By Stefan Hockenberger, Managing Director Europe
Our first blog in the series offered a perspective on supply chain visibility trends. Now, here are some thoughts about future trends for asset management and digital manufacturing. These two fields are intertwined with each other yet solve different challenges. The common denominator for both? It is big data, which is used to improve maintenance, productivity and safety.
Manufacturing and supply chains are not new to automation. In warehouses, it is used for picking, sorting and other manual tasks while manufacturing has automated production lines. Both examples illustrate some basic uses of digitalization; however, the systems are not often connected to the enterprise level. This divide leaves opportunities on the shop floor.
Companies see the benefits of integrating machines, materials, methods and people. It has inspired a migration to hyperautomation as part of their transformation to Industry 4.0, which takes advantage of connected opportunities from shared data and communications. Gartner defines it as dealing “with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.”
This next generation application of technology enables improved operations from new insights on the most efficient ways to work through the analysis of big data in real-time. For example, AI can determine the optimum design for a product, whether standard stock or customized. When the experiment outcomes are finalized, the specifications are then sent directly to the machines on the factory floor for production. The information can be communicated to the local plant or globally across a company’s multiple locations.
In another example, quality deteriorates on a production line. Through machine learning, it is determined that vibrations are the cause. Research and development can then adjust the design or intelligent programmable logic controllers (PLCs) that changes the machine settings to accommodate this difference. By finding the ideal solution, the company can continue to create the same consistent product that the customer expects.
The concept of digital twins has been around since the early 2000’s. Companies would program replicas on their computers or servers and input the necessary data to update the design or environment specifications. The information was rarely received in real-time. This methodology was a very clunky and slow way to render these virtual duplicates.
With the advent of IoT technology, such as smart sensors and cloud systems, digital twins became easier and cheaper to create, maintain and use. Now, we’ve reached the stage of intelligent enterprise asset management that is fed by the influx of connected information from digital manufacturing devices, hyperautomation and other sources.
Digital twins are becoming more prevalent in and out of the factory sector because these exact virtual replications of a system allow for greater experimentation with various scenarios to determine an outcome. It is a fail-safe way to test new ideas and different conditions. Positive results can then be communicated to the physical twin for implementation.
SAP notes “The idea of digital twin as a live digital representation can be applied to more complex physical structures, often combining connected assets and products with specific business outcome in mind.” The visualization allows companies to test for potential machine failures, optimize processes, plan for future capacity and more.”
While often thought of as a manufacturing concept, digital twins can serve a purpose in logistics and transportation as well. “Fleets of connected vehicles (e.g. forklifts, vans, trucks) or connected moving assets (like trains, ships, airplanes) add a spatial dimension to the digital twin; geo-fencing, track and trace, and other location-specific information (e.g. weather, road conditions) are commonly found when building solutions for logistics and transportation.1”
Digital twins are fed by zettabytes of real-time information that is analyzed quickly. The virtual realm becomes dynamic when machine learning and AI data enters the system. Projected scenarios can also be added. Therefore, digital twins offer companies in any industry a method to test high risk ideas and processes in a fail-proof manner because any conditions and results are sandboxed from the physical world. The greater visibility into potential outcomes enables insights that stakeholders can then apply to proactive decisions, or it can trigger predictive processes, that improve business at the shop and top levels.
These concepts aren’t trends necessarily because they are new, but because they are reaching a critical point of being more widely adopted. Companies now understand they must integrate their intelligence to stay competitive and deliver the optimum customer experience while moving employees to a higher purpose of work. The access to big data for applied analytics enables greater insights and visibility that power new business models, methods of efficiency and revenue opportunities.
If you’re still uncertain about these trends, review the following sobering statistic for the new decade: IDC predicts that 75% of organizations globally will become completely digitally transformed by 2027 – and the rest will go out of business.
It’s time to take the first step towards your digital transformation. Let’s get connected. We’re ready to help.