of providing services. Rolls Royce is putting to rest 30 years of development in a predictive maintenance service solution and replacing it with Microsoft’s Azure Suite. The business justification is obvious as these companies move toward a rental model for these deployments. It is nearly impossible to compete with the scale of companies such as Microsoft with an in house development. Deployments will become increasingly numerous as companies recognize that outsourcing infrastructure reduces the business risk associated with adding new services.