• Artificial intelligence has the potential to create the value of 1.4T to 2.6T in the marketing and sales of global businesses.Costs in supply chain management and manufacturing are 1.2T to 2T.
  • according toIDC dataBy 2021, 20% of leading manufacturers will rely on embedded intelligence to automate processes and increase execution time by up to 25% using AI, IoT and blockchain applications.
  • Deloitte saidMachine learning can improve the quality of discrete manufacturing products by 35%.
  • McKinsey said,Due to the heavy reliance on data, companies with artificial intelligence in the next five to seven years may have doubled their cash flow by 50%, leading the manufacturing industry to all industries.
  • By 2020, 60% of leading manufacturers will rely on digital platforms to support30% of its total income.
  • 48% of Japanese manufacturers see more opportunities to integrate machine learning and digital manufacturing technologies into their operations, rather than initially thinkingAccording to McKinsey's iconic research-digital manufacturing-escape the pilot purgatory.

Bottom line: 2019's leading growth strategy for manufacturers is to increase shop floor productivity by investing in machine learning platforms that provide the insight needed to improve product quality and yield.

Using machine learning to simplify every stage of production, starting with the quality of the warehousing supplier, from manufacturing planning to fulfillment, is now a manufacturing priority. according toA recent survey by DeloitteMachine learning reduced unplanned machine downtime by 15-30%, production volume by 20%, maintenance cost by 30%, and quality by 35%.

Here are ten ways in which machine learning can completely change manufacturing in 2019:

  • Artificial intelligence has the potential to create the value of 1.4T to 2.6T in the marketing and sales of global businesses, creating value for 1.2T to 2 dollars in supply chain management and manufacturing.McKinsey predicts that predictive maintenance based on artificial intelligence could bring manufacturers the value of 0.5 dollars to 0.7 billion dollars. McKinsey cited AI's ability to process massive amounts of data, including audio and video, meaning it can quickly identify anomalies to prevent failures. Machine learning can determine whether a particular sound is that the aircraft engine is operating correctly under quality testing or that the machine on the assembly line is about to fail. Source: McKinsey/Harvard Business Review.Most commercial uses of AI will be Michael Chui, Nicolaus Henke and Mehdi Miremadi Two fieldscomposition. 2019 year 3 month
Source: McKinsey/Harvard Business Review. Most commercial uses of AI will consist of two areas: MICHAEL CHUI, NICOLAUS HENKE and MEHDI MIREMADI. 2019 year 3 month
Source: McKinsey/Harvard Business Review. Most commercial uses of AI will consist of two areas: MICHAEL CHUI, NICOLAUS HENKE and MEHDI MIREMADI. 2019 year 3 month

  • With machine learning and predictive analytics extended on the cloud platform, manufacturers are gaining new insights on how to make them more sustainable.Process manufacturers are using Azure’s Symphony Industrial AI to deploy device models from a template library, which includes heat exchangers, pumps, compressors, and other assets that manufacturers rely on. Symphony AI's Process 360 AI helps users create predictive models of their processes. High-level processes are defined as items produced through equipment (such as chemicals, fuels, metals, other intermediate products and finished products). Examples of process templates include ammonia process, ethylene process, LNG process and polypropylene process. Process models help predict process disturbances and trips – equipment models alone may not be able to predict. Source: Microsoft Azure Blog,  Manufacturing predictive analysis using Symphony Industrial AI.
Source: MICROSOFT AZURE blog, using SYMPHONY INDUSTRIAL AI to implement manufacturing forecast analysis,
Source: MICROSOFT AZURE blog, using SYMPHONY INDUSTRIAL AI to implement manufacturing forecast analysis,

  • The Boston Consulting Group (BCG) found that manufacturers using artificial intelligence can reduce manufacturers' conversion costs by up to 20%, while reducing labor costs by up to 70% due to increased labor productivity.BCG found that manufacturers will be able to develop and produce innovative products tailored to specific customers through the use of artificial intelligence and deliver products in a shorter lead time, resulting in additional sales. The figure below illustrates how AI brings greater flexibility and scale to the production process based on BCG analysis. source:Boston Consulting Group, AI Future Factory, 2018 4 Month 18 Day.
Source: Boston Consulting Group, AI Future Factory, 2018 4 Month 18 Day.
Source: Boston Consulting Group, AI Future Factory, 2018 4 Month 18 Day.
  • Discrete and process manufacturers relying on heavy assets are using artificial intelligence and machine learning to increase throughput, energy consumption and hourly profit.Manufacturers with heavy equipment, including large machinery, are exploring the use of algorithms to increase yield, sustainability and yield. McKinsey found that AI can automate complex tasks and provide consistent and precise optimal set points, allowing the machine to operate in autopilot mode, which is critical to enabling light-off manufacturing on one or more production shifts. Source: McKinsey,AI is being produced: Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz Change the rules of the game for heavy asset manufacturers
Source: McKinsey, AI in production: ELEFTHERIOS CHARALAMBOUS, ROBERT FELDMANN, GÉRARDRICHTER and CHRISTOPH SCHMITZ change game rules for heavy asset manufacturers
Source: McKinsey, AI in production: ELEFTHERIOS CHARALAMBOUS, ROBERT FELDMANN, GÉRARDRICHTER and CHRISTOPH SCHMITZ change game rules for heavy asset manufacturers

  • Product defect detection and quality assurance based on AI and machine learning show the potential to increase manufacturing productivity by 50% or more.The inherent advantages of machine learning in discovering products and their packaging anomalies have the potential to improve product quality and prevent defective products from leaving production facilities. With deep learning based systems, improvements in defect detection up to 90% are feasible compared to manual inspection. Given the availability of open source artificial intelligence environments and the cheap hardware of cameras and powerful computers, even small businesses are increasingly relying on artificial intelligence-based visual inspection. In the AI-enabled visual quality check, a reference example is created by visually imaging high-quality and defective products from different angles to power the training of supervised learning algorithms. Resources:Intelligentization through artificial intelligence (AI)-what is in Germany and its industrial sector?(52 page, PDF, no choice to join) McKinsey & Company.
Source: Intelligent Artificial Intelligence (AI) - What is in Germany and its industrial sector? (52 page, PDF, no choice to join) McKinsey & Company.
Source: Intelligent Artificial Intelligence (AI)-What is in Germany and its industrial sector? (52 pages, PDF, no option to join) McKinsey & Company.
Easyai public number

  • Machine learning has the potential to reduce long-term labor shortages in manufacturing while finding new ways to retain employees at the same time.Manufacturing is facing a serious labor shortage today, and every manufacturer survey reflects that this problem is one of the three factors that affect industry growth. One of the most interesting companies to take on this challenge isEightfold. Their AI-based talent intelligence platform relies on a range of supervised and unsupervised machine learning algorithms to match the candidate's unique capabilities, experience and strengths. includeConAgraManufacturers withinEight timesImprove recruitment and rediscover the talent they need to equip their teams with talent and growth opportunities. The following diagram explains how the Eightfold Talent Intelligence Platform works:
HTTPS://EIGHTFOLD.AI/
HTTPS://EIGHTFOLD.AI/

  • Machine learning is helping manufacturers solve problems that were previously difficult to solve and reveal problems they never knew, including hidden bottlenecks or unprofitable production lines.. Improve the predictive maintenance accuracy of each machine in the shop, reveal how to increase the throughput/throughput of each machine and related workflows, optimize system and supply chain optimization. The diagram below illustrates how machine learning begins with increasing shop floor productivity from the machine level and then to the workflow and the systems it depends on. Source: McKinsey,Manufacturing: AnalysisPromotionProductivity and profitabilityBy Valerio DiLdaProvided by Lapo Mori, Olivier Noterdaeme and Christoph Schmitz
Source: McKinsey, Manufacturing: Analysis to increase productivity and profitability, provided by VALERIO DILDA, LAPO MORI, OLIVIER NOTERDAEME and CHRISTOPH SCHMIT
Source: McKinsey, Manufacturing: Analysis to increase productivity and profitability, provided by VALERIO DILDA, LAPO MORI, OLIVIER NOTERDAEME and CHRISTOPH SCHMIT

  • Machine learning can significantly improve product configuration, as well as the configuration that manufacturers rely on to produce products to order-the quotation (CPQ) workflow.Siemens' approach to selling, designing, and installing railway interlock control systems using AI and machine learning to find the best configuration for 1090A possible combination. Machine learning excels at defining the best configuration that best meets customer needs and is the most reliable manufacturing. Source: Siemens,The next level of AI-powered by knowledge graphs and data thinking, Siemens China Innovation Day, Michael May, Chengdu, 2019 5 Month 15 Day
Source: Siemens, next level AI - supported by knowledge maps and data thinking, Siemens China Innovation Day, MICHAEL MAY, Chengdu, 2019 5 Month 15 Day
Source: Siemens, Next Level AI-Supported by Knowledge Graph and Data Thinking, Siemens China Innovation Day, MICHAEL MAY, Chengdu, May 2019, 5

  • Machine learning in artificial intelligence and manufacturing is expected to surpass robotics in the next five years and become a major use case for manufacturing.The complexity and constraints of supply chain operations are ideal use cases for machine learning algorithms to provide a recommended solution. Manufacturers are looking for pilots for predictive maintenance, and those that are able to generate significant revenue growth are most likely to go into production. Source: MAPI Foundation,Manufacturing industry: manufacturing and artificial intelligence will change the future of the labor force: Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 page, opt-in)
Source: MAPI Foundation, the manufacturing industry: manufacturing and artificial intelligence will change the future of the workforce: ROBERT D. ATKINSON, STEPHEN EZELL, Information Technology and Innovation Foundation (PDF, 56 page, opt-in)
Source: MAPI Foundation, the manufacturing industry: manufacturing and artificial intelligence will change the future of the workforce: ROBERT D. ATKINSON, STEPHEN EZELL, Information Technology and Innovation Foundation (PDF, 56 page, opt-in)

  • Machine learning is revolutionizing how manufacturers protect each threat surface and rely on the Zero Trust Security (ZTS) framework to protect and scale their operations.Manufacturer is turningZero Trust Security (ZTS)Framework to protect every network in its supply chain and production network, cloud and on-premise platforms, operating systems and applications. Chief analystForrester's Chase CunninghamIs the main authority of Zero Trust Security, his recent videoZero Trust In ActionIt's worth watching to understand how manufacturers protect their IT infrastructure. You can find his blog here.. There are several companies worthy of attention in this area, including MobileIronCreated a mobile-centric, zero-trust enterprise security framework manufacturer that relies on today.CentrifyIdentity access management methods hinder the abuse of privileged accounts, which is the main reason for today's violations. Centrify  a recent survey,  Privileged access management in modern Threatscape,Find  74% of violations involve accessing privileged accounts. Privileged access credentials are the most common technique used by hackers to initiate violations, obtain valuable data from manufacturers, and sell them on Dark Web.
FORRESTER, ZTX meaning to suppliers and users 2018 year 1 month 23 day
FORRESTER, ZTX meaning to suppliers and users 2018 year 1 month 23 day

Additional reading:

2019 Manufacturing Trend Report, Microsoft (PDF, 72 page, no choice to join)

Accenture,In the future of manufacturing, artificial intelligence will drive the next wave of growth for industrial equipment companies(PDF, 20 page, no choice to join)

Anderson, M. (2019 years). Machine learning in manufacturing.Automotive design and production, 131(4), 30-32.

Bruno, J. (2019 years). How does IIoT change the business model.Manufacturing Engineering, 163(1), 12.

Greenfield, D. (2019 years). Suggestions for expanding the IIoT project.ProFood World

Hayhoe, T., Podhorska, I., Siekelova, A. , &Stehel, V. (2019). Industrial Sustainable Manufacturing 4.0: Multi-supply chain, network physical production systems and inter-departmental networks for artificial intelligence-driven decision making.Self magazine

Governance and Management Economics, 7(2), 31-36.

Honeywell,Honeywell Connection Factory, 2018 Year 6 Month(PDF, 36 page, no choice to join)

How and why to digitize your supply chain. (2019).Manufacturing.Net.

How emerging technologies can change the supply chain. (2019).Manufacturing.Net,

IRI offers AI and machine learning in a leading suite of analytical solutions. (2019).Manufacturing close-up

Kazuyuki, M. (2019 years).Digitalization of manufacturing processes and open innovation: findings of Japanese SMEs. St. Louis: Federal Reserve Bank of St. Louis.

Machine learning in manufacturing – use cases now and in the future, Emerj Artificial Intelligence Research, updated on 2019 5 Month 20 Day, published by Jon Walker

Machine learning, AI is the most influential supply chain technology. (2019).Material handling and logistics

MAPI Foundation,Manufacturing industry: manufacturing and artificial intelligence will change the future of the labor force: Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 page, opt-in)

McKinsey Global Institute, visualizing the uses and potential impacts of AI and other analyses, interactive visualization tools. 2018 year 4 month

McKinsey, The leading manufacturer of'Lighthouse'trend- Can the rest of the world keep up? -Led by Enno de Boer, Helena Leurent and Adrian Widmer; January 2019.

McKinsey,AI is being produced:By Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz Change the rules of the game for heavy asset manufacturers

McKinsey,Digital Manufacturing-Escape the Pilot Purgatory(PDF, 24 page, no choice to join)

McKinsey,Automation and artificial intelligence drive impact and scale, 2019 year 2 month (PDF, 100 page, no choice to join).

McKinsey,Manufacturing: AnalysisPromotionProductivity and profitabilityAuthor: Valerio Dilda, Lapo Mori, Olivier Noterdaeme and Christoph Schmitz, 2019 years 3 months

McKinsey/Harvard Business Review,Most of the business use of AI will be divided into two areas.

Morey, B. (2019 years). Manufacturing and artificial intelligence: commitments and pitfalls.Manufacturing Engineering, 163(1), 10.

Otto, S. (2018 years). How predictive maintenance improves asset efficiency.Machine design.

Reduce entry barriers in advanced analytics. (2019).Manufacturing.Net,

Seven ways of real-time monitoring are driving smart manufacturing. (2019).Manufacturing.Net,

Siemens,The next level of AI-powered by knowledge graphs and data thinking, Siemens China Innovation Day, Michael May, Chengdu, 2019 5 Month 15 Day

Smart Factory: Information Governance Issues Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, 2019 Year 3 Month(PDF, 68 page, no choice to join)

Intelligentization through artificial intelligence (AI)-what is in Germany and its industrial sector?(52 page, PDF, no choice to join) McKinsey & Company.

The team uses data and AI to predict battery life. (2019 3 month 28 day).Research and development

The future of artificial intelligence and manufacturing, Microsoft, Greg Shaw (PDF, 73 page, PDF, no opt-in).

Machine learning in industrial quality control papersThe application of ErikGranstedt Möller received a master's degree in engineering science. KTH Royal Institute of Technology, published in 2017. (PDF, 55 page, no choice to join)

Eight Data Science Use Cases in Manufacturing, ActiveWizards: Machine Learning Company Igor Bobriakov, 2019 3 Month 12 Day

Walker, I (2019 years). Armed Analysis: Manufactured as a martial art.Industrial weekly

Whittle, T., Gregova, E., Podhorska, I. , &Rowland, Z. (2019). Intelligent Manufacturing Technology: Data-driven algorithms in production planning, sustainable value creation, and operational performance improvement.Economics, Management and Financial Markets, 14(2), 52-57.

Why software will drive the future of smart factories and manufacturing. (2019).Manufacturing.Net

Zulick, J. (2019). Machine learning how to change industrial production.Machine design

This article is from Forbes,Original address

Easyai public number