Can we have the internet of things with operational data management?

We all talk about data from different devices etc. This is well and good but can you really have effective information if the data is not in context?

The challenge is how you gain this context and then sustain this context over many devices (things) without significant impact on the devices, how do add, remove and evolve devices (things). The role of an operational data management system that is a “yellow pages” of the system, providing the context, and relationship between devices and the operations.

Providing the ability to register new devices and associated data, input the associated context, while maintaining the detail in the device, but provide the bigger operational process alignment. This will also provide the association, other naming of that device so other applications, roles can find and interact. Often other systems, machines have a different outlook on the process and will use different naming and referring for the device. The Operational Data Management capability provides this association and ability to align many devices without having change the underlying applications or devices.

From a data to information point of view it provides the contact to gathering of data to shift it to information, so that big data analysis and other tools can be applied transforming that information into “knowledge”. Providing a pattern for contextualized operational data (e.g.: production, quality, machine status, etc.) integrated to templated collaboration activities (ODM) and ultimately broader supply chain management.

Without this companies have a real opportunity of just gathering significant more data without creating or having the ability to create the associated proportion of Information, knowledge and eventually wisdom. The diagram above shows the knowledge management pyramid and how on the right hand side companies have not go the top one which is blow out in data without the associated knowledge. The leaders will put architectures and systems into place which enable them to gain the contextualization while providing the “plug and Play” ability for devices and things to be added to the solution.

Which path are you on, how are you addressing this ODM concept?

"Smart, intelligent, brilliant devices" what is key is "self aware" devices

As I listen to “Youtubes," lectures on the Internet of Things, and in industry forums, I hear the terms of " smart, intelligent, and now brilliant" devices, as we move to smart cities, smart farms, smart airports, and smart industrial sites. I asked myself what is the core value and difference?

There is a significant leap and a significant switch from monitoring devices from a high application or humans, to " self aware" devices that monitor their own health and capability. Next you ask the difference between "smart" to " brilliant" devices and interestingly there is even amongst the marketing hype! The level of capabilities associated to the device that takes them from simple "self awareness" of asset health, to predictive and "machine learning" capability to move from the " as is" alarm to "to be " state."

However, it is all increased levels of embedded "self awareness" capability as close to the device or in the device to monitor, and understand it is effective condition to the "golden" operating condition, with the intent to sustain "operational continuity." The key is device level " awareness" and the connection of these devices to the Internet apart of a  holistic system( site, enterprise), raising exception conditions automatically, and acted on in a consistent, and “best practice procedure." With many of the same types of devices can now be aware of each other, learn from each other and grow in "self awareness" capability. Increasing the ability to shift to the "to be " state where exception conditions seen early,  corrective action can be taken fast and early to maintain " operational continuity."

As we go forward the " future" will also be incorporated into the device, eg high fidelity simulation models will be available for common devices, and will learn relative to the particular device setup and situation. Enabling not just the current condition but the future condition window to be seen, and " what if" to be played out but inexperienced operational staff to make well informed and correct decisions. This simulation model will either run locally or be called upon by the device to a remote simulation environment with discrete device models.

All of this device awareness is what I see as a paradigm shift the "Internet of things" brings and the opportunity to the industrial operational space for the next jump in productivity.

I have heard many times, and agree with the Internet of things, "self aware"devices that leverage their "brothers and sister devices" and other intelligence not applications, brings the next step change in output growth and GDP output. Called the "third industrial revolution" where and industrial revolution is when a significant step change in GDP productivity output is achieved, the first two been:

1/ the first industrial revolution with the steam engine, and then self pro population capability for production and transport.

2/ the Internet in 1992 to 2000, and beyond where human communication, collaboration and the " flat world was introduced" switching us from regional to global effectiveness.

3/ the Internet of things" self aware devices, and collaboration between devices, applications and people to address the fast moving world we live in and the requirement of “Now” decisions and satisfaction.

Too often all of this is associated with larger companies and utilities, but last week I visited a small food plant in rural Australia, to discuss their next step in operational change, and competitiveness. Two thought leaders where present, and it was great to have a very active and productive discussion around not problems, but opportunities. Leading to a powerful discussion on discovery investigations around these opportunities, and the key opportunities were again:

1/ People cost, therefore effectiveness

2/People efficiency in a dynamic and changing workforce

3/Brand integrity, and quality

4/Variable costs in materials but now energy

The discussion took us to the operational work space of the future the need for not data, but effective information that enables exception based operational management, supervisory control of the plant, and process.  Shift from process management to product production management, spanning all the processes and production units required to produce a product. A shift to activities that are required in order make the product and enable operational continuity.

The concept of "self aware plant” made up " self aware devices” and " self aware process" with embedded operational procedures was a reality, and not just for big companies, but even more critical for small to medium companies/sites that have fewer resources.

Maybe the new world is not that far away!

Expanding from a Process/ Plant to Holistic Landscape will fuel the debate of “on premise vs off premise”!

I had an an editor speak to me this week about “the shift to Multi Plant view across the industrial landscape, then he mentioned cloud is a dream in Industry”. Quickly correcting him that it should not be a shift to multi plant view versus an expansion on top of the existing local/ remote operational view, and “cloud” is truly real especially with virtual teams, and multi site. This confusion is common at the moment with some vendors and consultants pushing the Enterprise Historian view that really focuses on only one perpective in operations, that of the analyst performance team. That does not mean there is a swing to a new architecture, the architecture must support at least the two communities of interest with different views:

• Operational day to day teams: requiring a view focused on limited time span e.g.| 7 days and view one process or plant
• Planning, Management, Performance / optimization teams and expert support teams : wants to look at broader view across sites, and broader time span of months and years usually offline for analysis, and strategic thinking.

The expansion to holistic view will also push the debate of the industrial architecture, to a combination of:

• On Premise computing: The local plants with real time needs and short time spans will often have the required information and capability local on premise. This can apply also apply in a remote operational center infrastructure which is still local to the remote site, or on the site and just virtualized to the center.
• Off Premise computing: The shifting of data, analysis, and actions to an off premise computing, either specialised data center, or “cloud” etc, private or public, freeing up the local operational day to day infrastructure from this heavily lifting. Providing also the ideal opportunity merge and consolidate a view and capability across the industrial landscape of a company, this will involve storage, but also realtime model of the production, and operational awareness activities across the different teams.

In the blog on 2020 the following characteristics were expected:

· One in three employees will be working casual, part-time or project base vs career in a company.

· The average tenure in a job / role will be 2.4 years

· One in four workers will be working remotely and virtually.

The ability to see information and data in a near realtime, view according to my focus is key no matter what current device or location being used. The ability to connect, collaborate and share advice is key to decisions in the now and the ability share knowledge when the average experience in a role will be less than 2 years The holistic view will drive many parts of the architecture “off premise” into a managed service.

Three times in a week one in mining two in food, from companies across the world, I was asked to comment on an “End to End” view of information, and especially state. One for operational effectiveness and agility, the other for reduction in delayed time in achieving a “positive release” of products to customers. In both cases the company IT and thought leaders raised both “cloud” and “big data “ techniques. As I mentioned last week, the acceleration in acceptance of these technologies is astounding in the last 6 months.

The mixed reaction to last week blog many agreeing, many debating the reality, but for the second camp, I think they unaware of cost, technology updates, and the expectation of the new generation for doing things fast. Also, there is a shift to be able to buy this holistic capability as  services  vs total up front.

I fundamentally believe that, within 2,  years,  we will laugh at this debate of “on premise” vs “off premise” in the industrial landscape, the modern aligned and agile business will have  an industrial/ operational architecture that naturally applies both On and Off premise computing. “On premise” computing reducing to “only required data/ computing to satisfy the limited operational time span, e.g.| 7days even shorter” everything else will be “off premise”. The landscapes will enable users to view content device independent and across the landscape, but drilling to a plant and process, collaborating across the virtual team from site to corporate to worldwide experts.

Certainly over the next 2 years the expansion in Invensys’s portfolio capability spanning local to enterprise landscape, and include both “on premise” and “off premise” as well as managed services as not a specialty but a natural way to grow value from your systems.