Details about Industrial IoT tracks on June 30 at IIoT World Days 2020

June 30, 11:15 - 12:15 AM ET
Maximizing Business Resilience through Digital Services

Maximizing Business Resilience through Digital Services
Presentations in this track:
Is it possible to democratize IIoT technologies?
Topics to be discussed and questions to be answered during this presentation:
  • Could the democratization of IIOT technologies improve the competitiveness of SMBs?
  • What are the top challenges of Industrial IoT SMBs?
  • What are the top challenges of Industrial IoT Startups?
  • What should be the role of local, regional, national or EU Governments do accelerate adoption of IIOT in SMBs?
  • Collaboration or Competition with the Tech / Industrial Giants?
Delegates will get an overview of the current IIOT market and how SMBs are moving in their digital transformation journey with IoT.  I will provide recommendation to accelerate adoption of IIoT in SMBs and guidance to select the right IoT ecosystem.
The Outcome Economy: How the Industrial Internet of Things is Changing Every Business
Everything and everyone is connected. The Internet of Things is transforming the ways in which new value is being created in practically every business and every workplace. Welcome to the “outcome economy” — where companies create value not just by selling products and services, but by delivering complete solutions that produce quantifiable business outcomes. Buyers in the outcome economy aren’t looking to buy products, but rather an iron-clad guarantee of an outcome. In the Industrial IoT, roles must change, and rules are meant to be broken. This is where the value is. This is the new opportunity. The outcome economy is a significant shift from competing through selling products and services, to competing on delivering measurable results important to the customer. For most companies, this is a challenging prospect. They will need to develop a deeper understanding of individual customer’s business, how products and services are being used, and how customers evaluate outcomes. In this keynote, Joe poses and answers key questions to help you navigate the uncharted parts of the Internet of Things universe: How do you prepare for the outcome economy? What do you need to do to capitalize on this opportunity? How do you engage with your customers? How will you outdo the competition?


Maximizing Business and Operational Resilience through Digital Services
Today, organizations have realized the urgent need to improve their Business Continuity plans to quickly enable remote monitoring with connectivity to assets, remote expertise, staff augmentation for on-site workers, increased cyber security measures, guaranteed SLAs and spare parts planning. Investments in connected technologies and the Industrial  Internet of Things (IIoT), as well as the adoption of advanced analytics, are enabling companies to better control and monitor critical assets. Those organizations that partner with the digital service experts also reduce their total cost of ownership through longer lifecycle, more efficient use and analytics to optimize spending. With the acceleration of digital transformation, outcome-based services play a pivotal role in ensuring organizations remain focused on their core business activities. Stephane will discuss the findings of our new, Schneider Electric-sponsored IDC White Paper on current and future business needs while explaining how businesses are taking advantage of new digital and outcome-based service opportunities being presented to ensure business continuity.

Key takeaways:
  1. Learn what tactical steps you can take to improve your organization’s continuity plans
  2. Understand what business challenges your peers are prioritizing and how they plan to manage them with digital services
  3. Hear how your peers have used digital services to optimize processes and operations and ensure business resiliency
See more info about the speakers here. Register to this track by June 8th and get your free ticket!

June 30, 12:20 - 1:40 PM ET
Artificial Intelligence – how manufacturers address this disruptive force in 2020

Artificial Intelligence – how manufacturers address this disruptive force in 2020
In this track, four speakers will present the following topics:
Nine Ways Business-Side Managers Can Lead A Successful AI Program For Business Transformation
Business leadership today is under pressure to drive business transformation. What enables that transformation is new management technology, especially AI. What is the business meaning of new management technology? It is really all about supporting organizational decision-making. Decision-making is not just another management role — it is the management role, covering both short-term operational and longer term planning decisions. So, does the adoption of decision-oriented management technology, mean that the role of management is reduced? On the contrary, the role of management is increased. Here are nine ways that business-side managers can help lead a technology program for a successful business transformation:
  1. FOCUS ON DECISION-MAKING – The business case for big data, AI and other technologies is justified on better decision-making, especially examining questions of uncertainty, risk and decision frequencies.
  2. GUIDE BIG DATA PROGRAM – The only business meaning of “big data” is “too big for our systems” – and this means that we have to throw away a lot of our data. This concerns business policy.
  3. INVEST HARD-WON KNOWLEDGE – Any automation program will be enormously dependent on human knowledge for success, typically earned from years of human experience.
  4. TAKE RESPONSIBILITY FOR MODELS – Whether digital twins for machines, deployed AIs, or for fullon business system simulations, the executive must “open up the black box”. There is no competitive edge or legal protection in giving up responsibility to a black box.
  5. ENSURE NOT TO DROWN USERS – The economic principle is “data is cheap and business analysis is expensive”, and the inevitable temptation is to add ever more data outputs on some system. As a result alarm fatigue and information  overload in healthcare, transportation, oil exploration etc. is endemic.
  6. STRATEGY OF AUGMENTATION – The work of business will not be automated away. The recipe for automation success is augmentation, not substitution.
  7. FOCUS ON DATA STANDARDS – As decision oriented management technology begins to permeate an organization, the  requirement for high-quality data becomes more pressing. New data governance commitments are required.
  8. REALIZE IT IS  MANUFACTURING – The idea of “automation artefact manufacturing” is new for many organizations. But a big data program or an AI program is not a one-off. It is a commitment to a production cycle of new management tools.
  9. LEAD FROM STRATEGY – The question of overall strategy is outside the scope of this presentation.
Artificial Intelligence in Manufacturing: Case Studies on Predictive Maintenance and Predictive Quality
A reduction in unplanned downtime or a few percentages of scrap reduction can yield millions of dollars in savings for manufacturers. This compelling value proposition has resulted in significant investment and interest in using predictive maintenance and predictive quality solutions based on artificial intelligence (AI). Manufacturing firms are embracing Digital Transformation and leveraging AI and Big (Smart) Data metrics to achieve greater efficiency and productivity on the shop floor. Despite this interest, many companies are not realizing solutions that can provide this value for a variety of reasons. Some less than successful solutions do not incorporate domain knowledge, or have poor data quality, or have an incomplete business case, or a lack of robustness in the AI machine learning models and approaches. This presentation will:
  • Address many of those challenges and present compelling real-world business examples with demonstrated value for rapidly deploying AI in manufacturing.
  • Introduce a methodology including a workshop to define the business and technical problem, a state of the art and end-to-end analysis platform from data collection to the delivery of the health and process information, and lessons learned on how the solution can be maintained and improved over time.
  • Present real world manufacturing examples with ROI including stamping, casting, CNC machining, industrial robotics, among others. Examples in predictive maintenance that focus on reducing unplanned downtime will be shared along with case studies on predictive quality, in which the theme is improving quality and reducing scrap.
The presentation and case studies will shed the light on how manufacturers can transform from a “fail and fix” to a “predict and prevent” zero-downtime and zero-defect operation; keeping in mind cost, time-to-deploy, technology architecture, and scalability.
Check the speakers here.

June 30, 3:05 - 4:00 PM ET
[Panel Discussion] Where to start with IIoT and the true cost of integrating IIoT into your business

This panel will cover (amongst many things):

  • Where to start with IIoT
  • A breakdown of costs when integrating IoT into business processes and products
  • Why hardware has a critical role in successful IIoT solution
  • How product lifecycle affects your profitability

Check the speakers here

June 30, 4:05 - 5:40 PM ET
The Manufacturing Analytics Journey

The Manufacturing Analytics Journey
Presentations in this track:
Evolution of Predictive Maintenance
Predictive maintenance has made enormous progress over the past several decades.  General Electric (GE), in the 1980s, had a tool called GEN-X that was an expert system shell.  It was used for troubleshooting and predictive maintenance for jet engines as well as GE consumer appliances.  These expert systems incorporated domain-specific knowledge from existing troubleshooting manuals and enhanced by factory engineers with a background in engine performance analysis.  Symbolic artificial intelligence, also known as expert systems, dominated diagnostic and predictive maintenance in that era.  These expert systems were the precursor to condition-based maintenance that evolved to predictive maintenance.  Predictive maintenance today uses machine learning to do a classification or regression approach to maintenance issues.  This presentation will explore the evolution of predictive maintenance and how we can apply what we have learned to future applications in predictive maintenance.
How To Improve Profitability With Advanced Analytics
Waste is one of the biggest contributors to profitability loss in manufacturing. Learn how predictive and prescriptive analytics can reduce waste in your production processes – from excessive downtime to high scrap rates. With machine learning-driven insights, you’ll be able to proactively address production problems, optimize throughput without sacrificing product quality, and replicate your most profitable runs more consistently. This session will cover the fundamentals of advanced analytics and machine learning technology. We’ll discuss best practices for data acquisition and overcoming equipment diversity on the factory floor, along with enabling automated insights and predictive alerts that allow teams to solve problems faster.  As a result, you’ll be better equipped to reduce waste associated with excessive material consumption, unplanned downtime, and quality failures.
The Intelligent Manufacturing Journey
The Intelligent Manufacturing Journey can be thought of as a continuous progression to full manufacturing optimization, which includes process, machine, quality and supply chain elements. Organizations find themselves at varying levels of maturity. Most are at Level 1 or Level 2 today. The big step change is going from level 1 and 2 to level 3 and 4 with the realization that advanced analytics and customer inclusion are central to Manufacturing 4.0. Come learn how to progress from one level to another while dealing with islands-of-automation, monolithic manufacturing systems and apps. The challenge is to get insights that matter and gain control over key pain points. Then you can focus on tackling issues that drive business outcomes and provide pathways to greater maturity.
Where/when to invest time and resources to drive continuous value
The initial success of any Industry 4.0 implementation is so important, not just to demonstrate the value of the initial technology but to realize the belief in the benefits enabled through Industrial IoT technology. Unfortunately, IoT implementations have had a historically high rate of failure. Cisco produced a report of survey results indicating that companies considered 76 percent of their Industrial IoT initiatives failures. This has led to greater hesitation on the part of manufacturers to embark on digital transformation journeys. So what’s the solution? There is no silver bullet, but there is a starting point. As users gain confidence in the value of the technology, it’s essential to provide customers a roadmap to continuous innovation enablement. We call this roadmap the  manufacturing analytics journey. Visualizing your journey and where your organization lands within it will help define what steps you can take to create immediate value, where/when to invest time and resources, and most importantly how to advance forward to the next stages. Key Takeaways:
  1. Discover how to identify where your organization lands within the Analytics Journey to help define what steps you can take to create immediate value
  2. Understand where/when to invest time and resources to drive continuous value
  3. Learn about the MachineMetrics secret sauce: a use-case driven technology roll-out plan we call “the hybrid platform”
Analytics and Big Data, and how it underscores Industry 4.0
Industry 4.0 is increasingly becoming a priority for most high-tech industries. It calls for a future of agile, affordable manufacturing fueled by technology enablers such as the Internet of Things (IoT), Cloud computing, Mobile Devices and Big Data. While for most manufacturers it is a lucrative concept with the potential of bringing unforeseen benefits and synergies to their operations, the implementation of Industry 4.0 though remains a challenge. The question is: with all the buzz around Industry 4.0 and terms such as Cyber-physical systems, IoT, Cloud computing, Big Data, Machine Learning, 3D Printing, where should manufacturers start at to build their smart manufacturing journey? More about the speakers here.

Resources to download

white paper by IDC

A white paper by IDC, sponsored by Schneider Electric 

Download the Maximizing Business and Operational Resilience Through Serviceswhite paper to learn how global companies see the challenges and opportunities of digital transformation, and how third-party services help pave the way to success. 

The 14 IIoT Providers That Matter Most And How They Stack Up

In The Forrester Wave™: Industrial IoT Software Platforms, Q4 2019, a 24-criterion evaluation of industrial internet of things (IIoT) software platform providers, the research company identified the 14 most significant — ABB, Amazon Web Services (AWS), Bosch, C3.ai, GE Digital, Hitachi, IBM, Microsoft, Oracle, PTC, Samsung SDS, SAP, Siemens, and Software AG — and researched, analyzed, and scored them. This report shows how each provider measures up and helps infrastructure and operations (I&O) professionals select the right ones for their needs.

This report is sponsored by Siemens. 

LNS research

This e-book sponsored by Hitachi Vantara introduces:

  • The results of the 2018 Analytics that Matter survey and the unexpected (and interesting) responses; and
  • A data and analytics architecture that fits within the Oper­ational Architecture prescribed by LNS Research and that helps manufacturers achieve Digital Transformation goals.

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