As the world becomes increasingly interconnected, supply chain operations are more sophisticated than ever, and they must be both agile and resilient. Yet with many variables in play, disruptions are difficult to predict—making it crucial for supply chains to develop the capability to respond swiftly when issues arise.
According to research, supply chain managers name visibility and collaboration as their top two improvement priorities for effective, responsive operations. Companies that can access real-time, actionable data and communicate seamlessly across their networks are better positioned to adapt quickly, minimizing the impact of unexpected events.
This need for enhanced visibility and collaboration is where Real-Time Location Systems (RTLS) and digital twins step in, transforming supply chains with real-time, digital counterparts that mirror the physical flow of goods, assets, and operations. By offering up-to-the-second data on everything from inventory levels to equipment status, RTLS-powered digital twins enable supply chain managers to respond to changes with agility and precision, making proactive adjustments instead of scrambling to react.
This article explores the powerful impact of digital twins combined with RTLS on supply chain operations, examining how they not only address visibility and collaboration needs but also drive greater resilience and efficiency in an unpredictable world.
Table of Contents
What is a Digital Twin in the Supply Chain?
A digital twin is a virtual replica of a physical object, system, or process, created by integrating real-time data from sensors, devices, and other sources. This digital model is used to simulate, monitor, and analyze the performance of its physical counterpart. In the context of supply chains, digital twins can replicate not only physical assets like inventory, machinery, and vehicles but also processes and even entire supply chain networks. By continuously updating the digital model with real-time data from IoT devices and other sources, digital twins offer a dynamic, data-driven simulation of the entire supply chain, enabling better monitoring and optimization.
For example, Amazon uses digital twins to continuously optimize distribution by tracking shipments and managing product flows to support fast delivery times. In the pharmaceutical sector, GlaxoSmithKline employs digital twins to enhance vaccine production and improve temperature-controlled packaging.
Digital twins also offer the ability to simulate “what-if” scenarios, allowing businesses to test changes in their operations before implementing them in the real world. For example, Philip Morris International uses digital twins to simulate thousands of what-if scenarios, helping them align demand with supply, find optimal balances between resilience and efficiency, and evaluate returns on large investments across different timeframes.
The Necessity of Real-Time Visibility
The success of a digital twin depends on the continuous flow of real-time data. In supply chain operations, this means tracking the exact position, status, and condition of assets, inventory, and equipment as they move through various stages. If the data is inaccurate or outdated, the digital twin could misrepresent the actual scenario, potentially leading to costly mistakes.
RTLS is one of the core technologies enabling real-time visibility in digital twins. RTLS technologies track and provide the exact location of physical assets, inventory, and machinery in real-time, feeding this information directly into the digital twin. By doing so, RTLS serves as the eyes and ears for digital twins, ensuring they remain synchronized with the actual state of operations.
RTLS can capture several types of data beyond just location. Key data types include:
Environmental Conditions: Sensors can measure temperature, humidity, and other conditions, essential for sensitive items like pharmaceuticals.
Movement and Speed: By tracking the motion of assets, RTLS can provide insights into the speed and direction of goods moving through the supply chain.
Utilization and Dwell Time: RTLS can record how long assets or equipment remain in specific areas, useful for optimizing workflows and identifying bottlenecks.
Asset Status: Some systems can track operational status, such as whether a machine is active, idle, or malfunctioning, helping to improve maintenance and reduce downtime.
The integration of RTLS makes it possible to achieve an ongoing, seamless flow of data allowing digital twins to serve as living models of the supply chain, reflecting real-time conditions.
How RTLS Enhances Digital Twin Capabilities
RTLS brings vital, real-time data into digital twins, offering visibility that leads to smarter decision-making, improved efficiency, and streamlined operations. Three key areas in which RTLS enhances digital twin capabilities within the supply chain include asset tracking, inventory management and process optimization.
Asset Tracking
RTLS allows digital twins to precisely track assets such as pallets, machinery, and vehicles across the supply chain, resulting in:
Preventing Equipment Downtime: If a critical piece of machinery needs maintenance or is temporarily offline, RTLS data will reflect this immediately in the digital twin. This allows managers to reschedule tasks or reallocate resources without waiting for downtime reports.
Tracking Mobile Assets: RTLS helps digital twins track high-value mobile assets, such as transport vehicles, in real time. This enables supply chain managers to monitor routes, update estimated arrival times, and adjust schedules as needed.
Reducing Lost or Misplaced Assets: In busy environments like warehouses or distribution centers, assets can easily be misplaced. RTLS integrated with digital twins helps prevent this by continuously monitoring asset locations, ensuring that key items—such as tools, containers, or inventory trolleys—are always accounted for and quickly located when needed.
Inventory Management
RTLS enables digital twins to monitor the movement and placement of inventory with high precision. This helps digital twins:
Optimize Stock Levels: Digital twins use RTLS data to track inventory flow, helping managers maintain optimal stock levels and prevent both overstocking and understocking.
Reduce Picking Errors: By visualizing inventory placement within a digital twin, RTLS minimizes picking and storage errors. For example, if an item is mistakenly placed in the wrong storage zone, RTLS detects the discrepancy immediately, allowing for quick correction.
Process Optimization
By providing rich data on workflow patterns, RTLS enables digital twins to analyze and improve processes. This helps with:
Reducing Congestion and Delays: RTLS data helps digital twins highlight areas of frequent congestion within a warehouse, suggesting alternate routes to prevent bottlenecks.
Optimizing Workforce Allocation: With real-time insights into workflow and movement patterns, digital twins can help managers allocate workforce resources more efficiently.
Automating Routine Checks and Quality Control: Digital twins can use RTLS data to streamline quality control processes. For example, if a fragile item is handled too roughly, RTLS may trigger an inspection alert, preventing potential damage from going undetected.
Benefits of Integrated RTLS and Digital Twin Technology
The integration of RTLS with digital twins delivers numerous advantages to the supply chain including:
Increased Efficiency: Integrated RTLS and digital twins enable supply chain managers to make quicker, data-driven decisions with up-to-the-minute information about assets, inventory, and workflows. This leads to:
Faster identification of bottlenecks and delays, allowing teams to take immediate action.
Streamlined workflows by optimizing resource allocation based on real-time insights.
Reduced operational downtime, as issues can be flagged and addressed before they escalate.
Enhanced Predictive Maintenance: RTLS data plays a crucial role in helping digital twins predict and prevent equipment failures. With continuous tracking of assets, the system can identify signs of wear and tear and alert managers before issues disrupt operations. This includes:
Early detection of potential breakdowns based on usage patterns and movement data from RTLS sensors.
Scheduling maintenance proactively, reducing unplanned downtime, and extending the lifespan of equipment.
Minimizing production delays by ensuring that equipment is serviced before it fails, not after.
Improved Collaboration: With RTLS and digital twins providing real-time visibility, teams across various supply chain stages can coordinate their actions more effectively. This fosters:
Better communication between warehousing, logistics, and transportation teams, ensuring smoother transitions and handovers.
Greater transparency for all stakeholders, enabling everyone to track the status of shipments, inventory, and assets.
Reduced delays in decision-making, as teams have access to the same data, allowing them to respond quickly to issues as they arise.
Key Challenges and Solutions
While RTLS and digital twins offer great potential, there are a few hurdles that companies must address to maximize their benefits. Two particularly important ones are data accuracy and system integration and scalability.
Data Accuracy
High-quality, accurate data is crucial for digital twins to provide reliable insights. Inaccurate data can lead to operational inefficiencies, misinformed decisions, and costly errors. These inaccuracies can occur due to:
Sensor Inaccuracies: RTLS relies heavily on sensors for data collection. If sensors are malfunctioning, poorly calibrated, or not accurately positioned, the data they collect may be faulty, leading to inaccurate digital twin models.
Environmental Factors: Factors such as signal interference or environmental conditions (e.g., metal structures, and dense warehouses) can affect RTLS data accuracy.
Data Integrity: As data moves between various systems (from RTLS sensors to the digital twin model), the integrity of the data can be compromised due to poor network connections or system incompatibility.
To ensure data accuracy, companies can implement several solutions. Regular audits and calibration of sensors are essential, as frequent inspections and recalibrations can help minimize the risk of errors or malfunctions. Additionally, investing in high-quality RTLS sensors with advanced accuracy features, such as built-in error-correction algorithms, can significantly reduce sensor-related issues. Data validation checks at every stage—during collection, transmission, and integration—also play a crucial role in maintaining accuracy.
Integration and Scalability
Integrating RTLS technology into existing supply chain systems, and ensuring it scales effectively as the business grows, presents its own set of challenges. Some challenges to doing this effectively include:
System Compatibility: Existing systems or different technological platforms within the supply chain may not be compatible with RTLS solutions.
Data Silos: Different departments within the organization (e.g., warehouse management, logistics, and transportation) may use separate systems for tracking, which can result in fragmented data. This makes it harder to consolidate information into a unified digital twin model.
Scalability Issues: As a supply chain grows, the volume of data generated by RTLS systems increases, making it difficult for smaller systems or outdated infrastructure to handle the load. This may lead to slowdowns or errors as the business expands.
Businesses can adopt several strategies to address integration and scalability challenges. Middleware solutions and APIs are key tools for bridging the gap between RTLS and existing supply chain systems. These technologies facilitate smoother integration by ensuring compatibility between different platforms and technologies, reducing friction during implementation. Additionally, investing in cloud-based infrastructure for both RTLS and digital twin technology provides the scalability needed to support growing data volumes. A modular approach to RTLS systems further enhances scalability, allowing businesses to expand their RTLS capabilities incrementally.
Advancements in Digital Twin Technology
The next generation of digital twin technology will likely be driven by powerful advancements in various tech fields, such as IoT, AI, and 5G, each of which will significantly boost the capabilities of RTLS systems in supply chains.
IoT Integration for Hyperconnectivity: IoT will continue to revolutionize digital twins by providing an even more expansive and interconnected network of sensors and devices, allowing them to collect and analyze more granular data from a greater number of sources. This hyperconnectivity will give rise to digital twin models capable of simulating entire supply chain ecosystems with unprecedented detail and accuracy.
AI-Powered Automation: Artificial Intelligence (AI) will enhance the ability of digital twins to make proactive, automated decisions. AI could enable digital twins to predict disruptions, such as machinery breakdowns, and automatically initiate corrective actions, reducing the need for human intervention.
5G-Enabled Low-Latency Communication: With the rollout of 5G networks, the speed and capacity of data transmission will significantly improve. This will enable real-time, low-latency communication between IoT devices and digital twins, providing more accurate and immediate data.
The Future of Digital Twins in the Supply Chain
The future of digital twins in supply chains promises to transform operations by increasing automation. In the long term, digital twins are expected to provide prescriptive and autonomous capabilities, adjusting processes in real-time. Importantly, digital twins are designed to empower employees rather than replace them, using data to streamline decision-making and optimize productivity across the supply chain. Looking ahead we are likely to see:
Autonomous Warehouses: The rise of autonomous vehicles, robotics, and AI will likely give birth to fully autonomous warehouses. In these environments, digital twins will enable systems to collaborate seamlessly to manage inventory, sort shipments, and even perform quality checks—completely autonomously.
Smart Logistics Networks: Using real-time RTLS data, digital twins could continuously optimize the flow of goods, taking into account everything from weather conditions to traffic, and even fuel levels. This could lead to self-optimizing logistics, where transportation routes and delivery times are adjusted on the fly for maximum efficiency.
Predictive Supply Chain Management: As digital twins become more advanced, they could evolve into predictive tools capable of foreseeing potential disruptions before they happen. For example, by analyzing historical data and combining it with real-time insights from RTLS, a digital twin could predict periods of high demand, supply chain bottlenecks, or even potential labor shortages.
RTLS and Digital Twins: Changing The Face of Supply Chain
Digital twins, when paired with real-time tracking, empower companies to build resilience into their logistics operations. By creating a virtual mirror of their entire supply chain, businesses can experiment with different strategies, test outcomes, and rapidly implement changes to optimize performance.
As the logistics landscape evolves, the integration of real-time tracking and digital twins will become an essential component of future success. Companies that are able to harness these technologies will gain a significant competitive edge, positioning themselves as leaders in the industry. The ability to track assets in real-time and simulate operations with digital twins offers businesses the flexibility, foresight, and adaptability needed to thrive in an unpredictable world.
For businesses aiming to stay ahead of the curve in supply chain management, Deeyook’s cutting-edge RTLS solutions can help you harness the full potential of digital twins, ensuring more agile, efficient, and resilient operations. Reach out to Deeyook today to learn how we can transform your supply chain with real-time, data-driven insights.
