The Industrial Internet Consortium and Industrie 4.0 are seeking to transform not just manufacturing processes through applying IoT sensing for incremental improvement, but the entire process of Product Lifecycle Management, PLM. Their approach combines a number of technologies, and existing applications of technology, into the ubiquitous integrated and connected ecosystems of IoT. The concept of ‘Digital Twins’, the creation of both a physical entity with a corresponding digital entity, is core to this transformation.<!--break-->
In September 2015 The Economist headlined an article; ‘The digital Twin; could this be the 21st-century approach to productivity enhancements?’ The article went on to state; ‘The real advantage of the digital twin, however, materializes when all aspects, from design to real-time data feed, are brought together to optimize over the lifetime of the asset’.
In a similar article the World Economic Forum has drawn attention to the importance of Digital Twins under the headline, ‘Can the digital twin transform manufacturing?’. Yet outside of those directly concerned the concept is little recognized, or understood, even in many manufacturing companies. Certainly not in companies providing Machine Servicing and Support who will perhaps feel even greater impacts on their business model.
Creating a fully functional Digital Twin is a logical enhancement to the increasing sophistication in 3D design to develop the starting point for a complete Digital Services Product Lifecycle Management capability. Complex products are increasing using enhanced 3D designs to model the product for optimization in various aspects; simplicity for manufacturing, operational efficiency, maintainability, predicted wear and failure etc. IoT allows the performance of real installed physical machines to be compared with the predicted behavior observed in the Digital Twin pre production model, and in the resulting physical prototypes.
The simultaneous collection of data, using IoT sensing and connectivity, from operating physical machines can be used to run reiterations of the digital machine model to further increase the accuracy of predicted behavior. The manufacturers of complex products such as GE and Siemens, both leaders in applying Digital Twins to their products, have already adopted much of the necessary technology to improve their in-house design and manufacturing quality.
Riemer, the VP of Aerospace and Defense Strategy at Siemens describes the new business value as moving beyond ‘Digitisation’ of products to ‘Digitalisation’ of process by the use of IoT to form ‘golden threads’ of connected information. “It’s about relating information,” he said. “It’s about understanding the ‘why’ and the longer the reach of the digital thread from your company’s enterprise, the more likely it is to become a major influence on how you complete certain processes’.
The question is what are the certain processes? In the emerging market built around Digital Services, ranging from ‘Power by the Hour’ through to Maintenance Service contracts with tightly specified performance criteria, Digital Twins represents a new business proposition closely aligned to the Digital Services economy.
‘Manufacturing’ a Digital Twin at the same time as manufacturing the physical version provides the manufacturer with a Digital product as a basis for creating various new ‘Services’ products. In the Digital Services market place this is an important step as many manufacturers look for new revenue streams. Immediate possibilities building on current trends around OpEx cost provisioning allow the manufacturer to achieve ongoing cost optimization. The ability to load the model with actual data experienced by different customers allows individual customization of operating machinery provided on ‘pay per use’, or ‘power by hour basis’.
Manufacturers operating traditional Service Maintenance contracts gain the ability to increase the accuracy of predicted failure times and costs, as well as adjusting any settings to reduce wear. The result from a large number of deployed machines will enable cross use of the data for faster access to pinpointing insights to increase profitable operation. This is the point where Machine Learning brings true Business value by large-scale examination of results pinpointing best practice.
The impacts are likely to be rather different for existing Service Maintenance Businesses, particular if independent, and therefore lacking access to the Manufacturers Digital Twins data. Training, certification, and representation of a particular Enterprises products would need to extend to include access to some aspects of a Digital Twin on the manufacturers Cloud Service. At the very least a new degree of interactive data exchange would seem to be called for to ensure that Machinery under indirect maintenance provide data to manufacturers, the so called Golden Thread of connected data referred to earlier.
The manufacturer has the opportunity to supply new technology-based services to their aligned Enterprise Service Representatives including, as an example, Virtual Reality to guide Service Engineers working on their products. These are potentially both new revenue streams, but bring the customer benefit of faster, cheaper, better maintenance.
The question that this, and other associated changes arising from IoT, bring is that on one hand the market shift to interconnected and interactive Ecosystems of Business Partners increases the numbers of Player visibly competing for work. On the other hand it introduces the need for greater Business collaboration and alignment in sharing data.
Software Solution vendors such as Salesforce.com and SAP recognize the implications and are working on enhancing their current offerings, both can claim to improve Predictive and Actual Maintenance, even though their approaches are very different. Salesforce focus on making the performance of the person and their ability to act with the data much more effective; whilst SAP focuses on the creating more effective processes to driven the engagement. Both offer Service Maintenance companies’ highly effective ways to use IoT with its new forms of data transforming the effectiveness of Maintenance. Longer term the use of Digital Twins leads towards innovations such as instant spare parts availability through 3D printing generation.
The nature of the Ecosystem model with collaboration between manufacturers and Maintenance companies is reflected in new partnerships in the Technology Industry. Salesforce is partnered with GE in building a new level of interactive relationships with its customers that goes hand in glove with the interactive Business model of Digital Services. SAP is in a strategic alliance with Siemens to build and host advanced capabilities around IoT and maintenance.
The future path towards creating Digital Twins of many physical objects, possibly even including people eventually, looks to deliver significant enough business value to ensure that large complex machinery and devices will drive a rapid take-up. However that does not mean the every machine, or device is complex enough to warrant a full Digital Twin, and of course there is a very substantial installed base of existing machinery that requires some level of Physical and Digital alignment. Past blogs have dealt with the need to assemble contextual data with the event data from IoT devices, notably; ‘Why IoT devices need be digital assets’.
For independent Service Maintenance companies familiarization with the more readily accessible capabilities of Digital Assets are the starting point for building their own databases and perhaps customer models as a key strategic initiative. In the Digital Services ecosystem the ownership of data is a key differentiator, and that data could be on the Machine itself, or it could be on its deployed use at a customer site.
Both are needed and are likely to be important trade items in the future ecosystems of Machine based Services based business. Manufactures expertise may lay in the product Digital Twin, whilst Maintenance companies may own equal expertise in the deployment and use.
Digital Twins - GE
Digital Twins – Siemens;
Salesforce - Field Service Engineer
SAP – Predictive Maintenance and Service