Get Ready: How Digital Twins Will Transform Supply Chains in 2024

ByPaul Christiano Last Update on

Supply chain leaders, it‘s time to start your digital twin engines. Digital twin technology is set to drive immense transformation across global supply chains. But what exactly are digital twins and how can they help optimize your operations?

In this comprehensive guide, we‘ll explore:

  • What digital twins are and how they work
  • The top 5 supply chain use cases seeing results from digital twins
  • Real-world examples of digital twins in action
  • The technologies powering digital twin innovation
  • Challenges to overcome in implementation
  • The outlook for digital twin adoption in the future

If you want to future-proof your supply chain, boost resilience, and gain a competitive edge, you need to understand digital twins. Let‘s dive in.

Digital Twins 101: A Primer

Before we get into the use cases, let‘s quickly cover the digital twin basics:

A digital twin is a virtual representation of a real-world physical asset or process. The digital twin is powered by data from IoT sensors, cameras, drones, meters, gauges, operator inputs, and more.

Advanced simulation software creates a digital model that mirrors all the characteristics, configurations, and behaviors of its physical twin. Machine learning algorithms analyze data flowing from the physical twin to optimize the digital copy in real time.

Digital twins are being widely adopted across manufacturing, energy, automotive, aerospace, retail, and many other sectors. In the supply chain realm, digital twins provide a complete virtual replication of the end-to-end supply network.

According to Gartner, currently 13% of organizations implementing IoT projects are already using digital twins, while 62% plan to use digital twins by 2022.

The Top 5 Supply Chain Uses Cases

Now that you know what digital twins are at a high level, let‘s explore the top 5 ways they can transform supply chain operations:

1. Optimize End-to-End Supply Chain Processes

With an integrated digital twin of the entire supply chain, companies can identify process constraints and redesign workflows for greater efficiency.

For example, Lockheed Martin uses digital twins to optimize the performance of its production lines. By virtually modeling the various workstations, tools, and steps required to manufacture the F-35 fighter jet, they minimized aircraft production time by 20%.

Digital twins can also help assess sustainability. Unilever created a digital twin of their laundry detergent supply chain. The virtual model identified opportunities to optimize delivery routes, reduce miles driven, and decrease GHG emissions by 15%.

According to ABI Research, optimizing supply chain management processes is the leading application of digital twins today.

2. Pinpoint Bottlenecks in Real-Time

Digital twins give supply chain professionals complete visibility into every link and node in their network. With real-time monitoring capabilities, they can instantly flag any emerging bottlenecks.

For example, by creating a digital twin of a packaging line, a CPG company could identify that a particular machine constantly slows down production. Operators are notified to quickly take action and prevent slowdowns rippling through the supply chain.

Research from MarketsandMarkets predicts digital twins could yield 30% improvement in cycle times by simulating production processes and uncovering chokepoints.

3. Model Transportation Plans and Infrastructure

With data from digital twins, supply chain leaders can optimize their transportation fleets, routes, and infrastructure:

  • Predict demand surges or shortages by geography and rapidly reallocate transportation capacity
  • Identify underutilized trucks, drivers, routes, distribution centers, and docks
  • Determine optimal locations and transportation modes for new warehouses to minimize logistics costs
  • Assess impact of new product launches on transportation network bandwidth

For example, GE Transportation deployed digital twins to improve rail yard operations. By simulating rail car maintenance processes, they increased operating efficiency by 25%.

4. Improve Demand Forecasting and Inventory Optimization

Digital twins generate much more accurate demand forecasts by incorporating real-time point of sale data, customer ordering patterns, and production schedules.

Armed with better demand inputs, supply chain teams can optimize inventory. Simulations ensure the right stock levels in the right locations to avoid shortages while reducing carrying costs.

Companies integrating digital twins with inventory management systems have achieved:

  • 10-30% improvement in demand forecast accuracy
  • 20-50% reduction in excess and obsolete inventory
  • 25-80% decrease in stock-outs and lost sales

5. Design Optimized Packaging

Digital twin technology enables companies to simulate and test packaging designs virtually under various conditions.

Teams can assess how boxes, bottles, and crates will:

  • Withstand vibration, compression, and other stresses during handling and transport
  • Maintain seal integrity and prevent leaks or spoilage
  • Be impacted by environmental factors like heat and humidity
  • Interact with labeling and scanning equipment

Evaluating packaging with digital twins first prevents costly rework and failure later on. Companies can also assess packaging sustainability and optimize materials and recyclability.

The Tech Powering Digital Twins

The data and technologies fueling digital twins are advancing rapidly:

  • IoT Sensors: Devices like RFID tags, infrared cameras, and vibration sensors feed real-time visibility into physical asset conditions and environments.
  • Connectivity: 5G enables huge amounts of data to flow seamlessly from IoT sensors into the digital twin. Edge computing reduces latency.
  • Cloud Computing: Virtual digital twin models are hosted in the cloud for unlimited scalability and computational power.
  • Simulation Software: Physics-based simulation recreates physical asset behavior and performance digitally.
  • AI and Machine Learning: ML algorithms continuously analyze data to tune the digital twin to match evolving real world conditions.
  • AR/VR: Emerging applications blend physical and digital worlds through augmented and virtual reality.

Gartner predicts that by 2023, over 50% of large industrial companies will be using digital twins, up from less than 10% in 2019.

Overcoming Implementation Challenges

To achieve digital twin success, supply chain organizations should prepare for these common challenges:

  • Data Quality: Carefully cleanse and normalize incoming data streams to train digital twin models effectively. Maintain strong data governance over time.
  • Specialized Skills: Partner with digital twin platform vendors or systems integrators to obtain needed expertise. Upskill teams through hands-on training.
  • Change Management: Get leadership and staff onboard with proven pilot projects. Shift culture towards data-based decision making.
  • Interoperability: Integrate digital twin platforms with existing supply chain systems like ERPs and TMS. Define open data standards.
  • Security: Establish robust cybersecurity protections and access controls for digital twin data. Limit external connectivity.

Are You Ready for the Future?

The supply chains that will thrive into the future will be the ones leveraging digital twins today. With real-time visibility, simulation capabilities, and predictive insights, digital twins drive step-change improvements.

"Leading organizations must begin adopting digital twins now," emphasizes John Manners-Bell, Chief Analyst at Transport Intelligence. I couldn‘t agree more.

Now is the time to get up to speed on digital twin technology and start piloting use cases with the highest ROI. Partners skilled in design, implementation, and change management can help you accelerate adoption.

By digitally transforming your supply chain with this game-changing technology, you‘ll be able to rapidly respond to whatever the future brings.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

Similar Posts