The Maturity Path Of Digital Twins Along The Lifecycle Of An Infrastructure Asset.

“The Single Greatest Need In Business Today Isn’t Automation, Its Autonomous Insight. It Means Being Able To Leverage Vast Amounts Of Data – Behind The Scenes, Where Connected Devices And Machines Interpret What’s Happening And Why, And Then Act Accordingly, Autonomously”

The Digital Twin is used to create demonstrable business value along several axes. The twin models are continuously updated as the physical asset is operated.  At any moment the twin represents a faithful representation of the current state of the asset.  They are also scalable. Benefits are derived when hundreds or thousands of similar assets have their own digital twins. A twin tracking a single asset learns from similar assets. They are also adaptable, and can transfer learning to another part or asset class, or adapt to new scenarios or new factors.


Optimizing For Outcomes In Complex Process Manufacturing Environments | A Multi-Dimensional Algorithmic Challenge

ArcInsight Partners’ new report examines analytics innovations from three companies, bringing some field experiences solving domain-specific problems using IIoT analytics. Oil & Gas midstream refining and chemical process/batch manufacturing often throw the hardest multivariate problems for optimization of processes. Two are early technology companies with a track record combining domain specific models with AI to solve client process issues. The third, a business unit of a large global conglomerate. The three companies exemplify the use of deep-domain expertise with operating experiences in upstream & midstream of oil &, gas and downstream aromatics process and chemicals industries. One is based in Europe, the other in Asia, the third headquartered in the United States.


A Refinery Comprises Multiple Divisions, Including Units That Distil Crude Into Components Processed Into Fuels. And Those That Convert Heavy Residual Oils Into Lighter, More Valuable Products.

Repsol, the Spanish energy major, plans to deploy big data and artificial intelligence tools to optimize performance of its Tarragona integrated refining complex in eastern Spain, and ultimately across its five other refineries with a total capacity of 896,000 b/d. The Tarragona Industrial Complex processes 9.5 million tons of raw materials a year and has the capacity to distill 186,000 barrels of oil a day. The company already uses Tools such as Siclos which Repsol’s refinery control-panel operators use to forecast in real time, the economic impacts of operating decisions; or Nepxus, which increases planning, analysis, and agility in decision-making in the control rooms at these sites.

As part of Repsol’s broader digital transformation program, this new initiative will leverage Google’s ML Cloud to to analyze hundreds of variables that measure pressure, temperature, flows and processing rates among other functions for each unit at Tarragona. Repsol’s developers will build and deploy machine-learning models for refinery production.  Ultimately, the company plans to redesign more than 300 functions from its traditional operation to a digital version.


A Recent Industrial Incident Gives Us Reasons To Worry


Monetizing Industrial IoT

The “M2-R2 Test” For Industrial-IoT

Rethinking The Framework Of Doing Business

Looking For Value Opportunities In The Data Ecosystem

Exploding A Few Myths

SMART CITY: A Framework For Intelligent Value Creation

Smart Cities Are About Data-Driven Insights;

Smart Cities Are About Delivering Unique Contextual Citizen-Facing Services;

Smart Services Do Not Exist In A Vacuum. They Depend On A Pre-Existing Infrastructure Linked to A History Of Goals-Driven Economic Investments.

DIGITAL TRANSFORMATION: Enterprise Value Creation

Enterprises Must Transform Digitally To Face The Next Phase Of Their Competitive Lives.

Together With Digital Platforms, Content Ubiquity, Social Amplification And New Dimensions Of Customer Experience Insight Available Via Internet-of-Things, This Is A Transformation Opportunity Like No Other.


IIoT & Digital Transformation: How VOLTAS Turned Its Customers Around

Field service is a massive and growing opportunity, powered by companies that make machines and equipment we use every day.

Deploying AI With An Industrial-IoT Solution

ArcInsight Partners Profiles An Interesting Company That Has Developed An Artificial Intelligence Technique, Deployed On Several Field Industrial-IoT Installations.

The Hype and Reality of Blockchain

 The Hype And The Fomo (Fear Of Missing Out) Factor Have Led Hundreds Of Companies To Unveil So-called Blockchain Initiatives. Venture Capitalists Have Poured More Than US$2 Billion (S$2.6 Billion) Into Blockchain Ventures, Most Of It Over Just The Last Two Years.

Building A Successful Strategy For The Internet Of Things

Cisco’s Maciej Kranz & ArcInsight Partners’ Praas Chaudhuri Pick Out Best Practices That Make Industrial-IoT Intiatives Succeed In Large Enterprises.


Predictive Maintenance In Action

A Conversation With Patrick Bass, CEO of thyssenkrupp Elevators

Arcinsight Partners’ Principal Analyst Praas Chaudhuri Sat Down With Patrick Bass, CEO Of thyssenkrupp Americas Elevator Business To Gain Insights Into The Company’s Industrial-IoT Journey.



The United States Remains A Top Environmental Polluter

New Data Shows The United States Remains The Largest Source Of Air And Other Sources Of Pollution.

Surging Seas: A Risk Zone Map

Climate Change Is Is Accelerating The Need For Action At A Moment In Time When People Are More Open Then Ever To Doing Something For Their Community And For The World.

There's Excitement About Industrial-IoT: Startup Ecosystems Thriving In India & Isreal

Startups Bringing Digitization And IoT Infrastructure To Asset-Heavy Industries —Mfg., Logistics, Mining, Oil, Utilities & Ag. — Are Receiving Greater Shares Of Venture Deals Going To IoT Ecosystem.

ANALYTICS STARTUPS:   If You Say You Are Doing Artificial Intelligence, Then Demonstrate

  • What are the founders passionate about?

  • Are you solving a real-world problem?

  • Is AI core to your strategy?

  • What market are you going after?

  • How talented is your AI talent?

The bottom line: VCs who know the segment will quickly be able to see, or see-through, the depth of your AI knowledge. To ensure your credibility and be taken seriously as an artificial intelligence startup, if you say you’re doing AI, show it.

URBAN PLANNING INSIGHTS (Will Chilton and Paul Mackie - Mobility Lab)