What is a process digital twin?

If you can handle the suspense, we first need to explain what digital twin technology is, before we can dive into process digital twins.

A digital twin is a virtual model of a real product, piece of machinery, or other object. To make the digital replica more accurate than your standard from-scratch simulation (check out our related article on the differences that set them apart), digital twins are typically connected to sensors on their physical counterpart. They enable businesses to transform real-world operations with the speed, intelligence and flexibility of software.

So what is a process digital twin?

Process digital twins and object digital twins are more sibling technologies than twins themselves.

A process digital twin is a digital model of a business process. “But what about the sensors you said a digital twin uses? How do they work for something intangible like a process?” I hear you ask. A process digital twin’s answer to a sensor is a real-time data feed showing how the process is running in the real world. And businesses can use process mining to accurately extract and standardize this data from its source systems.

By creating digital twins of processes, businesses can monitor the various granular steps and components within them – as well as how they interact with each related process across the company. With this end-to-end transparency, businesses can rapidly optimize each process to improve the overall performance. These optimizations can even be automated.

With process twins, value is created through speed, efficiency and cash flow, whereas with physical objects the opportunities primarily concern preventative maintenance, product design and quality control.

Imagine an Order-to-Cash process at a large manufacturing firm

To take one example, process digital twin technology could be used to represent every step in the O2C process – from customers placing orders to the variety of steps required to fulfil them, such as receiving payment.

With real-time data feeding into the digital twin model of the process, the business could see the reality of how each of these steps is being executed and where there might be room for improvement, such as:

• Do we experience errors in processing orders?
• Are we moving fast enough to get customers’ orders ready?
• If we’re slow, where is the bottleneck?

With an accurate process model, the business could then simulate and compare potential optimizations before implementing those that achieve improvements. The insights gained could answer questions like:

• What if we changed the user experience for placing an order?
• Would we lower cycle times if we organized our inventory differently in production processes?
• Could we improve cash flow by prioritizing payment processes?

What can businesses do with a process digital twin?

Until fairly recently, digital twins have mostly been defined in the context of the Internet of Things (IoT), industrial applications, and physical assets such as aircraft engines, turbines and manufacturing equipment. An object’s digital twin makes it easier for businesses to track the location, performance and health of products, tools and machinery in a scalable way.
However, as businesses developed digital twins of these objects, it became increasingly clear that it’s also worth having a digital twin of the wider process or system in which the object operates.

That’s why Gartner defines a digital twin as “a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities, such as a power plant or a city, and their related processes.”

When every object has a digital twin, and every transactional event and global business service has a process twin, you can effectively create a digital twin of the entire business. This is game-changing when it comes to being able to anticipate and respond to business volatility.

At Celonis, the Process Intelligence Graph shows the bigger picture that process digital twins are designed to build and enable. It’s a live map showing every process and how they’re all interconnected, providing both an objective view of process performance and valuable business context that can be used to make technologies such as AI work for the enterprise.

How are businesses using process digital twins?

If you’re only just discovering process digital twins, you’re a little late to the party. Businesses are already using process twins to improve just about every kind of process in major industries such as manufacturing. Here are just a handful of processes that can be optimized using process digital twins:

Supply chain resilience ->

Process digital twins help businesses better analyze risk by modeling what-if scenarios such as disruption, stock-outs and supply volatility in a virtual environment. Modeling the impact of these challenges helps businesses understand the steps they need to implement to increase resilience and maintain service levels.

Accounts Payable

Finance teams can gain more granular visibility into the issues that lead to late payments, identify areas where cash discounts aren’t being fully utilized, and investigate errors in purchase orders to improve vendor relationships.

Accounts Receivable

Companies can track where invoices are typically lost or duplicated, model ways to streamline credit review and dispute processes (such as unnecessary approval flows), and identify opportunities for more effective dunning efforts that could improve cashflow.

Order Management

Process digital twins help detect upstream and downstream order errors to reduce cycle time, identify delivery and billing blocks that may impact on-time in-full delivery, investigate opportunities for automation, and trial reverse logistics to improve returns experiences.

Procurement

Businesses can use a digital twin model to diagnose discrepancies in purchase requisition, eliminate the occurrence of free text orders, more accurately model supplier lead times, track emissions in real time, and minimize excess stock to lower operating costs.

• For a practical guide to making these optimizations real, read Revolutionizing Workflows with Process Digital Twins.

What’s next? Process digital twins and AI

Businesses that are already implementing process digital twins are well on their way to surfacing and overcoming the hidden issues standing between them and operational excellence. With greater visibility and the ability to take action at the root cause, they have a clear path to better efficiency, improved cashflow, and positive outcomes for their customers and suppliers.

Combined with the progress made possible by process digital twins, artificial intelligence not only recommends targeted improvement opportunities for businesses. But AI also provides a scalable way to drive consistent, standardized processes that help optimize the entire enterprise.

If you want to start putting process digital twins to work in your business, take a look at how Celonis can help.