ENGLAND, United Kingdom, Nov. 09, 2020 (GLOBE NEWSWIRE) -- Data Bridge Market Research published a new report, titled, “Machine Tool MarketGlobal Growth, Trends, Opportunities and COVID-19 Impacts”. If business wants to thrive in this competitive age, get knowledgeable about the major accomplishments taking place in the market. Market research analysis and all the information lends a hand to businesses for the planning of production, product launches, costing, inventory, purchasing and marketing strategies.

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Machine Tools Definition:

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Machine tools are the machine used for handling machining metal or other rigid materials, milling machines are highly versatile and can perform a variety of functions, such as filleting, turning, chamfering, drilling and gear cutting among others. They also include some tools for shaping and cutting. In simple definition machine tools are machines that help the work of people easy.
The report gives explanation about market definition, currency and pricing, market segmentation, market overview, premium insights, key insights and company profile of the key market players. With this large scale Global Machine Tool Market report, it can also be estimated that how the actions of key players are Doosan Corporation, Amada (India) Pvt. Ltd, Makino, JTEKT Corporation affecting the sales, import, export, revenue and 4.90% CAGR values. The report also helps analyse the most appropriate method for the distribution of certain products.

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Key Market Competitors Covered in the report

• Doosan Corporation
• Amada (India) Pvt. Ltd
• Makino
• JTEKT Corporation
• Georg Fischer Ltd
• Komatsu NTC
• Okuma Corporation
• HYUNDAI WIA CORP
• CHIRON Group SE
• MAG IAS GmbH
• Haas Automation, Inc
• GROB-WERKE GmbH & CO.KG
• Spinner Werkzeugmaschinenfabrik GmbH
• YAMAZAKI Mazak Corporation
• DMG MORI
• Electronic HiTech Engineering Pvt, Ltd

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Global Machine Tool MarketScope and Market Size

Machine tool market is segmented on the basis of product type, automotive, industry segment and sales channel. The growth among segments helps you analyse niche pockets of growth and strategies to approach the market and determine your core application areas and the difference in your target markets.

• On the basis of product type, the machine tool market is segmented into milling machines, drilling machines, grinding machines and electrical discharge machines.

• On the basis of automotive, the machine tool market is segmented into CNC machine tools and conventional machine tools.

• On the basis of industry segment, the machine tool market is segmented into automotive and transportation, sheet metals, capital goods and energy.

• Based on sales channel, the machine tool market is segmented into events and exhibitions and dealers and distributors.

Machine Tool Market Scenario

According to Data Bridge Market Research the machine tool market is attaining a significant growth in developing economies during the forecast period of 2020-2027 due to factors such as increase in the demand for machine tools across the industries, rising demand for high quality, high productivity and reduced cycle time, rising demand in the industrial automation which will likely to create positive growth of the market.

Now the question is which are the other regions that machine tool market is targeting? Data Bridge Market Research has estimated a large growth in Middle East and Africa machine tool market in the forecast period of 2020-2027. The Data bridge market research new reports highlight the major growth factors and opportunities in the machine tool market.

Table of Contents: Machine Tool Market
Part 01: Executive Summary
Part 02: Scope of the Report
Part 03: Research Methodology
Part 04: Market Landscape

• Market Ecosystem
• Market Characteristics
• Market Segmentation Analysis

Part 05: Pipeline Analysis

• Pipeline Analysis

Part 06: Market Sizing

• Market Definition
• Market Sizing
• Market Size And Forecast

Part 07: Five Forces Analysis

• Bargaining Power Of Buyers
• Bargaining Power Of Suppliers
• Threat Of New Entrants
• Threat Of Substitutes
• Threat Of Rivalry
• Market Condition

Part 08: Market Segmentation

• Segmentation
• Comparison
• Market Opportunity

Part 09: Customer Landscape
Part 10: Regional Landscape

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• Geographical Segmentation
• Regional Comparison
• North America
• South America
• Europe
• MEA
• APAC
• Market Opportunity

Part 11: Decision Framework
Part 12: Drivers and Challenges

• Market Drivers
• Market Challenges

Part 13: Market Trends
Part 14: Vendor Landscape

• Overview
• Landscape Disruption

Part 15: Vendor Analysis

• Vendors Covered
• Vendor Classification
• Market Positioning Of Vendors

Part 16: Appendix

• List Of Abbreviations

See The Complete Table Of Contents And List Of Exhibits, As Well As Selected Illustrations And Example Pages From This Report.

New Business Strategies, Challenges & Policies are mentioned in Table of Content, Get Download Detailed TOC at https://www.databridgemarketresearch.com/toc/?dbmr=global-machine-tool-market

Key Pointers Covered in the Machine Tool Market Industry Trends and Forecast to 2027

• Market Size
• Market New Sales Volumes
• Market Replacement Sales Volumes
• Market By Brands
• Market Procedure Volumes
• Market Product Price Analysis
• Market Regulatory Framework and Changes
• Market Shares in Different Regions
• Recent Developments for Market Competitors
• Market Upcoming Applications
• Market Innovators Study

The report is prepared on the basis of detailed assessment of the industry by experts. To conclude, stakeholders, investors, product managers, marketing executives, and other experts in search of factual data on supply, demand, and future predictions would find the report valuable.

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Key Highlights of the Machine Tool Report:

Machine Tool Study Coverage: It incorporates key market sections, key makers secured, the extent of items offered in the years considered, worldwide Machine Tool and study goals. Moreover, it contacts the division study gave in the report based on the sort of item and applications.

Machine Tool Executive Outline: This area stresses the key investigations, market development rate, serious scene, market drivers, patterns, and issues notwithstanding the naturally visible pointers.

Machine Tool Production by Region: The report conveys information identified with import and fare, income, creation, and key players of every single local market contemplated are canvassed right now.

Machine Tool Profile of Manufacturers: Analysis of each market player profiled is detailed in this section. This portion likewise provides SWOT investigation, items, generation, worth, limit, and other indispensable elements of the individual player.

The major points that are covered:

• Overview:
  In this section, definition of the global Machine Tool is given along with the overview of the report in order to give a board outlook about the nature and contents of the research study.
  
• Industry Players’ Strategies Analysis:
  The market players will be benefitted from this analysis as it will help to gain competitive advantage over their competitors.
  
• Key Market Trends:
  In this section, in-depth analysis of the market’s latest and future trends is discussed.
  
• Market Forecasts:
  The research analysts have provided accurate and validated values of the total market size in terms of value and volume. Other offerings in the report include consumption, production, sales, and other forecasts for the global Machine Tool.
  
• Regional Analysis:
  Major five regions and its countries have been covered in the global Machine Tool report. With the help of this analysis, market players will have estimates about the untapped regional markets and other benefits.
  
• Segment Analysis:
  Accurate and reliable forecasts about the market share of the important segments of the Machine Tool is provided.

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Competitors – In this section, various Machine Tool industry-leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue.

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Supply and Consumption – In continuation of sales, this section studies supply and consumption for the Machine Tool Market. This part also sheds light on the gap between supply and consumption. Import and export figures are also given in this part.

Production Analysis – Production of the Machine Tool is analysed with respect to different regions, types and applications. Here, price analysis of various Machine Tool Market key players is also covered.

Sales and Revenue Analysis – Both, sales and revenue are studied for the different regions of the Machine Tool Market. Another major aspect, price, which plays an important part in the revenue generation, is also assessed in this section for the various regions.

Other analyses – Apart from the information, trade and distribution analysis for the Machine Tool Market.

Competitive Landscape – Company profile for listed players with SWOT Analysis, Business Overview, Product/Services Specification, Business Headquarter, Downstream Buyers and Upstream Suppliers.

Note – In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.

Thanks for reading this article you can also get individual chapter wise section or region wise report versions like North America, Europe, or Asia Etc.

Still if you have question Speak to Report Author @ https://www.databridgemarketresearch.com/speak-to-analyst/?dbmr=global-machine-tool-market

Customization Available
Data Bridge Market Research is a leader in consulting and advanced formative research. We take pride in servicing our existing and new customers with data and analysis that match and suits their goal. The report can be customized to include production cost analysis, trade route analysis, price trend analysis of target brands understanding the market for additional countries (ask for the list of countries), import export and grey area results data, literature review, consumer analysis and product base analysis. Market analysis of target competitors can be analysed from technology-based analysis to market portfolio strategies. We can add as many competitors that you require data about in the format and data style you are looking for. Our team of analysts can also provide you data in crude raw excel files pivot tables (Factbook) or can assist you in creating presentations from the data sets available in the report.

Research Methodology: Global Machine Tool Market
Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analysed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis. To know more about the research methodology, drop in an inquiry to speak to our industry experts.

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Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work.

There’s no questioning the promises of machine learning in nearly every sector. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand. And as many organizations and companies are learning, before you can apply the power of machine learning to your business and operations, you must overcome several barriers.

Three key challenges companies face when integrating AI technologies into their operations are in the areas of skills, data, and strategy, and Rackspace’s survey paints a clear picture of why most machine learning strategies fail.

Machine learning is about data

Machine learning models live on compute resources and data. Thanks to a variety of cloud computing platforms, access to the hardware needed to train and run AI models has become much more accessible and affordable.

But data continues to remain a major hurdle in different stages of planning and adopting an AI strategy. Thirty-four percent of the respondents in the Rackspace survey stated poor data quality as the main reason for the failure of machine learning research and development, and another 31 percent said they lacked production-ready data.

This highlights one of the main hurdles when applying machine learning techniques to real-world problems. While the AI research community has access to many public datasets for training and testing their latest machine learning technologies, when it comes to applying those technologies to real applications, getting access to quality data is not easy. This is especially true in industrial, health, and government sectors, where data is often scarce or subject to strict regulations.

Data problems crop up again when machine learning initiatives move from the research to the production phase. Data quality remains the top barrier when it comes to using machine learning to extract valuable insights. Data engineering problems also pose a significant problem, such as data being siloed, lack of talent to connect disparate data sources, and not being fast enough to process data in a meaningful way.

Above: Data accounts for most key problems in gaining actionable insights from machine learning models (Source: Rackspace Technology)

Both startups and established companies suffer from data problems, though scale seems to be the key differentiator between the two, according to Jeff DeVerter, CTO of Rackspace Technology. “Startups tend to be constrained with not all the right resources to implement a quality data pipeline and consistently managing it over time,” DeVerter said to TechTalks in written comments. “Enterprises usually have scale on their side and with that comes the rigor that’s required.”

The best way companies can prepare for the data challenges of AI strategies is to do a full evaluation of their data infrastructure. Eliminating silos should be a key priority in every machine learning initiative. Companies should also have the right procedures for cleaning their data to improve the accuracy and performance of their machine learning models.

AI talent is still in high demand

The second area of struggle for most companies is access to machine learning and data science talent. According to Rackspace’s survey, lack of in-house expertise was the second biggest driver of failure in machine learning R&D initiatives. Lack of skill and difficulty in hiring was also a key barrier in adopting AI technologies.

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Above: Many companies struggle with acquiring the talent to implement their AI strategies (Source: Rackspace Technology)

With machine learning and deep learning having reached mainstream use in production environments only recently, many smaller companies don’t have data scientists and machine learning engineers who can develop AI models.

And the average salary of data scientists and machine learning engineers matches those of experienced software engineers, which makes it difficult for many companies to put together a talented team that can lead its AI initiative.

While the shortage of machine learning and data science talent is well known, one thing that has gone mostly unnoticed is the need for more data engineers, the people who set up, maintain, and update databases, data warehouses, and data lakes. Per Rackspace’s figures, many initiatives fail because companies don’t have the talent to adapt their data infrastructure for machine learning purposes. Breaking down silos, migrating to cloud, setting up Hadoop clusters, and creating hybrid systems that can leverage the power of different platforms are some areas where companies are sorely lacking. And these shortcomings prevent them from making company-wide deployments of machine learning initiatives.

With the development of new machine learning and data science tools, the talent problem has become less intense. Google, Microsoft, and Amazon have launched platforms that make it easier to develop machine learning models. An example is Microsoft’s Azure Machine Learning service, which provides a visual interface with drag-and-drop components and makes it easier to create ML models without coding. Another example is Google’s AutoML, which automates the tedious process of hyperparameter tuning. While these tools are not a replacement for machine learning talent, they lower the barrier for people who want to enter the field and will enable many companies to reskill their tech talent for these growing fields.

“Lack of in-house data science talent is not the barrier it once was now that more of these services are able to use their own ML to help in this regard as well consulting firms having these talents on-staff,” DeVerter said.

Other developments in the field are the evolution of cloud storage and analysis platforms, which have considerably reduced the complexity of creating the seamless data infrastructures needed to create and run AI systems. An example is Google’s BigQuery, a cloud-based data warehouse that can run queries across vast amounts of data stored in various sources with minimal effort.

We’re also seeing growing compatibility and integration capabilities in machine learning tools, which will make it much easier for organizations to integrate ML tools into their existing software and data ecosystem.

Before entering an AI initiative, every organization must make a full evaluation of in-house talent, available tools, and integration possibilities. Knowing how much you can rely on your own engineers and how much it will cost you to hire talent will be a defining factor in the success or failure of your machine learning initiatives. Also, consider whether re-skilling is a possible course of action. If you can upskill your engineers to take on data science and machine learning projects, you will be better off in the long run.

Outsourcing AI talent

Another trend that has seen growth in recent years is the outsourcing of AI initiatives. Only 38 percent of the Rackspace survey respondents relied on in-house talent to develop AI applications. The rest were either fully outsourcing their AI projects or employing a combination of in-house and outsourced talent.

Above: Most companies rely on outside talent to plan and implement their AI initiatives (Source: Rackspace Technology)

There are now several companies that specialize in developing and implementing AI strategies. An example is C3.ai, an AI solutions provider that specializes in several industries. C3.ai provides AI tools on top of existing cloud providers such as Amazon, Microsoft, and Google. The company also provides AI consultancy and expertise to take customers step by step through the strategizing and implementation phases.

According to the Rackspace report: “A mature provider can bring everything from strategy to implementation to maintenance and support over time. Strategy can sidestep the areas where AI and machine learning efforts may lose momentum or get lost in complexity. Hands-on experts can also spare organizations from the messy work of cleanup and maintenance. Such expertise, taken together, can make all the difference in finally achieving success.”

It is worth noting, however, that fully turning over an organization’s AI strategy to outside providers can be a double-edged sword. A successful strategy requires close cooperation between AI specialists and subject matter experts from the company that is implementing the strategy.

“This is very similar to companies who move to a DevOps development methodology and attempt to outsource the entirety of the development. DevOps requires a close partnership between the developers, business analysts, and others in the business,” DeVerter said. “In the same way, AI projects require strategy and technical expertise — but also require a tight partnership with the business as well as leadership.”

Outsourcing AI talent must be done meticulously. While it can speed up the process of developing and implementing an AI strategy, you must make sure that your experts are fully involved in the process. Ideally, you should be able to develop your own in-house team of data scientists and machine learning engineers as you work with outside experts.

How do you evaluate your AI strategy?

Finally, another area that is causing much pain for companies embarking on an AI journey is forecasting the outcome and value of AI strategies. Given the application of machine learning being new to many areas, it’s hard to know in advance how long an AI strategy will take to plan and implement and what the return on investment will be. This in turn makes it difficult for innovators in organizations to get others on board when it comes to garnering support for AI initiatives.

Of the respondents of the Rackspace survey, 18 percent believed that a lack of clear business case was the main barrier to adopting AI strategies. Lack of commitment from executives was also among the top barriers. Lack of use cases and commitment from senior management show up again among the top challenges in the machine learning journey.

“AI often wanders around as a solution looking for a problem within organizations. I believe this is one of the greatest impediments to its wide-scale adoption within organizations,” DeVerter said. “As AI practitioners can demonstrate practical examples of how AI can benefit their specific company — leadership will further fund those activities. Like any business venture — leadership needs to know how it will either help them save or make money.”

Evaluating the outcome of AI initiatives is very difficult. According to the survey, the top-two key performance indicators (KPI) for measuring the success of AI initiatives were profit margins and revenue growth. Understandably, this focus on quick profits is partly due to the high costs of AI initiatives. According to the Rackspace survey, organizations spend a yearly average of $1.06 million on AI initiatives.

But while a good AI initiative should result in revenue growth and lower costs, in many cases, the long-term value of machine learning is the development of new use cases and products.

“Short-term financial gains can be myopic if they aren’t paired with a long-term strategy that can be funded by those short-term gains,” DeVerter said.

If you’re in charge of the AI initiative in your organization, make sure to clearly lay out the use cases, the costs, and the benefits of your AI strategy. Decision-makers should have a clear picture of what their company will be embarking on. They should understand the short-term benefits of investing in AI, but they should also know what they will gain in the long run.

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics. This post was originally published here as a series exploring the business of artificial intelligence.

This story originally appeared on Bdtechtalks.com. Copyright 2021

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