Real-World Business Challenges and Their Solutions by Digital Twins

• Digital Twin
• February 27, 2026

by Sumeet Thakkar

Digital twins solve many business problems, improving operational efficiency & cost savings, and making it a major motivation for around 79% of organizations planning to implement digital twins.

Digital twins solve various critical business problems by creating virtual replicas of physical assets, processes, or systems, enabling real-time monitoring, simulation, and optimization.

Be it unpredictable downtime, opaque supply chains, sustainability issues, limited asset lifecycle, or otherwise, when operations are disintegrated or misaligned, businesses face increasingly complex operational challenges. Thanks to digital twins, they help organizations deal with these challenges by providing appropriate solutions.

This blog explores how digital twins improve operational efficiency and reduce downtime by solving numerous business problems across industries.

It enables you to know everything and find the right company for digital twin services to build an excellent solution that helps you get rid of day-to-day business challenges.

Global Digital Twin Market Statistics

• The global digital twin market is anticipated to reach USD 384.79 billion by 2034, which was valued at USD 24.48 billion in 2025, exhibiting a CAGR of 35.40% during the forecast period.
• North America dominated the digital twin market with a market share of 34% in 2025.
• Another report from Grand View Research predicted the global digital twin market to reach USD 328.51 billion by 2033, from just 35.82 billion in 2025.
• This growth will be at a CAGR of 31.1% between the forecast period.
• This significant growth is attributed to the rapid adoption of Industry 4.0 practices, rising demand for predictive maintenance across industries, and the growing need for real-time monitoring of assets to reduce operational costs and downtime.

Core Business Problems and Their Solutions by Digital Twins

Businesses come across different challenges, including a lack of real-time visibility, unplanned downtime, process inefficiencies, limited asset lifecycle, supply chain, and more.

There are many digital twin examples, solving these critical business problems by creating virtual replicas of physical assets to enable predictive insights, optimize performance, and improve performance at reduced costs. Here’s how:

1. Lack of Real-Time Operational Visibility

There are businesses out there, operating with delayed reports and fragmented data, making it hard to see what’s happening on the floor or in systems in real time. This lack of operational visibility leads to slow responses and poor operational decisions.

Solution: Digital twin technology troubleshoots this challenge by creating a continuously updated virtual replica of physical operations. Digital twins do so by integrating data from sensors, IoT devices, enterprise systems, and historical records.

This live model mirrors real-world conditions in real time, allowing teams to monitor performance, detect anomalies, and understand operational dependencies as they occur.

With accurate, centralized visibility, managers are likely to respond faster to issues, test operational changes virtually, and make proactive, data-driven decisions instead of relying on delayed or fragmented reports.

French retailer Intermarché created digital twins of its store operations using data from IoT‑enabled shelves and systems, which enabled scenario testing like layout changes and inventory flows without disrupting physical stores.

2. Unplanned Downtime and Asset Failures

Traditional reactive maintenance leads to asset failures and unplanned breakdowns, disrupting production and inflating costs. Waiting until something fails before fixing it is inefficient and risky, and pushes businesses towards bearing huge losses.

Solution: Digital twins reduce unplanned downtimes and asset failures by monitoring asset behavior in real time and comparing it against historical and expected performance patterns. By analyzing variables such as vibration, temperature, and load, digital twins predict potential failures before they occur.

This enables condition-based and predictive maintenance, reducing unexpected breakdowns, extending asset life, and minimizing costly production interruptions.

Rolls‑Royce’s IntelligentEngine digital twin, for example, collects real‑time engine data to predict part wear, reducing unscheduled removals and improving aircraft availability.

3. Process Inefficiencies and Resource Waste

When there are inefficiencies in workflows and resource allocations, they lead to increased cycle times and waste materials or energy, hurting throughput and margins. Thanks to the digital twins, they troubleshoot this problem through their abilities in simulation, predictive analytics, and maintenance.

Solution: Process-level digital twins simulate workflows, material flows, and resource usage in a virtual environment, allowing teams to identify bottlenecks, test optimization strategies, and evaluate the impact of changes without disrupting live operations.

This leads to improved throughput, reduced waste, and more efficient use of time, materials, and energy.

Manufacturing plants modelled by digital twins reduce downtime and inefficiencies. For example, Siemens uses twin simulations in production to detect potential issues early and adjust workflows.

4. Limited Asset Lifecycle Insight

Without clear insights into the asset performance, historical data, and condition trends, it’s hard to know when equipment needs service or replacement, leading to shortened lifespans or missed optimization opportunities. Furthermore, it’s more likely to experience sudden breakdowns, limiting productivity and benefits.

Solution: Digital twins track assets across their entire lifecycle by continuously collecting performance, usage, and condition data through sensors or other data sources. This visibility helps organizations understand wear patterns and utilization levels.

As a result, maintenance, replacement, and investment decisions become more data-driven, maximizing asset value and longevity.

GE Vernova’s Digital Wind Farm is an excellent digital twins example implemented for wind turbines to continuously analyze performance and plan maintenance to maximize uptime and lifespan.

5. Supply Chain Fragility and Disruption Risk

Supply chains are vulnerable to shocks, supplier delays, transport issues, and demand surges. When companies lack efficient tools to model these risks, they’re likely to bear unpredictable downtime, inefficiencies, and business loss.

However, employing digital twins and AI in supply chain management helps end supply chain fragility and disruption risks.

Solution: Supply chain digital twins create an end-to-end virtual model of suppliers, logistics, inventory, and demand flows. By simulating disruptions such as delays or demand spikes, organizations can test contingency plans and optimize responses in advance. This helps businesses improve resilience, agility, and overall supply chain reliability.

US Transcom is among those companies using digital twin technology. It used a supply chain digital twin to map logistics flows and streamline operations, identifying better flow paths and reducing costs significantly (around $2 billion).

6. High Cost and Risk of Physical Testing

Physical prototyping or experimentation for assets is more likely to be slow, expensive, and sometimes dangerous. This not only stalls innovation but also leads to inflated costs and jeopardizes business progress.

Solution: Digital twins enable virtual testing of designs, configurations, and operational changes before physical implementation. With these solutions, businesses can simulate performance, identify risks, and refine outcomes without incurring the cost, time, or safety risks of real-world testing. This accelerates innovation while reducing experimentation costs.

Businesses using digital twin applications show reduced downtime and extended equipment life through virtual testing and simulation before physical changes.

7. Sustainability and Energy Performance Gaps

Organizations often struggle to measure and reduce energy usage and emissions because systems lack integrated insights. It makes sustainability targets hard to hit.

Solution: Using digital twins in renewable energy, or energy management in general, helps organizations model consumption patterns, emissions, and system efficiencies in detail. These systems allow organizations to simulate energy-saving strategies, optimize resource usage, and measure environmental impact accurately.

This supports sustainability goals while reducing operational costs and improving compliance with ESG requirements.

Energy digital twins in smart manufacturing reduce energy consumption, optimizing systems like heating tunnels through real‑time feedback loops.

8. Loss of Operational Knowledge and Expertise

Lack of operational knowledge and expertise is one of the major causes that reduces businesses’ efficiency. When experienced operators leave, their tacit knowledge about processes and exceptions goes with them, creating gaps that slow decision‑making.

Solution: Digital twins capture operational logic, best practices, and decision rules within system models rather than relying solely on individuals. This preserves institutional knowledge, supports training and onboarding, and ensures operational consistency even as experienced personnel leave or roles change.

Many manufacturing implementations embed operational logic from experts into twin models, ensuring decisions remain consistent even as personnel change down the line.

9. Challenges Managing Distributed or Remote Operations

Geographically spread assets make centralized monitoring and control difficult for organizations. It leads to blind spots in global operations. However, digital twins bridge the gap between remote operations and excellent management.

Solution: Digital twins centralize data from geographically dispersed assets into a single virtual platform. This enables remote monitoring, diagnostics, and coordination across locations, which improves control, consistency, and decision-making without requiring constant on-site presence.

Utilities and infrastructure operators use digital twins to integrate widespread data streams, unify reporting, and manage remote assets in real time.

10. Slow Incident Diagnosis & Root Cause Identification

Slow diagnosis and root cause identification for incidents is one of the challenges businesses encounter day in and day out. When failures occur, fragmented historic data slows analysis, delaying fixes and extending outages.

Solution: By combining historical and real-time data, digital twins provide full operational context during incidents. The system allows teams to trace events, respond to scenarios, and pinpoint root causes quickly. This shortens resolution times, reduces downtime impact, and improves system reliability.

Many companies using digital twin technology aim for condition‑based diagnostics in infrastructure like bridges, helping detect structural defects early through sensor data analysis.

11. Operational Safety Risks

Confronting safety risks is one of the challenges businesses come across in their day-to-day operations. Conventional safety assessments often fail to fully anticipate complex interactions or emerging hazards.

Solution: Digital twins mitigate operational safety risks by creating a real-time, virtual replica of physical assets using sensors, IoTs, and machine learning. This allows companies to simulate dangerous scenarios, predict equipment failures, and monitor hazards without real-world consequences.

By leveraging IoT data, they shift safety strategies from reactive to proactive, lessening human exposure to hazardous environments.

City‑scale digital twins, for example, Singapore’s Virtual Singapore, model infrastructure and environments to enable decision-makers to plan safety responses and urban resilience strategies.

12. Inconsistent Service Performance and Reliability

Service systems that lack real‑time performance insights are more likely to deliver poor user experiences or fail without warning. This can cause inconsistency and huge losses to organisations.

Solution: Service digital twins monitor performance indicators in real time and simulate service behavior under varying loads. This helps teams predict failures, optimize service delivery, and maintain consistent performance levels, improving customer satisfaction and SLA compliance.

Many companies using digital twin technology in logistics and supply services aim to reveal performance patterns enabling optimization, for example, a digital replica of warehouse flows improves throughput and reliability.

13. Siloed Decision-Making Across Functions

Different departments often use disconnected data sources. These siloed data, causing siloed decision-making across functions, lead to misalignment and conflicting decisions.

Solution: Digital twins act as a shared operational model that integrates data across departments. Engineering, operations, and leadership teams work using the same real-time insights, improving collaboration, alignment, and the quality of strategic decisions.

Smart city digital twins, for example, integrate data from traffic, utilities, and emergency services to coordinate planning and responses across agencies. Digital Twin Solution for Smart Cities is one of the excellent case studies, showcasing that the user experiences:

• 60% boost in citizen engagement
• 48% improved operational efficiency
• 4 times faster data-driven decisions

14. Lengthy Planning and Decision Cycles

Traditional forecasting and planning often rely on static reports and slow manual analysis, leading to not only slow decision-making but also limiting business efficiency, productivity, and profitability.

Solution: Digital twins enable real-time scenario modeling and forecasting, allowing teams to evaluate options quickly and with confidence. This reduces reliance on static reports, accelerates planning cycles, and supports faster, more informed decision-making.

For example, the simulation of factory processes like BMW’s digital twin of an assembly line enables faster planning of layout and production strategy.

15. Risky Scaling and Expansion Decisions

Scaling business operations without testing scenarios increases strategic risk and can lead to costly missteps. However, businesses using digital twins can confront these challenges with ease and convenience.

Solution: Digital twins reduce risky scaling and expansion decisions by creating a virtual replica of physical assets or processes. These systems enable companies to simulate “what-if” scenarios, test, and analyze those in a risk-free, cost-effective environment before implementing changes in the real world.

By leveraging real-time data from Internet of Things (IoT) sensors, digital twin models provide actionable insights that allow businesses to optimize performance, predict bottlenecks, and validate investments, thereby minimizing the dangers of premature or improper scaling.

Industries Benefiting Most from Digital Twins

Digital twins, which are dynamic, virtual replicas of physical assets, significantly benefit industries like manufacturing, aerospace, automotive, energy, healthcare, and more.

Companies using digital twin technology primarily aim to make these businesses efficient by enabling predictive maintenance, process optimization, and enhanced product development.

• Manufacturing: Businesses in manufacturing use digital twins for production optimization, factory simulation, and predictive maintenance, helping them reduce downtime and improve efficiency.
• Aerospace & Defense: Digital twins enable the simulation of aircraft performance, predictive maintenance of systems, and lifecycle management for ships and vehicles.
• Automotive: Organizations in the automotive industry leverage digital twins for vehicle design, testing, and simulation to enhance product development and manufacturing processes.
• Energy & Utilities: Digital twins help the energy & utilities industry in managing complex, real-time data for power systems, infrastructure monitoring, and predicting equipment failures.
• Healthcare: Organizations dealing in healthcare use digital twins for patient modeling, personalizing treatment plans, and simulating surgical procedures.
• Real Estate & Construction: Digital twins in construction & real estate help infrastructure management, traffic management, and simulate building performance.
• Logistics & Retail: Digital twins optimize supply chain management, warehouse operations, and in-store product placement.
• Other Industries: Additional industries using digital twins include mining, agriculture, telecommunications, and real estate, where virtual models help in optimizing resource management and operational efficiency.
• Urban Planning: Digital twins, enabled by AI in urban planning, are dynamic, 3D virtual replicas of cities that integrate real-time IoT, GIS, and building data to simulate, monitor, and optimize urban environments.

Digital Twins Solve the Challenges, and MindInventory Delivers the Solution

Digital twin technology turns data into actionable insights that solve core business challenges, from visibility and maintenance to sustainability and strategic planning.

When combined with expert implementation partners, organizations can not only adopt digital twins but tailor them for real business outcomes.

As a leading AI development company, MindInventory offers comprehensive assistance for digital twin solutions. With 6+ years of average experience, we have successfully delivered 7+ digital twins, one of which is the digital twin platform for solar planning, which we have built leveraging our capabilities in Unreal Engine development services.

This digital twin solution experiences:

• 58% faster issue detection & resolution
• 45% reduction in overall maintenance costs
• 32% increase in energy output efficiency

So, whether it’s digital twin development, system & data integration, digital twin modernization & expansion, or just digital twin consulting, we help you hire AI developers that provide complete solutions, meeting your needs.

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