How to Transform your Manufacturing Operations with Digital Twin Simulation Software


Hyper-competition, supply, and workforce disruptions are driving a digital transformation revolution in the manufacturing industry.

According to the World Economic Forum, industrial data and analytics are powering productivity, customer innovation, and helping to meet environmental impact goals.

Smart industrial leaders are on a mission to converge their IT and operations management systems, streaming data from historian software on the plant floor to the cloud for analysis. Digital twin process simulation is key to unlocking enterprise-wide value.

In this interview, we illuminate the journey with Steve Pavlosky, who is the Vice President of Product Management for Proficy software at GE Digital.

Getting started

How do you start the digital transformation process?

Transformation of any sort requires a clear understanding of a vision of what the future looks like. You also need executive buy-in, financial support, possibly new organizational models, and a cultural shift that drives a new mind-set in your employee base. A holistic approach is vital to seeing this through, and ensuring digital transformation is a phased endeavor that delivers sustained results. Transformation doesn’t matter unless you’ve got a culture of operational excellence that’s willing and able to embrace it.

By taking a step-by-step approach to creating a digital plant, you won’t overwhelm your existing infrastructure or your team. You can pace your change to what works for your organization and make it more successful in the end. It will also give you an opportunity to bring management along in your journey. Success will breed more success.

Also, industrial organizations can face challenges related to legacy automation devices, disparate software applications, and the constant need to keep up with ever-changing customer demands. Fortunately, proven solutions and processes provide the foundation for how to improve operational efficiency with digital transformation.

Overcoming barriers

What are some of the barriers to digitization projects?

Many manufacturers are still operating on paper or with Excel templates for operations information collection and reporting. An increased number of manual processes often leads to human error. No matter how experienced people are, they will inevitably make mistakes. This situation can be exacerbated when working across a range of equipment types within 24/7 operations, with multiple employees from different shifts potentially affecting the process over the course of a day.

Compared to digital data collection and reporting, the biggest sacrifices with paper-based environments are speed, accuracy, and traceability – along with impeding the ability to leverage analytics and optimize operations in real time.

Clearly, it’s time that we move away from paper-based manufacturing environments. When looking for a solution to help move from paper to digital, look for manufacturing and supply chain solutions that can interface with a wide range of industrial equipment and protocols, either directly or through relevant standards like OPC.

OT data and the cloud

With data being a critical foundation for digital transformation, what are your thoughts on OT data and the cloud?

Cost-effective cloud computing of OT data is a more recent breakthrough in manufacturing. In 2023, we see companies being able to quickly and cost effectively get their OT data in the cloud – finally! As part of an enterprise IT strategy, the growing trend of cloud-based industrial data management now facilitates a more simplified and reliable movement of OT data to the cloud, spanning from device level to enterprise. Companies can build on existing IT cloud investments including Microsoft Azure and AWS to integrate OT and enterprise data.

Why haven’t we been able to do this sooner?

Obviously, cloud suppliers and other database companies have provided cloud-based solutions. Some are even focused on time-series data. Unfortunately, the use case of storing web application metrics is not the same as storing high-volume, real-time OT data. These solutions lack the complete functionality of an operational historian. Often, they are key value pair or RDB-based technologies which lack the capabilities of an operational historian and are very expensive to use to store high-volume OT data streams.

The result is that industrial companies have had to make do with either expensive cloud-based storage solutions coupled with custom development to bring data to the cloud or opening up ports to enable cloud-based analytics solutions to reach down into the plant to acquire data from the local operational historians.

Neither of these solutions is optimal, but industrial organizations historically haven’t had an alternative.

What is the solution then for cloud-based OT data management?

With new cloud-native operational data historian innovation, cloud-based OT data management – at scale and affordable – is a reality and will grow as a trend in 2023. Our cloud-native historian is available as a Marketplace application that counts toward a company’s cloud provider volume agreement. Companies can fully deploy it in minutes in their AWS or Microsoft Azure VPC.

As an example, a GE Digital customer in the aviation space managed OT data across 32 manufacturing plants, each with a distinct deployment. They made the switch to a cloud-based OT data management solution using Proficy Historian for Cloud and reduced infrastructure costs by more than 20%. The company also improved system availability by eliminating more than a month of planned downtime and enabling a common data store accessible in real time by thousands of enterprise-wide employees.

Smart Factories and Digital Twins

Besides OT data management, we also hear a lot about “smart factories” using digital twins. Can you talk about those?

A smart factory must enable connected workers and operational agility, global enterprise-wide visibility and scalability, and resiliency that supports sustainability commitments. Applying AI/machine-learning and Digital Twin simulation solutions to recommend asset and process adjustments that optimize production is a big part of the digital transformation factories today are implementing.

Digital Twin technology can help manufacturers monitor production lines, evaluate equipment performance, quantify scrap rates, optimize energy usage, and solve quality-control problems. In addition to our solutions automatically adjusting setpoints to optimize operations, insights can be shared with everyone from operators on the plant floor to key management decision-makers, enabling them to have conversations that lead to improved quality and product consistency for their customers. They are better able to meet customers’ standards and stay on top of the digital curve. 

What are the results you’ve seen from digital twin technology?

Our GE Digital customers are seeing results with digital twins and simulation. One international food manufacturer decreased customer complaints related to product weight by 33%, improved product quality, and decreased waste.  Additionally, a pulp and paper manufacturer predicted Critical to Quality (CTQ) KPIs to improve productivity and eliminate wastewater regulatory issues. As a final example, a third company implemented an advanced process control solution to increase production system throughput by 10% using optimization technology.

Implementing Digital Twins

How can smart factories use digital twins to learn and test before deploying in a real-world environment?

First, take advantage of Digital Twin technology that provides simulation capabilities. With our Proficy CSense advanced analytics, manufacturers can run simulations before deploying in their real-world operational environment.  Second, manufacturers can partner with a provider that has the expertise in data science and analytics to demonstrate what Digital Twins can do for the business. The provider must be able to mine historical and real-time data and rapidly develop, test, and deploy simple calculations and predictive analytics to reduce variability. Once manufacturers see how Digital Twins used industrial advanced analytics to predict future asset and process performance, they will quickly see the benefits of deploying the technology in their smart factory.

How can manufacturers implement digital twins?

Manufacturers should select Digital Twin technology that allows their process engineers to be successful without requiring a team of data scientists. The process engineers are key. They are the domain experts and can drive results without the manufacturer having to add more resources. There are software solutions that manufacturers can quickly deploy that will help them implement Digital Twins in their operations. Companies will offer advisory services that jump start the deployment and set up to align with a business’ specific desired outcomes, and manufacturers should make sure they work with their partner to define the right roadmap to lay the groundwork for future success. Start with the low-hanging fruit, drive some early successes, and expand from there.