How to Implement a Supply Chain Digital Twin
Mark Vernall
Logistics/Supply Chain Specialist, SC SME - SC Project Management - SC Consulting - SC Advisor - SC System Implementation - Supplier Relationship Management - SC Digital Transformation - SC Solution Design - SC Jedi
Supply Chain digital twins have emerged as a transformational technology. These virtual replicas of physical Supply Chain networks allow organizations to simulate, analyze, and optimize operations without real-world risks.
Digital twins bridge legacy infrastructure with cutting-edge AI, IoT, and analytics. This integration facilitates the visibility, predictions, and prescriptions needed to meet rising customer expectations amid increasing complexity.
By mirroring flows, processes, and performance digitally, companies can uncover inefficiencies, bottlenecks, and risks in siloed or outdated processes. Supply Chain digital twins become living blueprints to guide incremental and continuous improvements.
A digital twin mimics a physical asset, system, or process through virtual representations that simulate real-time behaviors and performance.
In Supply Chain contexts, digital twins encompass entire networks - mapping interconnected nodes, product flows, information flows, and financial transactions from end to end. These virtual models integrate data from IoT sensors, enterprise platforms, and other sources.
The simulated scenarios reflect real-world variability without disrupting live operations. By digitally prototyping modifications, teams can forecast impacts on bottlenecks, disruptions, and performance across integrated and highly interconnected Supply Chain functions.
Artificial intelligence unlocks a digital twin's predictive potential for Supply Chain optimization. By ingesting historical data and leveraging machine learning, AI transforms snapshot model data into forward-looking insights.
It uncovers patterns around optimal parameters and delivers accurate forecasts of network conditions and performance - from demand variability to emerging disruptions.
AI capabilities also enable automation through continuously analyzing sensor data streams, sending alerts when thresholds breach, and recommending corrective actions for stakeholders.
Key benefits that supply chain digital twins empower organizations to unlock include;
Enhanced Forecasting: By aggregating data and applying AI, digital twins enable accurate demand predictions, risk forecasts, and optimization recommendations.
Increased Visibility: Integrated platforms with virtual visualizations break down silos, enhancing transparency across supply chain partners.
Logistics and Transportation Optimization: Simulating scenarios identifies routing and workflow bottlenecks to prescribe efficiencies.
Cost Reduction and Productivity Improvements: Simulations pinpoint underutilized assets and guide process enhancements to maximize throughput.
Agility and Innovation: Virtual testing capabilities accelerate the low-risk transformation of innovative strategies and technologies.
The downstream impact is increased customer satisfaction through improved quality, costs, and delivery timing.
Strategically defining digital twin requirements and objectives early on ensures maximum value alignment. This focused approach lets teams concentrate efforts only on the highest ROI capabilities that address the most pressing pain points, without scope creep.
When assessing your organization’s current Supply Chain operations, it's vital to highlight the most severe pain points limiting performance rather than trying to fix everything at once. You should target quick wins first - the "low-hanging fruit" vulnerabilities.
A good example is perhaps your Supply Chain is being hindered by paper-based processes causing visibility and tracking issues, information silos that obstruct collaboration, volatile freight expenses, and insufficient capacity and agility to meet customer delivery demands.
By prioritizing these primary weak points for digital twin transformation, you can address the most pressing inefficiencies without getting overwhelmed. This focused approach allows you to methodically layer on additional digitization towards full modernization based on quick initial returns.
The key is not letting the scope balloon initially. It is best to pick vulnerable spots, model the as-is challenges digitally, introduce incremental solutions via the digital twin, measure success, and gradually expand improvements once these first areas gain stability through increased efficiency and resilience.
Detail critical order-to-delivery processes spanning the interconnected network from suppliers to end consumers. Inventory, planning, and logistics foundations require focus first to establish an agile, scalable digital platform.
Production scheduling, procurement, and predictive maintenance can build on digitally upgraded foundations later for adjacent performance lifts.
Balance quantitative cost, speed, and quality KPIs with crucial qualitative capabilities like real-time visibility, automated analytics, mapped network models, and collaborative planning tools.
While optimization should target traditional metrics, establishing data visibility, integration, and access creates the digital infrastructure to enable continuous improvement through better decision-making.
Modeling the current network is a crucial step in understanding the complex relationships, connections, and interactions within the Supply Chain. By creating a visual representation of the network, organizations can identify inefficiencies, bottlenecks, and areas for improvement.
Using an iterative modeling approach allows organizations to deeply comprehend Supply Chain complexities; identify bottlenecks, validate assumptions, and readying data to support enhancement decisions. Once aligned with reality, you can transition from replicating the as-is to simulating the future, optimal system.
Detail the various suppliers, manufacturers, distributors, and interconnected logistics providers. Document associated procurement flows, production flows, distribution flows, and information flows.
Highlight potential bottleneck nodes causing delays due to constrained capacity as well as disrupted flows from communication breakdowns.
Connect siloed sources like ERP, WMS, TMS, and SCM systems. Incorporate external inputs from weather APIs, traffic data, and social media sentiment analytics.
Fusing inputs reveals factors influencing variability and performance across integrated planning and execution.
Compare modeled metrics and KPI simulations to real-world benchmarks, collect feedback from a diverse range of Supply Chain stakeholders.
Ensure model accuracy to build confidence and refine representations, estimating the impact of cost, time, and other dynamic variables.
As with any complex business transformation initiative, implementing a digital twin comes with an array of potential barriers that can undermine project success and value realization. Being aware of common mistakes upfront allows organizations to proactively mitigate risks through careful planning, project, change management, and governance.
o??? Lack of clarity around objectives and scope
o??? Insufficient stakeholder engagement and buy-in
o??? Poor data quality and availability
o??? Technology limitations and compatibility issues
o??? Inadequate resources and funding
o??? Complexity and difficulty in validating the digital twin
o??? Limited adoption and usage by stakeholders
o??? Failure to continuously update and refine the digital twin
o??? Inability to integrate with other systems and platforms
o??? Ignoring security concerns and failing to implement proper security protocols.
By keeping these pitfalls top of mind, orchestrating executive alignment, allotting adequate budgets, and governing vigilant project management, organizations can overcome obstacles that have thwarted others seeking digital twin adoption. It requires acknowledging upfront that challenges will emerge and putting in place the mechanisms, checks, and balances to promptly identify root causes and course-correct.
No implementation goes as smoothly as we'd like - success lies in quickly identifying, diagnosing and addressing inevitable hiccups. (It is better prepare and prevent, rather than repair and repent).
With current-state Supply Chain models established, the next phase involves enabling dynamic simulations through real-time data integrations and embedded AI capabilities.
Ingest inputs from IoT sensors, enterprise systems, weather data, and other sources to enable an integrated view of flows, inventory, equipment status, and personnel availability. Big data analytics, cloud platforms, and AI facilitate fusing siloed inputs.
Map out every node and dependency from suppliers to end consumers. Reflect fluctuations in demand, disruptions in supply continuity, logistics constraints, and the complex interrelationships governing performance.
Empowering a digital twin to uncover data patterns, trends, and signals that humans cannot detect involves leveraging advanced technologies such as machine learning, natural language processing, and computer vision. These capabilities enable the digital twin to provide valuable insights for demand forecasting, risk detection, and prescriptive recommendations.
Machine learning algorithms can be applied to the data generated by the digital twin to identify complex patterns and trends, allowing for more accurate demand forecasting and risk detection. By analyzing historical and real-time data, machine learning models can uncover hidden correlations and make predictions that are beyond human capacity.
Natural language processing (NLP) enables the digital twin to understand and interpret human language, allowing it to process unstructured data from sources such as customer feedback, social media, and market reports. This capability is valuable for gaining a deeper understanding of customer needs, market trends, and potential risks.
Computer vision technology empowers the digital twin to analyze visual data from images and videos. In the context of Supply Chains, this can be used to monitor and analyze the condition of physical assets, identify potential issues, and make prescriptive recommendations for maintenance or optimization.
With a living model capable of running scenarios, organizations can proactively mitigate disruptions through redundancy planning, balance inventories based on demand sensing, and identify optimization pathways for maximum throughput at the lowest sustainable cost.
Here is a list of digital twin providers that you can have a closer look at if you plan on outsourcing the digital twin build process. Oracle, SAP, IBM, and Dassault Systèmes are probably the most well-known and largest players in the market, what follows is a list of a few of the better not-as-large providers.
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If you would like to research further you can go to G2 and they list and rate 55 digital twin providers. (Please be aware that I am not endorsing, supporting or recommending any of the suppliers listed, it is for you dear reader, in case you would like to research further).
According to market forecasts, the digital twin market is expected to grow from USD 10.1 billion in 2023 to USD 110.1 billion by 2028, at a CAGR of 61.3% during the forecast period.
A fully operational digital twin empowers organizations to extract maximum value across three key dimensions.
The digital twin’s analytical insights on bottlenecks, capacity utilization, demand forecasting, and other operational dynamics enable data-driven decision-making. Simulation capabilities allow low-risk testing of decisions like inventory balancing, production scheduling, and logistics changes.
By detecting signals in advance, organizations can anticipate disruptions from suppliers, regulations, weather, and other sources - evaluating alternatives and contingency plans through simulation. Recovery strategies can also be shaped quickly amid active disruptions using the digital twin’s real-time visibility.
Connecting the digital twin across enterprise platforms like ERP, CRM, WMS, TMS, and SCM provides integrated data access, improving accuracy and consistency. API integrations facilitate real-time data flows while warehouses allow flexible retrieval. Proper data governance protocols ensure security and quality standards.
This unified operational view, powered by AI-driven analytics, creates a dynamic capability to course-correct Supply Chain processes as conditions change - balancing costs, agility, and risk. The digital twin unlocks continuous adaptation toward the optimal system design while continually improving performance and efficiency while building resilience and flexibility into the end-to-end Supply Chain.
When implementing a digital twin solution, it's important to customize it to fit your organization's unique Supply Chain needs. This means considering factors such as the specific products you produce, the complexity of your Supply Chain, and the regulations and standards that apply to your industry.
To customize the digital twin effectively, you'll need to work closely with your IT team and other stakeholders to understand the specific requirements of your Supply Chain.
This may involve creating a tailored implementation plan that takes into account your organization's existing systems and processes, as well as any additional hardware or software components that may be needed.
Work closely with IT teams and business stakeholders to tailor the digital twin to your organization’s unique products, Supply Chain complexity, industry regulations, and other factors influencing your operations.
Create an implementation roadmap aligned to existing infrastructure, systems, and processes while allowing for modular expansions and include room for scaling up.
Select adaptable solutions leveraging open standards and APIs for scalability across changing business needs. Prioritize modular and customizable cloud-based platforms over rigid on-premise options.
Partner with experienced suppliers equipped to support tailored integrations across diverse use cases, make you strategic partner your best friend, build the relationship and don't be afraid to push them hard.
Define the scope and objectives
o??? Clearly define the scope of your digital twin project, including which parts of the supply chain you want to replicate.
o??? Determine the objectives of your digital twin, such as cost reduction, efficiency improvement, or customer satisfaction enhancement.
Choose the right technology
o??? Select a suitable platform or software for creating your digital twin, considering factors such as scalability, interoperability, and ease of use.
o??? Consider using open-source platforms or APIs to integrate with other systems and avoid vendor lock-in.
Use data analytics and machine learning
o??? Utilize data analytics and machine learning algorithms to analyze historical data and real-time data streams to gain insights into your supply chain.
o??? Train your digital twin to learn from data and make predictions about future behavior.
Validate against real-world entities
o??? Validate your digital twin against real-world entities, such as sensors, IoT devices, and external data sources.
o??? Use validation techniques such as comparison, inference, and abstraction to ensure that your digital twin accurately represents the physical supply chain.
Incorporate feedback loops
o??? Establish feedback loops between your digital twin and the physical supply chain to continuously update and refine your digital representation.
o??? Use feedback loops to validate assumptions, identify gaps, and improve the accuracy of your digital twin.
Monitor and maintain the digital twin
o??? Assign dedicated resources to monitor and maintain your digital twin, ensuring that it remains updated and accurate.
o??? Continuously evaluate and improve your digital twin, incorporating new data and insights as they become available.
Foster collaboration and communication
o??? Encourage collaboration and communication among stakeholders, including supply chain partners, customers, and employees.
o??? Use your digital twin to facilitate dialogue and decision-making, fostering a culture of transparency and trust.
Address security and privacy concerns
o??? Implement appropriate security measures to protect sensitive data and prevent unauthorized access to your digital twin.
o??? Adhere to privacy regulations and standards, ensuring that personal data is handled responsibly and ethically.
Phase pilot projects before broad rollout and continuously refine to stay relevant. Maintain rigorous validation through actual benchmarks and user feedback. Leverage performance monitoring to meet defined objectives around accuracy, reliability, and stakeholder adoption.
With real-time data feeds and AI capabilities activated, the virtual model transitions from a static replica to a dynamic simulation platform for testing hypothetical scenarios and prescribed optimization strategies.
Continuously pipe Supply Chain sensor data, transaction records, and other dynamic inputs into an integrated central platform to enable a mirrored view of workflows and KPIs.
Analyze patterns in data flows, inventory velocities, quality variations, and cost drivers. Machine learning uncovers correlations between variables that humans can easily misinterpret as random variation or noise, the overall aim is to unearth new highly optimized strategies.
To maximize adoption and value, it is critical to equip supply chain professionals across various roles and job titles with tailored digital twin analysis capabilities catering to their unique responsibilities, priorities, and KPIs.
Build interactive dashboards aligning to the specific metrics and operational insights most relevant for planning teams, warehouse managers, Supply Chain key stakeholders, logistics, operations procurement specialists, etc. Empower each function with self-service access to visualize and slice data pertinent to their mandate and typical areas of focus.
Allow users to configure alerts and notifications when key performance indicators breach defined targets. This could include alerts for anomalies like lower-than-expected output volumes, excessive lead times, higher-than-forecasted expenses, or sudden shifts in quality.
Proactive alerts enable rapid diagnosis and intervention when metrics stray outside of expected ranges based on the digital twin’s simulations.
Traditionally, running digital twin experiments and simulations requires requesting modeling support from specialized internal teams or external consultants. By democratizing easy-to-use simulation configuration tools for common what-if scenarios, more employees can self-serve virtual experiments to evaluate the systemwide impact of local changes under consideration. This capability shifts from centralized modeling to decentralized simulation promotes usage, learning, and innovation.
With each new analysis tool and simulation feature released, it is critical to ensure thorough and ongoing training. Both live and self-paced options combining interactive tutorials with practice environments accelerate proficiency.
As the platform scales to more enterprise users, administrator controls can govern access privileges appropriate to each role ensuring the right guardrails are in place to mitigate risk.
Congratulations! on successfully implementing your organization’s Supply Chain digital twin. You now have a tremendously powerful tool that can simulate various scenarios, optimize, and continually improve your organization's Supply Chain performance and efficiency. As well as helping you and your Supply Chain team make better more informed decisions a whole lot faster.
Check out part 2 of this 2-part series. "How to Implement a Digital Supply Chain Control Tower", you can read my LinkedIn article here .
Coming soon part 3 of 3 in this series, "How to Integrate a Digital Supply Chain Twin and Control Tower".
[And, if you need a remote Supply Chain specialist, Subject Matter Expert, Advisor, Consultant, Project Manager or know someone that does, please feel free to connect & message me directly on LinkedIn.]
Check out my LinkedIn article; “Supply Chain Control Towers a Key Trend in 2024”, here .