Today’s digital twins and data analytics tools empower organizations to optimize operations and drive performance. These technologies are key enablers of the fourth industrial revolution, which is also known as the digital transformation of manufacturing.
The ability to use sensors, virtual models, and data analytics programs make it possible to analyze performance in real-time, predict failure before it happens, streamline maintenance routines and reduce energy consumption.
This blog post will explore what digital twins and data analytics can do for sustainability efforts in your organization — if you’re ready to dive in deeper, check out our white paper on Digital Twins & Data Analytics Power Sustainability.
Topic Overview
What is a Digital Twin?
A digital twin is a virtual representation of a physical asset — such as a piece of machinery, a building, or a supply chain. This digital representation can communicate with other digital systems across the value chain, such as sensors, maintenance management systems, asset management systems, and more.
When used in manufacturing, a digital twin can be used to virtually simulate production processes and help to identify inefficiencies and other opportunities for improvement. It can also be used to help optimize the use of resources and create a more sustainable production process.
What do digital twins and data analytics have to do with sustainability? When used in manufacturing, digital twins can be used to virtually simulate production processes and help identify inefficiencies. They can also be used to help optimize the use of resources and create a more sustainable production process.
Data Analytics and Manufacturing
As a connected digital network, sensors and data analytics programs can be used to digitally capture the state of an asset (in real-time) and its surrounding environment. This information can be used to create a virtual model that can be shared across the entire organization.
When used with a digital twin, this virtual data can be used to make predictive and prescriptive decisions — like the ability to predict failure before it happens. Data from sensors and real-time analytics programs can also identify trends, predict and prevent downtime, optimize maintenance routines and reduce energy consumption.
This data can also be used to create transparency within the supply chain — giving organizations the ability to see where their products are coming from, what is being transported during each shipment and how long it will take to arrive at the final destination.
Organizational Benefits of Digital Twins & Data Analytics
These tools are designed to collect data and create transparency within the entire value chain — which is why they can be so powerful for organizations. Predictive Maintenance – Predictive maintenance can help organizations avoid unplanned downtime, improve operational efficiency and reduce the costs of maintenance.
Predictive maintenance can help identify what maintenance routines should be performed and when they should be performed in order to prevent sudden breakdowns and other costly equipment issues.
Visibility across the value chain – Organizations can use data analytics programs and sensors to create transparency within the entire value chain — giving them the ability to see where their products are coming from, what is being transported during each shipment, and how long it will take to arrive at the final destination. This can help organizations identify potential issues within the supply chain before they become a problem.
Sustainability Benefit 1: Reduce Manufacturing Costs
When used to capture data and create transparency across the value chain, data analytics programs and sensors can help organizations identify where costs are being unnecessarily added to their supply chain. This can then be used to enact change where necessary to reduce costs to create a more sustainable manufacturing process.
When paired with a digital twin, this data can be used to virtually test different production processes. This can help organizations identify areas where costs can be reduced — such as the amount of energy used to power production or the number of raw materials being used. By reducing these costs, organizations can create a more sustainable manufacturing process.
Sustainability Benefit 2: Increase Product Quality
The data collected from sensors and data analytics programs can be used to identify factors that can negatively impact product quality. This can be used to take action to prevent potential issues from impacting quality. With a digital twin, organizations can virtually test different production processes and identify potential issues before they become a problem — such as the amount of energy used during production or the number of raw materials being used.
By identifying these issues, organizations can take action to reduce these potential issues from impacting product quality.
Sustainability Benefit 3: Improve Supply Chain Transparency
The data collected from sensors and data analytics programs can be used to better understand the entire supply chain — from raw materials being used to create the product all the way through shipping and delivery. Data analytics programs can be used to track the location of raw materials being used to create a product and track where that product is being shipped and delivered.
This can be used to create transparency within the entire supply chain and give organizations the opportunity to identify potential issues before they become a problem. This data can also be used to create monitoring programs to track product quality and progress as it moves through the supply chain. This can be useful when tracking product that is perishable or has a long shelf life.
Sustainability Benefit 4: Increase Employee Engagement
The data collected from sensors and data analytics programs can be used to create a more meaningful workplace. Digital tools and real-time analytics can be used to create a more engaging workplace by giving employees access to the data being collected.
This can be particularly helpful for employees in quality control, suppliers, and other areas that might not normally receive this data. Employees’ access to this data can help drive engagement and create a sense of ownership. This data can also be used to create a culture of sustainability by giving employees the ability to see the impact their decisions are having on the environment.
For example, if a supplier is polluting a nearby river and employees are able to see this data, they can take action to help solve the issue.
Conclusion
Digital twins and data analytics can be used to create a more sustainable manufacturing process. This can be achieved by reducing manufacturing costs, increasing product quality, improving supply chain transparency, and creating a more engaging workplace. When used together, digital twins and data analytics can be powerful tools that can help organizations achieve their sustainability goals.
