Breaking barriers: How to overcome common hurdles in Digital twin adoption.

For companies looking to model, monitor, and optimise real-world systems, digital twins have become a game-changer. Digital twins facilitate real-time, data-driven decision-making in a variety of contexts, including ports, cities, infrastructure, and energy networks. However, many organisations continue to encounter major obstacles when attempting to implement digital twin technologies, even in spite of their increasing popularity and shown benefits.

Entopy collaborates with customers in dynamic, intricate settings to ensure that the adoption of digital twins is not only feasible but also significant. The most typical obstacles to adoption are examined here, along with solutions.

1. Fragmented and siloed data

Getting access to clean, organised, and unified data is one of the most difficult aspects of creating digital twins. It is challenging to obtain a comprehensive picture of operations in many businesses because data is kept in silos across departments or systems.

Solution: The platform from Entopy is made to combine several, disjointed data sources into a single, organised framework. It is simpler to connect data across systems and provide insightful findings thanks to our ontology-driven models, which comprehend the relationships between entities.

2. Complexity of real-world environments

A single, sizeable digital twin is frequently insufficient to represent the dynamic and intricate nature of real-world systems. Particularly in settings where things are always changing, like supply chains or transportation networks, the scope can seem daunting.

Solution: Entopy uses micromodels, which are tiny, targeted AI models created for certain procedures or parts, to deconstruct complexity. Together, these micromodels in the digital twin provide the entire operating picture while remaining scalable, controllable, and flexible.

3. Lack of internal expertise 

Many businesses don’t have the technological know-how of staff to create, implement, or manage digital twins. Delays, an excessive reliance on outside experts, or unsuccessful pilots may result from this.

Solution: By combining our technology with professional support, Entopy provides an Intelligence-as-a-Service approach. Without requiring extensive technical expertise from your end, we collaborate closely with your team to implement proof-of-concept solutions, scale wisely, and guarantee long-term success.

4. Concerns about data privacy and security 

Organisations may be reluctant to centralise or share sensitive operational data, particularly among stakeholders, because digital twins frequently demand access to this data.

Solution: Security and privacy are key to Entopy’s design. We use stringent data governance procedures, encryption, anonymisation, and role-based access. Without sacrificing confidentiality, our solutions make sure that only the appropriate data is distributed to the appropriate parties.

5. Uncertainty around ROI

Digital twins continue to be perceived by some as a futuristic investment with an uncertain financial return. Leadership could find it difficult to measure the worth or comprehend the long-term advantages.

Solution: By using focused proof-of-concept initiatives, our fast-start, phased methodology enables you to evaluate and gauge impact. To show value early and gain the trust of stakeholders, we concentrate on measurable results, such as fewer delays, increased visibility, and better resource allocation.

Moving forward with confidence 

You don’t have to be overwhelmed by digital twins. Organisations may develop more robust, effective, and intelligent operations and gain valuable insights with the correct approach, resources, and partner. That journey is not only feasible but also realistic and validated at Entopy. We assist our clients in moving more quickly from data to decisions by removing adoption hurdles.