However, only a handful of the manufacturers are close to what was envisioned in the Industry 4.0 framework. This is evident in the challenges that manufacturing businesses continue to grapple with – like activating data-driven collaboration, closed-loop quality, better cost control, and achieving high asset RoI. These were the value propositions of Industry 4.0 – and they can still be achieved with cognitive digital twins and digital threads.
In this article, see how these two underutilized levers can be applied to evolve manufacturing to the next level of digital maturity, and how manufacturers can realize disruptive value propositions in the process.
Manufacturing challenges amidst ongoing transformation
Despite ongoing digitization efforts, a few challenges continue to afflict businesses today. Here are some of them:
- Collaboration: Data remains siloed, which makes it difficult to get design, engineering, production, logistics, and service teams talking to each other. Moreover, driving collaboration between partners, vendors, and customers remains all the more difficult.
- Asset RoI: Most manufacturers still struggle to realize maximum returns on their assets. Maintenance schedules remain suboptimal, and MRO costs eat into profit margins.
- Visibility across the value chain: While manufacturers have realized the value of end-to-end visibility, few have been able to achieve it. Inefficient data architectures and lack of cross-system integration are the major causes.
- Cost control: Even after industrial automation, most manufacturing organizations are still struggling with efficient planning and execution. These inefficiencies represent a significant cost control opportunity that can be realized with advanced process optimization.
- Quality gaps: Lastly, manufacturers find it difficult to catch quality issues early on in the product life cycle. This contributes to higher wastage, scrap, repairs, and recalls.
Closing the gaps with cognitive digital twins and digital threads
The above challenges are addressed in the Industry 4.0 framework by digital twins and digital threads. So, what do these technologies refer to?
What is a cognitive digital twin?
Digital twins are essentially digital replicas of physical products or processes. For example, a digital twin for an airplane turbine would enable you to observe its real-time status in a digital environment. A cognitive digital twin offers intelligent insights along with this real-time replica – for instance, lower turbine RPM may be caused by an issue with the compressor.
What is a digital thread?
Digital threads build a complete lineage of a product from design and engineering to production and deployment in the field. It builds a single source of truth about all aspects of a product, including changes in Bills of Material (BoM), design updates, test metrics, repair history, and so on. Moreover, digital threads will also provide insights into each capability of each line across the shop floor.
Driving synergies with the two levers – Digital twins and threads
One technology is not superior or inferior to the other. Instead, they are complementary technologies that enable synergistic outcomes. For instance, the life cycle digital twin could help track the real-time performance of an asset in deployment, whereas the digital thread can enrich this data with additional context in the form of that product’s history.
Advancing Industry 4.0 maturity with cognitive digital twin and digital threads
Digital twins and digital threads can enable strategically advantageous outcomes in the modern manufacturing organization. They drive these outcomes by unifying data from multiple functions and building a single source of truth for each domain – product, engineering, production, service, and so on.
This lends collaboration a new meaning. For instance, the engineering teams have access to product performance data recorded from the field, which they can leverage to make improvements in the product design. Engineers no longer need to update other stakeholders on design changes by exchanging static files on emails. Feedback loops become faster, and manufacturers are then able to bring innovations faster to the market.
Beyond better collaboration, digital twins and digital threads also enable targeted improvements in the quality, maintenance, service, and other core processes.
Key outcomes of cognitive digital twin and digital threads
#1. Improved product quality with closed-loop manufacturing
With cognitive digital twins, engineering teams can run simulations to weed out any performance and quality issues in the design. Moreover, digital threads offer a comprehensive picture of shop floor capabilities, which ensures that the product can be manufactured by the existing facilities flawlessly.
The data from the lifecycle digital twin can be utilized by engineering teams to bring improvements in the next iterations, thus enabling them to address quality issues at the design stage. Likewise, factory digital twins offer visibility into each step of production. By applying computer vision and intelligent imaging, QA teams can catch quality issues at the earliest step – thus minimizing wastage.
#2. Enhanced asset availability with predictive maintenance
One of the most valuable outcomes of lifecycle digital twins is the ability to optimize maintenance operations for assets on the shop floor, as well as products in deployment. As a result, the lifecycle digital twin is an essential part of as-a-service business models in manufacturing.
Lifecycle cognitive digital twins not only track real-time performance metrics of assets in the field but also apply sensor technology to continuously monitor their health. This data is then leveraged to predict failures with precision and to schedule maintenance tasks just before the failure point. The digital thread can provide further context to maintenance teams, regarding part details, repair history, and expected outcomes of the scheduled repair tasks.
#3. Optimized processes and better cost control
By implementing both digital twins and digital threads, manufacturing organizations can unlock new potential for process optimization.
For instance, engineers can optimize product designs for ease of production with available shop floor capabilities. Similarly, what-if simulations can help identify bottlenecks and help plant managers determine optimal production schedules. A birds-eye view of the production facility drives each production decision, thus enabling each role to make informed decisions.
These levers enable more optimal processes, resulting in better asset and resource utilization, and ultimately enhanced cost control. Finally, enhanced collaboration based on a single source of truth saves time of valuable resources and ensures that each decision is backed by actuals instead of dated information.
Birlasoft’s Gen AI-enabled Digital Thread (Prod Weaver)
