Digital Manufacturing - 5G Network-Connected Machines

Digital Manufacturing - 5G Network-Connected Machines

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • 5G
  • Connected Intelligence

Publication | Update: Oct 2020

With 5G, manufacturers can connect factory devices and quicker adoption of new technologies, and given the low latency provided by 5G, around 10 milliseconds, as well as allowing for up to one million sensors per square kilometre, they can be monitored in real time to improve productivity.

“The more parts that need to be transported, the more production steps and vendors, the more distributed the set-up is, the higher the benefits from 5G industrial digitisation,” says Bela Virag, managing partner at technology management consultancy Arthur D. Little in an interview in Raconteur.

 “A robot can come with an inbuilt SIM that is easily connected to the 5G network, so operators can plug and play, as opposed to establishing a new network for it, which creates barriers to adoption,” says Guido Jouret, chief digital officer at ABB in an interview in Raconteur. “A factory with good wireless connectivity can produce more because robots can work 24/7.”

Using 5G-enabled technologies for increased data capture, MTU Aero Engines, a company that produces bladed disks for engines, working with Ericsson and Germany’s Fraunhofer Institute for Production Technology, managed to reduce its process design phase by 75 per cent, with annual savings of approximately €27 million.

 “Manufacturing needs to be adaptable to cater for increasing demand for more personalised products,” says Mats Norin, programme manager at 5G For Industries, Ericsson Research.

The 5G network is more easily segmented, so factories could even provision additional network slices, as and when needed, to support changes in the volume of production.

 “Slicing offers manufacturers a dedicated system which they can fully control” says Dritan Kaleshi, head of technology for 5G at UK innovation centre Digital Catapult. “It provides reliable communications with guaranteed quality of service, along with cloud-based computation that is under the operator’s absolute control.”

A study conducted by Ericsson and BT found that compared with conventional networks, network slicing is the best and most economic model for IoT service delivery and can provide a 150 per cent economic benefit.

We encounter the emergence of new flexible production business models and as Guido Jouret, chief digital officer at ABB says increased flexibility will enable manufactures to say yes to more work they would otherwise have to turn down. “Any factory is in some sense inflexible because it’s optimised to produce certain things; however, if it’s possible to easily reprogram equipment, manufacturers can produce items in smaller batches, for example in less than 10,000 units, which they typically find hard to do today,” he concludes.

According to Assembly magazine, during the next decade, 5G promises to dramatically transform the way that conveyors, fastening tools, robots and other production equipment perform and interact on the plant floor. It will drive numerous Industry 4.0 initiatives, improving the automation of production processes and real-time monitoring of machine conditions.

The technology provides the ability to connect multiple devices at once and move more data faster than ever. As 5G is adopted, it will improve the ability of engineers to deploy artificial intelligence (AI), data analytics, digital twins and other smart factory tools. It will also enable millions of devices, such as actuators, cameras, motors and sensors, to be connected wirelessly with each other.

Early 5G trial deployment projects at European manufacturers hint that bringing 5G connectivity to the factory floor will decrease maintenance costs by 30 percent and increase overall equipment efficiency by 7 percent.

“Safer, flexible and more efficient manufacturing systems will be possible thanks to the ultra-low-latency and reliability of 5G connectivity,” says Jens Jakobsen, development manager at HMS Labs, a company that specializes in connected devices and networks. “From a technical perspective, 5G technology has the potential to meet all the requirements,” claims Jakobsen.

“The industrial world is undergoing its fourth revolution, and the goals are to increase flexibility, increase automation and improve productivity, while also maintaining a high degree of safety and sustainability,” explains Jakobsen. “Therefore, using 5G is the perfect solution for enabling smart wireless connectivity in the factory.”

“In the context of industrial applications, 5G is much more than just an enhanced version of 4G,” adds Leo Gergs, 5G analyst at ABI Research, a global tech market advisory firm. 5G allows completely new applications for connectivity on the factory floor. Supporting the connectivity of between 1,000 and 1 million devices per square kilometer will enable setting up highly dense wireless sensor networks, enabling the permanent monitoring of production processes and production machine conditions,” says Gergs.

According to ABI Research, by 2026 there will be more than 5 million 5G connections on the factory floor. And, the market for 5G cellular connections in manufacturing will reach billion by 2030, growing at a compound annual rate of 187 percent.

According to Kamphuis, the biggest misconception associated with 5G technology is the adoption curve. “Initially, 5G will see the most traction in the hotspot and smartphone market,” he points out. “Connected manufacturing techniques that leverage 5G capabilities specifically will lag [behind] a few years, due to a lack of coverage densities,” says Kamphuis. “We’ll also see a slow adoption of 5G radios embedded in shop floor equipment, as well as manufacturers’ slow adoption of connected approaches to managing the shop floor. One of the biggest myths with 5G technology is that it’s the most important thing in tomorrow’s high technology and connected manufacturing; it is not,” claims Kamphuis. “It’s just data coverage and a lot more bandwidth.

The promise of reliable, low-latency and high-bandwidth wireless connectivity is opening up new possibilities and benefits across manufacturing operations, such as automation, asset efficiency, cost reduction and supply chain agility. 

Machine Connectivity

5G technology will improve the performance and connectivity of production equipment such as conveyors, fastening tools and robots. For instance, a robot connected to the cloud via 5G could use machine learning to find the best way of navigating its environment and performing tasks without being specifically programmed in advance.

At last year’s Hannover Fair in Germany, several automation suppliers showcased how 5G will change factories in the near future.

Bosch Rexroth’s lineup included a mobile control panel that enabled human-robot collaboration and integrated industrial Ethernet over 5G. Festo featured displays that highlighted artificial intelligence, integrated connectivity and predictive maintenance applications.

Weidmüller showcased a 5G-enabled energy monitoring system for use in welding control applications. The system’s analysis unit receives data directly from the welding process and feeds it via a 5G modem and 5G network to an energy flow visualization unit.

Zeiss displayed an inline process control system for the auto industry. Its AICell measures all key characteristics of every single car body component as it passes through the production line, thereby delivering much more accurate and reliable process monitoring and control data than random testing. It is equipped with an array of inline sensors that inspect and measure body features and topographies, checking for cracks, flushness and other characteristics.

In addition, Ericsson teamed up with Comau to show a 5G-powered digital twin of an automotive assembly line. Ericsson is a leading supplier of antennas, base stations, routers and other types of wireless equipment.

Using a virtual reality (VR) headset, visitors were immersed in the line and could “move” within it, monitoring key process parameters such as pressures, temperatures and vibrations. A VR digital dashboard, which could be used with a standard tablet device, identified situations that could create slowdowns or interruptions in the process by providing instructions to tackle the problem effectively.

“The features of 5G connectivity allow [us] to collect a stable, continuous and massive flow of data in real-time that is vital for automation processes,” says Maurizio Cremonini, head of marketing and the digital initiatives platform at Comau. “Thanks to 5G low latency, the digital twin shows information related to the real robot in the form of visual outputs, which make it possible to understand how the robot activity will evolve in the cell. From the data analysis, it is possible to foresee faults and malfunctions, and identify which components must be repaired or replaced, suggesting which actions to take to operate effectively,” claims Cremonini. “5G becomes the enabling technology for every analytics and digital intelligence remote activity on

“Bandwidth and low latency, main features of the new 5G technology, are the crucial factors that will allow [us] to accelerate the digitization and automation processes, enabling cutting-edge use cases in smart manufacturing and Industry 4.0,” adds Magnus Frodigh, head of research at Ericsson. “5G deployment in the industrial environment will allow [manufacturers] to increase productivity and reduce costs.”

Ericsson also has a strategic partnership with ABB Robotics to apply 5G technology to its machines. During the recent World Economic Forum in Davos, Switzerland, the companies teamed up for a demonstration using two cloud-connected ABB robots operating via an Ericsson-powered live 5G network. The goal was to showcase human-robot collaboration and control over wide distances utilizing the real-time communication capabilities of 5G.

“Today, the flexibility of factories is limited by the amount of data that can be processed, because of the lack of reliable, low-latency and high-bandwidth connectivity,” claims Sami Atiya, president of ABB’s robotics and discrete automation business. “Replacing traditional hard wires with 5G mobile networks will take the interconnection between machines, materials and people to a new level.  [This will help] drive the shift from mass production to mass customization, by supporting the shift to flexible manufacturing cells where manufacturing lines can be constantly reconfigured to accommodate changing manufacturing needs,” says Atiya.

According to Atiya, 5G technology will result in several benefits to manufacturers, including: large networks of sensors for predictive maintenance of machines and robots on the factory floor; cloud robotics will enable smaller, cheaper robots that can be centrally controlled and untethered in any environment; identification and tracking of goods in the end-to-end value chain; and remote quality inspection and diagnostics with high-resolution 3D video or haptic feedback, thermal and other sensors.

“The traditional connectivity paradigm is being challenged by flexible production and wireless industrial IoT (IIoT),” adds Asa Tamsons, senior vice president and head of business area technologies and new businesses at Ericsson, which operates a state-of-the-art 5G factory in Tallinn, Estonia. “Currently, most IIoT [applications] are based on wired connections. However, as the evolving cellular capabilities are challenging industrial ethernet solutions, wires will in many cases become redundant, introducing opportunities for more flexible production and faster line changes,” claims Tamsons.

“Digitization of factory assets, equipment, vehicles and processes means the number of connected devices will increase exponentially,” adds Tamsons. “The estimated number of connected devices needed in a typical smart factory is 0.5 per square meter. Manufacturers will gradually adopt supportive applications to increase efficiency and quality in their activities, from augmented reality (AR) to digital twins.”

For example, at Ericsson’s factory in Estonia, inspection of assets and products with AR technology has resulted in consistently improved product quality with reduced lead times and costs.

Compared to their European counterparts, American manufacturers have been slow to jump on the 5G bandwagon. However, many experts believe that will soon change.

For instance, Whirlpool Corp. is currently testing 5G technology at its Clyde, OH, washing machine factory. Engineers are converting a fleet of automated guided vehicles to run on the technology instead of traditional Wi-Fi, which is susceptible to interference issues.

But, European manufacturers are leading the 5G charge, spurred on by widescale Industry 4.0 initiatives. For example, Daimler AG recently implemented the world’s first 5G network for automobile production at its cutting-edge “Factory 56” in Sindelfingen, Germany. The 105-year-old facility assembles Mercedes-Benz S-Class sedans.

“We’ve started leveraging 5G to simplify factory IT operations, improve support to manufacturing and accelerate factory digitization,” says Luke Durcan, Ph.D., director of EcoStruxure at Schneider Electric.

According to Durcan, 5G applications leverage better network quality, faster response times and secure indoor coverage to validate a range of use cases along various aspects, such as:

  • Enhancing the real-time augmented reality systems used by maintenance technicians and field workers.
  • Improving predictive maintenance through more robust data analytics.
  • Enabling factory robots to send video streams and sensor input, and receive real-time instructions to perform tasks.

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology


We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

  • EMIS: EMIS Insight and EMIS Dealwatch

  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

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  • Bloomberg

  • Standard & Poor’s Industry Surveys

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  • Reuter 3000 Xtra

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  • IDC

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  • Moody’s

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  • Forrester Research

  • Computer Economics

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  • SIA / SSIR

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  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

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  • GSA

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  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

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  • Profound

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  • ISS Corporate Governance

  • BoardEx

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  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.