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Smart Robot Market Report Scope & Overview:

Smart Robot Market Revenue Analysis

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The Smart Robot Market size was valued at USD 13.39 billion in 2023. It is expected to hit USD 111.6 billion by 2032 and grow at a CAGR of 26.63% over the forecast period of 2024-2032.

Intelligent robots are willing to reform supply chain processes. This increased production is essential to manufacturing in that it reduces reliance on humans and allows for scale. The rapid advancements in robotics technology create new opportunities for increased efficiency and reduction of human labor. This, combining with the expanding preference for local production and consumption is likely to propel phenomenal expansion in smart robot market. Industrialization affects the adoption of robots which is then accelerated by automation. Manufacturers of any size can be positively impacted by automated robotics, because they are more accessible and affordable than ever before.

The 2024 World Intelligence Expo boasted the newest in high-tech gadgetry - highlighting robotics as a tool for production. This should have been my fate, but inexplicably I ended up marrying procreation and all its promises instead of the robots in Men-only Heaven with whom guests are encouraged to mess around - bionic robots and human-like ones as well as robotic dogs.

The show also featured remarkable aerial tech, like drones and flying vehicles of all sizes including a cargo-carrying helicopter from a German firm. While robotics are notably more than just the industrial arm off their old and new applications, robotic arms use 5G, AI along with many of today's latest technology to produce some incredibly sharp camera work. But the exhibition was more than a showcase - it also played host to events showcasing what these technologies are capable of, such as the Asia-Pacific Robotics World Cup andtheWorld Intelligent Driving Challenge. This event gave companies the chance to show off their new solutions and explore how far-reaching business opportunities are enabled by smart technology, including digital transformation.

Hyundai Motor Group is boosting smart offices with robots! The DAL-e delivery robot from Hyundai and Kia operating at Factorial Seongsu in Seoul, South Korea features AI facial recognition (claimed to be accurate up to 99.9%) for autonomously traveling inside building premises and delivering parcels directly into the hands of residents The Hyundai WIA Parking Robot parks itself as well, taking control of the wheel to park the car in multiple rows and save space. There is a Max of 50 they can handle for their Smart Parking Control System and in the future, will partner with Hyundai & Kia's Automatic Charging Robot service for EV charging. Hyundai plans to expand the "Robot Total Solution" in more buildings where evolution of workplaces begins with robotic transformation.

Market Dynamics

Drivers

  • Increasing adoption of Industry 4.0 practices, there is a growing need for smart robots

  • Increasing demand for smart robotics in the Non-Industrial sector will drive the global service and industrial robots market.

Those smart robots are now longer contained in factory walls anymore. The developers are building robots for different industries resulting in a boom of the market. Robots are a helping hand today while artificial intelligence (AI) and machine learning is taking over better than humans! You truly read this right, it's not sci-fi. Our healthcare sector has already started new surgeries with the help of these robots and they're becoming a part day-by-day in livesages folks. Imagine robots performing complex missions in the future! Robeta is just one more example of an area where robots will be prevalent in the future, with agriculture seeming to have particular promise for an industry that has trouble finding workers willing or able and also spends lots on. Money's no free. At the moment, cyborgs are harvesting crops where they carefully pick fruits and vegetables without damaging them. Ultra-intelligent robots and the best vision technology out there, only a robot with an excellent knowledge can make an accurate call on where produce is ripe. Drones and autonomous tractors are the now-common reality of the robotic revolution, buzzing overhead and checking on crops. Purpose-built robots are also fueling growth in the robot market outside factories, but to an extent rarely seen for automation products.

  • Artificial intelligence is going to change the name of the business world with its advanced robots.

AI is sort of a power booster for robots. This allows the robots to learn, adapt and deal with complex situations by simulating human-like intelligence. Imagine photo-realistic AI training grounds doing hundreds of challenges in seconds, one after the other whose data can be used to train robots more accurate and intelligent. The rising demand for AI in robots is a no brainer. These are as a part of the cognitive area, so that robots can learn to understand natural languages (natural language processing) etc. This is also used in robotics for free interaction with humans. Robots are becoming stringently precise, intelligent and commercially vogue setting a new benchmark in the intelligent robot industry while their upcoming boom is at par with all things future.

Restraints

  • Rising concerns over data privacy will restrict industry growth.

Increasing software services for robots are raising ethical concerns related to the ownership of data, which would hinder market growth. Use of domestic robots in not just for education, recreation, but also chores incremented the number of cloud-based data belonging to the individuals. And that data also leaves the companies premises often bought by unauthorized users or third party such as marketing agencies. The data from robots is access in proprietary way, sometimes enables enterprise confidential getting infringed and misused. In the military, defense and healthcare sectors, data breaches carry heavy consequences. Thus, growing data security is becoming a challenge for robots adoption recently and it could significantly suppress the market growth.

  • Worries about data privacy and strong regulations

With robot helpers appearing higher and better in households for jobs, education, or even simply mere leisure reasons ultimately a privacy alarm is growing. These robots also collect your personal data (such as what time you leave in the morning, or if you have a chronic health condition) that can be uploaded into cloud storage. This could then put your data in danger! It can be bought by third-parties, say marketing agencies for instance and this has led to your privacy being infringed upon directly. Think of voice-controlled robots such Amazon's Lynx and Google's Roomba, they are being recorded all the time. Ensuring data security is paramount. There's also many ethical questions to be considered by governments, as who gather the data that robots do and its possibilities for researching with it.

Segment Analysis

By Type

  • Personal/Domestic Robots

  • Professional Robots

Professional robots presently claim 55% share of the market with respect to smart robots. They are very prevalent in industries like manufacturing, logistics and healthcare where their soundness as well strength is crucial. These robots are ideal for automating dull and dangerous tasks, as they demonstrate good performance in complex assignments at the highest level. A growing demand for these skilled assistants examined the use of automation is increasing, as well there simply are not enough workers. If you see robots in the factories or at hospitals - that's pretty much one of the most demanding things about intelligent robotics field.

Smart-Robot-Market-By-Type

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By Mobility

  • Mobile

  • Fixed/Stationary

At 56% of the smart robot market, fixed robots now lead they have been around longer and many are traditional manufacturing tasks that occurred on the assembly lines. Basic tasks on these understandable yet affordable machines that are proven, hardworking). This is especially true when dealing with very complicated and nuanced movements that require precision and repeatability, something which mobile robots inherently struggle to accomplish due to the limitations of navigation/stability issues. Even so, the dominance of mobile robots is growing. Navigation technology and battery life are improving their capabilities, and new applications in areas such as logistics and healthcare have showcased the advantage of mobility. Well, the current title holder is actually stationary robots; however, mobile robots are holding Court to win it in future!

By Application

  • Inspection and Maintenance

  • Material Handling and Sorting

  • Security and Surveillance

  • Education and Entertainment

  • Sanitation and Disinfection

  • Others

By End-User

  • Manufacturing

  • Healthcare

  • Agriculture

  • Military and Defense

  • Logistics and Warehouse

  • Education and Entertainment

  • Others

Regional Analysis

Regional Analysis

The Asia Pacific commands the largest share in this market with 45% of total smart robot industry. A rapidly growing industrial base in this region, is accelerating the necessity for robotics as it helps to cater huge requirement especially from manufacturing and logistics sector. In this region, governments are like parents of a prodigy - offering financial and regulatory help to robotics as it grows up. Innovation in technology! Indeed, some of the world's most advanced robotics research is taking place in countries like China, Japan and South Korea. That is how Asia Pacific commands the largest market share in smart robots - they have everything perfect to be ruling as robot overlords damn it!

The leading direct return market of the Smart robot industry in a 25% share, North America has maintained rapid growth as demand for industries such as car manufacturing / healthcare/ food production continues. Growth is primarily being driven by better robotics, trade rules work in countries' favor and the more workers make per hour. Use of automation in the region stimulates local robotic knowledge and appeals to investment

Smart-Robot-Market-By-Region

REGIONAL COVERAGE:

North America

  • US

  • Canada

  • Mexico

Europe

  • Eastern Europe

    • Poland

    • Romania

    • Hungary

    • Turkey

    • Rest of Eastern Europe

  • Western Europe

    • Germany

    • France

    • UK

    • Italy

    • Spain

    • Netherlands

    • Switzerland

    • Austria

    • Rest of Western Europe

Asia Pacific

  • China

  • India

  • Japan

  • South Korea

  • Vietnam

  • Singapore

  • Australia

  • Rest of Asia Pacific

Middle East & Africa

  • Middle East

    • UAE

    • Egypt

    • Saudi Arabia

    • Qatar

    • Rest of Middle East

  • Africa

    • Nigeria

    • South Africa

    • Rest of Africa

Latin America

  • Brazil

  • Argentina

  • Colombia

  • Rest of Latin America

Key Players

The major key players are Hanson Robotics Ltd., KUKA AG, Amazon Inc., SoftBank Corporation, ABB, Honda Motor Company Ltd., OMRON Adept Technology Inc., YASKAWA Electric Corporation, Blue Frog Robotics, DeLaval, Intuitive Surgical, Samsung Electronics Co. Ltd., Hyundai Motor Group and other.

Recent Development

  • In 2024, Hyundai deployed robots for deliveries and parking in a Seoul office building, showcasing their self-driving and AI capabilities.

  • In April 2022, Yaskawa Company developed an industrial robot equipped with artificial intelligence that can accurately identify the color and shape of objects, efficiently transporting them to their designated positions.

Smart Robot Market Report Scope:
Report Attributes Details
Market Size in 2023 US$ 13.39 billion
Market Size by 2031 US$ 111.6 Billion
CAGR CAGR of 26.63% From 2024 to 2032
Base Year 2023
Forecast Period 2024-2031
Historical Data 2020-2022
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • By Type (Personal/Domestic Robots, Professional Robots)
• By Mobility (Mobile, Fixed/Stationary)
• By Application (Inspection and Maintenance, Material Handling and Sorting, Security and Surveillance, Education and Entertainment, Sanitation and Disinfection, Others)
• By End User (Manufacturing, Healthcare, Agriculture, Military and Defense, Logistics and Warehouse, Education and Entertainment, Others)
Regional Analysis/Coverage North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America)
Company Profiles Hanson Robotics Ltd., KUKA AG, Amazon Inc., SoftBank Corporation, ABB, Honda Motor Company Ltd., OMRON Adept Technology Inc., YASKAWA Electric Corporation, Blue Frog Robotics, DeLaval, Intuitive Surgical, Samsung Electronics Co. Ltd., Hyundai Motor Group
Key Drivers • Increasing demand for smart robotics in the Non-Industrial sector will drive the global service and industrial robots market.
• Artificial intelligence is going to change the name of the business world with its advanced robots.
RESTRAINTS • Rising concerns over data privacy will restrict industry growth.
• Worries about data privacy and strong regulations

 

Frequently Asked Questions

Ans. Rising demand for HEVs/PHEVs and clean energy storage are driving the Smart Robot market.

Ans. The Smart Robot Market size was valued at USD 13.39 billion in 2023. It is expected to hit USD 111.6 billion by 2032 and grow at a CAGR of 26.63% over the forecast period of 2024-2032.

Ans North America region is anticipated to record the Fastest Growing in the Smart Robot Market.

Ans .The Professional Segment is leading in the market revenue share in 2023.

Ans. Asia Pacific is expected to hold the largest market share in the Smart Robot Market during the forecast period.

TABLE OF CONTENTS

1. Introduction

1.1 Market Definition

1.2 Scope

1.3 Research Assumptions

2. Industry Flowchart

3. Research Methodology

4. Market Dynamics

4.1 Drivers

4.2 Restraints

4.3 Opportunities

4.4 Challenges

5. Porter’s 5 Forces Model

6. Pest Analysis

7. Smart Robot Segmentation, By Type

7.1 Introduction

7.2 Personal/Domestic Robots

7.3 Professional Robots

8. Smart Robot Segmentation, By Mobility

8.1 Introduction

8.2 Mobile

8.3 Fixed/Stationary

9. Smart Robot Segmentation, By Application

9.1 Introduction

9.2 Inspection and Maintenance

9.3 Material Handling and Sorting

9.4 Security and Surveillance

9.5 Sanitation and Disinfection

9.6 Others

10. Smart Robot Segmentation, By End User

10.1 Introduction

10.2 Manufacturing

10.3 Healthcare

10.4 Agriculture

10.5 Military and Defense

10.6 Logistics and Warehouse

10.7 Education and Entertainment

10.8 Others

11. Regional Analysis

11.1 Introduction

11.2 North America

11.2.1 Trend Analysis

11.2.2 North America Smart Robot by Country

11.2.3 North America Smart Robot By Type

11.2.4 North America Smart Robot By Mobility

11.2.5 North America Smart Robot By Application

11.2.6 North America Smart Robot By End User

11.2.7 USA

11.2.7.1 USA Smart Robot By Type

11.2.7.2 USA Smart Robot By Mobility

11.2.7.3 USA Smart Robot By Application

11.2.7.4 USA Smart Robot By End User

11.2.8 Canada

11.2.8.1 Canada Smart Robot By Type

11.2.8.2 Canada Smart Robot By Mobility

11.2.8.3 Canada Smart Robot By Application

11.2.8.4 Canada Smart Robot By End User

11.2.9 Mexico

11.2.9.1 Mexico Smart Robot By Type

11.2.9.2 Mexico Smart Robot By Mobility

11.2.9.3 Mexico Smart Robot By Application

11.2.9.4 Mexico Smart Robot By End User

11.3 Europe

11.3.1 Trend Analysis

11.3.2 Eastern Europe

11.3.2.1 Eastern Europe Smart Robot by Country

11.3.2.2 Eastern Europe Smart Robot By Type

11.3.2.3 Eastern Europe Smart Robot By Mobility

11.3.2.4 Eastern Europe Smart Robot By Application

11.3.2.5 Eastern Europe Smart Robot By End User

11.3.2.6 Poland

11.3.2.6.1 Poland Smart Robot By Type

11.3.2.6.2 Poland Smart Robot By Mobility

11.3.2.6.3 Poland Smart Robot By Application

11.3.2.6.4 Poland Smart Robot By End User

11.3.2.7 Romania

11.3.2.7.1 Romania Smart Robot By Type

11.3.2.7.2 Romania Smart Robot By Mobility

11.3.2.7.3 Romania Smart Robot By Application

11.3.2.7.4 Romania Smart Robot By End User

11.3.2.8 Hungary

11.3.2.8.1 Hungary Smart Robot By Type

11.3.2.8.2 Hungary Smart Robot By Mobility

11.3.2.8.3 Hungary Smart Robot By Application

11.3.2.8.4 Hungary Smart Robot By End User

11.3.2.9 Turkey

11.3.2.9.1 Turkey Smart Robot By Type

11.3.2.9.2 Turkey Smart Robot By Mobility

11.3.2.9.3 Turkey Smart Robot By Application

11.3.2.9.4 Turkey Smart Robot By End User

11.3.2.10 Rest of Eastern Europe

11.3.2.10.1 Rest of Eastern Europe Smart Robot By Type

11.3.2.10.2 Rest of Eastern Europe Smart Robot By Mobility

11.3.2.10.3 Rest of Eastern Europe Smart Robot By Application

11.3.2.10.4 Rest of Eastern Europe Smart Robot By End User

11.3.3 Western Europe

11.3.3.1 Western Europe Smart Robot by Country

11.3.3.2 Western Europe Smart Robot By Type

11.3.3.3 Western Europe Smart Robot By Mobility

11.3.3.4 Western Europe Smart Robot By Application

11.3.3.5 Western Europe Smart Robot By End User

11.3.3.6 Germany

11.3.3.6.1 Germany Smart Robot By Type

11.3.3.6.2 Germany Smart Robot By Mobility

11.3.3.6.3 Germany Smart Robot By Application

11.3.3.6.4 Germany Smart Robot By End User

11.3.3.7 France

11.3.3.7.1 France Smart Robot By Type

11.3.3.7.2 France Smart Robot By Mobility

11.3.3.7.3 France Smart Robot By Application

11.3.3.7.4 France Smart Robot By End User

11.3.3.8 UK

11.3.3.8.1 UK Smart Robot By Type

11.3.3.8.2 UK Smart Robot By Mobility

11.3.3.8.3 UK Smart Robot By Application

11.3.3.8.4 UK Smart Robot By End User

11.3.3.9 Italy

11.3.3.9.1 Italy Smart Robot By Type

11.3.3.9.2 Italy Smart Robot By Mobility

11.3.3.9.3 Italy Smart Robot By Application

11.3.3.9.4 Italy Smart Robot By End User

11.3.3.10 Spain

11.3.3.10.1 Spain Smart Robot By Type

11.3.3.10.2 Spain Smart Robot By Mobility

11.3.3.10.3 Spain Smart Robot By Application

11.3.3.10.4 Spain Smart Robot By End User

11.3.3.11 Netherlands

11.3.3.11.1 Netherlands Smart Robot By Type

11.3.3.11.2 Netherlands Smart Robot By Mobility

11.3.3.11.3 Netherlands Smart Robot By Application

11.3.3.11.4 Netherlands Smart Robot By End User

11.3.3.12 Switzerland

11.3.3.12.1 Switzerland Smart Robot By Type

11.3.3.12.2 Switzerland Smart Robot By Mobility

11.3.3.12.3 Switzerland Smart Robot By Application

11.3.3.12.4 Switzerland Smart Robot By End User

11.3.3.13 Austria

11.3.3.13.1 Austria Smart Robot By Type

11.3.3.13.2 Austria Smart Robot By Mobility

11.3.3.13.3 Austria Smart Robot By Application

11.3.3.13.4 Austria Smart Robot By End User

11.3.3.14 Rest of Western Europe

11.3.3.14.1 Rest of Western Europe Smart Robot By Type

11.3.3.14.2 Rest of Western Europe Smart Robot By Mobility

11.3.3.14.3 Rest of Western Europe Smart Robot By Application

11.3.3.14.4 Rest of Western Europe Smart Robot By End User

11.4 Asia-Pacific

11.4.1 Trend Analysis

11.4.2 Asia-Pacific Smart Robot by Country

11.4.3 Asia-Pacific Smart Robot By Type

11.4.4 Asia-Pacific Smart Robot By Mobility

11.4.5 Asia-Pacific Smart Robot By Application

11.4.6 Asia-Pacific Smart Robot By End User

11.4.7 China

11.4.7.1 China Smart Robot By Type

11.4.7.2 China Smart Robot By Mobility

11.4.7.3 China Smart Robot By Application

11.4.7.4 China Smart Robot By End User

11.4.8 India

11.4.8.1 India Smart Robot By Type

11.4.8.2 India Smart Robot By Mobility

11.4.8.3 India Smart Robot By Application

11.4.8.4 India Smart Robot By End User

11.4.9 Japan

11.4.9.1 Japan Smart Robot By Type

11.4.9.2 Japan Smart Robot By Mobility

11.4.9.3 Japan Smart Robot By Application

11.4.9.4 Japan Smart Robot By End User

11.4.10 South Korea

11.4.10.1 South Korea Smart Robot By Type

11.4.10.2 South Korea Smart Robot By Mobility

11.4.10.3 South Korea Smart Robot By Application

11.4.10.4 South Korea Smart Robot By End User

11.4.11 Vietnam

11.4.11.1 Vietnam Smart Robot By Type

11.4.11.2 Vietnam Smart Robot By Mobility

11.4.11.3 Vietnam Smart Robot By Application

11.4.11.4 Vietnam Smart Robot By End User

11.4.12 Singapore

11.4.12.1 Singapore Smart Robot By Type

11.4.12.2 Singapore Smart Robot By Mobility

11.4.12.3 Singapore Smart Robot By Application

11.4.12.4 Singapore Smart Robot By End User

11.4.13 Australia

11.4.13.1 Australia Smart Robot By Type

11.4.13.2 Australia Smart Robot By Mobility

11.4.13.3 Australia Smart Robot By Application

11.4.13.4 Australia Smart Robot By End User

11.4.14 Rest of Asia-Pacific

11.4.14.1 Rest of Asia-Pacific Smart Robot By Type

11.4.14.2 Rest of Asia-Pacific Smart Robot By Mobility

11.4.14.3 Rest of Asia-Pacific Smart Robot By Application

11.4.14.4 Rest of Asia-Pacific Smart Robot By End User

11.5 Middle East & Africa

11.5.1 Trend Analysis

11.5.2 Middle East

11.5.2.1 Middle East Smart Robot by Country

11.5.2.2 Middle East Smart Robot By Type

11.5.2.3 Middle East Smart Robot By Mobility

11.5.2.4 Middle East Smart Robot By Application

11.5.2.5 Middle East Smart Robot By End User

11.5.2.6 UAE

11.5.2.6.1 UAE Smart Robot By Type

11.5.2.6.2 UAE Smart Robot By Mobility

11.5.2.6.3 UAE Smart Robot By Application

11.5.2.6.4 UAE Smart Robot By End User

11.5.2.7 Egypt

11.5.2.7.1 Egypt Smart Robot By Type

11.5.2.7.2 Egypt Smart Robot By Mobility

11.5.2.7.3 Egypt Smart Robot By Application

11.5.2.7.4 Egypt Smart Robot By End User

11.5.2.8 Saudi Arabia

11.5.2.8.1 Saudi Arabia Smart Robot By Type

11.5.2.8.2 Saudi Arabia Smart Robot By Mobility

11.5.2.8.3 Saudi Arabia Smart Robot By Application

11.5.2.8.4 Saudi Arabia Smart Robot By End User

11.5.2.9 Qatar

11.5.2.9.1 Qatar Smart Robot By Type

11.5.2.9.2 Qatar Smart Robot By Mobility

11.5.2.9.3 Qatar Smart Robot By Application

11.5.2.9.4 Qatar Smart Robot By End User

11.5.2.10 Rest of Middle East

11.5.2.10.1 Rest of Middle East Smart Robot By Type

11.5.2.10.2 Rest of Middle East Smart Robot By Mobility

11.5.2.10.3 Rest of Middle East Smart Robot By Application

11.5.2.10.4 Rest of Middle East Smart Robot By End User

11.5.3 Africa

11.5.3.1 Africa Smart Robot by Country

11.5.3.2 Africa Smart Robot By Type

11.5.3.3 Africa Smart Robot By Mobility

11.5.3.4 Africa Smart Robot By Application

11.5.3.5 Africa Smart Robot By End User

11.5.3.6 Nigeria

11.5.3.6.1 Nigeria Smart Robot By Type

11.5.3.6.2 Nigeria Smart Robot By Mobility

11.5.3.6.3 Nigeria Smart Robot By Application

11.5.3.6.4 Nigeria Smart Robot By End User

11.5.3.7 South Africa

11.5.3.7.1 South Africa Smart Robot By Type

11.5.3.7.2 South Africa Smart Robot By Mobility

11.5.3.7.3 South Africa Smart Robot By Application

11.5.3.7.4 South Africa Smart Robot By End User

11.5.3.8 Rest of Africa

11.5.3.8.1 Rest of Africa Smart Robot By Type

11.5.3.8.2 Rest of Africa Smart Robot By Mobility

11.5.3.8.3 Rest of Africa Smart Robot By Application

11.5.3.8.4 Rest of Africa Smart Robot By End User

11.6 Latin America

11.6.1 Trend Analysis

11.6.2 Latin America Smart Robot by Country

11.6.3 Latin America Smart Robot By Type

11.6.4 Latin America Smart Robot By Mobility

11.6.5 Latin America Smart Robot By Application

11.6.6 Latin America Smart Robot By End User

11.6.7 Brazil

11.6.7.1 Brazil Smart Robot By Type

11.6.7.2 Brazil Smart Robot By Mobility

11.6.7.3 Brazil Smart Robot By Application

11.6.7.4 Brazil Smart Robot By End User

11.6.8 Argentina

11.6.8.1 Argentina Smart Robot By Type

11.6.8.2 Argentina Smart Robot By Mobility

11.6.8.3 Argentina Smart Robot By Application

11.6.8.4 Argentina Smart Robot By End User

11.6.9 Colombia

11.6.9.1 Colombia Smart Robot By Type

11.6.9.2 Colombia Smart Robot By Mobility

11.6.9.3 Colombia Smart Robot By Application

11.6.9.4 Colombia Smart Robot By End User

11.6.10 Rest of Latin America

11.6.10.1 Rest of Latin America Smart Robot By Type

11.6.10.2 Rest of Latin America Smart Robot By Mobility

11.6.10.3 Rest of Latin America Smart Robot By Application

11.6.10.4 Rest of Latin America Smart Robot By End User

12. Company Profiles

12.1 Hanson Robotics Ltd.,

12.1.1 Company Overview

12.1.2 Financial

12.1.3 Products/ Services Offered

12.1.4 The SNS View

12.2 KUKA AG

12.2.1 Company Overview

12.2.2 Financial

12.2.3 Products/ Services Offered

12.2.4 The SNS View

12.3 Amazon Inc.

12.3.1 Company Overview

12.3.2 Financial

12.3.3 Products/ Services Offered

12.3.4 The SNS View

12.4 SoftBank Corporation

12.4.1 Company Overview

12.4.2 Financial

12.4.3 Products/ Services Offered

12.4.4 The SNS View

12.5 ABB, Honda Motor Company Ltd.

12.5.1 Company Overview

12.5.2 Financial

12.5.3 Products/ Services Offered

12.5.4 The SNS View

12.6 OMRON Adept Technology Inc.

12.6.1 Company Overview

12.6.2 Financial

12.6.3 Products/ Services Offered

12.6.4 The SNS View

12.7 YASKAWA Electric Corporation

12.7.1 Company Overview

12.7.2 Financial

12.7.3 Products/ Services Offered

12.7.4 The SNS View

12.8 Blue Frog Robotics

12.8.1 Company Overview

12.8.2 Financial

12.8.3 Products/ Services Offered

12.8.4 The SNS View

12.9 , DeLaval

12.9.1 Company Overview

12.9.2 Financial

12.9.3 Products/ Services Offered

12.9.4 The SNS View

12.10 Intuitive Surgical.

12.10.1 Company Overview

12.10.2 Financial

12.10.3 Products/ Services Offered

12.10.4 The SNS View

12.11 Samsung Electronics Co. Ltd

12.11.1 Company Overview

12.11.2 Financial

12.11.3 Products/ Services Offered

12.11.4 The SNS View

12.12 Hyundai Motor Group

12.12.1 Company Overview

12.12.2 Financial

12.12.3 Products/ Services Offered

12.12.4 The SNS View

12.13 Others

12.13.1 Company Overview

12.13.2 Financial

12.13.3 Products/ Services Offered

12.13.4 The SNS View

13. Competitive Landscape

13.1 Competitive Benchmarking

13.2 Market Share Analysis

13.3 Recent Developments

13.3.1 Industry News

13.3.2 Company News

13.3.3 Mergers & Acquisitions

14. Use Case and Best Practices

15. Conclusion

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.

 

The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

We at SNS Insider have divided Primary Research into 2 parts.

Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

Primary Research

Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.

Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.

Step 3: Data Bank Validation

Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.

Data Bank Validation

Step 4: QA/QC Process

After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.

Step 5: Final QC/QA Process:

This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.


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