Manufacturing Global speaks to executive leaders at EY, Infor and GE Digital to get to the bottom of this question

Whilst the drive to digitally transform the manufacturing industry has been a topic of conversation for the last decade, recent events have only increased the need for the agility, scalability and resilience that Industry 4.0, smart manufacturing capabilities can provide. Speaking with Cobus Van Heerden, Senior Digital Product Manager at GE Digital, Mark Powell, Partner, EY (UKI Consulting), and Phil Lewis, Vice President, Solution Consulting EMEA at Infor Manufacturing Global looks at how technologies that harness AI and Big Data can help manufacturers unlock real-time operational visibility to achieve improved process reliability and performance.

What are the current applications of artificial intelligence (AI) and Big Data in the manufacturing industry?

CVH: Industrial AI uses a combination of targeted AI technologies, data, physics, and deep domain knowledge to solve key industrial business challenges. Traditional AI mimics human intelligence, whereas industrial AI builds upon it to unlock insights and determine causal knowledge in high-stakes, dynamic, and variable industrial environments. In Manufacturing, Industrial AI can be used to detect and predict key process and asset problems to help companies optimize their operations including capacity, quality, and cost structures.

PL: Textbook definitions of AI or Big Data miss the point that industries differ and will have drastically different demands for the technology.  It is about the application of a given technology to a specific issue that a business may be experiencing.  This issue may be an ‘industry-standard’ one or something that arises in the configuration of the technology.  But there is the most value in the application of tools such as Big Data and AI to the critical 10% of a business that is truly idiosyncratic.  We classify this as a 60/30/10 split and it is how we look to apply these technologies to drive maximum value.

For manufacturers looking to adopt Industry 4.0, smart manufacturing capabilities, why should manufacturers use AI and Big Data to do so?

CVH: Smart manufacturing deploys industrial advanced analytics to predict future asset and process performance using real-time and historical data and optimizing in a closed loop. This involves the use of AI and machine learning to enable process engineers to combine data across industrial data sources and rapidly identify problems, discover root causes of issues in the plant, predict the future performance of assets, and automate actions employees can take to improve quality, productivity, and operations.

MP: Digitisation is forcing manufacturers to reimagine their supply chains. As an example, most companies use internal data to track demand-supply balances and it is challenging for them to foresee external events impacting their supply chains. Using AI techniques that understand unstructured external data sets, such as social media and other data on events, manufacturers can plan for supply chain disruptions much sooner.

In addition, manufacturers can use AI and Big Data to build digital replicas of their manufacturing operations and tap into transformative possibilities of reducing cycle time in production, adding manufacturing capacity and predicting unplanned maintenance activities etc.

PL: Some of the poster child statistics for AI and Big Data simply demand attention.  Recently, Siemens automated one of its factories in Germany, with 75% of the processes digitised or having increased automation. Productivity improved by 1,400%. That is game-changing for any business. This means many manufacturers are now looking at how they plug AI and Big Data into their plans for the future. 

What is the best strategy for manufacturers striving to realise the value of AI and Big Data in their operations?

CVH: Process engineers have exceptional domain expertise to put together process models – or Process Digital Twins – and be able to interpret the models. This is the foundation for improving competitive advantage and success with analytics. To drive analytics and improve processes, manufacturers should put together a strategy that can align domain expertise to five capabilities: Analysis – automatic root cause identification accelerates continuous improvement; Monitoring – early warnings reduce downtime and waste; Prediction – proactive actions improve quality, stability, and reliability; Simulation – what-if simulations accelerate accurate decisions at a lower cost; and Optimization – optimal process setpoints improve throughput at acceptable quality by up to 10 per cent. 

All process engineers can and need to develop capabilities in analytics and machine learning to remain competitive. Over time, engineers can go from small projects to pilots to multi-plant optimization with deep application of analytics. Their deep domain expertise provides a foundation for modelling processes and developing the analytics that are game changers in very specific applications. 

Most importantly, get started with analytics. “Trystorm” some projects; put your intuitive ideas to the test and put data and analytics behind them. Don’t wait to become a data science expert. That isn’t necessary. Leverage proven easy-to-use industrial analytics tools fueled with your domain expertise. That’s going to drive big improvements quickly.

PL: Businesses – including manufacturers – tend to assess digital projects with a focus on either customer, supply chain, internal efficiency or people – those are the four main drivers for any foray into digital.  These are often organic and arise from an ongoing ‘how can we do better’ attitude. This has been accelerated by concerns of competition as companies are now fearful of being left behind competition and disruptive entrants.  There is palpable fear around being digitally relevant and this is promoting a lot of investment.

However, it is worth noting that many manufacturers have already invested heavily in technology (even before COVID forced a move to digitalisation) so the first point of definition is to align AI and Big Data to existing technology.  When businesses assess their technology in use today, they need to bear in mind not only a short-term perspective of will the technology handle current processes but also does it provide a platform for the future?  This latter perspective is built on data.  Both elements are equally important but the second ‘platform perspective’ demands big data.  It is no longer enough to choose a platform that just supports/tweaks the ongoing processes – there has to be future capabilities built-in.

There is then the need to ensure that this technology is deployed in the best way possible. This necessitates an open, cloud-based application landscape so a business can seize new opportunities such as Big Data or AI without having to go through a cumbersome integration and bolt-on process.  This makes an organisation more agile, focusing on the creative application of the technology to the needs of the business, such as identifying new opportunities for revenue.

What are the challenges when it comes to adopting AI and Big Data analytics into manufacturing operations? 

CVH: Manufacturers are challenged with reducing waste, costs, and risk while meeting customer demand. The combination of AI and data provides acceleration of digitisation through analytics-based solutions that empower workers with data in context so that people, assets, and processes work together efficiently.

Another challenge for companies is just getting started. They want to learn more about how to use analytics in their operations but don’t see it as a job for their current workforce. Fortunately, Industrial AI solutions can help and not require process engineers to be data scientists.

MP: The key challenge in adopting AI will come down to manufacturers’ ability to establish alignment across the organisation on some of the high-value areas where AI will make an impact. For example, using machine learning and computer vision to predict and identify faults in equipment before they occur, thus reducing production downtime and decreasing maintenance costs. Another challenge is establishing a culture of infusing AI into their processes through a test-and-learn culture.

For too long, organisations have talked about becoming ‘data driven’ and this has generally not worked as well as it had been hoped. Manufacturers need to take a different approach that starts with understanding where value can be driven from new insights and then focus on the data needed to drive the insights that can then drive business value. Organisations need to become ‘insight-driven and data enabled’  and not simply ‘data driven’ – only then will they really leverage the power of AI and big data.

PL: It is all about how attitudes towards data have changed.  It was previously seen as a necessary evil but is now the number one asset in a business. Typically this drives an obsession with big data labels but it is what you do with the data that matters – using the likes of AI / BI / IoT etc to turn that data into a truly valuable asset. The automotive industry is the prime example – using and selling the data produced by a car. Interestingly, we now almost take ‘cloud’ for granted – had we answered this question 24 months ago, cloud would have been the first consideration, but it is now table stakes.  It is no longer if a business will go cloud but more a question of what type of cloud/cloud use? – We have moved far beyond the infrastructure conversation –the how and into the what – and into the why a business looks to embrace digital.

Is artificial intelligence (AI) and Big Data driving the fourth industrial revolution (Industry 4.0)?

CVH: The combination of Industrial AI and data produces what we call a Process Digital Twin which helps manufacturers to rapidly troubleshoot continuous, discrete, or batch manufacturing process performance by mining insight from available sensor and production data. This technology, which utilises predictive analytics, enables users to analyse operating scenarios, qualifying the impact that operational changes will have on key performance metrics and identifying causes for performance variation. Digital Twins inspire continuous improvement, a key goal of the future of the industry by looking back to historical data as well as real-time to move forward rapidly.

PL: We see daily increases in AI/ML uses – inventory optimisation, maintenance, faster finance processes are all key areas that we see arise many times. For this to continue, and return on investment to continue, AI needs to be plumbed in and ready to go with other systems, rather than a bolt-on, or businesses face a hefty, and costly integration project. In terms of the next specific technology, it really depends on the maturity of the individual company or project – businesses are only just reaching the point of a digital fabric rather than a bunch of digital projects.  Prescriptive working, driven by AI and fed by masses of sensor data, holds a huge amount of promise for the B2B / industrial markets and we see some very encouraging early shoots in asset maintenance and field service.

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The recent study report on Big Data Analytics Tools market aims to provide an end-to-end analysis of this industry vertical with respect to drivers, challenges, opportunities that will influence the business growth in coming years. Furthermore, the report elaborates the industry segmentation in great length to uncover the top growth prospects for the stakeholders in the upcoming years.

According to industry analysts, the Big Data Analytics Tools market is predicted to garner considerable gains with a CAGR of XX% during the forecast period 2021-2026.

Considering the latest updates, the outbreak of COVID-19 has severely impacted several businesses worldwide, leading to uncertainties in economic conditions. Although the pandemic hasn’t affected some industries, a significant number of businesses are being forced to cut down on costs and alter their strategies. Our detailed insights into the changing market dynamics post the COVID-19 pandemic aims to help the partakers develop strong contingency plans to ensure strong returns in the future.

Key highlights of the Big Data Analytics Tools market report:

• Prediction of growth rate of the market and its sub-markets during the analysis timeframe.
• Global COVID-19 impact on industry growth trends.
• Major opportunities.
• Statistical coverage of overall sales volume and revenue.
• Advantages and disadvantages of indirect and direct sales channels.
• Vitals regarding the top traders, dealers and distributors.

Big Data Analytics Tools market segments covered in the report:

Regional bifurcation: North America, Europe, Asia-Pacific, South America and Middle East and Africa

• Country-wise analysis.
• Figures pertaining to total sales and returns captured by each geography.
• Market share held by each region.
• Information on estimated growth rate values as well as revenue secured by each region during the forecast period.

Product types: Cloud-based and On Premise

• Product pricing patterns and market share accounted by each product type.
• Sales and revenue generated by each product category.

Application spectrum:

• Large Enterprise and Small And Medium Enterprise

• Pricing of the given products based on their application scope.
• Sales volume and revenue of each application segment.

Competitive backdrop: Hadoop , BOARD , Tableau , Domo , Cloudera , Hortonworks , QlikView , TIBCO Spotfire , Google , SAP , Oracle , Vertica , BIRT and Alteryx

• Products and services offered by major players.
• Manufacturing units and operational regions of leading competitors across the regional markets.
• Evaluation of each participant using SWOT analysis.
• Critical information regarding the pricing model, revenue, sales, gross margins and industry share held by each company.
• Analysis of commercialization rate, well-known business strategies, market concentration ratio, and other business-centred aspects.

Major Points in Table of Contents:

1 Big Data Analytics Tools Market Overview

2 Big Data Analytics Tools Market Company Profiles

3 Market Competition, by Players

4 Big Data Analytics Tools Industry Size by Regions

5 North America Big Data Analytics Tools Revenue by Countries

6 Europe Big Data Analytics Tools Revenue by Countries

7 Asia-Pacific Big Data Analytics Tools Revenue by Countries

8 South America Big Data Analytics Tools Revenue by Countries

9 Middle East & Africa Revenue Big Data Analytics Tools by Countries

10 Market Size Segment by Type

11 Global Big Data Analytics Tools Market Segment by Application

12 Global Big Data Analytics Tools Market Size Forecast (2021-2026)

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The key focus of Big Data in E-commerce market report is to evaluate the performance of the industry in the ensuing years to help stakeholders take better decisions and expand their business portfolio. The document highlights the key growth trends as well as the opportunities and how they can be exploited to generate maximum profits. In addition, it empowers industry partakers with methodologies that can be adopted to effectively deal with the existing and upcoming challenges. Besides, it gauges the impact of COVID-19 on this business sphere and attempts to monitor its future implications on the market scenario for a stronger realization of the growth prospects.

Key pointers from COVID-19 impact assessment:

• Socio-economic impact of COVID-19 on the global economy and Big Data in E-commerce market.
• Shifts in supply and demand share.
• Predicted long-term COVID-19 outlook on the growth of the industry.

Summary of the regional analysis:

• Geographically, the Big Data in E-commerce market is split into North America, Europe, Asia-Pacific, South America, Middle East & Africa, South East Asia.
• Contribution of each geography to the overall growth in given in the report.
• Growth rate, revenue, and sales of each regional market are discussed extensively.

Other crucial pointers from the Big Data in E-commerce market report:

• The Big Data in E-commerce market, based on the product terrain, is categorized into Cloud-based andOn-premises.
• Information regarding the estimated revenue and volume share of ever product type is documented.
• Data pertaining to growth rate, market share, and production pattern of each product category over the forecast timespan is given as well.
• The report segments the application spectrum of the Big Data in E-commerce market into Online Classifieds,Online Education,Online Financials,Online Retail,Online Travel and Leisure andOthers.
• Each application segment’s market share and predicted growth rate are thoroughly discussed.
• Leading organizations influencing the market dynamics are Dell Inc,Teradata Corp,Data Inc,Facebook,Hewlett Packard Enterprise (Hpe),Palantir Technologies, Inc,Hitachi, Ltd,Twitter,SAP Se,Whatsapp,IBM Corp,Oracle Corp,Microsoft Corp,Splunk Inc,Amazon Web Services, Inc andSAS Institute Inc.
• The study examines the mentioned firms with respect to their market share, gross margins, market remuneration, pricing pattern, production capacity, and product & service portfolio.
• The document elaborates the prevailing competition trends and their implications on businesses.
• A granular analysis of supply chain, including details about providers, consumers, as well as manufacturers is encompassed in the study.
• Moreover, the study determines the investment viability of a new project through several practices such as Porter’s Five Forces analysis and SWOT assessment.

Key features of the report:

• Intricate details of each organization.
• Information regarding market share, product sale price, manufacturing base distribution, total revenue generated, and sales.
• Latest developments of the leading players.
• Sales amassed by each company with respect to their operational areas.

Highlights of the Report:

• Accurate market size and CAGR forecasts for the period 2020-2025
• Identification and in-depth assessment of growth opportunities in key segments and regions
• Detailed company profiling of top players of the global Big Data in E-commerce market
• Exhaustive research on innovation and other trends of the global Big Data in E-commerce market
• Reliable industry value chain and supply chain analysis
• Comprehensive analysis of important growth drivers, restraints, challenges, and growth prospects

The scope of the Report:

The report offers a complete company profiling of leading players competing in the global  Big Data in E-commerce market with a high focus on the share, gross margin, net profit, sales, product portfolio, new applications, recent developments, and several other factors. It also throws light on the vendor landscape to help players become aware of future competitive changes in the global Big Data in E-commerce market.

Reasons to Buy the Report:

• Upgrade your market research resources with this comprehensive and accurate report on the global Big Data in E-commerce market
• Get a complete understanding of general market scenarios and future market situations to prepare for rising above the challenges and ensuring strong growth
• The report offers in-depth research and various tendencies of the global Big Data in E-commerce market
• It provides a detailed analysis of changing market trends, current and future technologies used, and various strategies adopted by leading players of the global Big Data in E-commerce market
• It offers recommendations and advice for new entrants the global Big Data in E-commerce market and carefully guides established players for further market growth
• Apart from the hottest technological advances in the global Big Data in E-commerce market, it brings to light the plans of dominant players in the industry

Table of Contents:

Industry Overview of Big Data in E-commerce Market

Industry Chain Analysis of Big Data in E-commerce Market

Manufacturing Technology of Big Data in E-commerce Market

Major Manufacturers Analysis of Big Data in E-commerce Market

Global Productions, Revenue and Price Analysis of Big Data in E-commerce Market by Regions, Manufacturers, Types, and Applications

Consumption Volumes, Consumption Value, Import, Export and Sale Price Analysis of Big Data in E-commerce by Regions

Gross and Gross Margin Analysis of Big Data in E-commerce Market

Marketing Traders or Distributor Analysis of Big Data in E-commerce Market

Global and Chinese Economic Impacts on Big Data in E-commerce Industry

Development Trend Analysis of Big Data in E-commerce Market

Contact information of Big Data in E-commerce Market

New Project Investment Feasibility Analysis of Big Data in E-commerce Market

Conclusion of the Global Big Data in E-commerce Market Industry 2020 Market Research Report

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Manufacturing firms around the world have responded to the COVID-19 pandemic by accelerating their investments in disruptive digital technologies such as artificial intelligence, robotics and cloud platforms, a new study has found.

Most manufacturers were caught off-guard the COVID-19 pandemic began spreading across the world, and the vast majority of them were forced to shut down their operation temporarily. Worse, they found themselves severely hampered by reduced orders and an inability to provide a safe workplace when they finally reopened.

But the industry has bounced back quickly as manufacturers around the world responded by stepping up their investments in some game-changing tech.

That’s according to a new report from Google LLC, which found the key to the manufacturing industry’s transformation has been their embrace of “digital enablers and disruptive technologies.”

In a blog post, Dominik Wee, managing director for manufacturing and industrial at Google Cloud, said Google’s survey of more than 1,000 senior manufacturing executives in seven countries shows that more than 40% of manufacturing firms responded to the pandemic by stepping up their use of public cloud platforms, data, analytics and digital productivity tools. The number varies by country, though: In the U.S., for example, 64% of manufacturers increased their use of digital technologies.

Google found that 46% of manufacturing firms were hit by lower productivity because of the COVID-19 pandemic, with 44% reporting lower sales and 39% complaining about increased lead times, possibly from supply chain disruptions. Some 35% of manufacturers also reported reduced customer demand, while labor shortages were a problem for 34% of respondents.

Manufacturers responded to these challenges in several ways. Seventy-seven percent said they were forced to re-evaluate their operating model strategies in light of the pandemic because of their inability to collaborate effectively with value chain partners and employees. They also cited a lack of technology needed to operate without large numbers of onsite workers.

Ultimately, disruptive technologies such as artificial intelligence and robotics proved to be the answer to many of the challenges COVID-19 throw up, Wee said. Some 76% of manufacturers responded by increasing their use of cloud, AI, data analytics, robotics, 3D printing and additive manufacturing, “internet of things” and augmented and virtual reality to transform their business operations.

As a result, 82% of manufacturers say they’re now in a position where they believe they can navigate any future pandemics. Wee said this sentiment is likely the result of of a large part of the industry switching to new product areas such as manufacturing personal protective equipment, as well as their increased investment in digital factories.

The sustained nature of the COVID-19 pandemic impacted supply chains in ways never seen before, said Bob Parker, International Data Corp.’s senior vice president of enterprise applications, data intelligence, services and industry research.

“As a result, we’re seeing an urgency from manufacturers to quickly put the right technological levers in place, sooner rather than later,” Parker said. “While there may have only been initial conversations about digital transformation in the past, we’re now seeing a rapid acceleration of critical tools and technologies being adopted within the industry.”

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The report on global Python Web Frameworks Software market, is a comprehensive overview of different aspects based on various parameters, such as production base, distribution channel, and potential customers. The key players in market include different regions. Moreover, it uses effective analytical methodologies, which focuses on each and every stage of the businesses. To identify the strengths and weaknesses, SWOT analysis is used. Finally, it focuses on recent developments, and upcoming innovations to bridge the gap.

The influence of the latest government policies is mentioned to focus on standard procedures, to comprehend the growth of the market. It studies the forecast period of the market for 2020 to 2027 year, which helps to increase the clients at domestic as well as global level. The research report is classified into different segments, on the basis of attributes, such as consumption, growth rate and market shares.

The study throws light on the recent trends, technologies, methodologies, and tools, which can boost the performance of companies. For further market investment, it gives the depth knowledge of different market segments, which helps to tackle the issues in businesses. It includes effective predictions about the growth factors and restraining factors that can help to enlarge the businesses by finding issues and acquire more outcomes. Leading market players and manufacturers are studied to give a brief idea about competitions. To make well-informed decisions in Python Web Frameworks Software areas, it gives the accurate statistical data.

The following manufacturers are covered in this report:

Pyramid, TurboGears, jam.py, Django, Web2py, Bottle, ArcGIS for Developers, BlueBream, Tornado, CherryPy, Sanic, Flask, Tornado.

Competition Analysis

This report examines the ups and downs of the leading key players, which helps to maintain proper balance in the framework. Different global regions, such as Germany, South Africa, Asia Pacific, Japan, and China are analyzed for the study of productivity along with its scope. Moreover, this report marks the factors, which are responsible to increase the patrons at domestic as well as global level.

Global Python Web Frameworks Software Market Segmentation:

On the Basis of Type: Type 1, Type 2, Type 38

On the Basis of Application: Application 1, Application 2, Application 38

Regions Covered in the Global Python Web Frameworks Software Market:
• The Middle East and Africa (GCC Countries and Egypt)
• North America (the United States, Mexico, and Canada)
• South America (Brazil etc.)
• Europe (Turkey, Germany, Russia UK, Italy, France, etc.)
• Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)

The Python Web Frameworks Software market is expected to grow in the upcoming 2020 to 2027 year. Different risks are considered, that helps to evaluate the complexity in the framework. Progress rate of global industries is mentioned to give a clear picture of business approaches. Various factors, which are responsible for the growth of the market are mentioned accurately.

The global Python Web Frameworks Software market is divided on the basis of domains along with its competitors. Drivers and opportunities are elaborated along with its scope that helps to boosts the performance of the industries. It throws light on different leading key players to recognize the existing outline of Python Web Frameworks Software market.

Key Influence of the Python Web Frameworks Software Market report:

Comprehensive assessment of all opportunities and risk in the Python Web Frameworks Software Market.
Python Web Frameworks Software Market recent innovations and major events.
Detailed study of business strategies for growth of the Python Web Frameworks Software Market-leading players.
Conclusive study about the growth plot of Python Web Frameworks Software Market for forthcoming years.
In-depth understanding of Python Web Frameworks Software Market-particular drivers, constraints and major micro markets.
Favorable impression inside vital technological and market latest trends striking the Python Web Frameworks Software Market.
To provide historical and forecast revenue of the market segments and sub-segments with respect to four main geographies and their countries- North America, Europe, Asia, and Rest of the World (ROW).
To provide country level analysis of the market with respect to the current market size and future prospective.
Table of Content (TOC):

Chapter 1 Introduction and Overview
Chapter 2 Industry Cost Structure and Economic Impact
Chapter 3 Rising Trends and New Technologies with Major key players
Chapter 4 Global Python Web Frameworks Software Market Analysis, Trends, Growth Factor
Chapter 5 Python Web Frameworks Software Market Application and Business with Potential Analysis
Chapter 6 Global Python Web Frameworks Software Market Segment, Type, Application
Chapter 7 Global Python Web Frameworks Software Market Analysis (by Application, Type, End User)
Chapter 8 Major Key Vendors Analysis of Python Web Frameworks Software Market
Chapter 9 Development Trend of Analysis
Chapter 10 Conclusion

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

In the event that you don’t find that you are looking in this report or need any particular prerequisites

About CDI: Contrive Datum Insights (CDI) is a global delivery partner of market intelligence and consulting services to officials at various sectors such as investment, information technology, telecommunication, consumer technology, and manufacturing markets. CDI assists investment communities, business executives and IT professionals to undertake statistics based accurate decisions on technology purchases and advance strong growth tactics to sustain market competitiveness. Comprising of a team size of more than 100analysts and cumulative market experience of more than 200 years, Contrive Datum Insights guarantees the delivery of industry knowledge combined with global and country level expertise.

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