5 Levels of Intelligence in Industrial IOT

http://blog.fluturasolutions.com/2014/08/5-levels-of-intelligence-in-industrial.html
There is a seismic shift under way in the engineering industries. The decreased cost of sensors, the increased amount of instrumentation on assets and need for new revenue streams are forcing engineering firms to re-imagine business models. The fusion of “atoms with bytes” promises to unlock new value previously unrecognised which generate additional revenue streams predicated on intelligence generated from the data. As machines increasingly become nodes in a vast array of industrial network, value is shifting towards the intelligence which controls machines. Intelligent Platformization of machines has begun

Keeping in mind this fundamental shift in value from atoms to intelligence, Flutura has defined 5 levels of maturity to assess the machine intelligence quotient of an engineering organisation. The highest level of maturity is “Facebook of machines” with ubiquitous sensor connectivity and the lowest is an asset which is “unplugged” where the device is offline. As organisations embark on a journey to intensify the intelligence layer in their IOT offering it makes sense to map where they are in their current state of maturity.
The 5 levels of machine intelligence with specific illustrative examples are outlined below

Level-1: UNPLUGGED

This is the lowest level in the maturity in the maturity map. At this level of maturity, the device or sensor is ‘unplugged’ from the network. There are no “eyes” to see the state of the machines at any point in time. The machine is offline to the engineering organisation. A vast majority of engineering firms manufacture assets which fall into this category. For example a vast variety of industrial pumps still are completely mechanical devices with no sensors to instrument them
Level-2: EDGE INTELLIGENCE

This is the next level of machine intelligence which exists in the maturity curve. At this level of intelligence the device is connected to the network. There is also rudimentary intelligence exists on the device to take corrective healing action. Examples of assets having edge intelligence include cars which can alert the drivers to basic conditions which need intervention. Other examples include a boiler which has edge intelligence to switch on/switch off valves based on steam pressure
Level-3: REMOTE MONITORING

At this stage the device can be remotely monitored and monitored from a central command centre network. For example Flutura was working with an asset service provider who was monitoring the health of connected buildings geographically dispersed and monitored in real time. This requires the ability of the platform to ingest billions of events from boilers, chillers, alarms etc. in real time and make sense of which assets need intervention from the command centre and which assets are healthy.
Level-4: PREDICTIVE

This is taking the intimate understanding of assets to the next level. This involves triangulating patterns from historical asset data, its ambient conditions etc. to predict failures, defects etc. At this stage, there is enough causal knowledge available to model when the device would break down and proactively trigger an intervention be it a field visit or a part replacement.
Level-5 : “FACEBOOK OF MACHINES”

This is the most evolved state of engineering intelligence where All assets the organisation has deployed is connected in real time seamlessly to field force, head office engineers and command centre observers in real time. Very few of global engineering firms are at this level of maturity.

Closing thoughts
As business models evolve driven by pervasive hyper connectivity of devices across industries like Utility, energy, Oil n Gas, Intelligent building management systems etc, competitive advantage will shift towards differentiated value adding intelligence platforms. Flutura intends to leverage its Cerebra Signal Studio Platform to accelerate signal detection and deliver value added business outcomes.

Machine-to-Machine (M2M)

Machine to machine (M2M) is a broad label that can be used to describe any technology that enables networked devices to exchange information and perform actions without the manual assistance of humans.

M2M communication is often used for remote monitoring. In product restocking, for example, a vending machine can message the distributor when a particular item is running low. M2M communication is an important aspect of warehouse management, remote control, robotics, traffic control, logistic services, supply chain management, fleet management and telemedicine. It forms the basis for a concept known as the Internet of Things (IoT).

Key components of an M2M system include sensors, RFID, a Wi-Fi or cellular communications link and autonomic computing software programmed to help a networked device interpret data and make decisions. The most well-known type of M2M communication is telemetry, which has been used since the early part of the last century to transmit operational data. Pioneers in telemetrics first used telephone lines — and later, on radio waves — to transmit performance measurements gathered from monitoring instruments in remote locations. The Internet and improved standards for wireless technology have expanded the role of telemetry from pure science, engineering and manufacturing to everyday use in products like home heating units, electric meters and Internet-connected appliances. Products built with M2M communication capabilities are often marketed to end users as being “smart.”

Currently, M2M does not have a standardized connected device platform and many M2M systems are built to be task- or device-specific. It is expected that as M2M becomes more pervasive, vendors will need to agree upon standards for device-to-device communications.

IT skills gap

 

The IT skills gap (information technology skills gap) is the difference between existing IT workplace knowledge and the knowledge required to fulfill business objectives.

Closing the IT skills gap by aligning the current state of workforce IT knowledge with forecasted future needs is a complicated proposition for C-level executives. Today, employers often struggle to locate and retain qualified tech talent, especially individuals with application development, security and data analysis skills.

Common approaches to closing an IT skills gap include recruitment process outsourcing, social recruiting, off-site training, employee mentor incentives, in-house turnkey training and partnerships with universities.

In many instances, an IT job will remain unfilled for an extended period of time when an employer needs to hire someone who has a very specific set of skills. In recruiting lingo, such candidates are referred to as purple squirrels. Because squirrels in the real world are not often purple, the implication is that finding the perfect job candidate with exactly the right qualifications, education and salary expectations can be a daunting – if not impossible — task

Madware

 

Madware is a type of aggressive advertising that affects smartphones and tablets. The name, which combines the words mobile and adware, was coined by the security vendor Symantec to describe a type of intrusive advertising that currently affects smartphones and tablets.

Typically, madware gets installed on a device when an end user agrees to allow ads in exchange for a free mobile app. Some madware can function like spyware by monitoring end user behavior and making undesirable changes to the device such as flooding the device with text message ads, replacing the phone’s dial tone with an audio ad and deliberately hiding from ad detectors. Madware banners often takes up valuable screen real estate, causing the end user to accidently click on the ad while navigating the website that is displaying the advertisement.

Madware can best be avoided by taking time to read each new app’s end user agreement before checking “I accept” and being extra cautious when installing apps that request access to the local system. End users should also close mobile apps when not in use, disable pop-up and extensions in mobile device browser settings and install mobile antimalware software.

7 Best Questions to Ask Consulting Clients

Here are the 7 questions.

“What is your number one priority for this business unit during this fiscal year?”

“What do you believe needs to be strengthened in order to support achieving this?”

“What options have you looked at to achieve this…?”

“Is there anything that you or your employees are doing that may be getting in the way of achieving this result?”

“What is unique about your business compared to your competitors?”

“What was the main reason that you wanted to meet with me?”

“Who will be making the final decisions on this project and who will be in charge of implementation?”

Inside the Diagram to Success

by Michael Zipursky |
Chasing-Success

One of my coaching clients earned $823,000 last year. He has no employees or contractors working for him.

Another did $1.2M before we started working together and in one year grew the business to over $2.3M.

You’ll notice that people Chasing Success spend the majority of their time talking about their ideas.
Not all of my clients have revenues at that level. Many are doing $8k to $25k a month.

I’m honored to work with such dedicated professionals (here) to help them grow their businesses and achieve greater lifestyle and freedom.

One thing that I’ve noticed about the most successful consultants, regardless of their industry or country, is their approach and the way they take action.

It’s not so much about the marketing tactic they choose, it’s how they think about business and the mindset and habits they have.

I created this graph to represent the big difference between people Chasing Success and those that have Reached Success or well on their way to reach it.

Success-Diagram-Consulting

You’ll notice that people Chasing Success spend the majority of their time talking about their ideas. They also talk about why they’ll be successful. More often than not however, they talk about their disadvantages and focus on why they can’t get from A to B, instead of taking action to fix it.

In addition, they spend a great deal of time thinking. Thinking and planning aren’t bad ways to spend your time. When they hold you back or provide you with little time to take action, they do hold you back from making progress.

On the other side you have people Reaching Success. They spend a good chunk of their time creating plans and gaining clarity on them. They communicate what needs to happen. But as you’ll notice from the diagram, the vast majority of their time is spent on taking action.

This is the key difference between the two groups – taking action.

Look at how you’re spending your day, week and month. Which side of the diagram are you on?