The document discusses how industrial Internet-of-Things (IIoT) delivers value through analytics. It notes that IIoT differs from traditional enterprise analytics by focusing on creating monetizable value and enabling new business models through impactful analytics. IIoT analytics deals with big, unstructured data from machines in complex ecosystems, requires contextual industry knowledge, and focuses on forward-looking scenarios and quantifying uncertainty rather than retrospective reporting. The role of analytics is critical to IIoT's ability to deliver value from vast amounts of machine-generated data.
Quantifying the Value of Digital Transformation in Manufacturingrun_frictionless
At its most basic definition, Industry 4.0 is a real- time approach to decision-making, enabled by integrated and reliable data. Industry 4.0 is built on the industrial Internet of Things (IIoT), which enables manufacturers to collect, analyze, and present real- time data and analytics in easy-to-understand and highly customizable formats.
https://runfrictionless.com/b2b-white-paper-service/
Technical Data Management from the Perspective of Identification and Traceabi...ijtsrd
In a Manufacturing Industry, be it of any scale, the entity of utmost importance is the technical data. As the quantum of the generation of such necessary data is large, it paves the way to the need of establishing a data management tool such that would aid ease of access and clarity of thought. Such a tool may be in the form of software or in the form of a set personal routine or procedure that is sincerely adhered to. Technical data literally forms the backbone of the Industrys progress. Just like the nervous system is highly dependent on the well being of the backbone, almost all the departments in an Industry are highly reliant on the Technical Data Pool available. This paper highlights the importance of Technical data management from the key perspective of identification wherein a document can be easily identified and traceability wherein the document can be quickly traced for the origin as well as the locations where it is currently used. Certain recommendations shall be appended for a reference towards improved functioning of various departments in the Manufacturing Industry. A conclusion shall thereafter be drawn highlighting the utility and importance of Technical Data Management. Gourav Vivek Kulkarni "Technical Data Management from the Perspective of Identification and Traceability in the Manufacturing Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26389.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26389/technical-data-management-from-the-perspective-of-identification-and-traceability-in-the-manufacturing-industry/gourav-vivek-kulkarni
Every organization is operating with an urgency to improve upon the current state of performance and create a competitive advantage by bringing excellence in everything they do thereby creating a better value proposition for the stakeholders. Organizations can achieve this by deploying continuous, reliable and scalable processes at an optimized cost; in other words, by achieving “Operational Excellence”. Operational Excellence is the achievement of world class performance through the alignment of people, systems and tools to deliver safe, reliable and profitable production.
The word Lean comes from the ability to achieve more with less resource, by continuous elimination of waste. The lean manufacturing process is a comprehensive way to reduce waste of all types. It could be a waste of time or material, it is still waste.
Lean manufacturing is a manufacturing strategy that seeks to produce a high level of throughput with a minimum of inventory e.g. suppliers deliver small lots on a daily basis, and machines are not necessarily run at full capacity. One of the primary focuses of lean manufacturing is to eliminate waste; that is, anything that does not add value to the final product gets eliminated. A second major focus is to empower workers, and make production decisions at the lowest level possible.
The Forrester Wave Enterprise Business Intelligence Platforms, Q3 2008Cezar Cursaru
SAS was among the select companies that Forrester invited to participate in its 2008 Forrester Wave report, The Forrester Wave: Enterprise Business Intelligence Platforms, Q3 2008. In this evaluation, SAS was cited as a leader in Enterprise Business Intelligence Platforms.
Digital Business Models I Best Practices I NuggetHubRichardNowack
What new business models are made possible by digitization? Digital business models are based on connected service and digital platforms. In this business best practice slide deck you learn how to develop, prototype and implement digital business models based on platforms and connected services.
We provide you with the following best practices:
- Introduction
- Digital Platforms, Strategies and Services
- Operating Models
- MVPs and Prototyping
- Platform Design
Quantifying the Value of Digital Transformation in Manufacturingrun_frictionless
At its most basic definition, Industry 4.0 is a real- time approach to decision-making, enabled by integrated and reliable data. Industry 4.0 is built on the industrial Internet of Things (IIoT), which enables manufacturers to collect, analyze, and present real- time data and analytics in easy-to-understand and highly customizable formats.
https://runfrictionless.com/b2b-white-paper-service/
Technical Data Management from the Perspective of Identification and Traceabi...ijtsrd
In a Manufacturing Industry, be it of any scale, the entity of utmost importance is the technical data. As the quantum of the generation of such necessary data is large, it paves the way to the need of establishing a data management tool such that would aid ease of access and clarity of thought. Such a tool may be in the form of software or in the form of a set personal routine or procedure that is sincerely adhered to. Technical data literally forms the backbone of the Industrys progress. Just like the nervous system is highly dependent on the well being of the backbone, almost all the departments in an Industry are highly reliant on the Technical Data Pool available. This paper highlights the importance of Technical data management from the key perspective of identification wherein a document can be easily identified and traceability wherein the document can be quickly traced for the origin as well as the locations where it is currently used. Certain recommendations shall be appended for a reference towards improved functioning of various departments in the Manufacturing Industry. A conclusion shall thereafter be drawn highlighting the utility and importance of Technical Data Management. Gourav Vivek Kulkarni "Technical Data Management from the Perspective of Identification and Traceability in the Manufacturing Industry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26389.pdfPaper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/26389/technical-data-management-from-the-perspective-of-identification-and-traceability-in-the-manufacturing-industry/gourav-vivek-kulkarni
Every organization is operating with an urgency to improve upon the current state of performance and create a competitive advantage by bringing excellence in everything they do thereby creating a better value proposition for the stakeholders. Organizations can achieve this by deploying continuous, reliable and scalable processes at an optimized cost; in other words, by achieving “Operational Excellence”. Operational Excellence is the achievement of world class performance through the alignment of people, systems and tools to deliver safe, reliable and profitable production.
The word Lean comes from the ability to achieve more with less resource, by continuous elimination of waste. The lean manufacturing process is a comprehensive way to reduce waste of all types. It could be a waste of time or material, it is still waste.
Lean manufacturing is a manufacturing strategy that seeks to produce a high level of throughput with a minimum of inventory e.g. suppliers deliver small lots on a daily basis, and machines are not necessarily run at full capacity. One of the primary focuses of lean manufacturing is to eliminate waste; that is, anything that does not add value to the final product gets eliminated. A second major focus is to empower workers, and make production decisions at the lowest level possible.
The Forrester Wave Enterprise Business Intelligence Platforms, Q3 2008Cezar Cursaru
SAS was among the select companies that Forrester invited to participate in its 2008 Forrester Wave report, The Forrester Wave: Enterprise Business Intelligence Platforms, Q3 2008. In this evaluation, SAS was cited as a leader in Enterprise Business Intelligence Platforms.
Digital Business Models I Best Practices I NuggetHubRichardNowack
What new business models are made possible by digitization? Digital business models are based on connected service and digital platforms. In this business best practice slide deck you learn how to develop, prototype and implement digital business models based on platforms and connected services.
We provide you with the following best practices:
- Introduction
- Digital Platforms, Strategies and Services
- Operating Models
- MVPs and Prototyping
- Platform Design
Enterprise Architecture Management (EAM) I Best Practices I NuggetHubRichardNowack
Enterprise architecture management is a "management practice that establishes, maintains and uses a coherent set of guidelines, architecture principles and governance regimes that provide direction and practical help in the design and development of an enterprise's architecture to achieve its vision and strategy. In this business best practice slide deck you learn how to assess and setup Enterprise Architecture and Digital Architecture frameworks as well as a transformation plan.
We provide you with the following best practices:
- Need for Enterprise Architecture Management
- Enterprise Architecture Approach
- Architecture Target Picture Development
- Implementation Roadmap
Modernizing the Enterprise Monolith: EQengineered Consulting Green PaperMark Hewitt
Are you an enterprise that recognizes the business liability inherent in the monolithic or otherwise dated enterprise software applications you have built? Does your technology represent an impediment to the needed agility and flexibility required to meet the needs of today’s business environment?
Historically, enterprise software development focused on an approach that incorporated all functionality into a single process, and replicated it across servers as additional capacity was required. Today, these large applications have become bloated and unmanageable as new features and functionality are added. And, as small changes are made to existing functionality, the requirements to update and redeploy the server-side application becomes an intractable juggernaut.
Forward-thinking organizations like Amazon and Netflix led the way toward agile processes, deconstructed software stacks, and efficient APIs. Both large and small organizations serious about embracing modern practices have followed by decoupling the front and back end of their enterprise applications, employing microservices and cloud technologies, and adopting agile methodologies. These very steps can serve to highlight additional technical deficits in old solutions and codebases, which in turn become stumbling blocks to modern development practices.
As these technology trends continue to evolve, how can your company keep pace and remain viable?
In this green paper, we discuss how CIOs, CTOs, and VPs of Engineering can lead the needed modernization with their counterparts in marketing and the business to ensure that their organizations remain competitive in today’s customer-driven and technology-led economy.
Key questions addressed include:
• Why is technical modernization vital for the business?
• What types of modernization projects are there?
• How does modernization fit into your organization?
In this presentation, Gerhard Botha, Head of Group MES architecture at Sasol, discusses the major technological trends that are set to challenge businesses. He will take a look at opportunities as well as ways to respond to the challenges.
HfS Webinar Slides: How Cognitive Systems like ignio™ Simplify Batch Jobs Man...HfS Research
Batch jobs are the lifeblood for thousands of businesses—many of which run millions of batch jobs every year. Unfortunately, managing these high volumes of batch jobs has become a huge nightmare: numerous errors require a large amount of resources to validate and isolate the problems. Even then, batch jobs still run into unexpected outages, while Service Level Agreement (SLA) violations threaten the proper operation of the business.
This webinar demonstrates how ignio™, the world’s leading cognitive system, has been helping customers tackle this complex problem. We share real world examples on how ignio™ is implemented and highlight the lessons learned from these implementations.
Attend and learn:
Why batch job management issues are affecting business operations
How Cognitive systems like ignio™ solve the complex issues of batch jobs management
How implementing ignio™ resolves customer problems—using real world examples
Insights from practitioners on how this is implemented and the lessons learned from these implementations.
Speakers:
Dr. Maitreya Natu, Digitate
Dr. Thomas Reuner, HfS Research
Victor Thu, Digitate
Tracking technology trends that will change the future of the industry. Fostering innovation. Megatrends and transitions are occurring in months rather than years. From mobility and video to cloud and network programmability, there is no end in sight. The implications of this are amazing. Faster rates of new product introduction. Increasing product complex- ity. And a highly volatile technology landscape, where disruption occurs more easily. To continue advancing the technological frontier, and encouraging global economic growth, we need a comprehensive vision of where the IT industry is heading. Cisco Technology Radar meets this need. It is the foundation of Cisco internal and external innovation strategy. The Corporate Technology Group coordinates the radar for the Cisco Chief Technology and Strategy Office. The program builds on Cisco employees’ passion for technology combined with data-driven inputs from the latest trends in academic research, patenting activity, and venture capital funding.
STKI researches and publishes once a year a complete Market Study about the Israeli Information Technology Scene. This is a version 2 that includes changes that were found after companies presented (again) their 2018 results and STKI analysts accepted the changes.
UiPath: Insurance in the Age of Intelligent AutomationUiPath
This paper will explain what benefits Robotic Process Automation (RPA) brings to the Insurance industry, how
it tackles the most sensitive pain points and offers guidelines on
building a successful RPA capability.
Real uses cases will illustrate how other companies developed their RPA deployments. You will also find out what’s in store
for intelligent process automation (IPA), as AI and cognitive tools merge with RPA.
Finally, the paper will demonstrate that insurers must catch the RPA train before it is too late if they want to stay relevant in an ever so challenging and rapidly evolving market.
Realising Business Outcomes & Digital Transformation with IoT by Jayraj Nair,...Wipro Digital
The changing dynamics of today’s world, is forcing enterprises, irrespective of industries, to re-invent themselves; be it in products, services or business processes. For enterprises to remain relevant and sustainable, Digital Transformation of their business is key and the success of the transformation is a function of outcomes realized. In this session you will hear our point of view on how to realize value, design, build and operate solutions using digital transformation framework for a multitude of use cases and how ecosystem partnerships are a key to delivering robust end-to-end IoT solutions.
MIT Enterprise Forum of Cambridge Connected Things 2017 panel discussion on "IoT Analytics: Using Analytics to Generate High Value from IoT in the Real World"
Ant tail white paper lora nb_iot__gprs mesh -v2Mark Roemers
his white paper gives an overview of new solutions provided by telecom operators to support the “Internet of Things” (IoT) vision. This scan describes the developments and planning and compares three technologies; AntTail’meshnetworks, LORA and NB-IoT.
Report of the LTE breakout session (NB-IoT) by Mediatek Inc. (Session Chair)Yi-Hsueh Tsai
7.16 WI: Narrowband IOT
(NB_IOT-Core; leading WG: RAN1; started: Sep. 15; target: Mar. 16; WID: RP-151621)
Time budget: N/A
Overall: At this meeting we need to determine the scope of the work. Which parts of LTE TSes to be reused, which parts are not applicable, which parts need change. Identification of issues and candidate solutions. The mindset should be that Requirements in TR 45.820 shall be fulfilled.
Enterprise Architecture Management (EAM) I Best Practices I NuggetHubRichardNowack
Enterprise architecture management is a "management practice that establishes, maintains and uses a coherent set of guidelines, architecture principles and governance regimes that provide direction and practical help in the design and development of an enterprise's architecture to achieve its vision and strategy. In this business best practice slide deck you learn how to assess and setup Enterprise Architecture and Digital Architecture frameworks as well as a transformation plan.
We provide you with the following best practices:
- Need for Enterprise Architecture Management
- Enterprise Architecture Approach
- Architecture Target Picture Development
- Implementation Roadmap
Modernizing the Enterprise Monolith: EQengineered Consulting Green PaperMark Hewitt
Are you an enterprise that recognizes the business liability inherent in the monolithic or otherwise dated enterprise software applications you have built? Does your technology represent an impediment to the needed agility and flexibility required to meet the needs of today’s business environment?
Historically, enterprise software development focused on an approach that incorporated all functionality into a single process, and replicated it across servers as additional capacity was required. Today, these large applications have become bloated and unmanageable as new features and functionality are added. And, as small changes are made to existing functionality, the requirements to update and redeploy the server-side application becomes an intractable juggernaut.
Forward-thinking organizations like Amazon and Netflix led the way toward agile processes, deconstructed software stacks, and efficient APIs. Both large and small organizations serious about embracing modern practices have followed by decoupling the front and back end of their enterprise applications, employing microservices and cloud technologies, and adopting agile methodologies. These very steps can serve to highlight additional technical deficits in old solutions and codebases, which in turn become stumbling blocks to modern development practices.
As these technology trends continue to evolve, how can your company keep pace and remain viable?
In this green paper, we discuss how CIOs, CTOs, and VPs of Engineering can lead the needed modernization with their counterparts in marketing and the business to ensure that their organizations remain competitive in today’s customer-driven and technology-led economy.
Key questions addressed include:
• Why is technical modernization vital for the business?
• What types of modernization projects are there?
• How does modernization fit into your organization?
In this presentation, Gerhard Botha, Head of Group MES architecture at Sasol, discusses the major technological trends that are set to challenge businesses. He will take a look at opportunities as well as ways to respond to the challenges.
HfS Webinar Slides: How Cognitive Systems like ignio™ Simplify Batch Jobs Man...HfS Research
Batch jobs are the lifeblood for thousands of businesses—many of which run millions of batch jobs every year. Unfortunately, managing these high volumes of batch jobs has become a huge nightmare: numerous errors require a large amount of resources to validate and isolate the problems. Even then, batch jobs still run into unexpected outages, while Service Level Agreement (SLA) violations threaten the proper operation of the business.
This webinar demonstrates how ignio™, the world’s leading cognitive system, has been helping customers tackle this complex problem. We share real world examples on how ignio™ is implemented and highlight the lessons learned from these implementations.
Attend and learn:
Why batch job management issues are affecting business operations
How Cognitive systems like ignio™ solve the complex issues of batch jobs management
How implementing ignio™ resolves customer problems—using real world examples
Insights from practitioners on how this is implemented and the lessons learned from these implementations.
Speakers:
Dr. Maitreya Natu, Digitate
Dr. Thomas Reuner, HfS Research
Victor Thu, Digitate
Tracking technology trends that will change the future of the industry. Fostering innovation. Megatrends and transitions are occurring in months rather than years. From mobility and video to cloud and network programmability, there is no end in sight. The implications of this are amazing. Faster rates of new product introduction. Increasing product complex- ity. And a highly volatile technology landscape, where disruption occurs more easily. To continue advancing the technological frontier, and encouraging global economic growth, we need a comprehensive vision of where the IT industry is heading. Cisco Technology Radar meets this need. It is the foundation of Cisco internal and external innovation strategy. The Corporate Technology Group coordinates the radar for the Cisco Chief Technology and Strategy Office. The program builds on Cisco employees’ passion for technology combined with data-driven inputs from the latest trends in academic research, patenting activity, and venture capital funding.
STKI researches and publishes once a year a complete Market Study about the Israeli Information Technology Scene. This is a version 2 that includes changes that were found after companies presented (again) their 2018 results and STKI analysts accepted the changes.
UiPath: Insurance in the Age of Intelligent AutomationUiPath
This paper will explain what benefits Robotic Process Automation (RPA) brings to the Insurance industry, how
it tackles the most sensitive pain points and offers guidelines on
building a successful RPA capability.
Real uses cases will illustrate how other companies developed their RPA deployments. You will also find out what’s in store
for intelligent process automation (IPA), as AI and cognitive tools merge with RPA.
Finally, the paper will demonstrate that insurers must catch the RPA train before it is too late if they want to stay relevant in an ever so challenging and rapidly evolving market.
Realising Business Outcomes & Digital Transformation with IoT by Jayraj Nair,...Wipro Digital
The changing dynamics of today’s world, is forcing enterprises, irrespective of industries, to re-invent themselves; be it in products, services or business processes. For enterprises to remain relevant and sustainable, Digital Transformation of their business is key and the success of the transformation is a function of outcomes realized. In this session you will hear our point of view on how to realize value, design, build and operate solutions using digital transformation framework for a multitude of use cases and how ecosystem partnerships are a key to delivering robust end-to-end IoT solutions.
MIT Enterprise Forum of Cambridge Connected Things 2017 panel discussion on "IoT Analytics: Using Analytics to Generate High Value from IoT in the Real World"
Ant tail white paper lora nb_iot__gprs mesh -v2Mark Roemers
his white paper gives an overview of new solutions provided by telecom operators to support the “Internet of Things” (IoT) vision. This scan describes the developments and planning and compares three technologies; AntTail’meshnetworks, LORA and NB-IoT.
Report of the LTE breakout session (NB-IoT) by Mediatek Inc. (Session Chair)Yi-Hsueh Tsai
7.16 WI: Narrowband IOT
(NB_IOT-Core; leading WG: RAN1; started: Sep. 15; target: Mar. 16; WID: RP-151621)
Time budget: N/A
Overall: At this meeting we need to determine the scope of the work. Which parts of LTE TSes to be reused, which parts are not applicable, which parts need change. Identification of issues and candidate solutions. The mindset should be that Requirements in TR 45.820 shall be fulfilled.
The Real Impact of Digital - As Seen From the "Virtual Coalface"thisfluidworld
A new study by INSEAD and this fluid world challenges some of the common assumptions and beliefs about the positioning of “digital”. The study approaches the issue of digital from a fresh direction: the real perceptions and experiences of managers on the ground and “in the coalface” of business. The results, as well as 21 insights and recommendations for the 21st century, are highlighted in this report The Real Impact of Digital - As Seen from the “Virtual Coalface”.
Talks by Carmelo Floridia, Senior Engineer (Data Processing) at BaxEnergy, at the Big Data for You event "Recommendation Systems: Talks & Workshop" (Catania, Feb. 25th, 2017).
Carmelo offers a panorama view of BaxEnergy activities, in particular about Solar power plants, on how they analyse power curves and on how they can provide a real-time monitoring service and forecasting.
Carmelo also describes the primary characteristics of Big Data and Machine Learning, and mention the Microsoft Azure Technology as the one currently used at Bax Energy.
Although one can't see this from the slides, Carmelo gave us a live demonstration of Human model learning (based on kinect)!
IoT Seminar (Jan. 2016) - (4) friedhelm rodermund - lwm2m and lpwaOpen Mobile Alliance
Slides from the OMA and oneM2M IoT Seminar on January 21, 2016
Speaker 4:
Friedhelm Rodermund, IoT Consultant, Vodafone
Presentation Title: “LWM2M and LPWA”
Friedhelm Rodermund is an IoT consultant working with Vodafone Group R&D where his current focus is on IoT standards development and strategy. He has more than twenty years of experience within the mobile industry in various areas such as technical project management, technology innovation and evolution, strategy development, development and introduction of new services, intellectual property, and standards development. He was actively involved in the development of key standards for mobile communications and service enablers across standards development organizations such as 3GPP, ETSI, GSMA, OMA and oneM2M.
AWS re:Invent 2016: Understanding IoT Data: How to Leverage Amazon Kinesis in...Amazon Web Services
The growing popularity and breadth of use cases for IoT are challenging the traditional thinking of how data is acquired, processed, and analyzed to quickly gain insights and act promptly. Today, the potential of this data remains largely untapped. In this session, we explore architecture patterns for building comprehensive IoT analytics solutions using AWS big data services. We walk through two production-ready implementations. First, we present an end-to-end solution using AWS IoT, Amazon Kinesis, and AWS Lambda. Next, Hello discusses their consumer IoT solution built on top of Amazon Kinesis, Amazon DynamoDB, and Amazon Redshift.
AWS re:Invent 2016: Visualizing Big Data Insights with Amazon QuickSight (BDM...Amazon Web Services
Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuckSight’s in-memory calculation engine – you can go from data to insights, faster than ever.
To meet the new connectivity requirements of the emerging IoT segment, 3GPP has taken evolutionary steps on both the network side and the device side. A single technology or solution cannot be ideal to all the different potential IoT applications, market situations and spectrum availability. As a result, the 3GPP standardizing several technologies, including Extended Coverage GSM (EC-GSM), LTE-M and NB-IoT.
LTE-M, NB-IoT and EC-GSM are all superior solutions to meet IoT requirements as a family of solutions, and can complement each other based on technology availability, use case requirements and deployment scenarios. The evolution for these technologies is shown in figure #5. Technical studies and normative work for the support of Machine Type Communication (MTC) as part of 3GPP LTE specifications for RAN began in 3GPP Release 12 and are continuing with the goals of developing features optimized for devices with MTC traffic.
Happiest Minds have worked extensively with Industrial and Manufacturing companies to provide customized and value rich IoT consulting and product assessment services. Our comprehensive tools and frameworks combined with our talent rich pool of IoT consultants have helped shape the IoT journeys of our customers.
Lean Digital Enterprise Evolution in a Hyper Connected World VSR *
In the digital era, every enterprise is Digital Enterprise and every digital enterprise must be Lean. Lean digital enterprises are future proof & future ready. This white paper highlights about the nature of software applications and paradigm shift required in Global Delivery Model to support Lean Digital Enterprises....
17 Must-Do's to Create a Product-Centric IT OrganizationCognizant
Tightening IT-business alignment and embracing Agile, DevOps and Lean Startup principles, while transcending traditional project management disciplines by incorporating product engineering rigor, are critical to creating an effective, digitally enhanced business.
Pursuing a Single Version of the Truth, From Product Creation to Service
This IDC Manufacturing Insights White Paper summarizes the critical challenges the industrial equipment industry faces today and outlines the dramatic changes the industry will encounter going forward.
The paper highlights how today's fast-paced business environment calls for industrial equipment manufacturers to increase the speed of decision making along the entire product life cycle, from concept to design, from engineering to manufacturing and to service.
IDC Manufacturing Insights suggests industrial equipment organizations modernize their IT landscape to speed up decision making, streamline business processes, and break organizational silos. To do so firms will have to create a unique platform that — supporting the entire product life-cycle process, end to end — offers a single data source from product creation to service.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
Similar to MONETIZABLE VALUE CREATION WITH INDUSTRIAL IoT (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
1. Industrial Internet-of-Things
MONETIZABLE VALUE DELIVERY:
How Industrial IoT Delivers With
Analytics
A Whitepaper from
ArcInsight Partners
( S A N F R A N C I S C O | N E W Y O R K | H A M B U R G | S I N G A P O R E | B A N G A L O R E )
JULY 2016
CONVERGENCE | MONETIZATION
2. About ArcInsight Partners
Based in San Francisco & New York, ArcInsight Partners is a global research & strategy advisory group
with focus on Industrial Internet of Things (IIoT), Enterprise Digital Transformation & Smart City Evolution
strategies.
The firm has spent several years studying developments from its vantage point in Silicon Valley, and
works with companies that inspired industry adoption of tools, standards and new platforms at leading
edge of IIoT. As seasoned analysts, the group has deep understanding of use-cases, technologies and
evolutionary paths in multiple industries, as companies re-orient their business models.
Analyst Focus
Our industry experience & research methodology is a valuable partnership for enterprises looking to
position themselves for an IIoT-driven world, and a structured path to the market.
Analyst Briefings - IIoT Ecosystem Companies: Management Sponsors / Technology Teams /
Clients.
Independent Research: Industry Vertical Developments / IIoT Use-Cases / Emerging Models
Industry Conference & Events: Presenter / Session-Lead / Panelist / Panel-Moderator
Sponsored Corporate Events: Presenter / Session-Lead / Panelist / Panel-Moderator
Sponsored Content: White-papers / Marketing & Branding Collaterals / Audio & Video-Based Exec.
Communications
Advisory Focus
IIoT Investment Decisions: Facilitation Of Visioning & Planning Sessions; Build/Validate Decision;
Define IIoT Road Map: Client & industry Context Specific Planning.
Assess New Target Markets: Client business model context
Business Drivers: Validate Drivers For New Business Model
New Service Models: Design & Model New Service Opportunities
Define Monetization Path: Map Revenue Path
Map In-house Competencies: Gap Analysis
IIoT Ecosystem: Structure Appropriate Partner Ecosystems For Effective Value Delivery
Define Infrastructure Elements (In Collaboration With Partners): Platforms; Analytic Tools;
Technology; Domain Skill-Set
Acquisition/Partnership Advisory: Due-Diligence & Negotiation Support For Potential
Acquisition/Partnership Targets
3. About The Author
Praas Chaudhuri is co-founder & principal strategy advisor at ArcInsight
Partners. A global management professional, He brings over 20 years of deep
experience with a diverse array of clients (large global organizations and high-
growth enterprises) on strategic aspects of business. His prior experiences in
strategy & organizational transformation were shaped by new possibilities that
technology brings to a range of industries.
As principal consultant at KPMG’s Silicon Valley office and at a Monitor group firm , Praas studied
& commented on ever-changing trends in technology and their influence on strategic thinking around the
world. Some trends are now mature and part of mainstream business, while others remain in evolution.
He built on this knowledge as part of its strategy consulting team advising clients across software,
banking & financial services, manufacturing, life sciences, healthcare and digital media industry globally
from its Singapore, Mumbai, London & Silicon Valley Offices.
Subsequently, working with HBS Prof. Robert Kaplan & David Norton at the Balanced Scorecard
Group (Boston) he guided client organizations map their strategic contexts, frame corporate-level
business strategies and enterprise transformation initiatives.
In his last corporate role leading Hewlett Packard’s industry strategy at Palo Alto (California),
Praas helped identify new growth opportunities, leveraged new research emerging from the companies
erstwhile HP Labs, and engaged with its key global enterprise clients helping frame their technology-led
transformations.
Praas Chaudhuri began his early career as a hands-on chemical engineer working with chemical
process and oil-refining industry on industrial cooling water and environmental management problems.
Subsequently he wrote process control software for managing manufacturing processes, long before IIoT
became a buzzword. From his vantage point in Silicon Valley, Praas and his team now use the unique
confluence of opportunities arising from his process-control experiences, a vast industry vertical research
base, new emerging analytics approaches, and knowledge of emerging enabling IIoT technologies to
frame industry developments & to provide guidance to IIoT-focused enterprises.
He earned a bachelors degree in Engineering, a MBA in Strategic Marketing & Finance (as an
ADB scholar), and carried out post-graduate level work in Advanced Decision Analytics (Stanford
University). He continues to publish well-received perspectives on the subject of analytic insight as the
core value of Industrial IoT. Some recent ones: An Experience Layer For Industrial IoT | Mind Of The
Machine | GE’s Predix Platform: Looking At The Road Ahead | A Litmus Test For Enterprise Digital
Transformations | The Rise Of Data Capital |
Reach him at:
Praas Chaudhuri (Co-Founder | Principal Analyst & Strategy Advisor)
ArcInsight Partners
(San Francisco | New York | Hamburg | Singapore | Bangalore)
Phone: 1-510-364-8536
Email: pchaudhuri@arcinsightpartners.com
Skype: prasenjit.chaudhuri5
LI: https://www.linkedin.com/today/author/praaschaudhuri
4. TABLE OF CONTENTS
1. Revisiting Industrial-IoT’s Raison d’Etre Page 6
2. Who Drives IIoT’s Monetizable Value Delivery Conversation Page 8
3. What's Driving Enterprise Spend On IIoT Page 9
4. The Critical Role Analytics Plays For IIoT Page 11
5. Taking The Strategic Path To Build IIoT-Analytics Competency Page 13
6. Ecosystem Thinking Is Foundational To IIoT Value Creation Page 15
7. PLM And AR Complement IIoT's Value Delivery Goals Page 17
8. The Coming Commoditization Of IIoT's Value Chain Page 19
9. Avoid Getting Lost In The PR Cacophony Page 22
10. In Conclusion Page 23
6. Revisiting Industrial-IoT’s Raison d’Etre
Mention about the new age of Industrial IoT to a manufacturing veteran, and you will get a hearty laugh.
IIoT is not a new concept. It has a history going back over 30 years. It was known by a different name
then – Embedded Process Control. It lacked the benefits of digital technology enablers (digital sensors,
cloud, cheap and open-sourced tools for advanced analytics & visualization, offsite data-storage and
data-management tools and finally, the ability to manage processes remotely via the Internet.
We have the ability to look at the big picture of manufacturing production strategy, while simultaneously
zooming into fine-grained details of individual production component performance. Often without human
intervention human to make it happen.
At its heart, industrial IoT is a hierarchy of management across multiple asset classes, all connected by
process-engineering design principles and limited by physics-design principles.
At the narrow top of this hierarchy lies Asset Health – an individual pump, a compressor, a wind-
turbine, a jet-engine, a HVAC chiller. Each machine is “sensored” to generate a stream of data about
parameters that indicate their health. In a perfect world, one may monitor every heart-beat from every part
of the machine thereby making available perfect information about its functional health. In real life
however, monitoring parameters are limited by our physical ability to sense and measure performance
parameters directly. Hence the importance of inference. Think of Asset Health as a non- linear equation
where the machine operation is a function of a set of parameters with unknown degrees of importance.
ASSET-HEALTH = a*(Parameter 1) + b*(Parameter 2) + c*(Parameter 3) + …….
Examples of specific metrics used in a plant environment are “Time to Failure” & “Probability of
Failure”. How an asset ages relative to its operation and exposure (fatigue, stress, corrosion and other
indicators) can be predicted. The information can then be used to optimize maintenance vs. mission
reliability of each asset as well as the entire operating system. Prognostic models can be used to detect
causes for improved asset failure mode. Life-duration models and anomaly detection models can
increase accuracy of production machine life curves and further personalize maintenance needs,
management and reduced unplanned downtime. For example, the health score of a valve in an oil-
refining facility may take sensor data on flow, input pressure, output pressure along with data from an
infra-red sensor inspection equipment. It may also take into account text inputs from maintenance
records.
7. The next level is Process Health. A manufacturing operation usually has many such processes, each
enlisting the support of multiple and types of machines to deliver on its goals. Think of this as a higher
order equation where process output is a function of a set of interconnected machines all with an equal
degree of importance. The importance of equal importance lies in their interconnectedness. In a chain of
machines, a failure in one is designed to shut down the entire process. However, even machines of the
same design type may exhibit differential behaviors under different environments.
PROCESS-HEALTH = x*(Asset 1) + y*(Asset 2) + z*(Asset 3) + …….
Decisions enabled by the Process-Health metric may include local optimization of materials and labor
based on throughput and timing as a function of variation and interdependencies. It also enables a new
paradigm where maintenance is scheduled based on condition rather than on fixed calendar dates.
GE Power (among others) leverage plant thermodynamic models to predict plant performance under
different operating conditions, dispatch modes and grid (or customer) requirements both under steady
state as well as transient operation. The model uses GE gas turbine power plant design knowledge and
advanced computational methods to accelerate the execution time and enables real time decision-
making. Engineers define each plant component and how those components connect to each other. A
heat-balance engine contains the fundamental physics of each component in the power plant as well as a
“solver” that controls how these components interact such that the final result complies with the laws and
principles of thermodynamics. This creates the as-designed model of the power plant. (See an earlier
article “An Experience Layer For Industrial Internet”)
At the broad base of this hierarchy lies overall productivity of a manufacturing operation Productivity
Health. This metric relies on the coordinated operation of multiple processes to maximize a specific
production output specified by its process-design, and controlled by its owner’s business goals. An
additional layer of complexity occurs in the case of fleets, where an owner is required to orchestrate a
combination of multiple production operations and their individual behavioral “idiosyncrasies”.
PRODUCTIVITY HEALTH = A*(Process 1) + B*(Process 2) + C*(Process 3) + …..
A specific metric used is OEE defined as (Availability) * (Performance) * (Quality). Decisions enabled
by Productivity Health metric include determinations to Make/Buy Plant Locations, Late Point
Customizations, Inventory & Delivery Strategies to fulfill demand and lower cost.
Machine-Driven Learning In An Industry 4.0 World
In an analog manufacturing world with operator-run machines, the long experience of handling the same
machine repeatedly led operators to develop a sixth-sense about their behaviors. They didn’t always
need to open-the-hood to know what went wrong to diagnose failure causes. They could even sense
ahead of time about an equipment condition often by listening to it. In the new digital world soon to be run
by millennials, these plant operators are dying breeds. They carried a gold-mine of process knowledge in
their heads. Knowledge that the world will soon lose forever when they start to retire, a process already
begun. Millennials born into a digital world look at mobile apps as primary tools to run the world. Without
the right training & knowledge-management strategies to capture manufacturing knowhow, and transfer
this vast collective repository to the next generation, millennials would be hard pressed to find operations
guidance.
Industrial players are painfully aware of this silent thread. Many have launched initiatives to
evolve technologies and management systems that capture collective operator knowledge built up over
years of running plants. They are building systems and tools that enable newbie operators to come up to
speed quickly. With an average company tenure of two years, “loyalty-lite” millennials change jobs more
quickly than previous generations. The process of training & onboarding to reach target competency
levels must be accelerated. Millennial work styles and preferences are company approaches to training
and tools. Honeywell, for example, has developed sophisticated competency assessment tools as part
8. of its UniSim Competency Suite to retain knowledge in the workforce as seasoned employees retire.
UniSim Tutor is a game-like tool in the suite that teaches users through its Diagnose and What-If
evaluation methods based on plant-specific scenarios. Applications like Honeywell Pulse allow
employees to connect with the process through global and personalized alerts and watchlists.
An alternate strategy to tackle loss of knowledge repositories is applying analytics innovation in a
massively scalable manner. Capturing bits of knowledge through supervised & unsupervised learning
algorithms allow them to codify thought-processes of experienced plant operators into predictive analytics
tools, without the barrier of prior deep domain knowledge of plant operations.
Who Drives IIoT’s Monetizable Value Delivery Conversation
Monetizable Value Creation Must Be Led By Owners Of The IIoT Use-Case. Analytics Is The Last-
Mile On This Chain.
I wrote about this last year in an earlier piece (“GE Must Drive Value In Industrial IoT”). Value creation in
IIoT rests with the player that “owns” the use-case and has ability to instrument its unique ecosystem
entirely. There are a handful of large global industrial players among large enterprises that can own,
manage and drive value creation across entire value chains, and across a diverse portfolio of business
use-cases. Some are familiar names – Honeywell, ABB, Siemens, Rolls Royce, United Technologies, GE,
FlowServe. Industrial players with multi-business portfolios spanning multiple industry verticals.
Every other participant in the value-chain plays a supporting role. The diversity of use-cases and problem
domains that lend themselves to machine-learning driven predictive analytics is quite large.
See my earlier piece on emerging monetization models “IoT As An Evolving Paradigm”
9. What’s Driving IIoT Enterprise Spend
IT Spend Budgets Are Shifting
The centralized IT spend budgets that drove decision for software and hardware are now fragmenting
steadily, and reside in multiple CXO boxes.
Love In The Time Of Convergence: OT Meets IT In The Enterprise
Up until the past decade, corporate IT and manufacturing IT typically referred to as operations technology
(OT)—could not have been more separate. Even today the divide between the two persists at most
industrial facilities. But this separation is growing narrower as Ethernet becomes the de facto plant floor
network, remote access and data analysis initiatives proliferate, edge computing applications are
deployed and supply chain network connections increase.
-
10. IIoT Spend Influence Is Moving To Enterprise Mainstream. Remains Distributed.
The OT organizational silo that was restricted to making technology decisions about manufacturing &
productivity is now beginning to include enterprise data as part of a broadening of IIoT’s definition and its
impact. As a result multiple decision-makers must weigh-in and collaborate on an IIoT initiative in order to
get a project off the ground. Depending on the country and its typical management culture, this
collaborative team includes business-unit heads, finance, IT and others.
1. A survey by IDC pointed to four different stakeholders playing the role of decision centers for IoT.
- The enterprise IT organization
- The internal business units
- Newly created IoT business unit
- External stakeholders – customers, partners, vendors.
2. While IT still broadly dominates budget decisions related to IIoT initiatives, there is a lot of variation
across countries, partly driven by management cultures.
3. Awareness of IIoT’s path to value creation is one that remains a challenge among many large and
medium enterprises. Less than 4% of enterprises report creating any monetizable value due to poorly
thought out strategies underlying their IIoT initiatives.
11. The Critical Role Analytics Plays For IIoT
How Does IIoT Analytics Differ From Traditional Enterprise Analytics
The goal of IIoT analytics is to create monetizable value and to enable new business model discovery.
• IIoT Is About Impact Through Analytics. Not About Gadgets Or Hardware – sensors,
communications protocols, platforms, bridges, tools.
• It Differs From Prevailing Notions Of Enterprise Analytics – Rear-view mirror dashboarding &
reporting is built on static or slow-refresh data. Simple Math, simple trendlines, pretty charts.
• It May Be Unstructured Data – It is data from machines. Machines are subject to real-world
environments. Can be error-prone. M2M data may not fit neatly into predefined data structures, nor
be utilized FIFO. Many will wait for value discovery.
• It May Fall Into Big Data Territory – Depending on the volume, variety, and velocity of data, one
could expect between 10MB-10TB of data per industrial asset per day.
• Contextual Knowledge Is A Key Differentiator – Industry Domain Experience. IIoT Architectures.
Ecosystem Partnerships.
• The “Smart” Data-Ecosystem May Be Complex – Identifying sources of data can be difficult
when multiple players are involved.
• IIoT Analytics Focuses On Forward Looking Scenarios – Demands interpreting data streaming
from multiple sensors. It places probabilistic over deterministic. Places critical value on quantifying
future uncertainty. Their correlation with our ability to predict future loss/failure.
• IIoT Analytics Complete The Strategic-Decision Loop. It enables new learning about a process
to inform a go-forward decision different from that typically made in the past. Analytics lies at the
heart of our argument for IIoT’s monetizable value delivery capability. Players follow a wide range of
practices. Some organizations building their their own proprietary analytics models & solutions for
deployment on data generated from their own equipment installed-base. Others use vendors to
integrate into their IIoT solution. A third group may choose to system integrator to bring in their own
resources.
Horizontal Machine-Learning Software Applications
The missing element for value creation really is an ability to leverage an extensive (and fast growing)
body of analytics algorithms and statistical tools made available by horizontal software application
vendors, and bring them to bear on what are truly vertical industry specific problem domains. The secret
sauce of monetizable value creation is an ability to build the right models that take in a minimum number
of input-variables (gathered from sensors), de-prioritizing the remainder, and (following a period of trial-
and-error) generates the ability to explain anomalous behavior of operational equipment with accuracy
enough to be actionable, and also detect subtle drifts in incoming sensor data to identify as a new signal
and to learn this set of newly emerging behaviors.
Machine learning algorithms have a broad applicability, including the ability to
1. Differentiate healthy-state from a non-healthy state of a machine
2. Predict when a machine is reasonably close to failure
3. Resolve a set of complex signals to identify multiple operating issues
4. Group a set of machines with similar design-parameters into clusters based on their operational
parameters
12. 5. Find the weak link. Benchmark a machine’s output performance, based on operational data from a
set of similarly situated machines.
6. Manage and prioritize a large set of input variables into smaller subsets of monitoring parameters that
most impact their performance
The primary goal of IIoT analytics in manufacturing is to sharpen focus on
multiple goals in any production environment
• Optimize cost of maintenance and repair by real-time fact-based scheduling decisions.
• Minimize cost from catastrophic failures through early prediction of such possibilities
• Predict possibilities of output disruptions
• Minimize decision errors and fine-tune implicit business rules for management
A strong data-signal of impending catastrophic event in a physical machine is often the function
of tiny (noisy) signals being emitted from smaller, subliminal events upstream to it. Events that a
human operator may not prioritize in the course of a day's normal operation at a power-generation
or an oil-drilling rig.
13. Take A Strategic Path To Building IIoT-Analytics Competency
Enterprise IIoT Analytics Competency Evolves Along A Three-Stage Maturity
The ArcInsight Partners’ methodology broadly describes this as below.
1. Descriptive Analytics: dashboards and visualizations. Report on past, present or future data with
visualizations
2. Real-Time Analytics Using Rules Engines:
3. Predictive Analytics Using Statistics: predictive analytics performed as a post process yielding a
prediction and confidence level
Look to predictive analytics solutions for “real” monetizable business-value.
But do recognize Predictive Analytics is half data-wrangling, and half statistical model building. A data
scientist’s view of the design for a machine-learning based predictive system typically comprises the
following components working together.
1. Raw Data: The raw data source (database or IoT gateway access) or files.
2. Data Views: Views on the problem defined as queries or flat files
3. Data Partitions: Splitting of data views into cross-validation folds, test/train folds and any other folds
needed to evaluate models and make predictions for the competition.
4. Analysis Reports: Summarization of a data view using descriptive statistics and plots.
5. Models: Machine learning algorithm and configuration that together with input data is used to
construct a model just-in-time.
6. Model Outputs: The raw results for models on all data partitions for all data views.
7. Blends: Ensemble algorithms and configurations designed to create blends of model outputs for all
data partitions on all data views.
14. 8. Scoreboard: Local scoreboard that describes all completed runs and their scores that can be sorted
and summarized.
9. Predictions: “Submittable” predictions for uploading to dashboard used for benchmarking equipment
performance against a universe of hundreds (thousands) of similar equipment.
An Overview Of The Machine Learning Process (Source: NIST)
15. Ecosystem-Thinking Is Foundational To IIoT Value Creation.
Look For Value In Ecosystem Data
Too often we focus too narrowly on the local problem at hand. While this generates a great local solution,
often this does not translate into monetizable value for the enterprise. To uncover and define a real
business problem may often require instrumenting the extended enterprise. In the process of framing an
operational problem for predictive analytics, it helps to step back and look at the bigger picture. Machine-
learning is not a solution for every problem. (See an earlier piece about Enterprise Digital
Transformations “Digital DNA”)
A shift in our own perspective serves the client better than hitting every nail using the only
hammer in our tool belt.
Connected Data-Ecosystems Drive Better Decisions With Analytics
An Example From Financial Services Industry: InsurTech represents a macro trend taking over the
auto insurance industry by forcing a rethink their insurance business models, by identifying modules
within their own value chain that need to be transformed or reinvented with the help of technology and
predictive analytics. An auto insurance company with 10 million policyholders may write price insurance
policies based on individual driving behavior and local driving conditions. To do this, the company uses
hourly records from thousands of weather stations – more than a million records a month, while pulling
real time telematics data from more than 2 million vehicles. In the process, the company creates a
monetizable insight collected from over 10 billion miles of driving data. The new process not only created
new data-sets that allowed better risk-pricing models, it also enabled improved relationships with its
customers by capturing numerous touchpoints during the customer journey not done previously. No
surprise therefore that UBI based pricing models are poised to take on a growing share of an auto
insurance company's premium writing business.
An Example From Automotive Industry: Our advisory work with a specialty high-performance race tire
manufacturer recently transformed our problem perspective from failure-prediction focus to a better
digital-engagement focus for their retail clients. We will publish a case study soon about this experience.
A Data Ecosystem Map Example
16. IIoT Strategy Advisory Firms Have Skills To Help Bridge Insight Gaps.
IIoT strategy advisory groups are typically comprised of strategy consultants with broader skillsets that
understand the engineering aspects of their problem domain, as well as the business aspects. They have
arms-length relationships with vendors in the technology value chain, and as such are under no
pressures or obligations to push one solution over another.
Additionally they have a much clearer cross-industry view that allows them to see interconnections across
the ecosystem for their clients, much more clearly than they themselves sometimes can. Because of their
operating experiences in multiple industry verticals, geographies and technologies, IIoT strategy advisors
often see ecosystem data-chain opportunities much earlier than their clients. Additional, their training in
bisociate thinking also allows them the unique ability to cross-pollinate ideas from one problem domain to
an entirely unrelated problem domain.
17. PLM & Augmented Reality Complement IIoT’s Value Delivery Goals
Its interesting to observe the increasing importance of tools and systems we have seen before in a
different avatar.
Product Lifecycle Management: PLM as we have known them before take on a whole new
dimension in the context of IIoT. The implication of smart, connected devices in IIoT is the presence of
embedded software and the consequent need to manage the lifecycle of that software. “In the old days, it
took ages to design a high-efficiency, high-temperature blade. Today, we simulate it, thanks to our digital
factory PLM simulation system. We simulate the airflow, the cooling system, and the coding, which is
important because the temperature at the edges of that turbine blade goes up 1,600 degrees Celsius
when it’s in use, so we’ve got to really understand what the cooling system is all about and how we
minimize the gaps in efficiency. Once we’ve done the simulation, we print the blade. “ Additionally, in the
context of products-as-a-service (the most promising monetization model for IIoT), PLM is expected to
support the importance of this new service provision context as manufacturers and producers undertake
in-service monitoring, providing incremental improvements to products at the same time. As
software/firmware updates become a standard part of the service lifecycle, products will need to be
defined in a way that allows for future modifications and accommodates sufficient scope for change.
“Product performance data, whether flow and temperature in a process plant, structural deformation of
structures or energy consumption in communications equipment, can be processed for short-term
corrective action but also for re-simulation as part of long-term continuous improvement and next-
generation design.”
Augmented (& Virtual) Reality: The recent Pokémon Go launch has instantly popularized
“augmented reality” (AR) — a digital technology that makes “virtual” objects part of the world around us (it
differs from “virtual reality” (VR) where the user is immersed in an entirely alternate universe). However,
this new app is a turning point, as it leverages advances in computing power, big data and geo-location
software to change the way we perceive and
experience the world.
While experience delivery remains the goal in most
consumer use-cases, AR is now looking at problem
resolution and new insight into industrial problems as
its primary target, with stakes running into hundreds of
millions of dollars. Field service personnel can use
mixed reality devices to converge 3D virtual
information and real time data with physical systems
to fix and optimize equipment.
GE is piloting an extensible "field maintenance
manual" intended to replace the need to deal with bulky printed maintenance manuals for industrial
18. equipment. The app enables an operator to take any part of the complex assembly being worked on and
digitally "explode" it, expanding it so that its interlinked component parts are visible. This kind of 3D
exploded view is superior to a printed page because it can be zoomed and manipulated, and individual
parts can be directly addressed in the app, rather than having to refer to additional printed pages. At
PTC’s recent LiveWorx 2016 conference in Boston, I had a chance to observe PTC’s Vuforia AR being
used to look-under-the-the-hood of a power generator, virtually. The capability was enabled simply by an
asset-identifier (similar to a QR code) that pinpointed the machine under observation and proceeded to
pipe-in and visualize a data-stream emanating from sensors attached to it indicating its machine-health,
as well its CAD design—model pinpointing its individual parts. All without once touching the machine.
Take this ability a step further. Replace the static CAD model of the machine with a living breathing
virtual-model built entirely from the physics of the actual operating asset. And you have the first prototype
of a Digital Twin. I have written more about this in two previous articles: “The Experience Layer Of The
Industrial Internet” & “The Mind Of The Machine”.
3D Printing:
“We use a lot of 3D printing already. We print small-volume prototypes, and that’s a very important
method of speeding up innovation. 3D printing is also a huge help in bridging the
gap between scale and scope. Scale used to mean that if you did 5,000 blades, it was cheap, and if you
did only five blades, it was very expensive. Today, it doesn’t matter because those five blades can be
produced by 3D printing. If you take the scalability out of the equation, you can expand your scope — and
have a lot size of one.”
19. The Coming Commoditization Of IIoT’s Value Chain
In the three years since industrial IoT arrived as a shiny new bandwagon, things have evolved quite
rapidly. The consumer side of the IoT flamed-out early thanks to failed promises of monetizable value
creation. The industrial side took a more pragmatic approach given its high stakes in the game. It tested
each step for relevance and value potential. More recently however, parts of the IIoT value chain appear
to be commoditizing rapidly. That may not be a bad thing for the industry’s evolution.
Sensors
Sensors have become cheaper, smaller and more capable. In some ways they are subject to a version of
Moore’s Law. Sensors and semiconductors are a relatively small part of an IoT solution cost. Excluding
specialized sensors, economies of scale demands a steady reduction in costs and rapid commoditization
demands a rapid fall in price of sensors as well. The margin on these sensors is minimal and constantly
shrinking. . While every endpoint has multiple sensors, not all data generated is utilized for value creation.
Gateway Hardware
As processing moves closer to the sensor, there is a potential need to to efficiently push analytics to the
edge as well. Evolving edge-based computing systems must be able to quickly configure and manage
thousands of networked measurement devices and push a myriad of analytics and signal processing to
those nodes. Looking ahead, companies may transition to smarter, software-based measurement nodes
to keep up with the amount of analog data they’ll be producing. Over the past several years, hardware
device prices are declining. In addition, competitive products are always emerging. R&D costs for
developing new hardware continues to increase. Hardware costs account for a significant portion of the
cost of an IoT solution (between 20-30 %). However, competitive pressures force suppliers to constantly
provide rebates on list price to win bids. Most components are built on commodity hardware and are
subject to intense pricing pressures. Margins following industry trend stay low or decline constantly.
Connectivity Services
Higher bandwidth wireless connectivity (LTE & 5G) lend themselves to active monitoring and analytics-
driven use-cases. Edge-devices generating high sample-rate high resolution data such as those
monitoring high-speed rotating equipment require fat-pipes supported by LTE to transmit data. There will
also be low-power mesh networks (LoRa, SigFox) to support low-bandwidth or passive higher latency use
cases. Smart-meters, asset-tracking, streetlight sensors and environmental monitoring devices generate
small bits at periodic intervals, and don’t require continuous connectivity or constant handshake signals to
verify status.
20. However, when evaluating applications for the Internet of Things, few generate over a dollar a connection
per month. Applications such as smart meters (AMIs) or home security typically generate between $0.75
and $1.50 per connection per month. Connectivity margins are diminishing as customers want to spend
less and less per month on connectivity but want more connected devices. And this is just on the cellular
front. It is much harder to generate revenues for fixed line, ZigBee, Z-wave, Bluetooth, Wi-Fi and other
devices. Revenues for those communication protocols are typically a few cents to fractions of a cent. This
has driven larger carriers towards creating ‘solutions’, platforms and integration services, from simple
connectivity services. As purely a communication service provider (CSP), revenues and margins are a
race to zero.
Data-Storage & Cloud Providers
The proliferation of sensors in IIoT is signaling a massive explosion of data ahead. As an example, a
freight train hauling cargo across the country (visualize a common machine with an engine, some
boxcars, and lots of wheels), can have over 250 sensors measuring 150,000 data points per minute in the
latest models. A running Boeing jet engine produce 10 terabytes of operational information for every 30
minutes. A four-engine jumbo jet can create 640 terabytes of data on just one Atlantic crossing. Multiply
that by the more than 25,000 flights each day. The challenges of data explosion is not restricted to sensor
data alone. Within asset monitoring applications, traditional measurement systems log every data point to
disk, even when nothing substantial with the physical phenomena being measured is happening. This can
result in gigabytes and potentially terabytes of data from thousands of deployed systems that need to be
analyzed and sifted through offline.
However, the issue potentially gets vastly more complex in an Industry 4.0 scenario where operational-
technology data is expected to converge routinely with data from enterprise IT data. Often, this collected
data becomes what is termed dark data - operational data collected yet not being used. Most companies
document 22% of the data they collect, but they can analyze on average only 5% of it. Hence, the data
wasted, and still, organizations pay in bandwidth, server space, inefficient retrieval, and overhead to
maintain this data.
But costs for cloud storage is falling steadily. Vendors such as Microsoft Azure and Amazon Web
Services (AWS) have played a key role in supporting development of applications on their cloud platform
for the past 10 years. Platforms such as AWS and Azure have positioned themselves as the backend
storage infrastructure. IBM has one too.
IoT Data-Aggregation Infrastructure, Apps Development & Services Delivery
Platforms
The IoT data-management platform space is witnessing the launch of a new platform nearly every week.
Platforms are the core foundation upon which all devices and software are developed. From application
development to connectivity management to user interfaces and middleware for integration, platforms
enable IoT solution development and integration. Platform vendors are competing with companies that
give away their core IoT platform for free (or for a minimal investment).
It is also now well acknowledged the market for data-management platforms is terribly fragmented. Nearly
every vendor likes to call themselves ‘platform providers’. Nearly 400 offerings vie for mind-share, some
well entrenched big brand mainstream players – IBM Watson IoT Platform, AWS IoT Hub/2lemetry,
Microsoft Azure IoT Suite, Google, SalesForce IoT which entered the IoT space extending their current
platform as a service (PaaS) offerings with IoT-specific capabilities. These platforms have leverage
through their strong legacy distribution models and sophisticated partner ecosystems as incumbent PaaS
players. A few such as ThingWorx and Xively were early movers.
Other smaller IIoT-focused platform services with niche appeal built on predictive platforms are a more
recent trend. Nearly all major industrial use-case owner are now embarking on building their own brand of
21. IIoT platforms, often using mainstream cloud platforms as backend. SAP's Predictive Maintenance and
Service, Honeywell's Tridium, Siemens' Digital Enterprise Software Suite, Bosch ProSyst, PTC's
ThingWorx/Axeda/ColdLight suite, Zebra Technologies' Zatar, Eaton, Schneider Electric, Johnson
Controls, and other so-called cyberphysical system based automation platforms. GE’s Predix platform has
far stood as a clever model combining existing open source infrastructure platforms like Cloud Foundry
with a unique set of industry specific capabilities. It remains to be seen whether it can sustain its lead in
mindshare and platform capabilities.
Evaluating IoT-platforms in an enterprise context can be an exhausting and an unending exercise. It is
also becoming apparent elements of industrial IoT’s ecosystem are tracking a familiar evolutionary path
we saw before in consumer IoT – too many platforms, multiplicity of “me-too” messages, narrow value-
propositions, all vying for attention. The market is crowded with sophisticated platforms that provide the
backend and infrastructure capabilities required by IoT solutions. Beyond the crowded ecosystem, there
are only a handful of vendors that have achieved relevant traction with customers, developers, and
partners in the enterprise.
In an earlier article (“PREDIX: Looking At The Road Ahead”) I laid out a set of strategic success
metrics that drive value and differentiation for a IIoT platform. The set remains a good test of credibility,
and probably worth revisiting below.
1. Installed Base Of Equipment Hooked Up To Platform (OEM eqpt. maker)
2. Competitive Brands Sign-ups
3. Population size of comparable Platforms in marketplace.
4. The “Differentiation-Moat”
5. Size & Growth Of Developer Community Supporting The Platform
6. Vertical Apps Running On Platform (demos, proof-of-concepts, mockups, test-beds don’t count)
7. Differentiated Messaging
8. Proven Economic Value Offered To Platform User.
9. New Monetization-Models Created
10. Incremental Revenue Generated (direct & pull-through)
22. One Last Thing . . .
Avoid Getting Lost In PR Cacophony.
The IoT-Cheerleading Industry Is Growing Faster Than The IoT-Industry Itself.
The noise of IIoT-cheerleading has reached deafening levels lately. To imagine that a technology
(software or hardware) vendor with little prior pedigree could drive IIoT’s direction entirely on its own
marketing/PR steam is an absurd notion. We are observing this farce in many industry events lately.
Overcome by amusement and stiff boredom at a recent IIoT conference, I doodled a few thoughts
unconsciously on a notepad (generously provided by the conference-organizer).
I share this below for your own amusement.
.
Cheerleading takes many forms. Its quite common to observe a small pool of industrially deployed use-
cases making appearances under different corporate labels, or parading the same experts under different
sponsorship banners at industry conferences. The circus act appears to be growing lately. To us, this is a
sign we may have already reached the precipice of Gartner’s hype-curve peak, where an IIoT industry
correction is a healthier outcome than more of the same noise.
There Is A Very Small Pool Of Fully Deployed Field Use-Cases That Have Demonstrated
Monetizable Value Creation.
Most presented at IoT conferences exist in the realm of marketing slideware. Some are merely pilot
experiment wrapped in layers of marketing/PR in an attempt to establish credible associations.
Another emerging practice is of launching test-beds (variously called proof-of-concepts or mock-ups).
They make good stories, they manage to get people’s attention for a bit, in a few cases they may serve a
useful purpose in taking newly emerging knowledge forward.
However, it is our observation that most involve very few direct use-case owners (as we defined them
earlier) and certainly little apparent evidence of real buy-in. They serve as much purpose as what
PowerPoint slide-decks serve for a startup pitching to venture-investors. Don’t get us wrong. Many
startups have landed venture investments with that strategy.
Enterprise value creation on a massive scale is however an entirely different world.
23. In Conclusion.
The industrial IoT world is different from the consumer world. It has real
machine, it has real use-cases, and it has real operational problems that require
solutions. And a smart predictive analytics model built for a targeted problem-domain
does have power to impact hundreds of millions (if not, tens of billions) of dollars for
a globally operating industrial company.
But where are the competent data-science and statistical model builders with a willingness and courage
to get in bed with an industrial company. And commit themselves to solving that domain-problem. The
upside gains (revenue, reputation, brand, valuation) are significant for such focused players. But there
may be significant downsides for those failing to solve the domain problem. Sadly, most analytics
applications vendors have adopted risk-averse platform strategies. Approaching target markets with
approaches that focus entirely on selling licenses or cloud subscriptions (or in the case of systems
integrators, throwing a few techie-bodies that understand data-plumbing) is a half-hearted way to
addressing this market need.
Success in creating monetize-able value with IIoT is rooted entirely in our willingness to
roll up sleeves and jump headlong into new problem-domains wielding those predictive
analytic tools we have seen work in other domains before.