Digital twin technology offers transformative benefits—real-time visibility, predictive insights, and strategic agility. But successful deployment requires navigating a complex mix of technical, organizational, and data challenges.

In this article, we break down the most common obstacles to digital twin adoption and provide practical solutions based on industry best practices and real-world implementations.

Challenge 1: Siloed and Incomplete Data

Problem:
Supply chains often span disparate systems—ERP, TMS, WMS, IoT devices, spreadsheets—with inconsistent data formats and ownership.

Impact:
Without accurate and timely data, the digital twin cannot reflect reality or deliver actionable insights.

✅ Solution:

Conduct a data audit before modeling.
Prioritize high-impact, high-frequency data (e.g., inventory, lead times, shipments).
Use middleware or ETL tools to normalize formats across systems.
Establish data governance roles and routines.

Pro Tip: Start with semi-structured data sources (e.g., ERP APIs) before tackling edge IoT feeds.

Challenge 2: Over-Scoping the Initial Build

Problem:
Many teams try to model the entire supply chain on Day 1—leading to delays, complexity, and failed expectations.

Impact:
Projects stall before delivering value. Business users lose confidence in the twin’s practicality.

✅ Solution:

Focus on a Minimum Viable Twin (MVT) with a single high-impact use case.
Choose a well-bounded pilot: e.g., one product category, lane, or region.
Deliver quick wins like inventory optimization or demand planning to build momentum.

Framework: Think big, start small, scale fast.

Challenge 3: Change Management & User Adoption

Problem:
Even the best-designed twin will fail if users don’t adopt it. Resistance comes from fear of change, unclear workflows, or lack of trust in the system.

Impact:
Teams continue relying on spreadsheets and gut instinct instead of the twin’s recommendations.

✅ Solution:

Involve end users early in design and scenario testing.
Provide training on reading dashboards, running simulations, and interpreting alerts.
Identify internal “champions” who advocate for the twin across departments.
Visualize value with before/after KPIs.

Human-centric design is as important as technical capability.

Challenge 4: Technical Integration Complexity

Problem:
Integrating legacy systems and IoT hardware with modern platforms can be slow and expensive.

Impact:
Delays in deployment, unexpected costs, and loss of data fidelity.

✅ Solution:

Use platforms that support open APIs, EDI, and native connectors.
Avoid full system replacement—layer the digital twin on top of existing architecture.
Consider hybrid cloud/on-premise models to minimize disruption.

Tip: Hylios offers lightweight, low-code integrations designed for plug-and-play deployment.

Challenge 5: Unrealistic ROI Expectations

Problem:
Leadership expects transformational outcomes in weeks—without aligning on cost, scope, or organizational readiness.

Impact:
Projects are labeled failures despite delivering meaningful improvements.

✅ Solution:

Set realistic timelines (e.g., 6–12 months to ROI).
Align metrics to business pain points: stockouts, order accuracy, lead times.
Benchmark against similar companies or use case archetypes.

Challenge 6: Security, Privacy, and Compliance

Problem:
Digital twins ingest sensitive data—financials, supplier contracts, IoT device streams—which raises concerns about exposure and compliance.

Impact:
Security concerns can block executive buy-in or prevent partner collaboration.

✅ Solution:

Use role-based access control and data masking.
Ensure encryption in transit and at rest.
Align with GDPR, SOC 2, and other regional frameworks.
Keep simulation data separate from transactional systems when needed.

Bonus Challenge: Simulation Paralysis

Problem:
Too many “what-if” scenarios can slow decision-making rather than speed it up.

Impact:
Stakeholders hesitate to act, waiting for perfect simulation models.

✅ Solution:

Predefine a set of strategic scenarios (e.g., +30% demand, +15% lead time).
Tie simulations to KPIs and time-box reviews.
Emphasize agility over perfection—recommend best-next actions, not exact forecasts.

Remember: The goal is confident action, not perfect prediction.

Conclusion: Build Smart, Avoid the Pitfalls

Digital twins are game-changers—but only if implemented thoughtfully. By starting small, aligning stakeholders, prioritizing clean data, and focusing on value-driven outcomes, you can overcome the common pitfalls and unlock the full power of your digital twin.

Learn more about how intelligent deployment of a digital twin can help your company conquer its supply chain challenges:

Conquering Complexity In Supply Chains With Digital Twins | BCG
Next Up: Revisit 2.3: The Digital Twin Implementation Roadmap to structure your rollout strategy
Or explore 3.1: Best Practices for End-to-End Supply Chain Visibility to scale beyond the pilot.