In the last five years investments in the industrial internet of things (IIoT) has exploded. In 2016 the IIoT market was already valued at $115 billion and by 2023 it’s expected to touch almost $200 billion.
There are two main explanations for this growth.
First, companies are seeking to make fast, data-driven decisions that keep operations aligned with customer expectations and demands. IoT sensors and the data they gather are meant to help inform these decisions and ensure this alignment is achieved.
Second, companies are seeking to adapt quickly to the impact of disruptions and make the sorts of decisions that can help them respond effectively to those disruptions. By analyzing the data that is gathered companies can identify disruptions earlier and leverage their systems to overcome them.
But there’s a problem.
While it’s undeniable that there is value to be found in the data that IIoT systems generate and in the artificial intelligence (AI) and machine learning (ML) investments that are made to analyze this data, many business leaders feel they are missing out on real strategic value and not generating a true return on their investment.
In other words, while decision-makers have access to more data than ever, they aren’t generating a return on that data.
Here’s why.
IIoT systems gather data and that data is necessarily about the past. Historical data can tell a story and by analyzing this data and determining correlations, companies can learn more about how their businesses work.
But correlation is not cause, and trends in historical data cannot accurately predict the future. What’s more, an event that has never previously occurred cannot be predicted based on data from the past. If the future does not resemble the past – and which industrial leader truly believes that it will? – the investment in historical data will not help to set a good strategy for that future.
What’s more, even the most extensive IIoT systems cannot provide a total and unified view of an entire industrial system. The intricate interconnections and interdependencies are impossible to map with an IIoT or even truly unlock with AI or ML.
As a result, significant parts of the corporate value chain are left unanalyzed and the impacts of decisions in one part of the business on other parts of that same business cannot be quantified. It’s a limited view of the value chain and it can cost a company dearly.
So, what’s a company to do if they want to find their forward and generate that return on data?
Simulation Digital Twins (SDTs) offer industry decision-makers a solution to their IIoT problems.
SDTs are a complimentary technology to IIoT systems and the AI engines that analyze IIoT data. The dynamic simulation and optimization capabilities of an SDT coupled with the data-driven approaches of IIoT and their AI systems offer a true next-generation analytics capacity for decision-makers to leverage.
Combining an SDT with an IIoT system is straightforward. A company can begin by gathering their data and developing forecasts of relevant externalities (demand, machine obsolescence, failures, disruptions) based on that data. These forecasts act as inputs for the SDT simulator alongside the expertise of your teams, knowledge about your systems, rules and processes in your business, and the mechanics of your system.
Thus primed, the simulator allows you to experiment with any strategy, test any decision, and evaluate all of the impacts of any choice. You can learn from these simulation results, use them to explain a strategic or operational choice, or even automate decision-making.
More than this, though, an SDT also enables a company to exploit optimization algorithms to identify optimal strategies to achieve goals and corporate KPIs. These prescriptive analytics empower decision-makers to build robust and resilient plans and know in advance how their systems will progress.
Finally, connecting IIoT and AI systems to an SDT offers an opportunity to explore correlations with unsupervised learning and clustering approaches, training algorithms with successfully explored strategies, and acting as a training environment for AI and ML algorithms without operational constraints.
For companies that are struggling with underperforming IIoT and AI investments, SDTs offer a means to finally generate a return on that investment.
For companies that have already achieved data gathering and analytics maturity, an SDT offers a chance to augment and accelerate their corporate decision-making capacity and optimize their strategic choices.
Leveraging an IIoT system via an SDT offers a way to generate a real return on investment and a return on the data that’s being collected en masse. What’s more, an SDT can help train your AI algorithms via simulation experiments so that they learn faster, reinforce that learning, and move you closer to automation. Adopting an SDT as a complement to your AI and IIoT systems is the fastest way to improve efficiency, lift productivity, and find the optimal strategic forward, and all while building on the data investment that is already in place.
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