Business demands have forced manufacturers to be more agile. Smart manufacturers are adopting technology, including digital twins, to move faster. Using a digital twin in a manufacturing environment can fast-track the discovery of production bottlenecks, drive efficiencies, lower costs, reduce environmental footprint, and help manage risk.

However, mystery still surrounds the concept and use of digital twins, from defining what they are and their strengths. In this blog post, we define what a digital twin is, how manufacturers are using it, and share some keys for success.

What is a Digital Twin?

This definition of a digital twin comes from the Digital Twin Consortium:

“A digital twin is a virtual representation of real-world entities and processes, synchronized at a specific frequency and fidelity.”

In short, what exists in the real world like an assembly line is represented in a digital world. Both worlds are tethered so that data can flow, and the worlds mirror each other. Digital twins use real-time and historical data to represent past and present, as well as simulate probable futures.

Data drives the makeup of the digital twin. Many organizations use 3D models, but it’s not for everyone. A manager may require a digital twin expressed as a dashboard, while an operator will need an interface fit for a factory floor.

According to the Digital Twin Consortium, “digital twins can be tailored to use cases; they’re powered by integration, built on data, guided by domain knowledge, and implemented in IT/OTsystems.”

Digital twins can transform operations, accelerate a holistic understanding of an entire entity or process, drive optimal decision-making due to test-run scenarios, and result in proactive action. Once it works digitally, the decision will more likely generate the same result in reality.

Challenge – Multiple Use Cases, Single Canonical Data Model

Manufacturers typically have multiple use cases with complex tasks. As if the challenge wasn’t hard enough, it’s compounded by feeding the many use cases into one canonical data model.

Uptake Fusion addresses this challenge by thin slicing the use cases, allowing the canonical data model to fit specific uses and/ or required industry standards such as ISO 22400, which defines key performance indicators (KPIs) used in manufacturing operations management. Or ISO 50000, a standard for establishing, implementing, maintaining, and improving an energy management system.

Asset Performance Management – The Core of Digital Twins

As a virtual representation of the physical world, digital twins must respond to real-life challenges of the physical assets. Improvements in asset performance is a core use case.

Analyzing data (time-series, real-time, and transactional) unified in a data lake environment by Fusion can produce insights into shop floor operations. Technicians can put insights to work, improving product quality, addressing production processes, measuring environmental impact, understanding yield losses, identifying risks on asset reliability, or any number of other initiatives.

By leveraging data for insights, you can also infuse your manufacturing plant’s preventive maintenance program with predictive capabilities.

Digital Twins’ Keys to Success

Only sharing the promises of digital twins would ignore the perils. There are also keys to success. Here, we’ll document both.

1. Beware of the “One Size Fits All” Digital Twin

No single platform can serve the digital twin needs of a manufacturer. The era we’re in now demands flexibility, which means assembling the best components per use case. The trick is to ensure that these components promote integration with Open APIs.

2. Cleanse and Catalog Data

Digital twin data comes in different types and from multiple sources. It’s time-series, transactional, structured, or unstructured data. It comes from a historian, a control system, smart sensors, enterprise systems, or external sources. All that disparate data needs to be cleaned and organized.

3. Unify Data in the Cloud for User Actions

Unifying data in the cloud provides scalability to support new user interactions from 3D engineering tools to geospatial environments that improve operations.

4. Pursue Business Goals

A digital twin excels when serving business needs. However, being viewed as a shiny object for a digital transformation program can result in wasted spending. Defined use cases and key performance can guide manufacturers to tangible value.

5. Keep Current with Aging Assets and Maintenance Records

Assets degrade over time and require ongoing maintenance. Whether a repair, revamp, or replacement, your digital twin must keep up with asset lifecycle changes.

Uptake for Industrial Intelligence

For process manufacturers seeking agility, digital twins that apply the digital world to reality on the shop floor can help, all while solving business problems. Having asset data unified in a cloud environment also opens the door to analytics to help your facility.