How does data become knowledge and actionable insight? Usually, it’s a question of context. Manufacturing data without context is, ultimately, just data—often overwhelming in volume, hard to interpret, and difficult to use in practice.
For manufacturing businesses, this can be a day-to-day challenge. That 75° reading from a temperature sensor is meaningless unless you know which line it’s on, which product it’s producing, what ambient conditions exist in the plant, what range of readings is expected, what historical track record it has for accuracy, and so on.
While existing control systems can be configured to raise an alarm if readings exceed predefined thresholds, they typically still fail to provide that extra context, especially when important maintenance information is hidden away in unstructured reports, diagrams, or pictures.
The missing link between
data and insight
The key question for manufacturers is
therefore how to get this much-needed
context to turn raw data into actionable
insight—and do so with automation that
brings the scale and pace required by modern
manufacturing plants. This, ultimately,
is how to unlock the highest-value data-driven
use cases, in areas like predictive
maintenance and autonomous operations.
It’s true that recent years have seen real progress in manufacturing data architectures. In particular, many businesses have been investing in data lakes. By bringing manufacturing data together in one place, these have proved to be a powerful way of overcoming traditional siloed data architectures and releasing key data that was previously locked away in spreadsheets or fragmented systems.
However, data lakes by themselves cannot typically provide the critical, extra context required to unlock value. In particular, they generally fall short of providing the structured information and insights that engineers and operators need to actually improve manufacturing operations.
Digital twins as a solution
The real game-changer here is the digital
twin. By creating real-time virtual representations
of physical systems, digital
twins not only bring data together from
multiple sources, but also unify and contextualize
that data.
In effect, this provides a ‘one-stop-shop’ resource of contextualized manufacturing data for business users, engineers, operators, and even other industrial applications and algorithms to use. This, in turn, enables new use cases such as supply chain and production simulation, and predictive intelligence at scale.
One of the most compelling aspects of a digital twin is its ability to store and structure information in a way engineers and operators can understand. This is important because having to consult a data analyst every time you want to understand or use a data set is simply not an efficient or sustainable solution.
Digital twins can also support faster and more accessible application development to solve day-to-day manufacturing challenges. For example, by adding modern low code/no code (LCNC) tools to the mix, manufacturing businesses can give their data engineers an intuitive and safe space for experimenting with new ways to optimize operations and ultimately improve quality, throughput, and efficiency.
A key step towards
autonomous operations
Another important benefit of the digital
twin is the way it can progressively digitalize
and formalize tacit manufacturing
knowledge as structured data. This is a key
pillar in building autonomous manufacturing
operations.
In tactical implementations, this structured data can be used to give engineers and operators contextualized alerts, helping them react faster—and in smarter ways—to improve operational performance. It can also be used to start developing a knowledge graph, making important conceptual connections across the organization’s data sets.
In strategic use cases, a digital twin’s structured data enables real-time event-based performance management. Systems can start tailoring descriptive and predictive insights and recommend the optimal action in each situation (whether performed by a human or a machine).
Ultimately, the goal is to create self-learning, autonomous, closed loop systems that can sense, interpret, and act by themselves. In turn, freeing up engineers and operators to focus on other critical activities. These autonomous solutions can also learn from the operation of the whole system and continuously refine and improve their actions within it.
Real-time data insights
provide a competitive edge
Embedding digital twins in supply chains
and manufacturing systems is a way for
manufacturers to contextualize their data
and start enabling some of the key next generation
use cases that undoubtedly
represent the future of manufacturing.
This is why all manufacturing organizations
should now be seriously considering digital
twin implementations as key enablers of
operational efficiency, cost optimization,
improved quality and customer satisfaction,
and competitive advantage.
