DXWorldEXPO LLC announced today that ICOHOLDER named “Media Sponsor” of Miami Blockchain Event by FinTechEXPO. ICOHOLDER gives detailed information and help the community to invest in the trusty projects.

Miami Blockchain Event by FinTechEXPO has opened its Call for Papers. The two-day event will present 20 top Blockchain experts. All speaking inquiries which covers the following information can be submitted by email to info@dxworldexpo.com. Miami Blockchain Event by FinTechEXPOalso offers sponsorship and exhibit opportunities.

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DXWorldEXPO LLC announced today that Kevin Jackson joined the faculty of CloudEXPO’s “10-Year Anniversary Event” which will take place on November 11-13, 2018 in New York City.

Kevin L. Jackson is a globally recognized cloud computing expert and Founder/Author of the award winning “Cloud Musings” blog. Mr. Jackson has also been recognized as a “Top 100 Cybersecurity Influencer and Brand” by Onalytica (2015), a Huffington Post “Top 100 Cloud Computing Experts on Twitter” (2013) and a “Top 50 Cloud Computing Blogger for IT Integrators” by CRN (2015). Mr. Jackson’s professional career includes service in the US Navy Space Systems Command, Vice President J.P. Morgan Chase, Worldwide Sales Executive for IBM and NJVC Vice President, Cloud Services. He is currently part of a team responsible for onboarding mission applications to the US Intelligence Community cloud computing environment (IC ITE). He is also a National Cyber security Institute Fellow. His first book, “GovCloud: Cloud Computing for the Business of Government” was published by Government Training Inc. and released in March 2011. His second book, released in 2012 by the same publisher, is titled “GovCloud II: Implementation and Cloud Brokerage Services”. His next publication, “Practical Cloud Security: A Cross Industry View”, will be released by Taylor & Francis in the spring of 2016.

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The Founder of NostaLab and a member of the Google Health Advisory Board, John is a unique combination of strategic thinker, marketer and entrepreneur. His career was built on the “science of advertising” combining strategy, creativity and marketing for industry-leading results. Combined with his ability to communicate complicated scientific concepts in a way that consumers and scientists alike can appreciate, John is a sought-after speaker for conferences on the forefront of healthcare science, marketing innovation and future tech.

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Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we’ll describe best practices for “configuration as code” in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and services, can be used to abstract from the infrastructure.

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• Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimising fab operations is achievable with machine learning.
• Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
• Automating quality testing using machine learning is increasing defect detection rates up to 90%.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimise manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionising manufacturing in 2018 include the following:

Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimising fab operations are is achievable with machine learning

Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimisation are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Asset management, supply chain management, and inventory management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today

The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimisation. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.

Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC

Analytics and MI-driven process and quality optimisation are predicted to grow 35% and process visualisation and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability

Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well

Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Automating inventory optimisation using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%

AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimisation across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimisation, Microsoft, March 1, 2018.

Combining real-time monitoring and machine learning is optimising shop floor operations, providing insights into machine-level loads and production schedule performance

Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimising the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more

Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning

The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE)

OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Ai Edam, 28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimisation, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45.

DXWorldEXPO LLC announced today that Dez Blanchfield joined the faculty of CloudEXPO’s “10-Year Anniversary Event” which will take place on November 11-13, 2018 in New York City. Dez is a strategic leader in business and digital transformation with 25 years of experience in the IT and telecommunications industries developing strategies and implementing business initiatives. He has a breadth of expertise spanning technologies such as cloud computing, big data and analytics, cognitive computing, machine learning and the Internet of Things.

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Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by FTC, CUI/DFARS, EU-GDPR and the underlying National Cybersecurity Framework suggest the need for a ground-up re-thinking of security strategies and compliance actions. This session offers actionable advice based on case studies to demonstrate the impact of security and privacy attributes for the cloud-backed IoT and AI ecosystem.

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Predicting the future has never been more challenging – not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.

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Hosting provider GoDaddy is going all-in on Amazon Web Services (AWS) moving the ‘vast majority of its infrastructure’ over to Amazon as well as maintaining an active interest in containerised apps.

The move, announced by AWS, will see GoDaddy utilise various services, including Amazon EKS, its elastic container service for Kubernetes launched at the company’s re:Invent event at the end of November. Other services being used include P2, AWS’ general purpose GPU instances ‘to substantially reduce the time it takes to train machine learning models’, as well as increase the performance of GoDaddy Domain Appraisals, the tool which helps customers understand the value of their domains.

AWS’ push on Kubernetes has been evident with the company joining the Cloud Native Computing Foundation (CNCF) – from where Kubernetes ‘graduated’ earlier this month – back in August. According to analysis from DZone at the start of this year, 63% of Kubernetes workloads were being deployed to the AWS cloud.

“As a technology provider with more than 17 million customers, it was very important for GoDaddy to select a cloud provider with deep experience in delivering a highly reliable global infrastructure, as well as an unmatched track record of technology innovation, to support our rapidly expanding business,” said Charles Beadnall, GoDaddy chief technology officer in a statement. “AWS provides a superior global footprint and set of cloud capabilities which is why we selected them to meet our needs today and into the future.

“By operating on AWS, we’ll be able to innovate at the speed and scale we need to deliver powerful new tools that will help our customers run their own ventures and be successful online,” Beadnall added.

To compare and contrast on what ‘all-in’ means, Mark Okerstrom, president and CEO of Expedia, told re:Invent attendees that they expected 80% of the company’s critical apps to be on board within three years.

Steve Cassidy