Digital Twin Markets And Industrial Simulation Platform Competition

Introduction

Digital twin markets arise where physical assets, factories, infrastructure, products, supply chains, or industrial processes are represented through continuously updated digital models. An industrial simulation platform may combine IoT sensors, cloud infrastructure, AI, engineering software, operational data, predictive analytics, and simulation tools to reproduce or forecast real-world conditions.

From a competition-law perspective, the important question is not merely whether a digital-twin platform is technologically sophisticated. The central issue is whether control over data, simulation models, APIs, interoperability, cloud infrastructure, software standards, or installed industrial ecosystems enables a firm to exclude competitors or make industrial customers dependent upon one platform.

The competition concerns can therefore arise under:

• abuse of dominance;
• refusal of access;
• interoperability restrictions;
• tying and bundling;
• self-preferencing;
• exclusive dealing;
• discriminatory access to industrial data;
• foreclosure through proprietary standards;
• switching costs and ecosystem lock-in;
• acquisition of complementary simulation technologies;
• algorithmic discrimination;
• leveraging cloud or operating-system power into industrial simulation markets.

1. Meaning of Digital Twin Markets

A digital twin is a digital representation of a physical object, process, system, or environment that is connected to real-world data and can be used for monitoring, prediction, optimization, testing, or simulation.

Examples include:

• aircraft-engine digital twins;
• automobile and battery twins;
• semiconductor-factory simulations;
• power-grid twins;
• logistics-network twins;
• oil-and-gas facility twins;
• smart-city infrastructure twins;
• industrial robots;
• manufacturing lines;
• warehouse systems.

A digital-twin platform may therefore operate simultaneously across several markets:

1. industrial IoT;
2. sensor-data management;
3. cloud computing;
4. simulation software;
5. engineering software;
6. AI analytics;
7. predictive maintenance;
8. industrial automation;
9. visualization;
10. digital identity of physical assets.

This creates a particularly difficult competition-law problem because market power in one layer can be transferred into another layer.

2. Structure of the Digital-Twin Competition Stack

A useful analytical model is:

Physical Asset → Sensors → Data Layer → Connectivity → Cloud → Simulation Engine → AI/Analytics → Digital Twin → Industrial Decision-Making

A firm controlling several layers may possess substantial ecosystem power.

For example, a platform might control:

• the sensors;
• the data format;
• the cloud environment;
• the simulation software;
• the API;
• the AI model;
• the visualization interface.

Competitors may consequently be unable to compete effectively even if they possess superior simulation technology.

3. Relevant Markets

Competition authorities should avoid automatically treating "digital twins" as one market.

Potential relevant markets include:

A. Industrial simulation software

Software used to simulate:

• manufacturing;
• machinery;
• engineering processes;
• energy systems;
• logistics.

B. Digital-twin platforms

Platforms integrating:

• sensor data;
• simulation;
• analytics;
• visualization;
• predictive modelling.

C. Industrial cloud services

Cloud infrastructure used to host digital twins.

D. Industrial IoT platforms

Systems collecting and processing operational data.

E. Engineering-design software

CAD, CAE, PLM and related engineering platforms.

F. Data-access markets

Markets for access to:

• machine-generated data;
• operational data;
• maintenance information;
• performance histories.

The relevant market may therefore be multi-layered and ecosystem-based.

4. Network Effects

Digital-twin platforms can exhibit strong network effects.

More industrial customers produce:

More operational data → better models → better predictions → more customers → more data

This can create a feedback loop.

A dominant platform may therefore develop an informational advantage that new entrants cannot easily reproduce.

The concern becomes stronger where historical industrial data are:

• proprietary;
• difficult to collect;
• expensive to reproduce;
• continuously generated;
• necessary for model accuracy.

5. Data as a Competitive Advantage

Data can constitute an important competitive input.

Suppose Platform A operates digital twins for thousands of factories. It may obtain information concerning:

• machine failure rates;
• production cycles;
• energy consumption;
• maintenance intervals;
• equipment utilization;
• supply-chain disruptions.

Platform B may technically be capable of providing equivalent simulation software but lack the historical data necessary to produce equally accurate predictions.

This can create data-based entry barriers.

However, possession of valuable data does not automatically establish competition-law liability. Authorities must demonstrate market power and an exclusionary or exploitative theory of harm where required by the applicable legal regime.

6. Interoperability and API Restrictions

Interoperability is particularly important.

A dominant digital-twin provider could theoretically:

• refuse API access;
• limit data exports;
• prevent third-party simulation engines from connecting;
• restrict real-time sensor feeds;
• impose technical conditions on competing applications;
• make APIs available only on discriminatory terms.

The consequence may be ecosystem foreclosure.

A customer might technically own its industrial data but still be unable to use that data effectively outside the incumbent's platform.

Thus:

Data ownership without practical portability may provide little effective competitive freedom.

7. Digital-Twin Lock-In

Industrial customers often make investments that are difficult to reverse.

Once a company has integrated a digital twin into:

• production planning;
• maintenance;
• engineering;
• procurement;
• safety systems;
• employee workflows;
• cloud infrastructure,

switching platforms may require:

• retraining;
• data conversion;
• API redevelopment;
• model reconstruction;
• validation;
• cybersecurity testing;
• regulatory re-certification.

These costs can create substantial switching barriers.

A dominant provider could potentially exploit such lock-in through:

• price increases;
• restrictive contracts;
• degraded interoperability;
• bundled services;
• discriminatory access.

8. Tying and Bundling

A powerful industrial platform could tie digital-twin software to another dominant product.

For example:

Dominant industrial cloud + mandatory simulation platform

or:

Dominant CAD software + proprietary digital-twin service

or:

Industrial operating system + exclusive digital-twin analytics

The competition concern is that customers may be prevented from selecting competing simulation providers.

The relevant legal question is whether the products are distinct, whether the firm possesses sufficient power in the tying market, whether customers are effectively coerced, and whether foreclosure is capable of harming competition.

9. Self-Preferencing

Suppose a dominant platform allows independent simulation applications to operate within its ecosystem while simultaneously offering its own competing simulation product.

It might allegedly:

• rank its own simulation engine first;
• provide its own product superior API access;
• give its own applications faster data feeds;
• expose competitors to technical delays;
• use privileged data unavailable to rivals.

This resembles broader digital-platform self-preferencing theories.

The competition concern is particularly acute where the platform is simultaneously:

infrastructure provider + marketplace + competitor.

10. Exclusive Dealing

A dominant industrial simulation platform might require manufacturers to agree that:

• all digital-twin workloads must run on its platform;
• sensor data cannot be supplied to competitors;
• customers cannot simultaneously use competing simulation engines;
• suppliers must use the platform's proprietary protocol.

Long-term exclusivity can be especially significant where the platform has already achieved substantial installed-base advantages.

11. Proprietary Standards and Protocol Dominance

Industrial digital twins depend heavily on technical standards.

A firm controlling a widely adopted protocol may gain structural power.

Competition problems can emerge if a dominant platform:

• makes its standard proprietary;
• refuses reasonable licensing;
• changes specifications to disadvantage competitors;
• introduces incompatible extensions;
• controls certification;
• prevents interoperability with rival platforms.

This can transform a technical standard into a competitive bottleneck.

12. Industrial Simulation and Merger Control

Digital-twin competition is also relevant to merger control.

A major industrial software company acquiring a simulation platform could combine:

• CAD;
• PLM;
• cloud;
• IoT;
• simulation;
• AI;
• digital twins.

The resulting ecosystem could foreclose competing suppliers through:

• bundling;
• interoperability restrictions;
• preferential APIs;
• exclusive licensing;
• data advantages.

The relevant theory may be vertical or conglomerate foreclosure rather than conventional horizontal concentration.

13. AI and Digital-Twin Competition

AI increases the strategic importance of digital twins.

An industrial AI model can use digital-twin environments to simulate:

• factory configurations;
• equipment failures;
• energy demand;
• autonomous robots;
• logistics;
• product designs.

The incumbent may therefore gain a feedback advantage:

Real-world data → digital twin → simulation → AI training → better predictions → more customers → more real-world data.

This can produce a powerful data–simulation–AI feedback loop.

14. Case Law

1. United Brands Company v Commission

The European Court of Justice established important principles concerning dominance and the assessment of market power.

Relevance

A digital-twin platform may become dominant where customers are unable to switch effectively because of:

• technical dependency;
• data advantages;
• ecosystem integration;
• high switching costs.

The case illustrates that dominance concerns require examination of the firm's actual economic power rather than simply looking at market share.

Digital-twin application

An industrial simulation provider with substantial control over essential industrial workflows could potentially possess significant market power even where several nominal competitors exist.

15. Case Law: Commercial Solvents v Commission

The Commercial Solvents litigation is important for the principle that a dominant undertaking cannot use its position in one market to eliminate competition in a related market.

Relevance

The same logic can be applied conceptually to digital-twin ecosystems.

For example:

Dominant industrial cloud → digital-twin simulation market

A firm possessing power over industrial cloud infrastructure could potentially disadvantage independent digital-twin providers by restricting access to necessary infrastructure.

Competition concern

The important question is whether infrastructure control is being used to foreclose competition in a downstream market.

16. Case Law: Bronner v Mediaprint

The case is particularly important for refusal-to-deal and essential-facility analysis.

The Court adopted a demanding standard for requiring a dominant undertaking to provide access to infrastructure.

Digital-twin relevance

An industrial simulation platform might claim that its:

• API;
• proprietary database;
• sensor interface;
• cloud architecture;
• simulation engine

constitutes infrastructure that competitors need.

However, competition law does not automatically require every dominant company to share every commercially valuable asset.

The demanding conditions associated with refusal-to-deal doctrine remain important.

17. Case Law: IMS Health v Commission

IMS Health is highly relevant to technology-driven markets involving intellectual property and interoperability.

The case concerned access to a structure protected by intellectual-property rights and the circumstances in which refusal to license could constitute abuse.

Digital-twin relevance

Suppose a digital-twin platform controls a proprietary:

• industrial data architecture;
• interoperability format;
• simulation interface;
• software structure.

A competitor could argue that access is indispensable for competing.

IMS Health demonstrates that IP rights and competition law must be balanced carefully, especially where refusal prevents the emergence of a new product or substantially restricts competition.

18. Case Law: Microsoft Corp. v Commission

The Microsoft litigation is one of the most important precedents for technology-platform interoperability.

The European Commission found concerns relating to Microsoft's refusal to provide interoperability information and tying conduct.

Digital-twin significance

The analogy is particularly strong because industrial digital twins may depend on interoperability among:

• machines;
• operating systems;
• cloud systems;
• engineering software;
• simulation applications.

A dominant platform that strategically restricts interoperability could potentially create an ecosystem where competitors cannot effectively operate.

The case demonstrates why technical interoperability can have direct competition significance.

19. Case Law: Google Shopping

The Google Shopping decision is relevant to self-preferencing by a platform that simultaneously operates infrastructure and competes with downstream services.

Digital-twin relevance

Consider an industrial platform that:

1. provides the underlying digital-twin marketplace;
2. hosts competing simulation applications; and
3. owns its own simulation product.

If it systematically gives its own product preferential access, ranking, visibility, or technical functionality, competitors may argue that the platform is leveraging its gateway position.

The central concern is not merely preferential treatment but whether the conduct distorts competitive conditions in the downstream market.

20. Case Law: Intel v Commission

Intel is important for understanding exclusionary conduct involving rebates and customer incentives.

Digital-twin application

An incumbent industrial platform might provide customers with:

• discounts;
• preferential cloud pricing;
• free migration;
• technical support;
• bundled simulation services

on the condition that customers substantially commit to its ecosystem.

The legal analysis must distinguish ordinary competition on price from arrangements capable of foreclosing equally efficient competitors.

21. Case Law: AKZO Chemie v Commission

AKZO is a foundational EU dominance case concerning exclusionary pricing and the use of pricing strategies to protect dominant positions.

Digital-twin relevance

A large industrial simulation platform could theoretically use:

• below-cost introductory pricing;
• cross-subsidisation;
• bundled pricing;
• predatory discounts

to eliminate smaller simulation competitors.

The case provides an important framework for distinguishing aggressive but legitimate competition from exclusionary pricing by a dominant undertaking.

22. Case Law: Bronner and Microsoft Compared

These cases illustrate two different dimensions of digital-twin competition.

Together, they show why competition authorities must distinguish between legitimate proprietary control and strategically exclusionary technical restrictions.

23. Main Competition Concerns

The principal competition risks can be grouped as follows:

A. Data foreclosure

Incumbent controls critical industrial data.

B. API foreclosure

Competitors cannot connect to the industrial platform.

C. Cloud leverage

Cloud dominance is leveraged into simulation.

D. Software bundling

Simulation is bundled with engineering or industrial software.

E. Self-preferencing

The platform favors its own digital-twin applications.

F. Exclusive contracts

Customers are prevented from multi-homing.

G. Standards control

Proprietary protocols become de facto industry standards.

H. Acquisition strategies

Incumbents acquire emerging simulation competitors.

I. Algorithmic foreclosure

The platform's algorithms selectively disadvantage competing systems.

J. Switching-cost exploitation

Customers become economically dependent on one ecosystem.

24. Industrial Customers as a Special Category

Industrial customers differ from ordinary consumers because their systems may have:

• very long asset lives;
• safety requirements;
• regulatory certification;
• complex engineering dependencies;
• enormous historical datasets.

A digital twin may remain operational for 10–30 years.

Consequently, a platform decision made today can determine competitive conditions for decades.

Competition authorities should therefore examine not only immediate price effects but also:

• future contestability;
• interoperability;
• technological neutrality;
• switching costs;
• innovation;
• access to data.

25. Remedies

Possible competition-law remedies include:

1. Interoperability obligations

Require technical interfaces allowing rival simulation providers to connect.

2. Data portability

Allow industrial customers to retrieve usable operational data.

3. API access

Require reasonable and non-discriminatory access where legally justified.

4. Non-discrimination

Prevent the platform from providing materially better technical conditions to its own downstream service.

5. Contractual restrictions

Limit exclusivity or long-term foreclosure arrangements.

6. Structural remedies

In exceptional cases, separation of infrastructure and downstream competitive activities may be considered.

7. Merger remedies

Require licensing, interoperability, data-access or behavioral commitments.

26. Competition-Law Analytical Framework

A competition authority examining an industrial digital-twin platform could proceed through the following sequence:

Step 1 — Define the market

Identify simulation, digital-twin, cloud, data, or industrial-IoT markets.

↓

Step 2 — Identify bottlenecks

Determine whether the firm controls:

• data;
• APIs;
• cloud;
• protocols;
• engineering software;
• industrial infrastructure.

↓

Step 3 — Assess market power

Examine:

• market shares;
• switching costs;
• installed base;
• network effects;
• data advantages;
• interoperability barriers.

↓

Step 4 — Identify conduct

Look for:

• tying;
• bundling;
• exclusivity;
• refusal to supply;
• self-preferencing;
• discriminatory access;
• predatory pricing.

↓

Step 5 — Assess foreclosure

Ask whether rivals are prevented from competing effectively.

↓

Step 6 — Evaluate efficiencies

Consider:

• cybersecurity;
• safety;
• system reliability;
• integration benefits;
• innovation;
• quality improvements.

↓

Step 7 — Select remedy

Use the least restrictive remedy capable of restoring effective competition.

27. Key Legal Principle

The central competition-law problem is not the mere existence of a successful digital-twin platform.

A platform may legitimately become large because its technology is better.

The competition concern arises where technological integration is transformed into an artificial barrier to competitive access.

Thus:

Innovation-driven digital-twin dominance is not itself unlawful; exclusionary exploitation of digital-twin bottlenecks may be.

Conclusion

Digital-twin markets create a new form of industrial platform competition in which data, software, cloud infrastructure, simulation, AI and physical assets converge into a single ecosystem.

The most important competition risks are:

1. data accumulation;
2. interoperability restrictions;
3. API control;
4. industrial cloud leverage;
5. self-preferencing;
6. tying and bundling;
7. exclusive dealing;
8. proprietary standards;
9. switching-cost exploitation;
10. acquisition of emerging competitors.

The principles developed in United Brands, Commercial Solvents, Bronner, IMS Health, Microsoft, Intel, AKZO and Google Shopping provide useful doctrinal foundations for analysing these emerging markets.

Ultimately, competition policy should preserve contestability of industrial simulation ecosystems. Customers should be able to change simulation providers, use competing analytical tools, export their operational data, and connect alternative technologies without facing artificial technical or contractual barriers. The long-term objective is therefore not to prevent digital-twin platforms from becoming powerful, but to ensure that platform power remains contestable and does not become permanent ecosystem control.