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The operational challenge
Travel, transportation, logistics and hospitality companies operate through tightly connected physical networks. An aircraft delay affects crews, gates and passenger connections. A warehouse interruption affects inventory, carriers and customer commitments. A hotel asset issue can quickly become a service, staffing or energy problem. The challenge is not a lack of data. It is that operational data often lives across assets, enterprise systems, partners and local workflows, making it difficult to see consequences before decisions are made.
The opportunity is to give leaders a trusted environment to test operational choices before committing scarce assets, people, capacity and time.
A digital twin provides the operational context: a connected representation of an asset, facility, process or network. Edge AI brings intelligence closer to cameras, sensors, vehicles and equipment. Cloud platforms provide enterprise visibility, governance and greater analytical capacity. Together, they create a faster loop from sensing to understanding, simulation and action.
Why now
The timing is important because aviation is entering a period of sustained growth and rising operational complexity. IATA projects that global air travel demand will more than double by 2050, placing increasing pressure on airlines, airports and transportation networks to make faster, more informed decisions. In this environment, dashboards alone are no longer enough. Organizations need real-time, predictive decision support that can connect operations, simulate scenarios and improve resilience at scale.
Digital twins are not new, but the surrounding technology stack has matured. A 2024 paper from the National Institute of Standards and Technolog (NIST) notes that advances in smart sensors, the Internet of Things, cloud computing, machine learning and AI have facilitated the development of digital twins in manufacturing. It also highlights continuing challenges around standards, implementation complexity and trustworthiness.
When combined with Edge AI, HCLTech SmarTwin can bring intelligence closer to assets, sensors and workflows. It connects operational signals with predictive analytics, simulation and enterprise governance to support faster decisions and action.
Digital twins provide context, Edge AI provides local responsiveness and cloud platforms provide scale and governance. The most effective starting point is a recurring decision that creates avoidable cost, delay, risk or service friction.
One operating idea, four industry value pools
The same digital twin principle can support different decisions across the sector. The twin should be shaped around the operational outcome, not the other way around.
| Sector | High-value starting point | Decision to improve |
|---|---|---|
| Aviation | Aircraft health, operational flow and disruption | Which action best protects reliability, capacity and the customer journey? |
| Transportation | Vehicles, infrastructure, ports and terminals | How will an asset or capacity constraint affect the wider network? |
| Logistics and warehousing | Inventory, labor, automation and facility flow | Which layout, resource or network decision performs best before we change the operation? |
| Hospitality | Property assets, energy, staffing and service flow | How can the property improve asset performance while protecting guest service? |
What the evidence says
Aviation provides several publicly reported examples. Airlines for America reported that the average direct aircraft operating cost for US passenger airlines was $98.41 per block minute in 2025. This should not be treated as the cost of every delay minute, but it shows why utilization and disruption matter. Airbus reported that easyJet avoided almost 80 cancellations over two months through predictive monitoring. Future Travel Experience reported that Schiphol’s digital twin-based passenger-flow approach prevented more than 4,000 minutes of crowding in 2024.
The evidence is broader but uneven across sectors. In rail, a 2025 systematic review of 91 papers identified digital twin applications across tracks, civil structures, vehicles and overhead contact line structures, with real-time monitoring, predictive maintenance and decision support as central themes. In warehouse logistics, a 2026 review identified applications in path planning, task allocation, inventory management and storage assignment, while noting that analysis of economic impact and ROI remains limited.
Hospitality is at an earlier stage of the public evidence curve. A 2025 peer-reviewed study reported a 25-35% reduction in check-in time and a 20% improvement in staff efficiency in scenario-based simulations grounded in historical hotel data. These were simulation results, not publicly reported outcomes from a named hotel deployment.
From signal to action
1 SENSE | 2 INTERPRET | 3 CONTEXTUALIZE | 4 SIMULATE | 5 ORCHESTRATE |
|---|---|---|---|---|
Capture relevant events from assets, cameras and sensors. | Use Edge AI for local, time-sensitive inference. | Update the twin with relationships and constraints. | Compare options before changing live operations. | Embed approved actions within governed workflows. |
This edge-to-cloud sequence is a recommended transformation model, not an industry standard. Each stage needs model validation, cybersecurity, data governance and human accountability.
Why HCLTech
At HCLTech, we bring together digital twin, Edge AI, cloud, IT, operational technology and engineering capabilities to help clients connect operational data, simulate scenarios and support faster decisions. HCLTech SmarTwin integrates design systems, engineering models, process simulation and IoT data to provide real-time updates, predictive analytics and what-if scenario analysis.
A platform foundation with room to grow
For travel, transportation, logistics and hospitality leaders, that matters because high-value decisions are highly contextual. An airline may need to protect aircraft availability and passenger recovery. An airport may need to rebalance passenger flows and gate capacity. A logistics provider may need to test warehouse labor, automation or routing changes. A hotel group may need to optimize asset performance, energy use and service flow without disrupting the guest experience. SmarTwin can provide a foundation for modeling those operating realities, testing scenarios and connecting insights back into execution rather than stopping at visualization.
Proving value before scaling
Across our digital twin services, clients have achieved outcomes including improved productivity, lower maintenance costs, reduced asset downtime and improved product performance. Results depend on the use case and operating environment, making it important to establish a baseline, validate the model against operational reality and scale only when the evidence, controls and users are ready. The right approach is to define one decision, establish a baseline, validate the model against operational reality and scale only when the evidence, controls and users are ready.
Where to begin
A focused first discussion can begin with three questions:
- Which operational decision repeatedly creates avoidable cost, delay or service risk?
- What data and systems already describe that decision and its dependencies?
- What measurable outcome would justify expanding the twin?
We can help convert those answers into a focused digital twin opportunity, a credible value hypothesis and a roadmap that expands as results are demonstrated.
