The concept of the digital twin, in which an organization creates a virtual representation of its business operation, has been around in various forms for decades, but has undergone a tremendous shift in recent years.
Previously, digital twins were created using spreadsheets to input data and see potential outcomes of various scenarios. Thanks to advances in artificial intelligence and virtual reality, digital twin technology has progressed with faster, more accurate projections, based on endless streams of data that can be accessed from anywhere in the world. It’s essentially creating a “metaverse” for manufacturing companies.
By using a direct feed of data and applying machine learning, digital twin technology allows a manufacturing plant to utilize the data in real time to become smarter and more efficient. It does this by identifying gaps in the manufacturing process, predicting the timing of maintenance or fault-production issues with increasing accuracy as it gathers information. Managing these issues early on or before they happen prevents downtime and productivity losses.
As the manufacturing sector grapples with a shortage of skilled labor, digital twin technology is helping leaders maintain a productive, safe factory floor despite resource limitations.
Supporting Lean Operations
With experienced manufacturing workers retiring and few skilled workers available to replace them, the U.S. industrial sector has been facing a skills shortage for several years. The Great Resignation, fueled by the COVID-19 pandemic, has only added to the challenge.
As manufacturers struggle to attract, onboard and train new talent, digital twin technology is serving as a lifeline to many manufacturers. Companies are using it to test different equipment setups to find the most productive options and prevent downtime. In addition, automation is allowing many manufacturing plants to continue production despite reductions in available labor. A single machine operator can run multiple machines autonomously, as the digital twin analyzes data to see how different inputs and processes will impact productivity.
Digital twin technology is especially useful in the prototyping stage of manufacturing, where teams are developing multiple iterations of a product to determine which design and materials will produce the best final piece. Digital twins can streamline the prototyping process to improve time to market while reducing costs. Digital twin prototypes are digital renderings that allow design teams to see how different models will perform in the real world, without devoting materials and production time to multiple possible iterations. The protype can contain information pertaining to the physical attributes, properties, operating parameters, bill of materials, part numbers and more. With a digital prototype, products can be built much earlier on in the process, and simulations can be run using virtual reality. Changes can be made quickly to improve designs before the physical machines are built.
If traditional time to market requires two to three prototyping cycles, the use of a digital twin in the early- to mid-stages can eliminate one whole prototyping cycle, reducing time, resources and cost.
Optimizing Workplace Conditions
By factoring in how many employees will be working at a given time, as well as their skill levels and equipment needs, a digital twin can simulate how productivity will be impacted by moving certain employees to different areas. It can balance the workflow and predict which combinations of workers and equipment will be most productive.
A digital twin can also predict failure based on factors such as age of equipment, materials used, maintenance records and other data points, allowing workers to prioritize preventive maintenance to avoid unnecessary downtime.
In the event of an equipment breakdown, digital twins enable virtual troubleshooting, allowing staff to investigate causes of failure and possible resolutions. This reduces the amount of unnecessary time and labor it would take to diagnose the problem and find a solution manually. Leaders can then enlist the help of the correct specialist to fix the problem, rather than going through each potential equipment issue. Because the digital twin runs on a high-powered computer, analysis and simulations can be done in minutes rather than hours or even days.
Steps for Implementation
Once senior leaders are on board with the potential gains of digital twin technology, manufacturing leaders can move forward with the implementation process. As the new technology is introduced, it’s important to take these key steps:
Setting up a digital twin can help manufacturers improve productivity and safety on the shop floor, but a few conditions must be met to ensure success. Providing access to accurate data is crucial, as is ensuring a secure network. Beyond the technical specifications, leaders need to provide sufficient training, communication and incentives to make sure all employees are onboard with the process. Effective change management is key to successful implementation. Providers need to show the benefits of digital twins while training staff on how to use the platform. It’s important to understand that while accuracy isn’t always 100%, especially in the beginning, even at 80% to 90% the digital twin can easily improve outcomes, and will continue to get better as it receives and analyzes more data.
Working with an experienced partner in this field also helps manufacturers realize value. A digital twin provider that understands the ins and outs of how a particular plant is set up, what types of equipment are used, and industry-specific challenges will be better able to create a digital twin that meets the company’s needs and goals.
CV Ramachandran is a digital transformation and operations improvement expert at PA Consulting. Vignesh Ramesh is a digital health expert at PA Consulting
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