This document discusses using machine learning and analytics for manufacturing applications. It begins with an overview of industry 4.0 and the increasing connectivity in manufacturing through technologies like the industrial internet of things. It then discusses how machine learning techniques like classification, regression, clustering and dimensionality reduction can be applied to common use cases in manufacturing around areas like order to cash, core manufacturing, and procure to pay. Specific case studies are presented on using machine learning for energy optimization at Infosys campuses and predicting churn for a automotive manufacturer's connected vehicle subscription services. Visualization and condition-based monitoring using artificial intelligence are also discussed.
Sponsored by Smeal College of Business at Penn State University, Infosys Consulting, Penkse Logistics and Korn Ferry, the 22nd Annual 3PL Study investigates digitization and automation in logistics, blockchain for supply chain, and risk and resilience in 3PL shipper relationships.
Developed with Forum for the Future, an international sustainability non-profit organization, and based on our own interviews and executive survey, Vision 2030: A connected future highlights the opportunities that experts and business leaders see for IoT, data and connectivity to create a sustainable future.
The report outlines a future vision for IoT driven connectivity and highlights the barriers that need to be overcome to realize this vision and concludes with recommended next steps.
Webinar on 4th Industrial Revolution, IoT and RPARedwan Ferdous
This is a summarized presentation on the 4th Industrial Revolution, the Internet of Things and Robotic Process Automation (RPA). especially for the undergrad students and recent graduates for getting an overview of the topics-based on global and local trends. Maximum contents are from online and those are cited with due respect at the end 3 slides.
The webinar was arranged by IEEE ISTT Student Branch, Bangladesh on 15th May 2020. The session was 2 hours long.
Note: Slide# 6 ~ 42 was taken from one of my earlier sessions, presented for the IEEE RU Student Branch. That slide can be found here: https://www.slideshare.net/RedwanFerdous/roadmap-to-4th-industrial-revolutioniot-iiot
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. Here is a five-step process for navigating it successfully.
In a day and age of rapid technological innovation, the public has come to demand an incredibly high level of technical sophistication from all aspects of their lives. Where the commercial sector has accelerated to meet this demand, the public sector has historically been resistant to change. The Government Technology (“GovTech”) sector arose in response to public demand for more efficient, affordable, and secure government processes from a segment plagued by antiquated systems and outdated procedures. Only recently have government entities begun leveraging innovative and agile solutions to meet increasing operational demands and produce more citizen value while remaining adherent to stringent budgets. This digital transformation has been catalyzed by federal programs and regulations, which are accelerating tech adoption by increasing capital investment and streamlining procurement processes. To capitalize on this trend, Catalyst is exploring GovTech businesses seeking to exploit the recent digital transformation of the public sector across North America and seize market share in a fragmented market.
Sponsored by Smeal College of Business at Penn State University, Infosys Consulting, Penkse Logistics and Korn Ferry, the 22nd Annual 3PL Study investigates digitization and automation in logistics, blockchain for supply chain, and risk and resilience in 3PL shipper relationships.
Developed with Forum for the Future, an international sustainability non-profit organization, and based on our own interviews and executive survey, Vision 2030: A connected future highlights the opportunities that experts and business leaders see for IoT, data and connectivity to create a sustainable future.
The report outlines a future vision for IoT driven connectivity and highlights the barriers that need to be overcome to realize this vision and concludes with recommended next steps.
Webinar on 4th Industrial Revolution, IoT and RPARedwan Ferdous
This is a summarized presentation on the 4th Industrial Revolution, the Internet of Things and Robotic Process Automation (RPA). especially for the undergrad students and recent graduates for getting an overview of the topics-based on global and local trends. Maximum contents are from online and those are cited with due respect at the end 3 slides.
The webinar was arranged by IEEE ISTT Student Branch, Bangladesh on 15th May 2020. The session was 2 hours long.
Note: Slide# 6 ~ 42 was taken from one of my earlier sessions, presented for the IEEE RU Student Branch. That slide can be found here: https://www.slideshare.net/RedwanFerdous/roadmap-to-4th-industrial-revolutioniot-iiot
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. Here is a five-step process for navigating it successfully.
In a day and age of rapid technological innovation, the public has come to demand an incredibly high level of technical sophistication from all aspects of their lives. Where the commercial sector has accelerated to meet this demand, the public sector has historically been resistant to change. The Government Technology (“GovTech”) sector arose in response to public demand for more efficient, affordable, and secure government processes from a segment plagued by antiquated systems and outdated procedures. Only recently have government entities begun leveraging innovative and agile solutions to meet increasing operational demands and produce more citizen value while remaining adherent to stringent budgets. This digital transformation has been catalyzed by federal programs and regulations, which are accelerating tech adoption by increasing capital investment and streamlining procurement processes. To capitalize on this trend, Catalyst is exploring GovTech businesses seeking to exploit the recent digital transformation of the public sector across North America and seize market share in a fragmented market.
Building Reference Architectures for the Industrial IoTCapgemini
Building Reference Architectures for the Industrial IoT - Alina Chircu, Bentley University; Eldar Sultanow, Capgemini Germany
Workshop on Smart Manufacturing in Aerospace and Automotive Industries
AIS Special Interest Group for Big Data Application in Processes (SIGBD), AMCIS 2017, Boston, USA
IOT in Bangladesh | Enterprise Resource Planning (ERP) Software System | Pridesys IT Ltd, Different Innovation and Excellence | Internet of Things Applications for Analytics
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
It might seem too soon to proclaim that the fourth industrial revolution, spurred by interconnected digital technology, has begun. But Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
When you look closely at the rapid pace of digitization in industry today, the name doesn’t seem hyperbolic at all. It is a signal of sweeping change that is rapidly transforming many companies and may catch others by surprise.
Borqs Technologies Inc operates as a software development company. It is engaged in software, development services, and products providing customizable, differentiated, and scalable Android-based smart connected devices and cloud service solutions. The company's segments include Yuantel and Connected Solution. Borqs derives most of its revenues from its Connected Solution which includes Software and Hardware.
Madison Park Group - EHSQ Software Market Update - Initial Report (2019)Madison Park Group
We are pleased to present our initial review of the environmental, health, safety, and quality (EHSQ) compliance & risk management software market.
Madison Park Group is a unique investment banking firm that takes a "strategy first" approach to advising software companies. Our partners have developed and advised numerous successful companies as operators, investors and investment bankers.
Jon Adler and Sean Stouffer spearhead the firm's efforts in the EHSQ software market.
How AI is Transforming Government Services in the UAESaeed Al Dhaheri
This presentation was given at the RIT - Dubai workshop about applications of Artificial Intelligence at the public and private sectors. Highlights the UAE AI strategy and what has been done to activate it. Also presents some use cases for AI from the UAE public sector.
Cloud Search Based Applications for Big Data - Challenges and Methodologies f...Accelerate Project
Presentation of Suciu et al. at "Workshop on Adaptive Resource Management and Scheduling for Cloud Computing", ARMS-CC 2015 ,
Donostia-San Sebastián, Spain, July 20th, 2015
Study Future PLM - Product Lifecycle Management in the digital age.Joerg W. Fischer
Product Lifecycle Management in the digital age.
The catalyst for IoT, Industry 4.0 and Digital Twins
“It is not primarily a matter of developing a digitalization strategy for your company. Rather, it is about aligning corporate strategy and processes so that your company can survive and succeed in an increasingly digitized world.”
Prof. Dr.-Ing. Jörg W. Fischer
This presentation was conducted at the future of smart manufacturing in Sharjah event, as organized by the Science Technology and Innovation Park and the American University of Sharjah, on 15/4/2019
Technology Trends for 2019: What it Means for Your BusinessPrecisely
Ninety percent of the world’s data was generated in the past two years, and 2.5 quintillion bytes of data are generated every day. Organizations across industries have an unprecedented opportunity to harness data to move their business forward – but they will need a solid strategy and the right tools to do so.
View this 30-minute webcast on-demand to learn about the technology trends you need to know to use data to your strategic advantage. Syncsort’s CTO, Tendu Yogurtcu, shares insights from our work with data-driven organizations around the world, on the top technology trends for 2019 – and how data governance is key.
The webcast covers opportunities, challenges and best practices for:
• Hybrid Cloud
• IoT
• AI and Machine Learning
• Blockchain
Building Reference Architectures for the Industrial IoTCapgemini
Building Reference Architectures for the Industrial IoT - Alina Chircu, Bentley University; Eldar Sultanow, Capgemini Germany
Workshop on Smart Manufacturing in Aerospace and Automotive Industries
AIS Special Interest Group for Big Data Application in Processes (SIGBD), AMCIS 2017, Boston, USA
IOT in Bangladesh | Enterprise Resource Planning (ERP) Software System | Pridesys IT Ltd, Different Innovation and Excellence | Internet of Things Applications for Analytics
Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II.
It might seem too soon to proclaim that the fourth industrial revolution, spurred by interconnected digital technology, has begun. But Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative.
When you look closely at the rapid pace of digitization in industry today, the name doesn’t seem hyperbolic at all. It is a signal of sweeping change that is rapidly transforming many companies and may catch others by surprise.
Borqs Technologies Inc operates as a software development company. It is engaged in software, development services, and products providing customizable, differentiated, and scalable Android-based smart connected devices and cloud service solutions. The company's segments include Yuantel and Connected Solution. Borqs derives most of its revenues from its Connected Solution which includes Software and Hardware.
Madison Park Group - EHSQ Software Market Update - Initial Report (2019)Madison Park Group
We are pleased to present our initial review of the environmental, health, safety, and quality (EHSQ) compliance & risk management software market.
Madison Park Group is a unique investment banking firm that takes a "strategy first" approach to advising software companies. Our partners have developed and advised numerous successful companies as operators, investors and investment bankers.
Jon Adler and Sean Stouffer spearhead the firm's efforts in the EHSQ software market.
How AI is Transforming Government Services in the UAESaeed Al Dhaheri
This presentation was given at the RIT - Dubai workshop about applications of Artificial Intelligence at the public and private sectors. Highlights the UAE AI strategy and what has been done to activate it. Also presents some use cases for AI from the UAE public sector.
Cloud Search Based Applications for Big Data - Challenges and Methodologies f...Accelerate Project
Presentation of Suciu et al. at "Workshop on Adaptive Resource Management and Scheduling for Cloud Computing", ARMS-CC 2015 ,
Donostia-San Sebastián, Spain, July 20th, 2015
Study Future PLM - Product Lifecycle Management in the digital age.Joerg W. Fischer
Product Lifecycle Management in the digital age.
The catalyst for IoT, Industry 4.0 and Digital Twins
“It is not primarily a matter of developing a digitalization strategy for your company. Rather, it is about aligning corporate strategy and processes so that your company can survive and succeed in an increasingly digitized world.”
Prof. Dr.-Ing. Jörg W. Fischer
This presentation was conducted at the future of smart manufacturing in Sharjah event, as organized by the Science Technology and Innovation Park and the American University of Sharjah, on 15/4/2019
Technology Trends for 2019: What it Means for Your BusinessPrecisely
Ninety percent of the world’s data was generated in the past two years, and 2.5 quintillion bytes of data are generated every day. Organizations across industries have an unprecedented opportunity to harness data to move their business forward – but they will need a solid strategy and the right tools to do so.
View this 30-minute webcast on-demand to learn about the technology trends you need to know to use data to your strategic advantage. Syncsort’s CTO, Tendu Yogurtcu, shares insights from our work with data-driven organizations around the world, on the top technology trends for 2019 – and how data governance is key.
The webcast covers opportunities, challenges and best practices for:
• Hybrid Cloud
• IoT
• AI and Machine Learning
• Blockchain
Key Contents -
Trends in the Manufacturing Sector
Key Statistics and Challenges
Digital Transformation Strategy Development Steps
Use-Cases in Manufacturing
Market Map Landscape - By Leaders, Star-ups, Segments & Sub Segments (Managing Technology Risks)
Drivers of M&A in Industry 4.0
Benchmarking the start-ups and investments/acquisition options for Market Leaders
Data Intelligence: come abilitare il valore aziendaleIDC Italy
IDC evidenzia che in media l’80% del tempo che un’azienda dedica ai dati viene speso in attività di gestione e soltanto il 20% in attività analitiche a valore. Grazie alla data intelligence, questa proporzione può essere invertita. Ecco la presentazione che Diego Pandolfi, Research & Consulting Manager di IDC Italia, ha portato all'IDC Data Intelligence Conference 2019 del 18 giugno a Milano
Certus Accelerate - Building the business case for why you need to invest in ...Certus Solutions
Becoming an analytically driven or cognitive business is a journey.
Businesses will be able to rapidly capitalize on new opportunities if they have invested in the foundations of their information management systems.
Capgemini’s Smart Plant Supervision (SPS) solution can improve plant and facilities management using the Watson IoT Platform to leverage data from a wide range of digital and analog sensors. The end-to-end solution for data aggregation, analytics and action—in near real time—is offered only by Capgemini on Bluemix. By leveraging our IOT Platform Service running on Bluemix, SPS enables predictive and preventive maintenance, higher machine uptime and maximum efficiency.
Intelligent Decision Making Assistant (IDMA) for SAL improvement.pptxMohammad Sabouri
Intelligent Decision Making Assistant (IDMA) for SAL improvement
IDMA based on AI techniques using Internet Of Things(IOT) according to movement map of Staff , Equipment and Machines
Authors:
Mohammad Sabouri- Robotics Engineering
Behnam Jabbari kalkhoran – Robotics Engineering
Loria Davide – Civil Engineering
IDEA:
An optimized methodology which leads to AI based software with the extraction of data from IOT sensors to analyze project data and provide real-time insights for intelligent decision making that leads to improved SLA in construction sites. With features like predictive analytics, resource optimization, and risk management.
In today’s globalized, competitive marketplace, being able to leverage technology to deliver faster turnaround times, meet lower pricing goals and provide customizable options can mean the difference between sustainability and irrelevancy. In this ebook, we’ll explore some of the leading solutions transforming the manufacturing industry:
- Automation for cost savings
- 3D printing for improved productivity
- Smart data for quality assurance
- Connectivity for safety and communication
- Security solutions to protect it all
Learn more: http://ms.spr.ly/6006Twegg
Similar to Demystifying Machine Learning for Manufacturing: Data Science for all (20)
Infosys commissioned an independent market research company, Vanson Bourne, to investigate the use of digital technologies and key trends in nine industries. We surveyed 1,000 senior decision makers from business and IT, from large organizations with 1,000 employees or more and annual revenue of at least US$500 million.
The report aims to discover:
a) the surging tide of digital technology adoption in organizations – what is used and where?
b) the promised land of digital technology use, and the hurdles organizations face to get there
c) the biggest disruptive digital trends within the next three years and why organizations see them as vital to future success
The summary here presents the survey results and highlights the digital outlook that will define the healthcare industry strategy over the next three years.
5 tips to make your mainframe as fit as youInfosys
Just like a periodic health check-up is important to assess your overall well-being, a detailed reexamination of the enterprise IT landscape is paramount. We take a look at the various ways an enterprise needs to revamp its mainframe and sharpen its functionalities to stay ahead of the game. While APIs aid you in providing superior customer service, migrating to the cloud provides you with scalability and resilience. These and many more sub-offerings from Infosys aid your organization in staying agile and equipped to leverage the latest technologies to cater to the ever-changing market. Learn more.
Human Amplification In The Enterprise - Resources and UtilitiesInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Resources and Utilities.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human Amplification In The Enterprise - Telecom and CommunicationInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Telecom and Communication.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human Amplification In The Enterprise - Retail and CPGInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Retail and CPG.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human Amplification In The Enterprise - Manufacturing and High-techInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Manufacturing and High-tech.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human amplification in the enterprise - Automation. Innovation. Learning.Infosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Automation, Innovation and learning.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human Amplification In The Enterprise - Healthcare and Life SciencesInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Healthcare and Life Sciences
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Human Amplification In The Enterprise - Banking and InsuranceInfosys
Infosys commissioned a study to develop a research methodology and get insights into the current nature of digital transformation enterprises undergo, across industry verticals. This deck provides industry specific insights from Banking and Insurance.
The study sought to understand a) the specific drivers of digital transformation for enterprises, b) the various facets of this transformation, c) expected and ensuing outcomes, and d) the role of Artificial Intelligence (AI).
Take a glimpse at few of our efforts that we made to demonstrate that efficient technologies can easily be deployed in large scale in a cost effective manner to make our campus environmental friendly on this World Environment Day 2015
The Information Services industry is in the eye of the digital storm. Two major contenders within this industry - traditional and new age media companies must adopt strategies for the significant mass of millennials and demanding consumers.
Infosys' session on IoT World - Systems Integration in an IOT world: A practi...Infosys
The installed base of IOT products is growing exponentially along with its economic impact. Innovators are seeking a new generation of technology solutions that will help them create, operate and maintain smart connected products. It is a system of systems world that is complex, heterogeneous with a mix of incompatible, non-standard, multi-vendor, smart & not-so smart, connected and not-connected products that generate incredible amounts of data to be analyzed for insight and value. In this presentation, Jayraj Nair, AVP and Head of IoT, Infosys Engineering Services will share his experience’s building real world IOT solutions, innovative ways in which a system integrator can enable the integration between the physical and digital worlds and accelerate the adoption of Internet of Things.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Mission to Decommission: Importance of Decommissioning Products to Increase E...
Demystifying Machine Learning for Manufacturing: Data Science for all
1. Infosys Confidential1 |1 Internet of Manufacturing Midwest 2018
DemystifyingMachine
Learningfor
Manufacturing:
Data ScienceforAll
Jeff Kavanaugh
June 7, 2018
2. Infosys Confidential2 |2 Internet of Manufacturing Midwest 2018
Today’s discussion
Research
Industry 4.0 maturity index and
framework
Future proof: learning and
communication through data
science and critical thinking
Machine Learning and
Analytics Background
IIoT and AI in Practice
Industrial IoT through facilities
energy management
Water Treatment Plant
Automotive OEM predictive
churn
4. Infosys Confidential4 |4 Internet of Manufacturing Midwest 2018
Industry 4.0: Beyond the POC… the time to scale is upon us
Industry 4.0 integrates the physical and virtual worlds through technology enablers, which brings the fungibility and
speed of software to manufacturing operations. The potential value created by Industry 4.0 vastly exceeds the low-
single-digit cost savings that many manufacturers pursue today (acatech, Infosys, BCG, McKinsey, et al).
Disruptive technology enablers for Industry 4.0 are at a tipping point
*McKinsey, acatech, Infosys, BCG research
100xdisruptive digitalinnovation is
100x faster than physical
disruption*
25Bconnected things forecasted to
ship by 2020.*
250Mconnected vehicles are forecasted
to have some form of wireless
network connection by 2020.*
$421Bin cost and efficiency gains
per annum
$907Bin annual digitalinvestments
$493Bin digital revenue gains per
annum
Industry 4.0 is changing manufacturing
But are we ready?....
5. Infosys Confidential5 |5 Internet of Manufacturing Midwest 2018
• Industry 4.0 announced atHannover
Messe 2011, butsystematic
implementation still only 18%
• Current speed of implementation
places the 2022 goalof 46% at risk
• Reason: data hurdles and piecemeal
POCapproach – unclear path
Approach toovercome barriers:
1. Evaluate your digital maturity
2. Proof of concepts to demonstrate
business value, then scaledaction
3. Set clear targets
4. Prioritize measures thatwill bring
the mostvalue to business
5. Demonstrate courage, persistence
Industry 4.0: Global study conducted on operations efficiency as a
driver for competitiveness
Dimensions
Maintenance
Efficiency
Information
Efficiency
Energy
Efficiency
Service
Efficiency
• Vast majority (82%) of
companies areaware of
the high potential in
implementingIndustry4.0
concepts
• 46% want to implement
Industry 4.0 solutions
systematically for enhanced
asset efficiency by 2022
• Only 30% have implemented
data-driven or intelligent
services
Potential recognizedSystematically implemented
Partly implemented No awareness
2017 2022
Directional findings
Source: Infosys and Institute for Industrial Management (FIR) at RWTHAachen study conducted in 2015 and updated in 2017.
Sample size: 433 executives across industrial manufacturing sectors from China, France, Germany, UK and USA
OPERATIONSEFFICIENCY
The opportunity
Performance
Efficiency
Engineering
Efficiency
15%
4%
32%
18%
35%
32%
18%
46%
6. Infosys Confidential6 |6 Internet of Manufacturing Midwest 2018
Humans still matter! Industry 4.0 maturity is about more than the
technology, and poor reasoning skills are constraining progress
Their No. 1 complaint?
Poor critical-reasoning skills
A survey by PayScale Inc., an online pay and
benefits researcher, showed 50% of employers
complain that college graduates they hire aren’t
ready for the workplace.
Source: UTD research study December 2017 and PayScale Inc., 2016
7. Infosys Confidential7 |7 Internet of Manufacturing Midwest 2018
Industry 4.0 maturity drives significant efficiency improvement,
and analytics is a fundamental requirement
Near-Term
Long-Term
Computerization
E.g. CNC milling
machinebutnot
connected
Business
applications
connected to
each other
Up to date
digitalmodel
(Digital
Shadow) to
showwhat’s
happening
now
BigData
analyticsto
understand
rootcauses
Advanced
analyticsfor
simulation&
identification
of mostlikely
scenarios
Automated
decision
makingand
actions
Source: Industrie 4.0 Maturity Index, acatechstudy supported by Infosys, 2017
8. Infosys Confidential8 |8 Internet of Manufacturing Midwest 2018
Machine Learning is an important component in Industry 4.0 analytics
Applied Machine Learning
Computer Vision
Unstructured Text
Analytics
Other AI Offerings
Deep Neural Networks
Data Analytics Cognitive
Time&AIInfrastructure
Predict
Categories?
Labeled
Data ?
Classification
Clustering /
Anomaly Detection
Predicting
values ?
Regression
Dimension
Reduction
Y
N
Y
N
Y
N High-Fidelity Speech
Synthesis
Video Analysis
Image Insights /
Comparison
Names Entity
Extraction
Chat Bots
Knowledge
Management
Language
Translation
Text Extraction
9. Infosys Confidential 9 Infosys Confidential|9 Internet of Manufacturing Midwest 2018
Machine Learning involves solving business problems using 25+
algorithms segmented into 4 groups
http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
Solving a Machine Learning (ML)
problem depends on finding the
right algorithms for the business
problem
Different algorithms are better suited
for different types of data and
different problems
We have found the Python scikit-learn
flowchart useful for selecting ML
algorithms specific to business
problem and available data
Yes/No;
quality
pass/fail
Addresses
too much
sensor data!
Old Faithful
(Describes
relationships)
Groups into
similar
characteristics
10. Infosys Confidential 10 Infosys Confidential|10 Internet of Manufacturing Midwest 2018
Machine Learning platforms (tools) have a large library of algorithms,
designed to address different types of business problems
Classification Regression
Clustering and Anomaly Detection Dimensionality Reduction
Identify category to which an object belongs
Applications:spam detection, image recognition, quality P/F
Support Vector
Machine (SVM)
Stochastic Gradient
Descent (SGD)
Classifier
k-Neighbors
Classification
Random Forest /
Decision Trees
Predicts a continuous-valued attribute associated with an object
Applications:forecasting,pricing determination
Stochastic Gradient
Descent (SGD)
Regressor
Lasso Ridge
Regression
Elastic Net
Automatic groupingof similar objects into sets
Applications: visualization, sensor feeds, efficiency improvement
K-Means
Clustering
Gaussian
Mixture Models
Mean Shift Spectral
Clustering
Reduce the number of random variables to consider
Applications: customer feed (Twitter) segmentation, groupingdata
Randomized Principal
Component Analysis
(PCA)
Kernel
Approximation
Locally Linear
Embedding
Spectral
Embedding
11. Infosys Confidential 11 Infosys Confidential|11 Internet of Manufacturing Midwest 2018
Machine learning techniques are organized by ability to learn
Supervised
Machine Learning
Raw Data Features /
Labels
Train and
Evaluate
Trained
Model
Deploy /
Improve
Unsupervised
Machine Learning
Raw Data Algorithms Cluster /
Anomalies
Use in supervised
learning
Reinforcement
Machine Learning
Goal Initialize Agent Environment
Action
Reward /
Penalty
Traditional Data Analysis
Raw Data Use Model/
Improve
Analyze and
Write Rules
“If” and “Else” decisions designed by humans,
coupled with functions ( e.g. Excel functions), to
process data or adjust to user input
Changing the task might require a rewrite of the model
The training data one feeds to the algorithm
includes the desired solutions, called labels
Based on learning, algorithm provides outputs for
new real time inputs
Examples: Classification, Regression
Only the input data is known, and no known output
data is given to the algorithm. They are usually
harder to understand and evaluate
Examples: Clustering (Identifying topics in a set of blog
posts, segmenting customers into groups with similar
preferences, detecting abnormal access patterns to a
website)
The learning system, called an Agent, can observe
the Environment, select and perform Actions, and
get Rewards in return, or Penalties in the form of
negative rewards
Examples : DeepMind’s AlphaGo, Walking Robot,
Automated Trader
12. Infosys Confidential 12 Infosys Confidential|12 Internet of Manufacturing Midwest 2018
Common machine learning use cases in a manufacturing context
Order to Cash Core Manufacturing Procure to Pay Record to Report
Demand estimation - order
quantities
Predictive maintenance
Contracts analysis for
named entity
FP&A Forecasting
Anomaly detection: credit
risk
Tech support / knowledge
base
Commodity price forecasting
Real-time monitoring of
foreign exchange
Order entry automation In-line quality inspection Consistent supplier terms
Automated inventory
stocking for service truck
Defect root cause and
corrective action
Long tail spend analytics
Simplification and
automation of manual
services billing
Production planning and
scheduling
Demand forecasting using
sales pipeline
13. Infosys Confidential13 |13 Internet of Manufacturing Midwest 2018
Industry 4.0:
Illustrative Case
Studies
13
Industrial
Examples
14. Infosys Confidential14 |14 Internet of Manufacturing Midwest 2018
Energy matters! Industrial IoT aids energy optimization in Infosys
campuses
46% reduction in per-capita energy consumption over 8 years
$100 million savings over 3 years
• Chillers
• HVAC
• Generators
• Elevators
• Sewage
Treatment
plants
• Solar power
plants
Large Campuses
80 million+ square feet Assets Managed
Central Command Center Demand Management
Digital Twin and Optimum
Operating Conditions
Predictive
Maintenance
Solution Approach
Business Benefit
Business Need Sustainability initiative at Infosys and implementation using IIoT solution
15. Infosys Confidential15 |15 Internet of Manufacturing Midwest 2018
Transparency Predictability AdaptabilityVisibility
Path of development
MaturityLevel/
BusinessValue
Visibility
Centralized command center for real-time visibility
Real-time data acquisition
Visibility to key operating parameters
16. Infosys Confidential16 |16 Internet of Manufacturing Midwest 2018
Transparency and the Digital Twin
Analyzing performance – As Designed vs As Installed vs As Operated
As Designed As Installed As Operated
Transparency Predictability AdaptabilityVisibility
Path of development
MaturityLevel/
BusinessValue
Plot of Critical Performance Parameters
• Condenser Water Delta (leaving temp – entering temp)
• Chiller Water Delta (leaving temp – entering temp)
• Evaporator Small Temp Diff (Ref. Sat temp– Chiller
Water leaving temp)
• Condenser Small Temp Diff (Ref. Sat temp–
Condenser Water leaving temp)
• Chiller Working Hours
Digital Model complements
physical assets
Study operating conditions,
trends and performance
17. Infosys Confidential17 |17 Internet of Manufacturing Midwest 2018
Predictability
Data Collection Data Cleansing
Correlation
Analysis
Exploratory Data
Analysis
Event Detection Prognostics
• Identificationof key performance indicators
• Exploratoryanalysis and visualization of data
• Event detection – Hotelling’sT-squared and quartile-
based method
• Prognostics– ARIMA model with xreg variable
• Knowledge model development
Implemented advanced analytics on chiller data for event detection and prognostics
Transparency Predictability AdaptabilityVisibility
Path of development
MaturityLevel/
BusinessValue
18. Infosys Confidential18 |18 Internet of Manufacturing Midwest 2018
Example: greenfield waste water treatment plant’s pumping station
• Plant: State-of-the-art waste water treatment plant in Europe
• Three operating scenarios:
1) Average flow
2) Average + Industry peak flow
3) Peak flow (heavy rain, flood)
• Three different design solutions for the pumping station to be analyzed
Case 1: 4 big pumps and 3 small pumps (original design requirement
from the Client)
Case 2: 3 big pumps and 3 small pumps
Case 3: 2 big pumps and 3 small pumps
FOCUS
The strict environmental permit must be
fulfilled which means that the effluent
from the pumping station to
environment is not acceptable
TARGET
To find optimal design solution to fulfil the
required availability and safety with minimum
lifecycle cost
EVALUATE
To create a RAMS simulation model of the
different design alternatives with different
operation and maintenance scenarios
RESULTS
To find out design solution to fulfil the
required availability and safety with
minimum lifecycle cost
19. Infosys Confidential19 |19 Internet of Manufacturing Midwest 2018
Moving from a traditional RAMS* to AI-based RAMS design enabled
faster decision-making with more accuracy
Integrated Operation
Defined design solutios:
- Design basis, specification,
objectives & requirements
Supplier-specific
Work Packages
Selected WP-
suppliers
Technical Performance & Availability
Operability & Maintainability
Safety & O&M Cost
Supply management
Anomaly?
Identify parameter
Healty Baseline
In-Situ MonitoringAlarm
Parameter Isolation
Failure DefinitionData-Driven Models
Physics of
Failures Model
RAMS database
Remaining Useful
Life Estimation
Yes
Continue
monitoring No
Instrumented process
Automated identification
and data capture
Application of
Prognostics and
Healty Management
Drishti 4.0 Operational
Excellence AI Platform
RAMS
Design
Process
System Design and Realization Processes
Identification of
critical RCM positions
Definition of Maintenance
Categories for RCM-
positions
Development and
Planning of CBM,
TBM and CM task
Optimization of the Plant specific maintenance
service program to achieve required availability
and safety with minimum costs
* RAMS = Reliability, Availability, Maintainability, Safety
20. Infosys Confidential20 |20 Internet of Manufacturing Midwest 2018
Visualization was an important tool to take decisions and
interventions based on analytics recommendations
Plant performance
KPIs:
RAMS and Risk
Index
Downtime insights:
Troubled asset, reason
for failures, cost savings
21. Infosys Confidential21 |21 Internet of Manufacturing Midwest 2018
Condition-based monitoring used AI to recommend pump
maintenance and proactive interventions
Results of RAMSanalyses of three design solution cases with currentmaintenance service program(without
CBM= Condition Based Maintenance)and with Drishti 4.0* operational excellence AI platform (=with CBM)
Pumping station
operational time 30 a
Case 1:
4 big and 3 small pumps
Case 2:
3 big and 3 small pumps
Case 3:
2 big and 3 small pumps
Maintenance service program Without CBM With CBM Without CBM With CBM Without CBM With CBM
Reliability and Availability
requirementsare fulfilled YES YES YES YES NO NO
Total Life Cycle costs (€)
2,307,358 2,107,910 1,743,175 1,568,232 1,486,811 1,308,584
Case 3:
Not acceptable
because of
violation of
environmental
permit
Case 2 with CBM: recommended
designsolution
1)No environmental risks
2)LCC cost are 740k€less than the
original design solution
RAMS designsavings 565k€
With CBMLCC savings 175k€
Case 1 without CBM:
The current design
solution
* Dhristri 4.0: dhristri.com
22. Infosys Confidential22 |22 Internet of Manufacturing Midwest 2018
Machine Learning
in action:
Churn Prediction
MajorAuto Manufacturer
23. Infosys Confidential23 |23 Internet of Manufacturing Midwest 2018
• Purchased vehicle
• Enrolled in trial (1 year)
• Converted to paid subscription
• Renewed paid subscription
The customer digital services cycle can be defined in the shape of a
funnel, and at each stage, there is churn. How do we reduce churn?
• At each stage of the funnel, we lose customers
– What can we do to reduce churn at each stage?
• By using customer and vehicle data across each stage,
we can use machine learning to predict a customer’s
likelihood to churn (i.e. customer does not progress to
the next stage of the funnel)
Find my car
Remote
Climate Start
Remote
Lock/Unlock
Typical
cloud
connected
features
Are there usage trends or customer behaviors thatcan
predict a customer’s likelihood of churning?
24. Infosys Confidential24 |24 Internet of Manufacturing Midwest 2018
Initial subscriptions present the biggest opportunity for improvement
Current annual sales: ~300,000
Luxury Brand
Annual sales (projected 2019): ~1,500,000
Mass Market
Brand
Select models
Increasing initial paid subscriptions is largest improvementopportunity
13%
33,600 vehicles
Enrolledintrial
and converted
to paid
64%
243,200 vehicles
Enrolledintrial but did
not convert to paid subscription
23%
64,400 vehicles
Did not enroll in
trial
9%
136,800 vehicles
Enrolledintrial and
convertedto paid
46%
699,200 vehicles
Enrolledintrial but did
not convert to paid subscription
45%
684,000 vehicles
Did not enroll intrial
25. Infosys Confidential25 |25 Internet of Manufacturing Midwest 2018
We gathered all relative usage and subscription data
• We gather data for new vehicles that were sold and enrolled in a trial of one month (in 2016)
– These vehicles were up for renewal in the following year, total vehicles in sample: ~24,000
Sep Oct Nov Dec Jan Feb Mar Apt May Jun Jul Aug Sep
Vehicle
Sale
Service
Renewal
• Next, we collected all usage and sales data for these
vehicles for the month before the renewal (~35,000 total commands)
– What specific commands were used by each vehicle? e.g. Remote Lock, Remote Start, Vehicle Finder, etc.
– What year / model was the vehicle?
– What was usage on the weekday vs the weekend for each vehicle?
– What metropolitan area was the selling dealer in?
– Did the customer subscribe to paid services?
Usage
Analysis
Trial Period
20172016
After getting the right data, we can build a model to answer the overarching question:
Which customers will subscribe?
26. Infosys Confidential26 |26 Internet of Manufacturing Midwest 2018
After a number of tuning iterations, the model enables churn
prediction on an individual basis
• The classification model was tuned over multiple iterations using Microsoft Azure, in order to find the
ideal level of accuracy and resiliency measured with the Receiver Operating Characteristic (ROC)
– Certain data was removed from the model to improve model quality
The tuned model enables us to determine the churn probabilityfor each customer
Infosys Churn Model POC, 2018
This curve would
indicate we could
predict every single
customer perfectly
(impossible!)
This straight line
would indicate we
are guessing
randomly
This is the
accuracy of our
model using limited
data – this can only
improve as we add
more data points,
e.g. demographics
VIN: SN0001
Model: SUV MODEL A
Weekday uses: 48
Weekend uses: 18
Remote: 0
Status: 0
Finder: 31
Lock: 35
Renewal
probability:
73.9%
Over the course of 100+ iterations, the machine learning
algorithm uses the training set to build a decision tree
based on the input data. Sample decision branches:
• Is weekend usage greater than weekday usage?
• Was the car purchased in an area with extreme weather?
repeatcustomers extraneousdata fields(e.g. VIN)
27. Infosys Confidential27 |27 Internet of Manufacturing Midwest 2018
Net Profit (in 000's)
PerYear, 1 yearold vehicles Only
CampaignConversion Rate
## 2% 3% 4% 5%
Probabilityofrenewal
5 $ 16 $ 38 $ 59 $ 81
10 $ 12 $ 49 $ 87 $ 125
15 $ (9) $ 44 $ 96 $ 149
20 $ (36) $ 30 $ 96 $ 161
25 $ (59) $ 13 $ 85 $ 158
30 $ (80) $ (3) $ 74 $ 150
35 $ (95) $ (15) $ 65 $ 145
40 $ (109) $ (26) $ 57 $ 141
We can now choose customers to address, to maximize profitability
• Incentives are 4% effective
• Customers < 20% likely to renew
• Profit in first year: $96k
• This is maximum cumulative profit for the scenario
How effectivecustomer
incentives are (“change
their minds”)
We choose the retention(renewal) probabilityof
customers to address
Additional net profit per
year (000s)
Based on incentive effectiveness, we can maximize value by choosing the targets
This is relevant for many manufacturing scenarios involving diminishing returns
0% likely
to renew
100% likely
to renew
Less than 10%
likelyto renew
~22,000 customers (VINs)
Less than 25%
likelyto renew
• We can choose which
customers to reach out to
• This enables better
efficiency of resources
VIN Scored Probabilities
SN00001 0.121978149
SN00002 0.48944521
SN00003 0.054602593
SN00004 0.196847379
SN00005 0.128807783
Actual model output
(VIN data isanonymized)
Sample implementation using
machine learning output*
* Using Azure Machine Learning Studio
28. Infosys Confidential28 |28 Internet of Manufacturing Midwest 2018
Continuing the conversation….
Jeff Kavanaugh
Partner, Manufacturing
Infosys Consulting
jeff_kavanaugh@infosys.com
Adjunct Professor
University of Texas at Dallas
jeff_kavanaugh@utdallas.edu
@jeffkav
www.infosysconsultinginsights.com
http://bit.ly/2qzanfr
www.infosys.design/plantio
Foundational skills for learning
in the age of AI (Amazon,B&N)
https://www.infosys.com
/age-of-ai/