Digital Twin in Logistics: Application, Benefits and Limitations

Digital twin holds powerful potential for the logistics sector. But where exactly does an investment in digital twin make sense? Building on my previous article, I would like to dive into use cases that illustrate the importance of digital twin for our warehouses and supply chains. I will also discuss prerequisites, current challenges, and what I consider the main benefit of digital twin: better and faster decisions.
|Written by Sabine Mueller

Supply Chain Trends | automation digital twin logistics supply chain warehouse

A warehouse showing boxes of shelves and digital statistics on inventory

Where We Stand Today

In their current stage of development digital twins can be used for applications ranging from “sensing and showing” to “thinking” and even “acting”.

Current use cases tend to focus on “sensing and showing” and “thinking”. For both, you need a solid foundation of structured business data.

“Thinking” applications let you simulate different scenarios in order to learn, predict, and analyze without incurring real-life costs. Here, too, application depends on a thorough understanding of all business rules, KPIs, etc.

“Act” is the most complex and advanced form of digital twin. Over time, becomes a self-optimizing mechanism, able to trigger appropriate actions.

Warehouse Digital Twin: Staying on Top of Changes

In this first use case, we’ll look at a digital twin focusing on material things, i.e., a warehouse with changing racks, staffing, use of robots, and layout changes.

This digital twin use case covers everything that happens within the warehouse walls, with the main focus being physical adjustments, i.e. changing racks, deploying fewer/more people, replacing people with robots, adapting layouts. These changes are simulated in a digital environment to enable sound decision-making.

A digital simulation lets us assess the operational and financial impacts of sudden changes. For example:

  • A customer requests additional volume due to an unexpected large order.
  • Issues with a carrier abruptly increase the cut-off time for orders.
  • Unexpected staff shortage due to illness/unrest creates bottlenecks.

Digital twin also helps us optimize the way we interact with and convince our customers, especially regarding warehousing design and investment decisions. A digital simulation lets us show the impact of modifications in 3D and real time: What happens if the customer were to invest in automation? How would this affect efficiency and costs?

Digital twin technology also boosts our flexibility and speed when designing facilities. We can model the optimum warehouse layout and configuration well in advance.

Supply Chain Digital Twin: Improving Responses

The simulation provided by a digital twin of a supply chain focuses on the process in a supply chain rather than on physical constraints. Some typical questions that digital twin can answer:

  • How much supply do I need to take each day from each location to minimize my costs?
  • How do I organize transportation, and
  • Which transportation modes should I pick?

Supply chain communication is still quite time-intensive. For example, in the event of a shortage, the supplier is responsible for informing the receiving party. This often happens through email, which is notoriously inefficient. The receiving party then has to come up with a solution, and choose from a variety of options. Digital twin can integrate this process, linking supplier communication to supply planning, and thus optimize (or automate) decision-making processes.

The digital twin recognizes disruptive events and can recommend (or even carry out) an appropriate response. If a shipment from a specific supplier is delayed, for instance, the digital twin will order the same material and quantity from a warehouse within its network. Using simulations and AI technology also helps you keep an accurate overview and reduce unnecessary safety stock. Last but not least, you become a better logistics partner: Digital twin lets you advise your customers on total purchase order quantities, thus offering them more than just execution.

Key Challenges to Consider

I don’t recommend that companies rush toward the digital twin trend blindly, as attractive as it is it comes with a set of unique challenges. Rather, I suggest they implement it in a way that is sustainable and value-adding over the long term. Here are five key challenges to consider before implementation:

  1. Standardize logistics operations: This seems to be a major hurdle, but it’s a prerequisite to uncovering the full potential of digital twin. A digital twin is most worthwhile in use cases with a high opportunity for standardization. The more variables in your business logic/systems, the more barriers there are to standardization, and the less attractive a digital twin solution becomes.
  2. Embrace continuous development: A digital twin is not an off-the-shelf product. Even a plug-and-play solution is a combination of technologies and data. A digital twin will need to be re-assessed, updated, and refined indefinitely.
  3. Manage change and build trust: Digital twin success heavily depends on users’ trust: How does it add value for them? The technology will offer new insights that may, at first, seem counterintuitive. Digital twin calls for adequate change management to teach users how to best collaborate with this technology.
  4. Go big or go home: Every digital twin solution needs to demonstrate that it solves a real business problem and delivers the desired results. In addition, digital twin should be implemented across the whole enterprise. If you only use one digital twin, the ROI is much too low.
  5. Avoid developing a digital twin application from scratch – the financial investment simply does not pay off. Several good plug-and-play solutions are already on the market, and I recommend them for every use case.

Digital twin and the Future of Logistics

Digital twin technology is an enabler. Through sophisticated intelligence, digital twin can help us make sound, data-driven decisions and even create self-optimizing systems. It will empower us to demonstrate and communicate complexity in an understandable way. And to simulate changes before implementing them.

In light of digitalization, and the huge amounts of data produced in our industry, we simply must explore the concept of digital twin, early on, through investing in R&D. Who will unlock its potential first? Understanding what digital twin can do for you – and which use cases stand to benefit – will help you to be ready to scale at the right moment.

However, there are many challenges we need to be aware of – standardization and scaling being the most important ones.

Are you considering digital twin technology for your business? Share your thoughts and questions with me in the comment section below, or directly on LinkedIn.

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