Global Artificial Intelligence in Manufacturing Market Size, Trend & Opportunity Analysis Report, by Component (Hardware, Software, Services), and Forecast, 2025-2035

Market Definition and Introduction

The Global Artificial Intelligence (AI) in Manufacturing Market, valued at USD 5.32 billion in 2024, is projected to skyrocket to USD 368.47 billion by 2035, expanding at an extraordinary CAGR of 47.0% during the forecast period 2025-2035. In the wave of digital transformation for manufacturing, AI is beginning to act as the central nerve in achieving operational excellence and operational efficiency by enabling factories to safeguard productivity through disruption anticipation, production optimization, and resource efficiency maximization. All kinds of manufacturers are employing AI to predict machinery failure, keep supply chains nimble, and improve product quality control systems, thereby converting traditional production lines into more intelligent self-learning ecosystems.

The past few years have witnessed an explosion in demand due to Industry 4.0 initiatives with regard to the AI-based automation systems, robotics, and data analytics being accepted for the good of smart factories with computer vision systems, predictive maintenance algorithms, and real-time process optimization tools in their stride. AI integration is not only revolutionizing output efficiency; it is changing the competitive landscape, allowing manufacturing businesses to maneuver with agility in fickle global markets.

On the demand side, technological giants and industrial innovators are stepping up their game to furnish market-ready hardware accelerators, state-of-the-art software platforms, and AI-driven services. These services help manufacturers in real-time monitoring, aberration detection in production almost instantaneously, and data-driven decision-making in real-time. Smart manufacturing has developed its momentum through rapid innovations in edge AI, allowing for analytics in the operational environment with minimal latency so critical decisions can take place within milliseconds. The synergy between AI, IoT, and advanced robotics is going to usher in an unprecedented manufacturing revolution.

Recent Developments in the Industry

1. In June 2024, Siemens AG announced a partnership with NVIDIA Corporation to integrate NVIDIA Omniverse and AI capabilities into Siemens. Industrial metaverse, allowing manufacturers to design, create, and simulate entire production environments to optimize them before deployment in the real world.

1. In February 2024, AI-enabled sustainability software for manufacturing was launched by IBM Corporation for predictive analysis of energy consumption and environmental impact, assisting manufacturers in the realization of their net-zero targets.

1. In September 2023, Microsoft Corporation expanded its Azure AI suite with advanced manufacturing-focused cognitive services, allowing seamless integration of machine learning into production workflows for anomaly detection and predictive quality control.

Market Dynamics

Needless to say, smart manufacturing and digital factory ecosystems will allow businesses to realize the more complex advantages that AI has in store for them.

Smart factory systems have rapidly gained acceptance among manufacturing companies as they grapple with the pressing need to boost productivity, enhance predictive capabilities, and improve agility in operations. Examples of AI applications include digital twins, machine vision, and emerging advanced process analytics, at the forefront of efforts to transform reactive operations to proactive strategies, accompanied by massive reductions of downtime and wastages.

AI Infusion for Predictive Maintenance and Operational Cost Reduction

AI-enabled predictive maintenance has made a big difference in manufacturing. AI algorithms are learning from sensor data to predict up to equipment failure before it happens, saving manufacturers from costly interruptions and planning repairs more appropriately. The shift from a previously scheduled maintenance model to a prediction-driven model promises significant savings as well as increased life for the assets used.

Government Initiatives and Industry 4.0 Driving Market Growth

In North America, Europe, and Asia-Pacific, governments are establishing subsidies, tax incentives, and policy measures to facilitate the rapid deployment of AI for manufacturing. These new budgets and programs, in conjunction with Industry 4.0, can motivate both SMEs and large companies to take up AI tools for enhancing competitiveness in global trade.

Attractive Opportunities in the Market

1. AI-Powered Predictive Maintenance - Reduces downtime, optimizes asset utilization, and enhances plant safety.
2. Computer Vision for Quality Control - Real-time defect detection improves production efficiency and product reliability.
3. Supply Chain Optimization - AI algorithms forecast demand fluctuations and mitigate inventory risks.
4. Robotics Integration - Autonomous robots streamline complex assembly and handling processes.
5. Edge AI Expansion - On-site analytics enables faster decision-making without cloud dependency.
6. Sustainability and Energy Efficiency - AI reduces waste and minimizes environmental footprints.
7. AI-Enabled Digital Twins - Virtual replication of assets enhances planning and simulation accuracy.
8. Cloud-Based AI Platforms - Facilitate scalable and collaborative manufacturing solutions across geographies.

Report Segmentation

By Component: Hardware, Software, Services

By Region: North America (U.S., Canada, Mexico), Europe (UK, Germany, France, Spain, Italy, Spain, Rest of Europe), Asia-Pacific (China, India, Japan, Australia, South Korea, Rest of Asia-Pacific), LAMEA (Brazil, Argentina, UAE, Saudi Arabia (KSA), Africa Rest of Latin America)

Key Market Players:Siemens AG, NVIDIA Corporation, IBM Corporation, Microsoft Corporation, Amazon Web Services, Google LLC, GE Digital, Bosch Global Software Technologies, Rockwell Automation, and ABB Ltd.

Report Aspects

Base Year: 2024

Historic Years: 2022, 2023, 2024

Forecast Period: 2025-2035

Report Pages: 297

Dominating Segments

Software Segment Leads the AI in Manufacturing Market Owing to Rapid Industrial Digitalization

The software segment dominates the largest AI in the manufacturing market as platforms for predictive analytics, computer vision, and digital twin simulations become core components to operate more efficiently. AI software will allow manufacturers to tailor solutions for special processes, allowing scalability across multiple facilities.

Hardware Innovation Optimizes Effectiveness and Scalability for AI Implementations

The hardware segment has been witnessing a stupendous growth curve financing demands with AI accelerators, high-performance GPUs, and IoT sensors delivering critical operational data to AI models. These components guarantee the smooth functioning of AI-oriented systems within extremely high-speed, data-intensive environments of manufacturing.

Service Providers Gain Traction with Manufacturers Exploring Specialized AI Implementation Knowledge

The services segment is on a rampant growth curve, as companies increasingly look to third-party providers for integrating AI systems and for training employees in the ongoing maintenance of these systems. These are the organizations that hold a relationship with AI technology providers to connect them with factory operations, ensuring a smooth transition and measurable ROI.

Key Takeaways

1. AI Adoption Surge - Rising deployment of AI systems accelerates Industry 4.0 transformation.
2. Software Dominance - AI platforms for predictive analytics and computer vision lead the market.
3. Hardware Expansion - GPUs and IoT sensors strengthen AI infrastructure in manufacturing.
4. Service Growth - Specialized AI deployment services gain traction among global manufacturers.
5. Digital Twin Utilization - Simulation technology enhances decision-making and operational planning.
6. Energy Efficiency Goals - AI reduces power consumption and production waste.
7. Autonomous Robotics - AI-driven robots redefine assembly and material handling workflows.
8. Edge AI Deployment - Real-time analytics improve operational agility.
9. Supply Chain Intelligence - AI optimizes forecasting, logistics, and inventory management.
10. APAC Growth - Rapid industrialization fuels AI adoption in emerging manufacturing hubs.

Regional Insights

North America is Leading the AI in Manufacturing Market With Strong Industrial Digitization and R&D Investments.

The North American market enjoys a robust position with heavy injections from AI research, advanced manufacturing infrastructure, and collusions between tech giants and industrial pacesetters. The U.S. is particularly vibrant in its AI-driven predictive maintenance, digital twins, and robotics applications, in automotive and aerospace manufacturing.

Europe Stays Strong on Traditional Automated Manufacturing

Europe keeps a sizable market share due to the region's stress on sustainable grounds for manufacturing and the adoption of Industry 4.0. Germany, France, and the United Kingdom are in the vanguard of this AI integration with the backing of government initiatives and high demand for precision engineering applications.

Asia-Pacific is emerging as The Fastest-Growing Market With large-scale deployments of Industrial AI.

Asia-Pacific is expected to record the highest-growth rate on the wings of industrialization, skilled manpower, and government facilitation. China, Japan, and India lead the race in embarking on large-scale deployments of AI in manufacturing to boost productivity and global export competitiveness.

LAMEA Slowly Awakens to AI-Powered Manufacturing Transformation

Latin America, the Middle East, and Africa are gradually introducing AI technologies into their manufacturing under the pressures of modernization, plus strategic partnerships with technology providers. Countries such as Brazil and the UAE are making heavy investments in AI infrastructure to nurture smart manufacturing ecosystems.

Core Strategic Questions Answered in This Report

Q. What is the expected growth trajectory of artificial intelligence in the manufacturing market from 2024 to 2035?

The global artificial intelligence in manufacturing market is projected to grow from USD 5.32 billion in 2024 to USD 368.47 billion by 2035, reflecting a CAGR of 47.0% over the forecast period (2025-2035). This remarkable growth is driven by the rapid adoption of AI technologies for predictive maintenance, quality inspection, and supply chain optimization across multiple manufacturing sectors.

Q. Which key factors are fuelling the growth of artificial intelligence in the manufacturing market?

Several key factors are propelling market growth:

1. Rising adoption of Industry 4.0 and smart factory initiatives.
2. Integration of AI with IoT, robotics, and digital twin technologies.
3. Increasing demand for predictive maintenance and quality control.
4. Advancements in AI-powered analytics, computer vision, and automation.
5. Government policies supporting manufacturing digitalization.
6. Need for energy-efficient and sustainable production processes.

Q. What are the primary challenges hindering the growth of artificial intelligence in the manufacturing market?

Major challenges include:

1. High initial investment and integration complexity.
2. Lack of skilled professionals in AI and industrial automation.
3. Data privacy and security concerns in interconnected factory networks.
4. Integration issues with legacy manufacturing systems.
5. Resistance to change in traditionally structured manufacturing environments.

Q. Which regions currently lead the artificial intelligence in manufacturing market in terms of market share?

North America leads the market, driven by advanced industrial capabilities, significant R&D spending, and early adoption of AI technologies. Europe follows closely, with key players in Germany, France, and the UK leveraging AI to enhance sustainable and precision manufacturing.

Q. What emerging opportunities are anticipated in the artificial intelligence in manufacturing market?

The market is ripe with new opportunities, including:

1. Edge AI applications for real-time process control.
2. AI-powered robotics for autonomous manufacturing.
3. Expansion of AI-based quality inspection systems.
4. Cloud-based AI platforms enabling global manufacturing collaboration.
5. AI-driven sustainability analytics to achieve carbon neutrality.
6. Advanced digital twin simulations for product and process innovation.

Key Benefits for Stakeholders

1. The report offers a quantitative assessment of market segments, emerging trends, projections, and market dynamics for the period 2024 to 2035.
2. The report presents comprehensive market research, including insights into key growth drivers, challenges, and potential opportunities.
3. Porter's Five Forces analysis evaluates the influence of buyers and suppliers, helping stakeholders make strategic, profit-driven decisions and strengthen their supplier-buyer relationships.
4. A detailed examination of market segmentation helps identify existing and emerging opportunities.
5. Key countries within each region are analysed based on their revenue contributions to the overall market.
6. The positioning of market players enables effective benchmarking and provides clarity on their current standing within the industry.
7. The report covers regional and global market trends, major players, key segments, application areas, and strategies for market expansion.

Chapter 1. Market Snapshot

1.1. Market Definition & Report Overview

1.2. Market Segmentation

1.3. Key Takeaways

1.3.1. Top Investment Pockets

1.3.2. Top Winning Strategies

1.3.3. Market Indicators Analysis

1.3.4. Top Impacting Factors

1.4. Industry Ecosystem Analysis

1.4.1. 360- Analysis

Chapter 2. Executive Summary

2.1. CEO/CXO Standpoint

2.2. Strategic Insights

2.3. ESG Analysis

2.4 Market Attractiveness Analysis

2.5.key Findings

Chapter 3. Research Methodology

3.1 Research Objective

3.2 Supply Side Analysis

3.2.1. Primary Research

3.2.2. Secondary Research

3.3 Demand Side Analysis

3.3.1. Primary Research

3.3.2. Secondary Research

3.4. Forecasting Models

3.4.1. Assumptions

3.4.2. Forecasts Parameters

3.5. Competitive breakdown

3.5.1. Market Positioning

3.5.2. Competitive Strength

3.6. Scope of the Study

3.6.1. Research Assumption

3.6.2. Inclusion & Exclusion

3.6.3. Limitations

Chapter 4. Industry Landscape

4.1. Market Dynamics

4.1.1. Drivers

4.1.2. Restraints

4.1.3. Opportunities

4.2. Porter-s 5 Forces Model

4.2.1. Bargaining Power of Buyer

4.2.2. Bargaining Power of Supplier

4.2.3. Threat of New Entrants

4.2.4. Threat of Substitutes

4.2.5. Competitive Rivalry

4.3. Value Chain Analysis

4.4. PESTEL Analysis

4.5. Pricing Analysis and Trends

4.6. Key growth factors and trends analysis

4.7. Market Share Analysis (2025)

4.8. Top Winning Strategies (2025)

4.9. Trade Data Analysis (Import Export)

4.10. Regulatory Guidelines

4.11. Historical Data Analysis

4.12. Analyst Recommendation & Conclusion

Chapter 5. Global Artificial Intelligence in Manufacturing Market Size & Forecasts by Component 2025-2035

5.1. Market Overview

5.1.1. Market Size and Forecast By Component 2025-2035

5.2. Hardware

5.2.1. Market definition, current market trends, growth factors, and opportunities

5.2.2. Market size analysis, by region, 2025-2035

5.2.3. Market share analysis, by country, 2025-2035

5.3. Software

5.3.1. Market definition, current market trends, growth factors, and opportunities

5.3.2. Market size analysis, by region, 2025-2035

5.3.3. Market share analysis, by country, 2025-2035

5.4. Services

5.4.1. Market definition, current market trends, growth factors, and opportunities

5.4.2. Market size analysis, by region, 2025-2035

5.4.3. Market share analysis, by country, 2025-2035

Chapter 6. Global Artificial Intelligence in Manufacturing Market Size & Forecasts by Region 2025-2035

6.1. Regional Overview 2025-2035

6.2. Top Leading and Emerging Nations

6.3. North America Artificial Intelligence in Manufacturing Market

6.3.1. U.S. Artificial Intelligence in Manufacturing Market

6.3.1.1. By Component breakdown size & forecasts, 2025-2035

6.3.2. Canada Artificial Intelligence in Manufacturing Market

6.3.2.1. By Component breakdown size & forecasts, 2025-2035

6.3.3. Mexico Artificial Intelligence in Manufacturing Market

6.3.3.1. By Component breakdown size & forecasts, 2025-2035

6.4. Europe Artificial Intelligence in Manufacturing Market

6.4.1. UK Artificial Intelligence in Manufacturing Market

6.4.1.1. By Component breakdown size & forecasts, 2025-2035

6.4.2. Germany Artificial Intelligence in Manufacturing Market

6.4.2.1. By Component breakdown size & forecasts, 2025-2035

6.4.3. France Artificial Intelligence in Manufacturing Market

6.4.3.1. By Component breakdown size & forecasts, 2025-2035

6.4.4. Spain Artificial Intelligence in Manufacturing Market

6.4.4.1. By Component breakdown size & forecasts, 2025-2035

6.4.5. Italy Artificial Intelligence in Manufacturing Market

6.4.5.1. By Component breakdown size & forecasts, 2025-2035

6.4.6. Rest of Europe Artificial Intelligence in Manufacturing Market

6.4.6.1. By Component breakdown size & forecasts, 2025-2035

6.5. Asia Pacific Artificial Intelligence in Manufacturing Market

6.5.1. China Artificial Intelligence in Manufacturing Market

6.5.1.1. By Component breakdown size & forecasts, 2025-2035

6.5.2. India Artificial Intelligence in Manufacturing Market

6.5.2.1. By Component breakdown size & forecasts, 2025-2035

6.5.3. Japan Artificial Intelligence in Manufacturing Market

6.5.3.1. By Component breakdown size & forecasts, 2025-2035

6.5.4. Australia Artificial Intelligence in Manufacturing Market

6.5.4.1. By Component breakdown size & forecasts, 2025-2035

6.5.5. South Korea Artificial Intelligence in Manufacturing Market

6.5.5.1. By Component breakdown size & forecasts, 2025-2035

6.5.6. Rest of APAC Artificial Intelligence in Manufacturing Market

6.5.6.1. By Component breakdown size & forecasts, 2025-2035

6.6. LAMEA Artificial Intelligence in Manufacturing Market

6.6.1. Brazil Artificial Intelligence in Manufacturing Market

6.6.1.1. By Component breakdown size & forecasts, 2025-2035

6.6.2. Argentina Artificial Intelligence in Manufacturing Market

6.6.2.1. By Component breakdown size & forecasts, 2025-2035

6.6.3. UAE Artificial Intelligence in Manufacturing Market

6.6.3.1. By Component breakdown size & forecasts, 2025-2035

6.6.4. Saudi Arabia (KSA Artificial Intelligence in Manufacturing Market

6.6.4.1. By Component breakdown size & forecasts, 2025-2035

6.6.5. Africa Artificial Intelligence in Manufacturing Market

6.6.5.1. By Component breakdown size & forecasts, 2025-2035

6.6.6. Rest of LAMEA Artificial Intelligence in Manufacturing Market

6.6.6.1. By Component breakdown size & forecasts, 2025-2035

Chapter 7. Company Profiles

7.1. Top Market Strategies

7.2. Company Profiles

7.2.1. BenevolentAI

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.2. Insilico Medicine

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.3. Atomwise Inc.

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.4. Exscientia

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.5. BioXcel Therapeutics

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.6. Recursion Pharmaceuticals

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.7. Deep Genomics

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.8. Cyclica

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.9. Cloud Pharmaceuticals

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

7.2.10. Aria Pharmaceuticals

7.2.1.1. Company Overview

7.2.1.2. Key Executives

7.2.1.3. Company Snapshot

7.2.1.4. Financial Performance

7.2.1.5. Product/Services Port

7.2.1.6. Recent Development

7.2.1.7. Market Strategies

7.2.1.8. SWOT Analysis

Research Methodology

Kaiso Research and Consulting follows an independent approach in making estimations to provide unbiased business intelligence. Our studies are not limited to secondary research alone but are built on a balanced blend of primary research, surveys, and secondary sources. This methodology enables us to develop a comprehensive 360-degree understanding of the industry and market landscape.

Supply and Demand Dynamics:

A. Supply Side Analysis:

We begin by assessing how suppliers contribute to overall market revenue growth. Our research then delves into their product portfolios, geographical reach, core focus areas, and key strategic initiatives. As most of our reports are based on a top-down approach, we begin by conducting interviews across the value chain. In the first round, we engage with manufacturers and companies, speaking with professionals from supply chain management, production, and sales. These discussions allow us to gather detailed insights into revenue generation, measured in millions or billions, segmented by type, platform, end-user, region, and other key parameters. This helps identify how companies are driving their products into mainstream markets and influencing the overall industry structure.

As the final step, we conduct a Pareto analysis to evaluate market fragmentation and identify the key players influencing industry structure. On the supply side, we evaluate how industry players contribute to overall market growth and revenue generation.

This includes an in-depth review of:

1. Product Offerings – range, categories, and applications covered.
2. Geographical Presence – regions of operation and market penetration.
3. Strategic Initiatives – new product development, product launches, distribution channel strategies, and key application areas.

B. Demand Side Analysis:

Once supply dynamics are assessed, we then examine demand-side factors shaping the market. This involves mapping demand across applications, geographies, and end-user groups. On the demand side, we conduct interviews with a network of distributors from the organised market to gain a deeper understanding of demand dynamics. This analysis covers revenue generation segmented by type, platform, end-user, and region.

Each subsegment is interconnected to understand patterns in:

1. Revenue contribution
2. Growth rate
3. Adoption levels

By aggregating demand from all subsegments, we estimate the magnitude of market-driving forces. Comparing supply and demand enables us to forecast how these dynamics influence future market behaviour.

Forecast Model (Proprietary Kaiso Engine):

Building on quantitative rigor, Kaiso integrates a Forecast Model that blends statistical precision with strategic scenario planning. Unlike generic projections, this model adapts dynamically to evolving market signals.

Our proprietary forecast engine incorporates the following layers:

1. Baseline Projection: Derived using historical patterns, econometric baselines, and validated macroeconomic inputs.

1. Scenario Forecasting: Optimistic, conservative, and base-case outlooks built with dynamic weighting of influencing variables (e.g., policy shifts, raw material volatility, supply chain disruptions).

1. AI-Augmented Predictive Analytics: Machine learning algorithms detect emerging weak signals, nonlinear patterns, and correlation anomalies that standard models may overlook.

1. Sector-Specific Modules: Tailored sub-models for fast-evolving industries (e.g., clean energy adoption curves, healthcare regulatory cycles, AI penetration trends).

1. Resilience Testing: Shock modeling to evaluate market response under “black swan” or disruption scenarios such as pandemics, trade wars, or technology breakthroughs.

Deliverable outcomes of our Forecast Model:

1. Granular projections by region, segment, and application (up to 2035)

1. Sensitivity-rank matrices highlighting critical drivers and risks

1. Dynamic update capability, ensuring forecasts remain current with real-time data

This ensures that our clients don’t just see where the market is heading, but also how robust that trajectory is under different conditions.

Approach & Methodology

At Kaiso Research and Consulting, we adopt an independent, data-driven approach to ensure objective and unbiased insights. Our methodology blends primary research, secondary research, and survey-based validation, giving us a 360° market perspective.

On average, for each market:

1. 45 primary interviews are conducted covering the entire value chain.
2. Interviews last approximately 28 minutes each, including a mix of face-to-face and online formats.

This rigorous methodology guarantees realistic, credible, and unbiased market analysis.

Key Player Positioning

We assess key companies on two major dimensions:

Market Positioning: measured through revenue, growth rate, geographical reach, customer base, strategies implemented, and focus areas.

Competitive Strength: evaluated through product portfolio, R&D investment, innovation, new product introductions, and overall competitiveness.

Conclusion

Our comprehensive methodology enables us to deliver high-quality, objective, and actionable market intelligence. By balancing both supply and demand perspectives, Kaiso Research and Consulting has established itself as a trusted and recognised brand in the research and consulting landscape.