Imagine a future where your business thrives on sustainability and where every decision is informed by insights that minimize your environmental impact. This future is not just a vision — it’s becoming a reality with the power of digital twins and Internet of Things (IoT).
And a tool like e-Magic’s TwinWorX® digital twins platform can be a vital asset that empowers your company to achieve its sustainability goals — enabling operational excellence, optimizing processes, and reducing carbon emissions.
Digital and Sustainability Strategies Align
To enable sustainability — reducing environmental impact, while saving on costs and aligning with societal expectations — organizations with large building portfolios need to take a strategic approach. From commercial real estate companies, to universities and governments, they need to integrate sustainability into their core business strategies in a holistic and phased way.
That starts with a commitment to long-term goals that drive meaningful change and benefit both the environment and your bottom line.
In the short term, this may mean assessing the carbon impact of your operations while achieving quick wins by improving energy efficiency, reducing water, and initiating waste reduction programs. But in the medium to long term, the focus will be more on integrating renewable energy sources, building retrofit programs, adding smart building technologies, introducing resilience planning, and strategizing on net zero carbon emissions. Digital twins are particularly useful for addressing all of these challenges.
What Is a Digital Twin?
McKinsey & Company describes a digital twin as a “digital representative of a physical object, person, or process, contextualized in a digital version of its environment.” It can help organizations simulate actual situations and determine their impacts, in order to facilitate their overall decision-making.
A digital twin approach that aligns with your sustainability strategy, then, can help you determine the impact you’re having on the world and where new efficiencies can be added. By creating digital twins of assets such as buildings or factories, companies can monitor operations in real time, utilizing analytics to make informed decisions that align with your sustainability goals.
This data-centric approach allows businesses to identify areas for improvement, track progress, and ultimately drive meaningful change towards a more sustainable future.
Collection, Analysis, and Utilization of Data for Sustainability
The 3 building blocks of digital twins are all about data: collecting it, analyzing it, and putting the resulting insights to use. Together, these can help businesses analyze their sustainability and act on the results.
Collection of Data
Collecting data is the foundational step in shaping a sustainability strategy. It serves as the backbone of a digital twin approach, providing real-time data for accurate digital twins and surfacing insights on various aspects of an organization’s operations and environmental impact.
Take the TwinWorX® digital twins platform as an example. TwinWorX® collects data from a wide array of sources — including building systems, meters, IoT devices, sensors, and other means. Then the computing power behind the scenes goes to work. TwinWorX® edge computing is powered by Intel®, which is pivotal in enabling the real-time data processing and accurate analysis essential for understanding a company’s environmental footprint and empowering informed decisions.
Structuring all the data collected at the edge is necessary for analysis to be effective. TwinWorX® utilizes Microsoft’s open Digital Twin Definition Language (DTDL) because it significantly helps address data management strategically.
Benefits of DTDL:
1. Standardized language: Ensures data consistency and reliability¹ 2. Interoperability: Based on JSON-LD, it’s programming-language independent¹ 3.Flexibility: Supports multiple versions, allowing companies to use a mix of models¹ 4. Semantic Modeling: Helps in managing large volumes of data effectively¹ 5. Single Source of Truth: A clear model for each entity establishes a single source of truth³
Analysis of Data
Once data is collected, the next vital step is analysis. Data analysis involves dissecting the collected information to derive meaningful insights. It helps in identifying trends, areas for improvement, and potential sustainability initiatives. For example, by analyzing energy consumption data a company can pinpoint energy-efficient opportunities, reducing costs and environmental impact.
Examples of Sustainability Metrics:
Energy Consumption: Percentage reduction in energy usage
Carbon Footprint: Metric tons of CO2 equivalent reduced
Resource Efficiency: Percentage reduction in resource usage per unit of output
Waste Reduction: Percentage reduction in waste production
Supply Chain Sustainability: Percentage of sustainable suppliers or percentage reduction in carbon footprint
Operational Efficiency: Increase in operational efficiency percentage
Renewable Energy: Percentage of energy consumed from renewable sources
Water Usage: Liters of water saved
Compliance: Number of compliance incidents avoided
Asset Management: Increase in asset lifespan
Sustainable Innovation Rate: Number of innovations developed
Advanced tools like data analytics and machine learning can also be employed to handle vast datasets, allowing for more accurate and actionable insights. TwinWorX® , powered by Intel processors, unlocks correlated trends and analyzes data from disparate building systems (including building management systems, utility and power quality meters, and access control) then combines this information with data from third-party sources (such as weather, occupancy, and IoT sensors) to prescribe operational adjustments in near-real time that improve building performance and tenant comfort.
Utilization of Data
Data’s true value is realized when it is put to practical use. Companies utilize the insights gained through data analysis to implement sustainable practices and policies.
Consider the case of a commercial real estate company embracing smart building technology. In this scenario, the company utilizes cutting-edge processors to gather data from a network of sensors installed throughout their buildings. These sensors could range from temperature monitors controlling heating and cooling systems to occupancy sensors that track the number of people present at any time. The data collected is then processed and analyzed in real time, often employing machine learning algorithms, to uncover patterns and optimize building operations.
Data-driven decision-making leads to more efficient operations, reduced environmental impact, and cost savings. Moreover, data empowers organizations to meet regulatory compliance requirements and enhance their reputation as eco-conscious entities. In essence, the utilization of data for sustainability is the transformative stage that propels companies toward a greener, more sustainable future.
Ready to Transform Your Sustainability Strategy?
To sum it up, digital twins are a powerful tool that can help companies achieve their sustainability goals by supporting data collection initiatives. By using these tools to support objectives such as energy reduction, companies can reduce their carbon footprint, save money on energy costs, and improve their overall sustainability performance.
In fact, embracing digital twin technology could be the game-changer your organization needs to meet and exceed its sustainability goals. The future of efficient, eco-friendly operations is here, and it’s powered by the sophisticated analysis and optimization that digital twins offer. So take the next step. If you’re ready to harness the power of digital twins for enhanced sustainability and operational excellence, let’s get started. Reach out to our team of experts today to explore how these cutting-edge solutions can be tailored to your organization’s unique needs and goals. Together, we can chart a path towards a greener, more sustainable future.
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(1) DTDL models – Azure Digital Twins | Microsoft Learn. https://learn.microsoft.com/en-us/azure/digital-twins/concepts-models
(2) Better Data Decisions on the Journey to a Single Source of Truth. https://www.spiceworks.com/tech/data-management/guest-article/driving-value-with-single-source-of-truth/
(3) Challenges in data management | Deloitte Insights. https://www2.deloitte.com/us/en/insights/industry/technology/challenges-in-data-management.html