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According to Gartner, artificial intelligence (AI) is not defined by a single technology but by an array of capabilities and research, from advances in algorithms to abundant computing power and advanced analytical methods such as deep learning. But for most of us, the AI experience doesn’t look like deep learning or sound like an algorithm. It sounds like the voice that responds from a smart speaker when we ask about the weather or to tune into a podcast.

A vast majority of CXOs are already relying on consumer technologies such as voice-activated digital assistants in their work, according to a recent survey by PwC. Imagine how much more powerful those virtual assistants could be with access to the collective knowledge of the enterprise and the ability to know individual users and what they need to work best. And imagine putting that power into the hands of users across the enterprise, in the meeting room, on a factory floor or in any device they use to do their jobs.

In fact, AI has the potential to democratize many applications that have been too costly and time-consuming to develop for individual functions and business lines. Machine learning is helping to create a highly personalized user experience in areas such as technical support. With AI, IT support can know who you are, where you are and which devices, applications and systems you use before you ask for help. And when you do ask for help, chances are you will be communicating with a bot.

Here are just a few examples of how AI is being put into the hands of users and transforming what’s possible.

  • Bots in the post-app world: Gartnerpredicts that by 2021, more than 50% of enterprises will spend more on bots and chatbot creation than traditional mobile app development. In the “post-app era,” chatbots will be the face of AI and bots will transform the way apps are built, according to Gartner. Some bots are already capable of communicating with other bots to prompt actions, such as reordering parts or setting up meetings.
  • Intelligent search: In the age of big data, all organizations face a common problem: how to make the best use of all that information. There is a quest to develop a magic button that will sort through all the structured and unstructured data available to the enterprise, to find meaningful connections, highlight patterns and predict possible outcomes. Cognitive engines can be trained to parse vast arrays of textual, visual and audio information just by asking.
  • The conference room of the future:Fast forward a few years and envision sitting down in a meeting room with a table, a screen and speakers—much like any meeting room today. To book your meeting, you speak directly to the room, giving instructions on what will be discussed and presented, who will attend and how they will participate—virtually or in person. Sensors in the room, using facial recognition, will note who is attending and, using voice recognition, automatically transcribe the entire meeting—even when multiple people are talking at the same time. Attendees can bring up documents, blueprints or any other stored information simply by asking out loud.
  • Mixed reality—changing the future of collaboration, engineering and training: If virtual reality represents a total immersion in a make-believe world, think of mixed reality as a more practical application of the same three-dimensional renderings, grounded in the real world—less Avatar and more holographic image of Tupac. For the enterprise, the ability to manipulate three-dimensional objects in a real space can make it possible for architects and engineers on different continents to look at a building blueprint and collaborate on its design. Interactive training modules can be designed with avatars crafted to help prepare employees for real-life situations. Three-dimensional, holographic representations of buildings, plants and equipment can save time and resources in everything from retooling machinery to repair work to disaster recovery.

Putting It All Together: The AI Platform

Publicis Groupe, the advertising and public relations giant, has spent the past year working very closely with its technology partner to develop Marcel, an AI platform designed to connect the group’s thousands of individuals and hundreds of agencies with one another and with the history and creative insights from the firm’s body of work, as well as with award-winning case studies from the broader advertising industry. The platform will be one way employees can contribute to ongoing projects or pitches, as well as learn more about clients and campaigns. Marcel will find and recommend people for specific projects, or they will be able to pitch ideas directly through the platform. Marcel will also have access to employees’ calendars and will be able to make recommendations that take the person’s workload and availability into account as well as that person’s experience and work preferences. Publicis also promises its employees that Marcel will cut time spent on tedious manual processes such as timesheets and expenses.

October is the month referred as the National Breast Cancer Awareness Month (NBCAM), America when hospitals and charitable institutions organize campaigns for the awareness that ultimately leads to fund raising for research and prevention attempts of this deadly disease. I take this opportunity to contribute to the cause through my paper where I have explained how emerging technologies as AI and Blockchain can contribute to breast cancer research and accurate diagnosis.


Breast Cancer, the Beast

As per National Breast Cancer Foundation, “Breast cancer is the most commonly diagnosed cancer in women. “Breast cancer is the second largest cause of cancer death among women”[1]. Every year approximately 40,000 women die out of breast cancer in USA [2]. However thanks to technology, with early detection this mortality rate can be brought down to a major extent.


Breast cancer survivors might have gone through many unpleasant experiences and procedures during the treatment that initiates with a needle biopsy. Lymph nodes found in women breasts might not be always a malignant tumour or cancer. First, following a clinical examination by breast surgeon, a radiologist evaluates and categorises the tumour between 1-5 through x-ray mammography (figure 1), sonomammography (figure 2) or MR mammography (figure 3). If suspicious, a needle biopsy is recommended followed by surgical removal of the lesion.


As per reports diagnostic errors play a role in around 10% of patient deaths [3] and breast cancer is no exception. Research says, “Overall, screening mammograms miss about 20% of breast cancers that are present at the time of screening. False-negative results can lead to delays in treatment and a false sense of security for affected women”[4]. On the other hand false-positive results would let patient go through unwanted painful and expensive procedures.


When Artificial Intelligence (AI) Meets Blockchain (BC)

Now let's find out how these two disruptive technologies AI and BC can work together to create wonder in the field of early detection of breast cancer in women.


AI is the stream of computer science where we create intelligent machines that have their own intelligence and behave like humans or may perform better. In healthcare space AI mostly uses machine-learning methods for creating value out of unstructured data which is mostly big data.  


2010 London-founded Deepmind, later acquired by Google, is a world leader in AI who has come up with products to empower women health by applying machine learning to mammography screening for breast cancer [5]. Usually these AI enabled tools have higher success rates of detection than radiologists. A similar AI program developed in UK has become very successful, as it claims to be 30 times faster in analysis than a human doctor and comes with 99% accuracy in cancer detection [6].


However such kind of data analytics would need huge amount of authentic patient data for deep analysis and creation of many different patterns for successful detection of tumours. In fact more the amount of data; more would be the chances of accuracy in analysis. This is where BC can help to provide data in a secure, tamper-proof way possibly from patients in many different countries and also from women of large number of different ethnic origins that would help fuel cancer research.


Born almost a decade back, Blockchain technology is still an infant who has brought the biggest disruption in 21st century. Worldwide spending in Blockchain is growing at an unbelievable rate of 81.2% [7] and healthcare Blockchain is projected to reach $176.8 million in 2018, and is estimated to grow over $5.61 billion by the end of 2025 [8].

Blockchain technology provides us a distributed secure ledger, which will log and store health-related experiences that research institutes and especially oncologists can get access to study patterns and find value out of it to serve cancer patients better. Too early to say but perhaps with Blockchain we are slowly moving towards a dream, i.e. global health governance.


Figure 1: X-ray mammogram shows a cancer in breast (arrows) (Courtesy: Dr. Rajul Rastogi)


Figure 2: Sonomammogram shows a cancer in breast (arrows) (Courtesy: Dr. Rajul Rastogi)


Figure 3: MR mammogram shows a cancer in left breast with measurements (Courtesy: Dr. Rajul Rastogi)


Breast Cancer Free World, Every Woman’s Dream

While many such technologies are still in research or in beta version, days are not far when they would actually be used in the hospitals in our next door. For any such research we need data from individuals both for evaluation of positive and negative scenarios. Patients who hesitate to share their data which may go global to research institutes must note that any such organization first strip down the personal data for patient’s privacy. It should be our combined effort, be it in technology, medicine or research that would one day lead to a “Breast Cancer Free World” that is every woman’s dream.



  1. Breast Cancer Facts - National Breast Cancer Foundation -
  2. How Common Is Breast Cancer? - American Cancer Society -
  3. What's Up, Doc? Diagnostic errors cause 10% of patient deaths -
  4. Mammograms -
  5. Applying machine learning to mammography screening for breast cancer -
  6. This AI software can tell if you're at risk from cancer before symptoms appear -
  7. Report Suggests Global Spending on Blockchain Tech Could Reach $9.2 Billion by 2021 -
  8. Blockchain healthcare IT set for huge growth by 2025 -
  9. Cover Image Source - By Nephron [CC BY-SA 3.0  ( or GFDL (], from Wikimedia Commons


Author Bio


Debajani Mohanty, author of Amazon bestseller “Blockchain from Concept to Execution” and “Ethereum for Architects and Developers” is a Senior Architect with NIIT Technologies Ltd, Delhi/NCR and has close to 17 years of experience in the industry. She has been involved in large projects and built many scalable enterprise B2B & B2C products from concept to market in Travel, e-Governance, e-Commerce and BFSI domains. Writing complex technical articles in easy language and with high readability is her forte that has earned her close to ten thousand followers on social media.


Debajani is a NASSCOM and UNICOM event speaker and honorary faculty at Amity University  and Kerala Blockchain Academy, the first Blockchain Academy in India. Debajani is also a woman activist and writer. She has been felicitated by Nobel Peace prize winner Mr Kailash Satyarthi for her outstanding contributions to women empowerment.

IoT is the sensational topic that is here for quite some time now. But in the recent past years, IoT has gained more relevance. All the major technology giants are amazed about what IoT has achieved in the past and what more it has in the future for the mankind. Most of us are not aware of the term IoT. Let us first understand what IoT actually is.

First, What is IOT?

IoT is known as the Internet of Things. It is a system of interconnected devices that share the real-time information among themselves in a network. When devices like home appliances, vehicles, weather forecast systems, navigations systems are interconnected over a network, they together make the Internet of Things.

Let us take an example to understand the beauty of IoT:

You have a meeting at 10:00 am. You got an email that the meeting is delayed due to some reason. The smart alarm system connected to the email system automatically delays the alarm according to stipulated time. Your coffee machine is also synched with your alarm system. As soon as your alarm hits, your coffee machines automatically brew coffee for you. You are ready to go and rain starts pouring outside. As a result, there is a huge traffic jam. IoT system automatically finds the best possible way of reaching your destination. Books ticket for you and you are ready to go. This is just an introduction of what IoT can achieve. IoT is a vast concept that can totally revolutionize the way things are done.

IoT will also help in building smart cities by improving the transportation, electricity supply, water distribution etc. It does so by finding all possible solutions to the problems and choosing the best solution. In the upcoming future, people will witness smart cities that are free from pollution, smarter transport, and smarter energy management.

IoT has a wide scope. In the new era of connectivity, it is going beyond laptops and smartphones. It is the technology which is going to connect vehicles, smart homes, smart cities, and healthcare. IoT is making more intelligent systems by bridging the gap between digital and the physical world.

But with great power comes great responsibilities too. IoT, if used for good, can change the whole scenario. But misuse of the technology can be devastating. So, it is important that the IoT system must be secure enough that can prevent data theft and any potential threat to our system. Let us discuss what are the various security risks and vulnerabilities involved in IoT and how we can prevent them.

1.Security Risks in IoT Systems

IoT system has a cloud database that is connected to all your devices. These devices are connected to the internet and it could be accessed by the cybercriminals and hackers. As the number of connected devices increases, chances for hackers to breach the security system gets increased.

Making IoT System more Secure

The security must be the main concern before implementing artificial intelligence and IoT systems. It is necessary that security of IoT system is to be considered at an early stage of development. Any unauthenticated access in the IoT network system must be detected at an early stage so that degree of damage can be mitigated. Meanwhile, many embedded devices are set up externally for the security purposes. For making secure IoT systems, two things must be kept in mind.

1.Data security: Data security must be on the top of the list of IoT security features. It is the initial step to prevent any unauthenticated access to the devices in the IoT network. Layered architecture must be used in the data security system. Therefore, any breach of initial security level does no expose all the data. Rather it must alarm the authorities about the potential threats and initial level security breach.

2.Authentication: Devices must be secured with the strong passwords for the authentication. Also, third-party software security tools can be used that makes devices more secure. This may include biometrics, facial recognition, voice recognition systems etc.


2.Vulnerabilities in IoT Systems

Let us discuss some of the vulnerabilities that IoT systems are facing:

1. The absence of Transport layer security: In most of the IoT systems data is stored on the online cloud servers, mobile phones or online databases. This data can be hacked easily as it is not encrypted in the transport layer before storing. This enhances the data security risk in IoT system.

2. Inadequate Security Features: With the growing competition and huge demand, technology giants want to launch their IoT software system as soon as soon as possible. Thus the important part of the software life cycle such as testing, quality assurance, and security vulnerabilities are not done properly.

3. Poor mobile security: Poor mobile security in IoT systems make it more vulnerable and risky. Data is stored in a very insecure way in mobile devices. However, iOS devices are more secure than the Android devices. If a user loses his smartphone and data is not backed up, he will be in a big trouble.

4. Storing data on cloud servers: Storing data on the cloud servers is also considered as a weak link in the security of IoT systems. Cloud servers have less security and are open to attackers from all the dimensions. Developers must make sure that data stored on the cloud servers must always be in the encrypted format.

5. Network attacks: Another big vulnerability in the IoT systems is the wireless connection that is exposed to the attackers. For example, hackers can jam the functionality of a gateway in IoT systems. This can bring down the whole IoT system.


In the nutshell, we can say that IoT is the one of the interesting and latest technology these days. Internet of Thing is used to define the network that consists of a number of electronic devices interconnected with smart technology. Smart Cities, smart cars, smart home appliances are going to be the next big thing that will revolutionize the way we live, work and interact. As we know every coin has two sides. Similarly, IoT has some risks and vulnerabilities too. By overcoming these threats, we can enjoy the services of the IoT and artificial intelligence applications.

Artificial Intelligence is more than just automating banking services. It’s the culmination of innovation and technology in an industry that relies heavily on automation. Customers can already perform many of their ATMs’ services and other banking requests on mobile devices rather than physically visiting the bank branch.


AI systems help banks save cost, improve RoI, and increase customer base. It is estimated that financial institutions that invest in human-machine collaboration and AI will boost their revenue and create new employment opportunities. BFSI firms in India have invested over USD 170 Mn in 2017 on AI related initiatives and are expected to invest over USD 4.5 Bn by 2025.


In order to fully benefit from the potential of AI, Financial Institutes in India need to accept that AI has the potential to rapidly transform their organization and the industry, work towards an enterprise wide strategy, and secure dedicated leadership support.


AI - An integral part of the BFSI ecosystem


The new banking ecosystem is based on the foundation of open banking, transparent blockchain based systems, and artificial intelligence. While still in the nascent stages of implementation, these solutions can significantly impact the cost and revenue structures of financial organizations.


BFSI firms in India are attracted towards AI based solutions because of their capabilities and changing business needs. Fueling this rapid advancement in AI and machine learning are key factors like - 

  •       Explosion and availability of structured and unstructured data
  •       Ability to get cost-effective compute power that can run powerful algorithms inexpensively and process massive amount of data
  •       Rising pressures due to new competition (including new age Fintech companies)
  •       Increased regulatory requirements (like GDPR and Data localizations regulations)
  •      Heightened consumer expectations


Using algorithms and machine learning, AI software can help to both assess risk as well as manage repetitive processes at the back end, including completing and filing claims and applications.


AI has a much better RoI in the areas where there is an opportunity to deliver personalization and relevance at scale. As consumers engage with their bank and more transactions and behavioral insights are collected, the consumer expects interactions with their bank to be more contextual and personalized. This can also result in enhanced RoI for banks and other financial institutes.


A few key examples of AI implementation in BFSI include -

  •        Targeted customer acquisition systems
  •        Robo Advisors for wealth management
  •        Algorithmic Trading
  •       Chat-bots for better customer service, and
  •       AML and Fraud detection


Financial service organizations should be aware of how Artificial Intelligence can be applied to their business to help them gather data, understand their markets, and be more productive and profitable. More than a replacement for human intelligence, Artificial Intelligence can work in tandem with people to deliver greater business impact and increased customer satisfaction.


To know more, download our latest research paper titled AI for BFSI

Recycling is getting is getting lots of media attention of late.  It’s been on this blogger’s mind based on personal experience with my own trash collector.  A previous blog was dedicated to the poor change management practices of that provider in instituting a price increase due to higher recycling costs.  I stand by my position that had this service provider practiced better change management, they could have saved themselves a lot of grief.  Since the previous blog, this author has attempted to learn more about the recycling industry and it has been an education!  The conclusion is, recycling is a manufacturing process in dire need of innovation.


What Happened?

In the US, consumer recycling began in earnest about 1990 to combat the growing amount of trash sent to landfills.  Enabled by favorable market conditions for scrap material, the advent of single stream recycling offered consumers the opportunity to do their part to help the environment.  This blogger participated willingly, but without any thought to the downstream process.  The hard truth is that a great deal of what we believe is recyclable is not.  For example, commingled grades and dirty paper, and broken or non-recyclable glass ends up in landfills anyway.  Most of the collected items got shipped overseas to be sorted and used to manufacture other products and therein lies the rub.


Historically, China has been the largest importer of recyclable material.  More recent government actions indicate a shift to more environmentally friendly policies.  Many believe that the 2016 documentary, Plastic China, was the driving force behind policy change.  A longer-term view, however, indicates the country has slowly been tightening the reins on imported solid waste for a decade or more.  The most recent, National Sword, went into effect in March of this year.  The policy bans importation of certain types of solid waste as well as impose strict contamination limits on acceptable waste. 

The change is having a ripple effect throughout the global recycling industry.  In the US, lack of a national recycling policy leaves states and municipalities to make their own rules in conjunction local materials recovery facilities (MRF).  Many US recyclers believe the current limit of 0.5 percent contamination rate imposed by China is nearly impossible to meet.  One local collector believes 1-2 percent contamination is the best it can achieve without capital investment.  However, volatile commodity prices and thin margins may preclude such investment particularly for smaller collectors.  As a result, waste is piling up at processing facilities with no place to go.  Much of what’s piling up will end up in landfills.  Smaller collectors who are unable to compete with either be acquired or close shop.  Worse, the future of recycling could be in jeopardy.     


What Now?

One solution is to look to countries such as India, Vietnam, and Malaysia to pick up the slack.  However, these countries may not have the capacity to compensate for losses in China nor is there a guarantee they will not follow China’s lead in the future.  Based on the experience of this blogger, the only sure thing is that recycling will get more expensive for all stakeholders. 


Efforts to improve the public’s recycling habits will make some contribution to reducing the amount of contamination and thus purer recyclable material.  AS costs soar, municipalities are stepping up their efforts to educate the public in proper recycling technique.  Citing contamination rates of between 30 – 40 percent, the city of Lowell, MA has hired recycling enforcement coordinators to reduce the high rate of contamination in that city’s program.  The coordinators work with consumers to educate them.  They also have the authority to issues fines of $25 to $200 for habitual offenders. 


Another solution is a national bottle deposit plan such as practiced in Norway where up to 97 percent of plastic bottles are recycled.  Here, plastic producers are subject to an environmental tax based on the amount of plastic these producers recycle.  The more they recycle, the lower the tax.  A collective target above 95 percent exempts producers from any tax, a target achieved for the last seven years.  How is that possible?  By keeping it simple.  At the heart of the plan is a focus on two PET resins and establishment of a value chain to support recycling them.  While this works in Norway, it may be difficult to duplicate elsewhere at this stage of the game.


Wanted:  Innovation in Recycling

BHS MAX AI              Innovative Automation from BHS MAX-AI 

While single stream recycling encourages consumers to recycle, the mixed waste stream that results requires a significant degree of sorting of the material into various constituents, i.e. paper, plastic, aluminum, etc.  Some facilities are large enough to justify the expense of machinery for this function, however, a fair degree of this most crucial step in the process is manual.  To this blogger and others, it appears the recycling industry is in dire need of need innovation to help reduce disposal costs, increase waste recovery potential, and save resources. 


Robotics, vision systems, and artificial intelligence are beginning to make their way to materials recovery facilities (MRF).  Modern robotic sorters utilize vision systems to see the material and deep learning to identify each item including the ability to distinguish between similar materials.  Proponents of these technologies admit that recycling centers are not as orderly as production lines and a large dataset will be required for the network to learn.  One such company, Bulk Handling Systems (BHS), believes its Max-AI Autonomous Quality Control (AQC) robotic sorter can equal and even surpass human proficiency.  According to the company, the system can even accurately identify never seen before items using probability. 


This is one example of the innovation underway in the recycling industry.  What is clear is that recycling is an industry undergoing a period of forced transformation and in need of innovative automation solutions to sustain it.  It's not all garbage - there is opportunity here for automation suppliers to leverage their expertise and technology in a new industry. 


“Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

For further information or to provide feedback on this article, please contact

 About the Author:

Paula Hollywood

Senior Analyst

Paula's focus area is Asset Lifecycle Management and Asset Performance Management, specifically Plant Asset Management and Asset Reliability. She also contributes to ARC's Process Automation and Field System teams.

ARC continues to take every opportunity to highlight examples of owner-operators that are leveraging IIoT-enabled solutions such as cloud-based analytics that enable them to continue their journey towards digital transformation.  In previous blogs ARC has highlighted its strong belief that strong technology partnerships are critical for companies seeking to fully leverage the true power of digital technologies to improve their operations and strive for operational excellence.


Cloud-based analytics empower digital transformation

BP announced recently that it has successfully deployed Plant Operations Advisor (POA), a cloud-based advanced analytics solution developed with Baker Hughes, a GE company, across all four of its operated production platforms in the Gulf of Mexico.  POA applies analytics to real-time data from the production system and provides system-level insights to engineers so operational issues on processes and equipment can be addressed before they become significant.

cloud-based analytics provides system-level insights to engineers

Photo courtesy of BP


Proof is in the pudding, cloud-based analytics really do work

The announcement comes after an initial deployment of POA proved the technology could help prevent unplanned downtime at BP’s Atlantis platform in the Gulf. The technology has now been successfully installed and tested at BP’s Thunder Horse, Na Kika and Mad Dog platforms – and it will continue to be deployed to more than 30 of BP’s upstream assets across the world.


Built on GE’s Predix platform, POA applies analytics to real-time data from the production system and provides system-level insights to engineers so operational issues on processes and equipment can be addressed before they become significant. POA helps engineers manage the performance of BP’s offshore assets by further ensuring that assets operate within safe operating limits to reduce unplanned downtime.


Cloud-based technologies are well suited for distributed and disparate operations

Now live across the Gulf of Mexico, POA works across more than 1,200 mission-critical pieces of equipment, analyzing more than 155 million data points per day and delivering insights on performance and maintenance. There are plans to continue augmenting the analytical capabilities in the system as POA is expanded to BP’s upstream assets around the world.


BP and BHGE announced a partnership in 2016 to develop POA, an industry-wide solution for improved plant reliability. The teams have built a suite of cloud-based IIoT solutions that have been tailor-fit for BP’s oil and gas operations. BP is currently in the process of deploying POA to its operations in Angola with additional deployments in Oman and the North Sea scheduled for 2019.


ARC encourages other owner-operators, independent E&P firms, and other oil & gas stakeholders to consider their road map for digital transformation and to reach out to technology partners that can help them navigate their journey as the benefits far outweigh any challenges or hiccups that may be encountered when any organization attempts to make changes to their operations, work processes and/or their culture.  ARC realizes that people and processes and the culture that is embedded in an organization’s DNA is as equally as important than the digital technologies designed to optimize performance and achieve real business value.


“Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

For further information or to provide feedback on this article, please contact

 About the Author: 

Tim Shea
Senior Analyst

As a senior analyst at ARC, Tim's research primarily focuses on upstream oil & gas automation as well as Digital Oilfield technologies.

Tim's focus areas include upstream oil and gas operational activities in support of the Digital Oilfield including multiphase flow metering, oilfield operations management systems, artificial lift optimization, leak detection systems, drilling optimization, compressor and turbine monitoring & controls, and general field devices such as radar and ultrasonic level measurement devices, and pressure transmitters, among others.

Digital transformation has become an imperative for industrial companies and infrastructure operators.  Whether it's called Industrial Internet of Things, Industry 4.0, or Digitization, companies want to leverage new technologies and broad connectivity to improve performance and enable new business models.  Managers of facilities see opportunities to improve operational efficiency and asset performance.   Equipment suppliers want to extend service offerings and increase the value of their products.  

These developments offer significant benefits, but many companies are concerned with the increased cyber risks.  Insecure devices can offer new entry points for attacks.  Insecure connectivity can enable lateral movement and expand attacker access to confidential information and critical control systems.  

Security Needs to Be Addressed Throughout IoT Device Supply Chains

Ensuring the security of IoT devices and deployments is an essential step in overcoming these cyber concerns.  Secure development lifecycle processes (SDLC) can help to ensure secure-by-design devices but this is no panacea.  Complex IoT device supply chains offer many opportunities for attackers to undermine device integrity, steal security credentials and compromise collected data.   While challenging, technology providers, device OEMs, distributors, implementers, IoT platforms and end users need to recognize and address all security gaps. 


ARC, Intel, and Mocana will be discussing some of the most challenging issues in IoT device supply chain security in a webinar on September 18, 2018.   I encourage all ARC clients to attend this event and learn more about the issues and available solutions.  It’s time for everyone to address the security roadblocks to broader deployment of industrial digital transformation initiatives.      

Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industries.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

 For further information or to provide feedback on this article, please contact


About the Author:


Sid Snitkin

Vice President, Cybersecurity Services

Sid's responsibilities include leadership of ARC's Industrial Cybersecurity practice, which develops products and services for protecting industrial facilities. Sid also supports ARC clients in Asset Lifecycle Information Management and the Industrial Internet of Things (IIoT).

Advanced metering infrastructure (AMI) measures energy, water or gas consumption and provides this information to users, suppliers and utility companies. Smart meters, communication network between customers and utilities, data acquisition and analysis systems are key components that make up an AMI. Many companies, big and smal,l have forayed into this space with solutions on power grid including AMI, remote health monitoring of infrastructure, cloud solutions for better access to data, data analytics, and multiple level security for access to this data.  

Several business needs and outcomes are driving users to invest in smart technologies:

  • Efficient transmission of electricity
  • Quicker restoration of electricity after power disturbances
  • Reduced operations and management costs for utilities, and ultimately lower power costs for consumers
  • Reduced peak demand, which will also help lower electricity rates
  • Increased integration of large-scale renewable energy systems
  • Better integration of customer-owner power generation systems, including renewable energy systems
  • Enhanced security of data – for consumers and utility providers alike
  • Customer Control – the right information and tools to make intelligent energy choices and track consumption usage in real-time.

L&T Technology Services is one such company that is investing in this technology and is helping renewable energy players maximize efficiency, operate smartly, and enhance reliability of energy supply to the grid. LTTS has implemented commercial mesh network for grids and have conducted extensive field testing of the mesh grid. Their collection devices called hypersprout collect data from individual meters and transmit it to the cloud. In addition, they engineered the connectivity from meters to the hypersprout and further from the hypersprout to the cloud. The solution also provides remote management of grid machinery and transformer unit, along with multiple level access security. The algorithms developed by them help iin assessing power forecast, expected life of transformer, expected failure of transformer, peak load and generation forecast with respect to time of the day and area, fault detection and resolution time for power failures/ shutdowns.

 There are numerous Business Benefits of implemeting a IoT-based AMI solution:

  • Automated processes: Automation of several manual processes such as meter reading and power suspension due to non-payment of bills leads to massive cost and time savings.
  • Line losses due to inefficient power transmission, which is as high as 30- 50 percent in developing economies, can be identified and eradicated.
  • Operational savings: Cloud automation helps in huge operational savings in the following areas:
    • Low metering/billing/collection efficiency
    • Electricity theft and tampering of meters
    • Low accountability of employees
    • Energy accounting and auditing not needed
  • Uninterrupted supply: Unwanted power cut due to unexpected transformer failure is avoided due to predictive health analytics. Grid providers are also able to extend the product life of their transformers with the help of proactive and corrective actions.
  • Load balancing: Insights on power consumption and consumer behavior can enable grid owners to execute proper load balancing and time-of-use rate plans for seamless energy distribution & management.

Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industries.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

 For further information or to provide feedback on this article, please contact


About the Author:


Amruta Kanagali

Senior Analyst

Amruta's main areas of focus are automation systems and solutions for process and hybrid industries, covering both India and the worldwide market. 

“When we talk about the Internet of Things, it’s not just putting RFID tags on some dumb thing so we smart people know where that dumb thing is. It’s about embedding intelligence so things become smarter and do more than they were proposed to do.” – Nicholas Negroponte

Sometimes, it feels as if everything that can be said about internet of things has already been said. We’ve been told that it’s the beginning of a new world, and we’ve been told that it’s the end of ours. Sci-fi stories have filled us with both awe and dread. We are never sure we are fully in control but, like a moth to a flame, we find ourselves pushing the envelope just that little bit more.

It might surprise many to know that IoT, even as a working model, has been around since the 1970s when a Coke machine at Carnegie Mellon University was hooked up to a server just so that students didn’t need to make a pointless trip down to an empty machine. Convenience has often been a key driver of progress in IoT, but it would be unfair to say that other developments have not influenced IoT as we perceive it today.

With accompanying improvements in data transmission speeds, miniaturising of chips, increased processing power and sensitivity in sensors and productive development environments, Internet of Things has now become a key component of the fourth Industrial revolution.

IoT – Then and Now

If we were to take the Coke machine as a benchmark, IoT has been around in various forms since then. Once the concept was proven and as computers became more propular, companies started to hook up their manufacturing machines to computers. These implementations were quite limited in scope and would probably fail modern expectations for IoT. Every space-ship sent out into space even in the eighties and nineties was an IoT system by itself – thousands of sensors monitoring every little detail, all of them interconnected and continuously feeding data back to their home stations.

IoT is a lot more sophisticated than simply saying that it consists of devices that talk to each other over the internet, Bluetooth, radio frequencies, intranet and other channels. IoT devices can be big, small or minuscule. They can be online all the time or just some of the time. They can be active, with their own power sources and control modules, or passive, activated only by the presence of another device. They can be pure-play sensors, sensor-equipped machines or simply machines that can be controlled from another location.

Since it has so many applications, it might make more sense to group the devices and solutions under the functions they serve: person, home, office and industry.

IoT For Person

Wearable technology has been around for some time now, but it is still a relatively new field. With recent advancements in miniaturisation, many of these devices have also become capable multi-functional touchpoints. IoT for person can be further drilled into three separate verticals based on their purpose – commercial (which would include tracking movement of people inside a facility, granting/denying access to classified areas, etc.), lifestyle (such as fitness trackers and smart watches), medical (blood-sugar monitors, REM trackers, etc.) and veterinary (microchipping of pets livestock and endangered animals)

The Welgovenden Game Reserve in South Africa, for instance, has gone beyond merely tagging the endangered species they have been tasked with protecting. They have put in place an entire network of IoT devices that monitor not only the animals themselves but also sections of the forest for movement and other disturbances. Interestingly, the data from the IoT devices are fed into (machine) learning systems that can then ‘learn’ to correlate causes and effects.

In developed countries, electronic shackles are already easing the congestion at jails. Primarily aimed at restricting the movement of convicts and suspects who are not considered critical dangers to their community, these shackles – which take the form of ankle monitors, shock-collars or simple boundary markers – have been around for a while now. With security implications being rather more obvious here, R&D has never stopped, nor is it likely to.

A quick glance at reveals more than 350 active projects around wearable technology, ranging from the absurd to the cool. This is just a fraction of the actual number of projects that are happening all over the world even as you read this. There are plenty of resources available today for programming wearable devices, and this has further democratised development in the field instead of restricting it to a few wealthy corporations.

IoT for Home

One of the first fillips to R&D in IoT came from home automation. At the time, the novelty factor paid for the expensive setup and maintenance costs. An elegant home automation solution was nothing if not an expensive indulgence, the equivalent of a private jet parked on the driveway for the neighbors to envy. Naturally, over time – and perhaps too quickly for companies who were just entering the space – the interest faded away. Success stories were the exception, not the norm.

But a combination of better broadband connectivity and advancements in circuitry has revived interest in the field once again, so much so that economies of scale are finally becoming a reality. Home automations, at least in part, have become affordable purchases today.

IoT @ Home can be classified into 3 subcategories based on its principal area of application: lifestyle, security and medical.

Lifestyle refers to the use of IoT for actions such as controlling the lighting (such as changing the brightness, colors or switching it on/off to give people the impression that the house is being occupied), air-conditioning (turning it on, for instance, when the homeowners are arriving), utilities (water and electricity supplies, quality control), etc.

Security refers to the use of IoT for securing the premises through the use of motion detectors, pressure pads, controlled access for domestic help to specific parts of the house in the owner’s absence, remote monitoring, locking and unlocking of doors and detectors for fire, flooding and gas leakages.

Medical IoT @ Home is typically found in places where there is a resident who needs constant medical monitoring, if not medical care itself. These devices might manage everything from period administering of medicines, motion-activated light sensing, smart locks (in case someone has an accident behind a locked door), hotlines to emergency personnel, automated escalation systems, etc. Experts believe that by 2024, medical applications are expected to be at least 25% of at-home implementations.

IoT for# Office

Business applications, on the other hand, already form a major part of spending on IoT these days. The solutions for businesses fall under one of three categories as well: administrative, movement and security.

Administratively, a major chunk of an organization’s fixed expenditures are due to consumption of electricity and water. Electricity usage, in turn, is driven principally by lighting and air-conditioning which are usually on 24x7, even when the office is less than optimally staffed. With sensors to monitor occupancies in every zone of the office, a smart business can turn resources on and off as required without needing manual oversight, freeing up an administrator for more unstructured work without affecting productivity of its employees. Philips has already invested in this field in a big way with their Lighting venture and the strides they’ve made makes for a fascinating study.

Another area where IoT can help administer an office is in housekeeping. By keeping track of water consumed and by employing sensors in the restrooms, canteens, storage tanks and pipes, an office is less than likely to be stumped by a failure in the systems. A few weeks ago, one of our contacts in the US told us about how his office was putting in place an IoT-driven system that was tracking restroom usage. According to the vendors putting it in, the system would keep track of consumables like soaps and tissues as well as fixtures, and be capable of initiating repurchase orders and repair requests as needed.  

Movement of personnel and material is also a key focus area for IoT implementations in the workplace. In offices where hundreds of employees work, or where physical assets such as files and equipment are in constant motion, it is essential that these are all tracked right down to the last foot. Otherwise, valuable hours would be lost in tracing and locating them!

Security is a rather obvious area of focus for IoT solutions for a business. Sensitive assets – physical and otherwise – have to be secured from unauthorized access; at the same time, this needs to be done unobtrusively so as to keep things moving efficiently. A typical office, after all, sees both employees and guests every day, and each of them will have to have their own accesses and limits.

IoT For Industry

Industrial IoT has played a massive role in bringing about the fourth industrial revolution. It has made operations smarter, more responsive. It has helped secure every part of the value chain and ensure quality and process controls. Even with decentralized systems, IoT has eliminated the problems of distance and ensured that plants can operate in sync with company norms.

A few months ago, we worked with a client who offered B2B services for automating legacy plants. We worked on the software, programming it to take in the data from the sensors the client would place on their automators, process it, run it through an algorithm to determine if the operation was still adhering to safety limits and, when it wasn’t, initiate appropriate corrective action. To a large extent, this is the fundamental application of IoT in industrial facilities.

But that’s still only scratching the surface of what IoT can do, as we’ve seen in recent times. In oil fields, sensors are used to detect leaks, determine pressures and flow rates, monitor emissions and wastages and convey all this information to servers situated far, far away. In automobile manufacturing units, sensors weigh metal pieces and robots – directed by computers that take in inputs from other sensors and machines – fit them together into a complete vehicle. In production plants, sensors regulate furnaces. In many of these cases, there are other IoT networks that operate in conjunction to track, detect and regulate the movement of man and material alike.

In other words, IoT in an industrial context could be one of many things. It could be a driver for operational efficiency

  • maintaining production schedules and environments at optimum levels and monitoring the running conditions for every piece of equipment to prevent a breakdown (or worse, a meltdown!)
  • tracking stock levels or evaluating inputs to production against benchmarks, resulting in less time lost to issues such as poor/unavailable raw materials
  • tracking produced goods through every step of the value chain until it leaves the industrial unit
  • controlling the use of shared resources such as water, electricity, fuel, etc., depending on the need

It could be a driver for securing the means of production by

  • detecting unsafe working conditions, such as leaks, fires or structural weaknesses
  • restricting access
  • tracking the movement of man and material that might interfere with production

It could even help with customized production for individual orders.

IoT in India

IoT’s presence in India was, until recently, restricted to businesses who could afford the high import costs. Indeed, there hadn’t been enough homegrown products and services in this field to make it easier for someone to adopt IoT. In fact, it has been logistics that has driven IoT adoption more than production, at least until the government’s Make In India campaign found enough traction to boost domestic manufacturing.

IoT can and has helped third-party logistics (TPL) service providers streamline their operations to a large extent. The bigger the warehouses, the more important are the IoT stacks that help operators ship packages in, through and out these facilities. One of the simpler ways, of course, is to tag all packages and personnel with RFID badges and then have a system monitor their movements. This has since evolved to include robot-powered assistance as in the case of GreyOrange, cellphone-driven communications pioneered by Airtel (in India, at least) and solar-power management modules at Writer’s.

In fact, production and adaptation of IoT devices are cheaper now than they have ever been. There are startups looking to actively solve interesting problems in IoT. Two major challenges await them, however – mass adoption in India is price-sensitive, while any high-value proposition must necessarily be backed by a proven track record of successes.

At the same time, the B2B space is more open to experimenting with a new approach than the customers of B2C business models. We’d even stick our necks out and say that this is a great time to get into IoT either as a customer or as a vendor. The entry barriers are low enough but the gains from delighting a customer could be significant. The race, one could say, is on… but far from over!


This article was co-authored by  Keyur V Bhalavat and  Sreeram Ramakrishnan of Plutomen Technologies, a new age technology company focusing on Augment & Virtual Reality, Mobility and emerging technologies.

Electric trucks have been in the news a lot lately, especially as Tesla has made multiple announcements regarding the highly anticipated release of its Class 8 semi-truck. However, the state of Tesla’s supply chain, along with the skepticism regarding the design of the truck, had the public wondering if it would actually come to fruition. But Tesla is clearly not the only company working to get an electric truck to the market. There are also a variety of electric trucks already on the road, most notably Class 6 trucks for parcel deliveries. With more interest in zero-emission trucks, the market is poised for growth.


electric trucksPic- Class 8 Electric Trucks


Tesla’s announcement of a planned electric semi-truck raised a lot of eyebrows, especially given the brash predictions from Elon Musk. Specifically, Musk has indicated that the trucks will be able to carry a full payload 600 miles on a single charge. While we will have to wait until the trucks are in full service to see if this rings true, Tesla has already received more than 2,000 pre-orders for the trucks, which range in price from $150,000 for the 300 mile range model to $180,000 for the 600 range model. Tesla did make a bit of splash last week  however, as it rolled out video of a prototype undergoing testing. Over the last few months, the trucks have been spotted in California, Nevada, Oklahoma, and Texas. Combine this with a recent California Highway Patrol post about an inspection of a Tesla semi (which went well), and maybe the company is getting closer to making its electric truck a reality on the road.


Daimler has unveiled two new all-electric Freightliner trucks. The unveiling of the Freightliner eCascadia heavy-duty truck and Freightliner eM2 medium-duty model took place on the same day the company announced the creation of the Automated Truck Research and Development Center in Portland. President and CEO Roger Nielsen introduced the eCascadia, which he said can offer a range of 250 miles, and can be recharged up to 80% of capacity in 90 minutes, offering an additional 200 miles of driving. He also debuted the eM2, which offers a 230-mile range and ability to recharge to 80% in 60 minutes, providing drivers another 180 miles. Nielsen stated the vehicles will be in “serious production in the next two-to-three years.”


Thor is another company looking to jump into the class 8 electric truck market. The company intends to bring multiple trucks to market in 2019, including the ET-1. This truck will have a 100-mile range and cost $150,000. The company has said that through savings, the truck will pay for itself in three to four years. Thor is also bringing another class 8 truck to the market, this one will have a 300-mile range and will cost $250,000. Thor is placing the majority of its focus on drayage, food and beverage delivery, and less-than-truckload fleets. The company is actively exploring opportunities in China and Europe.


Toyota has unveiled a new zero-emissions electric truck that builds on its first venture into the market. The new truck, known as Beta, will have an increased range of more than 300 miles per fill, which is at least 100 miles more than Toyota’s current all-electric test vehicle. Toyota has been actively testing the initial version of the truck, known as Alpha, at the ports of Long Beach and Los Angeles, logging over 10,000 miles of real world driving. Toyota said Beta’s two electric motors produce 675 horsepower and 1,327 pound-feet of torque. The truck is more versatile and will have greater maneuverability with the addition of a sleeper cab and a redesigned hydrogen-fuel cabinet combination that increases cab space without increasing the truck’s wheelbase.


Class 6 Parcel Trucks
The other side of the electric truck is in parcel, where there are already multiple companies focusing efforts on electric vehicles. As I mentioned a few weeks ago, UPS is partnering with the aforementioned Thor on to build a new electric truck. Thor is targeting a relatively short driving range of approximately 100 miles, but they are also planning for an even shorter range of 50 miles – though that version will be less expensive with a targeted production price “as low as $68,000.” The testing will “include off-road evaluation to address durability, battery capacity, technical integration, engineering and any items found during on-road testing.” UPS has already rolled out a number of electric vehicles in the past and looks to be taking an aggressive stand on the electric vehicle front for the future.


Not to be outdone, FedEx has also been exploring electric vehicles. The company announced that it has placed a reserve on 20 Tesla semi-trucks, which are scheduled to begin production in 2019. These trucks will be operated by FedEx Freight, its less-than-truckload unit. FedEx has also used smaller class 6 trucks for parcel deliveries. In fact, over the last ten years, FedEx has saved more than 158 million gallons of vehicle fuel by replacing vehicles with more efficient models and making greater use of electric vehicles, fuel cells, natural gas, hybrids and clean truck technologies. It only makes sense at this point for all parcel companies to explore these options.


In Europe, the electric truck battle also rages. Daimler has started delivering its small electric truck, which has a range of 62 miles and can transport 4.5 tons. And while UPS and DHL are using a small number of these trucks, DHL has decided to build it owns electric fleet. In 2014, the company bought a small startup called StreetScooter. Within 18 months, it had developed its own electric postal vans to navigate crowded cities, delivering post, parcels, and handling last mile deliveries. StreetScooter now makes fully electric pickups, vans, bikes, and trikes, which it also sells to third parties. It says that its 5,000 vehicles have driven more than 8 million miles so far and saved more than 16,000 tons of CO2 annually.


Final Word

These examples are just the tip of the iceberg when it comes to electric vehicles. As more and more attention is brought to emissions standards, electric vehicles seemed poised to take off. Companies have already shown success in building out both class 8 and class 6 electric vehicles for the trucking and last mile delivery industries. It will be interesting to see just how far these companies can go, and how effective the trucks can be.


Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation in the supply chain and logistics industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

 For further information or to provide feedback on this article, please contact


About the Author:


Chris Cunnane

Senior Analyst

Chris' key areas of research include transportation management systems, omni-channel logistics and fulfillment, business analytics, and retail technology. 


Artificial Intelligence is expected to have profound impact on almost all the industries and Banking & Financial Services is no exception to it. Banks are trying to deploy a combination of AI technologies like machine learning and predictive analytics to provide personalized and contextual services to customers.


Deployment Challenges

Like any other new technology there are many issues companies face venturing into AI. On one hand, it’s technically difficult to efficiently handle large amounts of data, on the other hand there is the challenge of training the deep learning systems to work efficiently with lesser data and organizing them as per the requirements.


Source: NASSCOM-CMR AI for BFSI Report


Banks are governed by multiple rules and regulations. Deploying AI systems which can manage security and privacy as per accepted standards and regulations is a big challenge followed by its ability to interpret questions and provide correct answers that meet the set objectives. Then, there is the challenge related to customers, who are still more comfortable interacting with humans, it will take them some time to adjust with the new automated AI systems (chatbots, voicebots, etc.).


Best Practices

Increasing market volatility and growing size of available data make implementation of AI and cognitive technologies the top most imperative for Financial Service players. Every AI application will have its own advantage, but there are some common best practices to follow.

  •        Data Management strategy: It is important to organize the data in a structured manner which can be interpreted by all the AI system. For AI systems to run successfully it is important to have right tools that can extract the data and store them in a centralized place which can be accessed by all AI systems.
  •        Feeding historical data: For an AI system to start delivering results immediately, the system requires to be fed with historical data, which the AI systems can use to self-learn. This can be very useful while answering customer queries as it needs to interpret old conversations to support the answers.
  •        Post implementation testing: Just because an AI system delivers the right outcomes from historical data doesn’t mean it will do so post Go-live. It is important for an AI systems to function independently for its success, as it may behave differently with actual data as compared to historical data. Therefore continuous testing using multiple parameters is extremely important to ensure accurate results.
  •        Combining public and private cloud: Designing an in-house AI infrastructure would be more apt to align the workloads that can match the exact requirements. Another option is to run AI workloads completely on a public cloud that can tap the cognitive services of an existing public cloud services using vendor like AWS, Google, or Microsoft via APIs.


To conclude

Bankers should primarily use AI to reduce human errors and create a robust business process system which can deal with manpower intensive processes. With time AI should reach a stage where it can use deep learning technology to continuously improve itself through self-learning to help increase accuracy and predict outcomes.


To know more, download our latest research paper titled “AI for BFSI”: 

According to the US Department of Energy, pumping systems account for almost 20 percent of the world’s energy consumed by electric motors and approximately 25 to 50 percent of the total electric energy usage in certain industrial facilities.  Among other applications, pumps move crude oil through vast pipeline networks, which in turn play an indispensable role in transporting hydrocarbons to key markets. Suppliers are already developing solutions such as digital twins that can leverage data analytics to optimize the performance of pump stations for crude oil and other pipelines.

Digital Twins and Oil & Gas 4.0 - Key Benefits

As digital replica modeling tools, digital twins support a digital culture of "fail fast, learn quick" by providing the perfect testing ground for innovative new ways of working.  Also, connectivity to the components and equipment provides real-time monitoring and adjustment capabilities.  The original concept came from the desire to take all information available on a piece of equipment or asset and then applying higher level analysis to that information.

digital twins and oil & gas - key benefitsthen applying higher level analysis to that information.
"Digital Twin of a Petroleum Refinery (Source: GE Ventures)"


Digital twins can help oil & gas companies:

  • detect early signs of equipment failure or degradation to move from reacting and responding to a failure to being proactive; which enables owner-operators to plan and implement corrective maintenance actions before failure occurs and often at much lower cost
  • model drilling and extractions to determine whether virtual equipment designs are feasible
  • gather real-time data feeds from sensors in an operational asset to know the exact state and condition, no matter where it is located

The real advantage of the digital twin concept, however, materializes when all aspects of the asset (from design to real-time operating and status data) are brought together to optimize the asset over its lifetime.  Companies can test pricing levels, logistics challenges, even potential safety hazards. A digital twin allows users to identify numerous plausible futures for an asset and consider their potential impact.

Best Practices of Oil & Gas 4.0

Recent research indicates that many of the oil and gas organizations implementing the Internet of Things (IoT) are already using or plan to use digital twins in 2018.  In addition, the number of participating organizations using digital twins will triple by 2022.  Several best practices in this area are emerging among the major engineering and oil & gas firms:

  • it's best to involve the entire value chain
  • establish well-documented practices for constructing and modifying digital twins
  • include data from the multiple sources (as-builts, operational data, costs, maintenance program, engineering detail, physical constraints, behavioral patterns, operating parameters, customer demands, and weather patterns)
  • look beyond the normal software development cycles to consider asset lifecycle issues

Initiatives Already Underway

Due to its asset-intensive nature and reliance on large pieces of highly instrumented equipment, often operating in remote, unsafe, and uncompromising locations, the oil & gas industry has had digital twins on its agenda for several years. 

Shell, together with Swiss engineering modeling and simulation technology company, Akselos and engineering research and development experts at LICengineering, a Danish consultancy firm specializing in the marine and offshore energy sectors, have recently signed up as participants in a two-year digital twin initiative.  The partnership focuses on advancing the structural integrity management of offshore assets by combining fully detailed cyber-twin simulation models.  Things are well under way with Shell North Sea assets, with the intention to improve management of their offshore assets, improve worker safety, and explore predictive maintenance.  There are two phases to this initial project:

  • First, to develop a condition-based model of its selected assets, enabling the company to analyze structural integrity with more accuracy and detail
  • Second, to combine this model with sensor data, to allow Shell to monitor the health of its asset in real time, which would enable the company’s operators to predict the future condition

The world's first “digital rig” is targeted to achieve a 20 percent reduction in operational expenditures across the targeted equipment and improve drilling efficiency.  The solution connects to all targeted control systems, including the drilling control network, the power management system and the dynamic positioning system.  Data is collected through individual IoT sensors and control systems, modeled and then centralized on the vessel before transmitting in near real time to GE’s Industrial Performance & Reliability Center for predictive analytics.  The system has already started to capture multiple anomalies and produce alerts of potential failures up to two months before they would occur.  The data models come from a digital twin of various physical assets, along with advanced analytics to detect behavioral deviation. Thanks to vessel-wide intelligence, personnel both on the vessel or onshore can gain a holistic view of an entire vessel’s health state and the real-time performance of each piece of equipment onboard.

In 2017, BP invested in the Beyond Limits start up to build upon existing NASA- and DOD-based experience in robotics. The intent was to operationalize new insights from operations to help them locate and develop reservoirs, enhance production/refining of crude oil, and increase process automation and efficiencies.  Extensive infrastructure was established, including supercomputers, and 2,000 km of fiberoptic cable and large investments were made to increase in data storage to six petabytes.  As a result, IoT sensors are collecting data about temperature, chemicals, vibration and more from oil and gas wells, rigs, and facilities.

The Gazprom subsidiary, GAZPROMNEFT-KHANTOS, has established an Upstream Control Centre that's pulled together already established solutions.  The objective is to improve upstream process efficiencies from a central operating center.  One of the most important components has been to establish the digital twin, developed for mechanical fluid-lifting built around hybrid models. This is further enhanced with machine learning tools and the ability to self-calibrate based on rapidly changing information, sourced from automated controls.  Information collated by the digital twin, new maintenance solutions, and other Gazpromneft-Khantos systems are accumulated at the Control Centre and can be displayed and visualized by multifunctional teams to take timely and well-informed decisions.  The functionality of the Gazpromneft-Khantos Upstream Control Centre will be significantly expanded in the future.  Currently, the company is completing testing of digital twins for formation pressure maintenance systems, energy supply systems, and treating and utilizing associated petroleum gas.

The Challenges Ahead

Development of the fourth Industrial Revolution defining technology is not for the casual "toe-dipper," as the journey to true digitalization is challenging for any enterprise.  Each aggregated digital twin is unique, ultimately enabling powerful data analytics, new machine learning, and potentially valuable information across the OEM network.  

Establishing digital twins requires a focused and cross-functional team that spans the organization, incorporating technical expertise across the infrastructure, the enterprise IT and OT applications from the OEM to the fully constructed and operational asset.


Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industries.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

 For further information or to provide feedback on this article, please contact


About the Author:


Jyoti Prakash


Jyoti’s research primarily focuses on oil & gas industry analysis, upstream oil & gas automation, and digital oilfield technologies.



The first industrial revolution marked the transition from traditional hand production to machine-driven production. The second one was characterised by rapid industrialization driven by technological innovations of the day, moving from small production units to massive factories where hundreds of people worked at any given point of time. The third industrial revolution could be credited to the World Wars – perhaps the only good to come out of the carnage and destruction. This IR saw the introduction of computers and robots to aid production, resulting in greater efficiency, throughput and quality.

We are now in the fourth revolution, which is commonly known as Industry 4.0.

Industry 4.0 has been powered by a significant number of factors. All transitions have been driven by economic considerations, but 4.0’s turn cannot be explained so simply. In the past few years, technology has evolved both in power and application; businesses are now even more complicated entities, involving a range of stakeholders right from the governments of the states they operate in to local communities who now have an active say in how their resources are consumed; customers, both corporate and individual, are more demanding. This is no longer the world in which “any color, as long as it is black,” is an acceptable business policy.

In an old world once, technology meant tethering beasts to flywheels to grind flour. Now it means everything – from the solar panels that power the motor, to the sensors that determine how finely the flour has been ground, to the robots that pack it for dispatch, to the climate-controlled trucks used for transport, to electronic billing and delivery, to alerts over mobile phones and delivery with drones. Technology itself is a broad umbrella under which other heads can be grouped under.




It is impossible for us - even those who grew up with rotary telephones and fat picture-tube television sets – to imagine a world without smartphones, let alone mobile phones. Statista reports that the number of smartphone users in the world is likely to hit 5 Billion by 2019. That’s more than 60% of the population in a world, up from single-digit figures barely half a decade ago.

It is no wonder then that advertising has moved from visual media – such as televisions and newspapers – to electronic media such as might be consumed on handheld devices. Internet costs have dropped across the world while infrastructure for access has scaled up simultaneously, which has also had a chicken-and-egg effect on smartphone usage. In emerging markets like India and China, more people consume data on their smartphones than on computers.

Smartphones have also opened up an entirely new ecosystem for businesses to reach out to and stay in touch with their customers. In an interesting statistic, there are as many billion-dollar companies started in the past fifteen years as were started in the sixty before it. Almost all of the former have relied on technology to drive their business models.

But the journey, as far as we can see, has barely begun. Smartphones have apps and apps are a surer way for companies to communicate back and forth with their customers. They offer fewer distractions, better engagements and mine more powerful usage data than any other medium can. As we move from 4G to 5G, even bandwidth will cease to be a constraint when it comes to connectivity.

Industry 4.0 will continue to be driven by connectivity solutions that don’t stop at smartphones. Sensors too have become more accurate and efficient. They consume less power, can detect greater ranges and can often be connected wirelessly to other devices to form a network that can monitor everything from one end of the business to the other. The air around is buzzing with a gazillion zeros and ones every second, data streams that connect hundreds of devices and control units.

Internet of Things, which is the more popular term for such an arrangement, has quickly moved from being the stuff of sci-fi (or, at the very least, top-secret) to a tangible technology we can see around us. Companies are no longer asking for proofs-of-concept, because there is no longer a need to prove that concept. Instead, they are asking us how we can help them add instrumentation and automation to their work – whether it’s production or service delivery. A project we did recently was for a company that automated legacy plants. We worked on the software that would monitor the feeds from the sensors, escalate appropriately when faults or warnings occur and deliver detailed reports on how the systems were performing.

IoT is proving particularly useful in home automation and logistics. In home automation, it is used to make living conditions more efficient – climate control, turning off fans and lights when rooms are vacated, running robotic vacuum cleaners, monitoring air quality and such. Logistics employs IoT to keep track of movement of people, product and vehicles.

Cloud services, in a way, are driving these innovations. Maintaining exclusive servers is still an expensive proposition, and the need for redundancies to fall back on makes it even more so. As a best practice, companies must opt for greater server performance than they need so that their stacks do not fail even at a time of high demand – yet, that merely means that for more than 95% of their running times, server stacks are rarely utilized in an optimal manner. That’s where cloud computing changed the rules of the game.

The cloud – a network of servers, some of them performing specific purposes – promises scalability, usage optimisation, failsafes, flexibility and near-perfect uptimes. This is often delivered with instantiation and load balancing algorithms that spread the traffic to the application so that there is very little chance of a system crash or a DDoS event. Even if one or two nodes of the cloud were to go down, neither data nor operational connectivity will be lost.


Data and Intelligence


Big data, artificial intelligence, machine learning and blockchain constitute three technologies that affect business intelligence. Companies are now able to connect data from diverse sources and make sense out of them, helping them identify correlations and behaviour patterns that might otherwise be missed. Big data – massive volumes of data – is used to identify and/or prevent fraud, price discovery, customer analysis, operational benchmarking, etc.

The first phase of big data left a lot to manual analysis, in that humans still had to make sense of the numbers and take appropriate decisions. The current and future phases, however, are augmented by machine learning. Self-learning programs or bots will be assigned the task of collating new pieces of information as they enter the system, figuring out the patterns for analysis, executing the analysis and eventually putting across an actionable summary for the managers.

But machine learning itself is not going to be restricted to a sidekick’s role for big data. ML is driving decisions in sectors such as pharma and medicine,  agriculture, transportation, hospitality and education. In his book, Kranti Nation: India and the Fourth Industrial Revolution, Pranjal Sharma talks in detail about how ML is changing the landscape even on governance. For instance, a Microsoft Azure project has been employed to track the successes and consequences of a health programme in south India, with a particular brief to understand how relapses offer. It is in fact a must-read for anyone looking to understand in more detail how industry in India is gearing up for modern-day tools and challenges.

Artificial Intelligence and Machine Learning are often mistaken for each other, and indeed, there are enough reasons to suggest why this shouldn’t be a big deal. At the same time, as a technocrat myself, it would be an unforgivable sin to take that path of convenience. AI and ML aren’t the same. In a manner of speaking, ML is a subset of AI.

Artificial intelligence refers to systems that can think, adapt and decide after taking in various factors, much as we would, even in an unstructured or fuzzy context. Machine Learning, on the other hand, refers to systems that are purposefully built to learn learning by themselves, often within a broad context or situation. I know, I know, it takes a while to get it… but it is an important distinction nonetheless.

AI, therefore, is implied wherever ML is employed. In addition to this, AI finds uses in systems where there are definite boundaries to what the system must do. For instance, in hospitality, AI is used by service providers to forecast demand based on key parameters, extrapolate it based on new conditions (such as the visit of a celebrity during a festival), determine staffing and material requirements, identify competitive price points and manage dynamic pricing, earmark high-value properties for last-minute guests (who won’t mind paying a premium if there is a shortage of rooms) and even put together additional packages of add-ons and third-party services to create a better experience for the guests.




3D, Augmented reality, virtual reality and mixed realities are no longer the tools of the elite they once were, at a time when the hardware requirements for running such solutions were prohibitively expensive. Now even a reasonably-capable smartphone can run AR/VR/3D applications.

Like AI and ML, AR and VR often end up getting clubbed together. Unlike AI and ML, however, AR and VR are not subset-superset. AR essentially refers to adding digital objects, such as images, text boxes, interactive buttons, videos, etc. to a real world context. In VR, that real-world context itself is not there. VR creates a completely virtual world, one in which you can look in any direction and find yourself within. The applications they can be used for, therefore, are also different.

AR is the recommended solution when context-specific overlays are needed. For instance, a brochure can be scanned and a 3D model can be superimposed on the images (known as markers in AR) so that the viewer gets a complete 360-degree look at the product. We’ve used this for our real estate, automotive and industrial machinery clients. In the US, AR is already playing a key role in medical training and diagnostics. As an educational tool, few can match the experiential value of AR.

VR, on the other hand, can be used in situations where the viewer might need to fully immerse themselves in an experience. For our real estate clients, we have created virtual apartments that visitors can navigate – either with a joystick or by moving around an area (clear of obstacles). VR would be great for teaching students about, say, the Jurassic era, for helping with phobia therapy, for experimenting with the look and feel of a room, for experimentation that would otherwise be impossible in the real world and as a marketing gimmick. Kids and adults alike love transporting themselves.

Mixed reality is, as the name suggests, a mix of real and virtual worlds. I’m going to let a video describe this one.

A common limitation to all three is that they are very personal technologies. It is only the viewer who has control, and everything is viewed from his/her perspective. Even if you were to display what the viewer is seeing on a big screen, others will see only what the primary viewer – the one wearing the headset and/or holding the device – is seeing.

There is an alternative, though.

Holograms, once the kind of technology Star Trek (and Total Recall?) fans drooled over, is making a comeback of sorts. Until a few years ago, holograms had found only limited popularity. More of a gimmick than a true solution, they weren’t interactive and required extensive, expensive hardware setups. That’s no longer the case now. Holograms can be made interactive, and, just as importantly, they can be run on kiosks.

Irrespective of the mode chosen, the success of visualization technology eventually hinges on how good the 3D modelling is. And that brings us to a heading which really doesn’t fit in with the three already listed.

Additive Manufacturing / 3D Printing

3D printing, experts argue, could eventually lead to the re-emergence of cottage industries. While 3D printing cannot be executed on the scale of hundreds of thousands of products a month, such as would happen in an assembly-line factory, it can still increase in multiples the normal throughput for small and cottage enterprises, letting them use their economies of scale to become profitable at their levels.

3D printing allows rapid prototyping and additive manufacturing.


Industry 4.1


At the risk of putting forth an unusual term, we have already moved beyond Industry 4.0 and into 4.1, which is where the convergence of these technologies are taking place. AI, for instance, is being used in AR applications to give you real-time, real-world data (such as scanning a car on the street, which pops up an electronic brochure and a choice of looks, all of which can be applied on the car then and there, virtually of course. Want to see how a silver Jag might look instead of the black one in front of you?)

Enterprise-level applications, such as Robotic Process Automation (RPA) systems, combine multiple technologies. RPAs themselves include machine learning, character recognition, IoT, mobility, et al. and are expected to disrupt the sectors they are being introduced in. Many corporations have already invested in automations across their entire business networks; others are commissioning feasibility studies for integrations that will be high on flexibility and low on maintenance in the coming years.

Indeed, one might even say that the revolution is over, done and dusted, and what we are seeing now is the new order of things. One where innovative change is the only constant, where every incremental percent of operational efficiency must be grabbed, where investments must be made for today and tomorrow.


Over the next few weeks, we will be looking at each of these technologies in detail. Stay tuned.

Process engineering simulation software has transformed the way process engineers do their job and is essential to maximize the return on capital investment in your plant or another industrial facility. The proven capabilities and security of the cloud can now transform the way manufacturing owner-operators and Engineering (EPC, EPCM) deploy simulation software.


Please join me for a webinar with Penn Energy. ARC Advisory Group will share best practices in transforming Engineering into agile, optimized operations delivering on Industry 4.0 outcomes.

Digital transformation, Industrial IoT and Industry 4.0 have all created the awareness and need for change.  Cloud services have re-defined process engineering and how changed the business dynamic for engineering organizations and the role of IT service and shared service organizations.

  • Provides ubiquitous infrastructure, enabling you to run simulations at any time, location, or on any device
  • Enables you to begin optimizing design right away without having to develop special skills
  • Provides all the benefits of engineering software without the overhead of installation, deployment, version control, and hardware maintenance
  • Increases engineering design agility
  • Doesn’t require long IT upgrade and computer refresh cycles to enable you to leverage the latest engineering software features and capabilities
  • Makes it easy to deploy models securely to enable you to share engineering models with partners and suppliers

Slide by AVEVA Digital TransformationSlide by AVEVA Digital Transformation


“Reprinted with permission, original blog was posted here”. You may also visit here for more such insights on the digital transformation of industry.

 About ARC Advisory Group ( Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.

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 About the Author:

Peter Reynolds

Peter performs research into process and technology areas such as optimization, asset performance management, and data analytics. He brings more than 25 years of professional experience as an oil and gas subject matter expert. 

Peter is a distinguished thought leader, strategist, and speaker, with an extensive history of practical experience in refinery automation, safety, and IT.  He has also published several whitepapers related to digital transformation and frequently speaks at technical conferences throughout North America and the EMEA region.

Most of the IT decision makers are of the opinion that AI is badly needed in BFSI segment and will lead to drastic improvement in operational efficiency and will fundamentally transform the core financial processes.


Conversational AI technologies are the way to go as they facilitate two-way interactions and allow banks to establish long term relationships with customers by providing seamless experience covering multiple banking aspects throughout the year.


With time the Conversational AI technologies will slowly reach a level towards an automated response system to contextualize and personalize conversations with their customer on-demand and in real time.


Major sub-segments to influence AI implementation in BFSI sector:

  •        Chatbots or Voicebots: This AI technology is programmed to converse with the customers on a 24x7 basis. It is programmed to self-learn and conduct intelligent conversations with humans over chat or audio.
  •        Robo Advisors: This AI technology uses different  platforms to offer investment advice to customers covering multiple issues with zero human intervention.
  •        Emotion AI: It is a branch of AI which helps in enabling machines to detect human emotions using advanced facial and voice recognition technologies. It further makes use of this information to provide advice to the customers depending on their mood and state of mind.
  •        Data Analysis: The AI systems record multiple data and use them to evaluate problems related to its own applications as well as to solve problems related to different areas of banking.


AI will enhance trustful conversations


One of the primary aspects associated with AI in banking is its ability to supercharge its customer using multiple touch points. The coming years will witness deployment of AI oriented Chatbots and voicebots which can interact with multiple customers using the most advanced conversation options. Voicebots are expected to be top priority with 52% banks surveyed planning for AI deployments in the next one year followed by chatbots with 48%.


Some of the major Indian banks to implement these services include:  

  •        State bank of India: SBI’s inTouch chatbot is using AI technology to address various customer queries powered by IBM Watson. The chatbot provides information on various banking products and services like loans, term deposit and many more. 
  •        HDFC Bank: The bank has introduced its own conversational banking chatbot called EVA exclusively for its customers. This AI powered virtual assistant chatbot designed by Bangalore-based AI start-up Senseforth is capable of answering millions of customer queries covering multiple channels.
  •        ICICI bank: The bank’s in-house build iPal chatbot has the capability to serve multiple customers to offer assignment on micro transactions like bill payment and fund transfer. Within a short span of time the chatbot has handled more than 6 million queries on its portal and mobile app iMobile providing information on multiple aspects with close to 90 percent accuracy. The bank claims that it is the only AI led Indian chatbot service available on the bank’s website as well as mobile application.


To Conclude

The main objective of most AI technology adopted by banks in India is to offer a more enhanced, proactive and personal customer experience at a lower cost. As the banks get more comfortable with these newly deployed AI systems, they will start using them more and more for back-end business processes which will help to reduce human error and improve turn-around-time where manual processes are required.


To know more, download our latest research paper titled “AI for BFSI”: Artificial Intelligence for Banking, Financial Services & Insurance Sector