The Value of Data to Mitigate Risks During Volatile Times

Although Mexico has a highly competitive market, the country is currently experiencing  a volatile context characterized by global inflationary pressures and the recovery process of supply chains. It is not my intention to sound pessimistic, not at all, but instead to point out how important it is for companies to find certainties during times of uncertainty.

For the industry sector, these certainties translate into risk mitigation and efficiency gains. These needs force the different stakeholders to think and work differently. In a digital world like ours, that means developing data-driven decision-making and predictive capabilities.

Today, every construction project, manufacturing plant, or infrastructure facility has hundreds of sensors that collect data every second. However, much of this data is stored or processed separately and not used as actionable metrics for informed decision-making. In other words, the key to mitigating risks and generating efficiency lies in better using the information available.  

The way to solve this problem is to transform workflows. Fortunately, companies nowadays have increasingly powerful technological tools to facilitate this task. As the adoption of the Internet of Things (IoT), artificial intelligence (AI), and cloud technologies expand, teams continue to break down information silos and find new ways to collaborate. These actions have enhanced knowledge and allowed them to reach new possibilities in every area. 

The convergence of these technologies has enabled the use of virtual models, which we refer to as digital twins. These models are exact replicas of physical assets or systems, such as e buildings, roads, manufacturing plants, or any other type of infrastructure. But beware, these are not just 3D model representations of physical objects. A digital twin also contains the data generated during the construction of the asset, as well as contextual data collected through IoT sensors, simulation, real-world performance, controls, and predictive information, to improve performance, reduce costs or improve sustainability. 

For example, let's take the construction of an office building. Throughout the design, preconstruction, construction, and operational stages of the project, relevant data is generated. When integrated into a model, it can create dynamic dashboards that provide real-time information and run what-if scenarios that predict an outcome. Continuing with this same case, let's assume that, after five years of operation, the model throws an alert about the need to change the filters in the HVAC system, thus preventing a sudden increase in energy consumption. In a nutshell, the digital twin's purpose is to simulate decisions to predict the outcome or anticipate potential risks. 

While digital twins have gained traction primarily in the architecture, engineering, and construction (AEC) sector, their adoption is not limited to these industries. On the contrary, their application benefits multiple sectors. For example, they contribute to the efficiency of the manufacturing sector by leveraging machine learning capabilities for predictive equipment maintenance; optimizing logistics routes to improve supply chain management; performing simulations before integrating new equipment to have visibility into the impact it will have on production, or even predicting demand patterns to avoid overproduction. 

According to Capgemini, organizations working with digital twins have already seen, on average, a 15 percent improvement in metrics, such as sales, turnaround time, and operational efficiency, as well as an improvement upward of 25 percent in systems performance.

Another peculiarity of digital twins is that they are not static. Like the assets they represent, they change over time and with use. They are also responsive and continue to evolve as more data is provided to them to offer new capabilities. These capabilities are sorted according to five levels:

1. Descriptive twin. It is a live, editable version of the asset’s design and construction data.

2. Informative twin. It integrates additional operational and sensory data.

3. Predictive twin. It leverages operational data to alert potential risks.

4. Exhaustive twin. It allows you to simulate what-if scenarios to anticipate results.

5. Autonomous twin. It can learn and act on behalf of users to achieve a specific goal.

Given the digitalization progress within the industry, the quality improvement of sensors, and the emergence of new wireless technologies, the potential applications of digital twins are increasing. To such an extent that MarketsandMarkets notes that it expects the global digital twin market to grow from US$6.9 billion in 2022 to US$73.5 billion by 2027, with a compound annual growth rate of 60.6 percent .

I understand that many macroeconomic variables are beyond organizations' control, generating a sense of uncertainty. Nevertheless, the way to counteract that feeling is to look at the data they already have and ask themselves what new things can be done if they unleash their potential, instead of holding them captive in spreadsheets and PDFs. That's where the actual digital transformation lies.