Co-authored by Ahmad Ali and Simon Clark
Being able to understand the status of production lines, predict and mitigate potential production issues and rapidly bring new products to market in a cost-effective way is vital to maximising overall equipment effectiveness, productivity and healthy top and bottom lines.
A technology known as “Digital Twins” is a key enabler of such capabilities. Digital twins are a digital representation of a physical asset such as a piece of production equipment, people, processes or even a whole production line.
Digital twins are part of the Industry 4.0 group of technologies and present organisations with significant opportunities to reduce costs and increase revenues by providing visibility and insights into many aspects of products and production processes.
In this blog, we explore:
Digital Twins; What are they, their applications and benefits
Application examples of Digital Twins
Julius and Clark observations on barriers for digital twin adoption
Digital Twins; What are they, their applications and benefits
Digital twins utilise simulation (3D graphics/virtual reality), data analytics, and machine learning to simulate the impact of product changeovers, usage scenarios, environmental conditions, and other variables.
They can be used throughout a product’s lifecycle to understand, predict, and optimize the product itself and the performance of physical assets before investing time, money and effort in making prototypes and or production line adjustments.
Julius and Clark see three main elements to constructing a digital twin:
Knowledge of the product and processes (often gained from sensors to get real time data about the physical equipment)
A robust data model
A set of data analytics often in the form of algorithms.
Digital twins can be applied on several levels within organisations depending on IT infrastructure and the appetite for digital twins. The levels are described below.
Element level – focused on a particular component within a piece of equipment of step of a process
Equipment level – conceiving a digital twin of one item of equipment within a production sequence
System level – Digital twin to monitor a group of equipment in an entire production line
Ecosystem level – this being the most macro-perspective level, which overlooks the end to end production process such as from Goods In through to Finished Goods out for a whole production site.
Various benefits can be observed from using digital twins
Increased reliability and reduced maintenance costs from improved predictive maintenance
Improved sustainability from less waste
Faster new product introduction vs traditional methods
Improvement in OEE
Increased production volumes
Reduced risks in scarcity of stock
Opportunities for flexibility in manufacturing, customisation and small-batch production
Optimised layout for greenfield and brownfield sites
Improved consistency and quality of products
Industry Example of Digital Twin Adoption
Having described digital twins and their benefits, below we share a couple of real world examples from different industries and different digital twin applications.
FMCG, Unilever digital twins for a step change in operations across the globe*
Unilever developed 8 digital twins to replicate existing production processes. Machines and equipment in a factory site were connected so that they could send a mass of data into a digital twin model. This created a representation of every machine and process, offering visibility across all levels of the plant. The collected data was mined for insights and patterns using advanced analytics and machine learning algorithms, which can predict outcomes based on historical data.
The algorithm reached levels of accuracy where it could be allowed to directly control part of a machine or process. This allowed operators to make better-informed decisions and frees them up from repetitive manual tasks for more value-added functions.
The digital twins have resulted in significant improvements to operations. Once Unilever switched the control of moisture levels in a soap-making machine to the digital twin algorithm, operators did not want it switched off because it gave them so much more control over consistency.
In another instance, the digital twin used data on how long it takes to produce one batch of liquid, such as shampoo or detergent, to predict the correct order of processes in order to get the most efficient batch time. The less time each batch takes, the higher the production capacity of the plant, fully utilizing the asset and avoiding having to invest in capability elsewhere.
Unilever’s digital twin solution was custom-built by Unilever’s engineering team in partnership with The Marsden Group, a Microsoft partner, and is hosted on Microsoft’s Azure platform.
Automotive, Porsche using digital twin to design a new production plant for its new Taycan.**
Space for the new factory on Porsche’s existing site was limited by existing buildings. There were also local building height restrictions for the new Porsche Taycan factory. Porsche utilised Siemens TIA portal to create a virtual factory to optimise layout, flow of vehicles and take any restrictions into consideration. This resulted in construction and commissioning of just 4 ½ months (about half the time of comparable projects), the best possible use of space and ultimate flexibility from vehicles moving through production on large autonomous guided vehicles (no fixed conveyors).
Beverage, Krones faster development of new beverage bottles***
To assist their customers in taking new products to market quicker Krones wanted to use precise simulation models to gain a deeper understanding of the product properties desired by its customers. The motivation came from the fact that beverage producers require ever shorter time-to market for new beverages and need optimized primary packaging.
The Krones engineers are using tools from the Siemens PLM software portfolio to simulate PET bottles throughout the production process. It is now also possible to simulate the production behaviour for different wall thicknesses in the blow-molding machine, the thermal behaviour, the material flow and the motion behaviour in the production line using the Siemens NX tools. Structural loading of bottles in the case of top load filling or the behaviour of filled and stacked bottles on transport pallets can also be simulated.
The ultimate goal at Krones is to reduce the development time for a new bottle from the typical three to four weeks down to three to four days. Progress toward this goal has already been made. For example, the top-load filling process for newly developed PET bottles can now be simulated much faster – with a time saving of 75 percent. A virtual modelling process for a package taking four to eight hours in the past now takes only one hour.
Barriers to Digital Twins
As we can see from the application examples digital twins can deliver significant benefits. However, a variety of barriers exist for organisations to adopt them. The most common barriers that Julius and Clark see are:
Lack of data or access to relevant data
Lack of skills and and capabilities to extract data and build a digital twin
Lack of technology and IT infrastructure (e.g. Sensors to capture data)
Ability to Keep digital twins accessible over time especially for products with a long lifecycle
The magnitude of these barriers are usually directly proportional to the complexity of the production landscape and appetite for applying digital twins. The good news is that the barriers are lowering as equipment providers and technology companies are beginning to offer digital twin cloud platforms in which data libraries of different equipment can be used to build digital twins much quicker and easier than the organisation having to source and attach sensors to existing equipment.
Conclusion
The benefits digital twins bring make overcoming the barriers worthwhile whether developing a new product, greenfield factory or improving existing production.
Costs of adopting digital twins can be controlled based on the immediate needs of the organisation from individual components up to whole processes. Once proven, a digital twins can be developed and expanded over time.
With more technology companies offering digital twin platforms Julius and Clark anticipate an increased adoption of this technology over the next 5 years.
Would you like to know more about digital twins and how they could help your organisation? If yes, then get in touch and we look forward to discussing this topic with you in greater detail.
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References
FMCG*
Automotive**
Beverage***