What are the hurdles to Real-Time Operational Excellence?

I see a significant increase in “operational Transformable projects” , but too often it is talk, or dreaming, and when we discuss the ideas people like, but they really miss the challenge. Too often they fall back into the traditional approaches can we get access to information, through reports and dashboards. 

Born out of the frustration to gain the transparency to “what is going on NOW”. Yes it is a journey for “operational excellence “ and it will not be done once or ever over in this ever “speeding , agile world”.

Taking a step back and understanding the hurdles to getting to “Managing by Exception”. I thought the image below simplified the discussion.

Understand where you are, and set a vision of where you want to be, and this goes back to shift towards “activities” design vs application or even role.

Above you can see how not having the data in context, or even accessible is key, this is seen in the two bottom challengers. As one customer said last week, how do eliminate cleaning data every 3 months. The answer is simple, capture data as close to the source, validate and structure it as close to source as possible, so now you are storing valuable, trusted information, and you can depend upon it.
But now you have the information people put it into reports, and dashboards, for decisions to be made, but did it get to correct person, did it get decided upon in timely manner, why it did not escalated, or collaborated to accelerate the decision. The system must provide this framework for escalation, and ability ask/ share.

With the changing roles, and people on plants, and the horizontal structure, do we know the decision was made, “accountability” is important when something is sent. Too often tradition alarms, notifications have no accountability, the only way a team works is that they understand their role, and responsibility for decisions.

Then you come to final hurdle “what do I do having made the decision”? This needs to consistent processes across different workers of different experience. Also the system has to shift to a “crowd sourcing” culture of continuous improvement and everyone is empowered to contribute.

This may seem so simple but it is fundamental to the “transformation in Work” yet so many programs are missing these basics.

I will follow this up next week again on why “People and Processes” are key to take the automation to the next level.

We need to improve the speed and accuracy of big data analysis in order for IoT to live up to its promise!

I was listening and reading the debate on IOT, and this article was layered with good amount of reality.

“As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of the year, the discussion has turned from what it is, to how to drive value from it, to the tactical: how to make it work.

We need to improve the speed and accuracy of big data analysis in order for IoT to live up to its promise. If we don’t, the consequences could be disastrous and could range from the annoying – like home appliances that don’t work together as advertised – to the life-threatening – pacemakers malfunctioning or hundred car pileups.”

http://www.wired.com/2014/11/iot-wont-work-without-artificial-intelligence/

This follows on from my discussion 2 weeks ago around the need to avoid just gathering data, vs gaining the proportional amount of knowledge and wisdom, which brings in a term you hear a lot “machine learning”.

“Wikipedia defines machine learning as “a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions.”

“The realization of IoT depends on being able to gain the insights hidden in the vast and growing seas of data available. Since current approaches don’t scale to IoT volumes, the future realization of IoT’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in almost every facet of our daily lives.”

In the industrial world this more applicable than nearly all industries, and in many cases we are already applying “machine levels” at different levels. A key part in the shift from “Information” to “knowledge” is having the tools to drill into historians based on events and discover learnings and patterns. Once validated and discovered these are turned into “self-monitoring” conditions to understand the current state of the device, and predict / recognize conditions well before they happen. Providing the “insight” to make awareness and decisions where the machines/ devices are telling you where the opportunities are. But a key part of machine learning is that this knowledge in not a once off step, it is a continuous evolution leveraging the gathering history data and developing increased amounts of knowledge.

The next step is to then apply proven or recommended operational processes to these decisions, so as a condition is recognized by the devices, either they take an action automatically or they recommend the action to the user in a timely manner with escalation. A key transformation IoT brings is the increased speed at which trustworthy knowledge is made available for actionable decisions to taken.
I like this phrase:


 “It’s time to let the machines point out where the opportunities truly are.”

Cost of Delay: Realtime Actionable Decisions Critical at all Layers in Industrial Operations

In a number of recent discussions with people when they have listened to sessions on the operational transform and the concepts of people and team transformation, they incorrectly feel the focus is only on the  “operator”. While the operator is the closest person to the “coal face” a fundamental concept of the operational transformation are the shifts to:

·         Actionable decisions performed as early as possible

·         Collaboration across the operational team in making the decision, and taking the action

·         Sharing of current situation and experience

·         Awareness of the situation as early as possible.

As seen in the diagram below if a situation is left then by the time it hits the weekly report even daily report the cost of the situation is significant.

Not all actions belong to the operator, some should be detected in control system, acted upon in that system, this is where apply some real time optimization tools is key like APC ( Advanced Process Control). Other actionable decisions may be on quality, safety issues or  maintenance issues that need to act upon early, and the escalation of the system should directly to the responsible role.

No longer is an alarming system good enough we need a intelligent notification system that brings awareness to responsible role, and immediate access on what ever device to the detail and ability to see situation and act.

The diagram below I talked about before but it continues to critical to understand that an alarm is “as is” situation, the cost of detection vs action is significant. The bottom axis is time, and the vertical axis is value/ cost. If we are able to move all roles to the “to be” state where decisions and actions are seen early and corrective best practice action is taken early.   

The cost of delay, unawareness is huge especially when cost is a measure of “operational continuity” and growing the production output. So when we talk about the Operational Transformation, the goal is to enable the operations to become more agile, and efficient, and this can only happen when business strategy / operational execution, and emergent situations are all acted upon is well “oiled real time machine” with each level role and application/ human acting in a timely manner, and consistent manner. 

Applied Knowledge/ Wisdom Foundational to Internet of Things, and "Time to Performance" of Operational Teams

For the last couple of weeks, Stan DeVeries and I have been brainstorming around articulating this core area of the operational transformation, "the ability to have a system that can absorb workforce change/ turn over". Good example of this is with one company on the 2025 vision of "all knowledge/ experience in the system", this is capturing as much of the tribal "applied knowledge" that the experience operational staff are making decisions, and taking actions on and moving it to the system. If then applied in a "activity/task" based operational experience, a younger skilled user has the ability to select a "activity" and the associated knowledge/ information, and action are presented to him. Dramatically reducing the "Time to Performance" and increasing the consistency of operations, while increasing flexibility in operational workforce management.

The results of the discussions has brought the discussion around "Federated Wisdom, applied knowledge":

The explosion of information across industrial operations and enterprises creates a new challenge – how to find the “needles” of wisdom in the enormous “haystack” of information.

One of the analogies for the value and type of information is a chain from “data”, through “information” and “knowledge”, to “wisdom”.  In the industrial manufacturing and processing context, it may be helpful to use the following definitions:

·         "data” – raw data, which varies in quality, structure, naming, type and format

·         “information” – enhanced data, which has better quality and asset structure, and may have more useable naming, types and formats

·         “knowledge” – information with useful operational context, such as proximity to targets and limits, batch records, historical and forecasted trends, alarm states, estimated useful life, efficiency etc.

·         “wisdom/Applied Knowledge” – prescriptive advice and procedures to help achieve targets such as safety, health, environment, quality, schedule, throughput, efficiency, yields, profits etc.

The cost to store and share data has dropped significantly, and a simplistic expectation is that although storage is growing by a factor of millions in only a few years, that somehow the following pattern evolves:

Although the pattern might seem to be convenient, it is actually a nightmare, because it becomes much harder to discover and translate knowledge and wisdom from another operation, especially in another location, to the local needs.  But there is a solution.

To understand the problem better, let’s consider the definition of “knowledge” – it includes context.  This context begins with local context – time, location, process or machinery configuration, raw materials, energy and products being processed or produced.  It is already valuable to have “wisdom” to achieve and sustain best performance for the community, customers and the corporation.  This local context only needs to know its immediate information, if it has enough “wisdom”.

Now let’s consider what happens when a single site, a fleet of similar sites, or an enterprise have numerous similar operations.  How can local “wisdom” be enhanced by using “wisdom” from the other operations, especially when all of these operations are sufficiently different?

The reason that solving this problem is important is for operations transformation, such as operating physical assets as one (in a chain or as peers), and by supporting the multiple operations with a flexible team of remote experts.

One approach to solving this problem is to take advantage of a technique used in distributed databases, where a technique called “federated information” is used, especially in industrial operations management architectures.  This technique does not change the local information’s naming or structure, but provides multiple translations, both across the database for multiple similar structures, and for multiple contexts such as what financial, technical support, scheduling, quality and other functions require.  This technique is an alternative to the fragility and complexity of attempting to force a uniform and encompassing naming and structure that attempts to satisfy all applications and users.

The same approach can be applied for “wisdom”.  Currently, hobbyists and enthusiasts around the world share “wisdom”, for restoring cars, making furniture, playing a musical instrument, gardening etc.  Anyone with no experience at all can ask for “where do I get started?”, and most respondents will provide kind advice; in the same forum, experts can share wisdom that is valuable and understandable by them at their level of experience.  This “wisdom” is extremely decentralized, and the experts are providing the translation.

In the industrial operations environment, federating “wisdom” is partially automated by expanding the local context.  This expansion includes information about adjacent operations, information about the chain or peers if these operations are being managed as one, and then “knowledge” is expanded by applying the context of group targets and performance.

Some enterprises have hundreds or as much as tens of thousands of similar operations, supported by dozens or fewer experts.  Discovery of wisdom is greatly enhanced by maintaining an architecture which enhances local context without modifying or attempting to force burdensome structures on local operations.

Expect this discussion to continue as expand on the systems, and approaches to make this real, while enable sustainable operational innovation. This will be core to Industrial Internet of Things as we align smart devices, operational practices and humans into a dynamic but coordinated operational force.

The Perfect Storm in Industrial Operations = New Paradigm in Operational Landscape

I decided to revisit a blog that I have enhanced from over a year as preparation for a discussion I have been having with many people in the last 2 weeks around "realtime Information Platform" vs "Historian" which I enter that discussion in the next blog. Too many people are looking at one aspect.

The most significant disruption in industrial operational strategy is happening in this post GFC (global financial crisis) era.  This is focused on determining an operational execution environment which enables timely contributions by the operational team for sustained high-performance plant execution.  The focus is currently on operational processes, but this will shift quickly to driving a new operational experience which enables a new operational execution plan. Traditional user interfaces will not “cut it” both in products, or the way they have been implemented.

A common thread around the world is the operational personnel challenge, especially the finding of people to replace the existing “baby boomer generation” and lack of experience available in the market.

This is only part of the most significant disruption in operational strategy in the last 20 years, even since the introduction of the PC.  There is a perfect storm happening, with these vectors:

•        Ageing workforce: the significant number of highly experienced operations, maintenance, process workers who will retire in the next 5 to 10 years.  Some mangers in “oil and gas upstream” talk about the fact that 80% of their current team will be gone in 5 years.
•          Operational Agility means Decision NOW: to be competitive, decisions must be made now, this has caused a change in thinking that workers need to be empowered to make more decisions, through more information, higher knowledge and access to experience, and a transition from “worker” to “knowledge worker”.  This also means that they have much more responsibility.  As one customer recently stated, traditionally they had an operator cover 5 to 10 wells; this was fine when you have 100 wells which lasted 20 years, but in the next 5 years he stated “we will have 20,000 + wells, but we will not have 2,000 operators”.
•          The rotating operational person means “time to experience” is shorter than ever:the experienced generation is retiring and transitioning to an age group 20 years their junior, and there is a new factor that people are not staying in their role or location longer than a year.  One company stated 10 years ago that people were in a role approximately 5 years, but now they are seeing rotations of 8 months.
•         Transition to digital native worker, with very different expectations, causes challenges with worker retention: The new generation is “digitally native”; they expect access to knowledge, they expect “touch experience”, they expect collaboration from anywhere, and they expect to learn on the fly.

Stepping back and looking at all of these vectors; we have significant disruptions for those in charge of industrial / manufacturing operational execution strategies.

Addressing that significant disruption will require a combination of techniques:

•          A new generation of user interface products with more than interface capability, but embedded knowledge access, experience access, actionable procedures and natural intelligence, really empower the operational workers in all roles.
•          A new operational experience design.  Notice that the traditional enabler -HMI (Human Machine Interface) does not express this new design: as it is a true “operational experience” that goes through the “day in the life” of the new generation of operational knowledge workers.
•          New alignment across the different systems, plant applications and sites to align context, actions. Keeping the sites, applications and systems “loosely coupled but tightly aligned”.

Why?  Because today is not about Control Rooms; it is about agility and timely decisions, and this requires the “Flexible Operational Team” that works naturally together to leverage their experience, in the NOW to have decisions of all sorts made in a timely manner (often earlier than “now” to prevent instead of react).

The above diagram shows the concept of Flexible Operational Team, where at the bottom the traditional User Interfaces (UI’s) would have been permanently manned.  These UI’s are becoming transiently manned, but all functions relative to the zone of responsibility are available.  The more central operational centers (increasingly remote) will have a “quarterback” operational controller who is calling the shots, with a transient in-plant team of different skill sets, and a virtual team of experts usually external to the plant.  The in plant team executes activities that must be done locally, e.g. Inspections, maintenance, and certain manual operations, and the virtual team are experts across the world who can be tapped on for experience and knowledge to work with the controller or the in-plant team.

The above requirements is driving customers to look at the concept of “Enterprise Control”, providing the unification, and evolution of existing systems to achieve the alignment required to enable the concept of “agility thru Operational alignment and decisions in the NOW”.

The Invensys Enterprise Control Vision is to provide a set of capabilities that enables customers to achieve "Operational Excellence” through three strategies:

•          Empowerment of Operational People

Operational personnel (e.g. Operators, process engineers, process experts, maintenance, quality, production management) are empowered real-time decisions through operational awareness, access to experience, collaboration, and best practices in a proactive system, to perform multiple tasks, in flexible roles, in multiple locations.

•          Unification through Federation across assets, applications and systems

Align the different assets and processes across the operational management layer (of the traditional automation levels) so that the industrial operations are more agile (can change equipment configuration and use of the equipment much faster and much tighter coordination).  These assets that reside within a plant, within a process and across multiple sites are aligned to business and operational processes and require consistent measures and information.  Each of the existing applications/ controls continue to run, but their information and visualisation models are aligned, and communication happens with orchestration execution, in order for the Operational Process to execute in the most timely and effective manner.

•          Built on a Sustainable platform of capability so that the system has longevity to evolve.

Enterprise Control will be implemented in stages and evolve in scope, breadth and functionality through its lifetime at each customer installation, which could be 20 years.  The system has been engineered and architected in a way that enables this evolution to occur in a sustainable way and caters to changing engineering teams and technologies.

New technology products will enable the above operational/automation paradigms, satisfying the changes in the market and workforce, compelling all of us to think and engineer differently as we evolve our operational systems.

I am hoping this refresh and discussion answers many of the questions people have asked me lately, as it is not just about the ageing workforce, the transition, the whole workforce culture and approach to work will be different in 5 to 10 years, and it really is a transformation to "smart work" in the industrial operational space. 

Is the Transition to Gen Y so Significant in the Industrial Environment?

This question was put to me by a magazine editor in Czech Republic this week, he was from Gen Y (born after 1980), and he was commenting after one of my presentations.  This is not the first time I have asked “do you genuinely think the transition to Gen Y will be that significant?” It is truly valid challenge, so I decided I needed answer why I believe it is a significant milestone or transition in the operational approach or culture in the Industrial Market.

As he asked the question he was texting and recording the interview on a Samsung PDA, and he had prepared his questions well, by researching me on Linkedln, and reading this blog. To him this was a natural way of doing research, yet an interviewer the week before from Gen X (early) had not done this research, he just had heard my presentation and asked questions based off this.

Yes, many of us from the  Bayboomer, Gen X generations have transitioned to living by our PDA, always “connected” and using email and text for communication, we do our research off Youtubes and forums, but while we transitioned it is not natural. We certainly do not share as well, yes some of us have Facebook accounts, but many do not. I asked a group of 120 people in the Gen X and Babyboomer generations how many had Facebook accounts it was less than 20%, but the small segment who were Gen Y had 100% with FaceBook accounts and all had contributed at least 1 you tube to public domain.

Gen Y has grown up in an environment where the internet is just a natural part of life, most would not remember a time without internet, and the same applied to mobiles that are used for more than voice. SMS texting comes before having an email account, where to Gen X we had email before Text, and tend to use email as the primary text communication vs SMS. The way Gen Y naturally searches on Google and filters the information and rapidly transverse the information to a desired result. They expect to use a map on PDA and see the closest banks, restaurants and other information. Wiki Pedia is a natural source of knowledge, and it is natural to contribute with comments, and material to Youtube and pedia style environments. The most significant transition of generation from Early Gen X and Babyboomer is the shorter time in a role and location, the willingness to transition their career more often. Remembering by 2020 the expectation is the average tenure in a role will be 2.4 years or fewer, people are expected to have at least 4 careers and over 20 jobs in these careers.

These are contributors to the transition, but given that many of the industrial supervisory and operational interfaces/ experiences created over the last 15 years, have been defined to control the process of a unit or equipment. Many are in isolation (islands of control) with limited inter application integration as the design was not done in a holistic view as the project had a deliverable goal and timeline. The navigation, and operations/ actions of the user interface had a fixed button navigation, and assumed a certain level of experience, and on how to use interface and control the process, this experience often came from training on the interface by the developer to the users.

Combining the holistic end to end operational control which requires multiple workers to run the system, often the operational stations are now transitional, so the workers will transition from one workstation to another executing the actions, the experience needs to be consistent to help smooth transition as they do their daily role, plus the ability to access the states elsewhere in the plant, based upon a notification they would have received maybe on a screen or mobile, and they require more detail than available on mobile, so they will drill in using a remote workstation. Now as the worker is executing his day, he is faced with a situation that is new to him, or requires some process experience, and he is unsure of the decision to take as he has only been in this plant 3 months. The operational interface requires for the user to collaborate with a remote expert on that process, sharing the situation, some screens and states, plus a live conversation, this should be natural in order to make a decision and take a correct action as soon as possible.

So when I talk about the significant evolution we going through in operational culture and approach, I am referring to the ability to maintain operational continuity while absorbing this constantly dynamic / rotating operational workforce with now limited experience in a role and location.

The growth in operational programs that are looking re-engineering their supervisory (HMI) systems, and operational interfaces to provide:

• Consistency of operational experience across workstations and devices
• Natural Collaboration with others of more experience or in the operational team.
• Multi workstation and mobile devices on a common system that interact
• The natural learning, and knowledge management and access
• Consistency in operational actions across workstations, devices and processes
• The shift to exception based operational control, using the ASM (abnormal Situation Management concepts) for faster recognition and action on the situation.

Is confirmation that it is not a technology upgrade only it is an operational culture approach that is driving the expectation significant increase operational agility?

Is your supervisory/ plant operational system ready to absorb a dynamic workforce, while maintain operational continuity in the agile world of increased new product introduction, and competitive pressures.

I would be interested in people’s comments.

Information vs Data Leads Discussion on the Future of Operations!

“Gather all the plant data and analyze afterwards” are common words you hear about the market, but when the discussion happens this approach a “putting head in the sand” approach, with limited bigger picture consideration. Today the key to agility is empowerment of decisions and actions in the NOW. This does not require data it requires trustworthy, in context information.  The last couple of weeks has enabled some fascinating and productive engagements. In a discussion,  last week at a Mining Thought leadership on the future a sizable group of interested people attended and took part in discussions.  

A key concept of “mine of the future” and actually for most industries oil and gas, power, food etc. is the agility to take more holistic operational view of the system and day to day operations. This requires alignment at 3 loops (the diagram illustrates these loops) of operations with the alignment in decision and actions. Foundational to this is the information that decisions are based on, requires not HISTORIANS but Plant/ Operational Information Systems, that align information and actions for effective use my different operational roles. Companies that make this foundational move will have a system where data structure, validation is “managed” not coded, that the system is trust worthy so people will depend on and use the system.

To many times the discussion with mining and process end users who are implementing an information system for increased decisions support, that they require a re look at the data sources and how to put it in context, and validated. An example of this was a coal company in South Africa they had spent significant time working on an information system and the historians and data warehouses, but lost effectiveness through:

• Data alignment across sources, E.g.| Finding different data streams that effected the same asset calculation for say energy.
• Data validation
• Data structure

The conversation remarkably quickly ended up going back to redo of the structure of data coming into the historians, and getting this data structured, validated before it went into historian. They had two choices either going to source in this case PLCs and adjusting the running code (not a smart idea), or put a structuring layer in which would structure the data, validate the data, and provide high availability and single names space to manage over the distributed historians.

This is a departure from the story he was told that just put a historian in and capture the data worry about analysis afterwards, that is old and in effective saying. As discussed in the “mine of the future” discussions  the challenge is to “federate” the existing data sources on a plant, E.g.| Historians, alarm event data logs, operator logs, and delay, downtime data bases, E.g.| The alignment of the data sources into effective information that actions can be taken. The concept of “self service” becomes necessary, as there was a lot of comment around of trying to minimize the process analysis phase and role in the data, and try to get effective information to operational people quickly. Another example is an oil and gas plant’s decision system that is effectively been run 24 hours out of phase with the plant, by only have decision able reports/ dashboards from the past 24 hours at 2 to 3 in the afternoon so. Again this delay was due to data gathering, data alignment, validation, in MS Excel manually done by 3 people, the company was exploring ways to eliminate this manual creation, so the whole process is “near real time”.

A clear message from the last couple of weeks is we need to step back, align the existing systems, to provide that key foundation for operational empowerment, absorb significant milestones such as advancement in the communication infrastructures; Example putting 4G communications in the Pilbra mining area (remote north western Australia), providing significant data capability. (A topic for next week). This need to absorbed into the industrial / operational Architecture, internet will be a natural part of the backbone, leveraging computing power remotely for functions, such as storage, analysis, model running, help accelerate the decision support.   

Dynamic Simulation needs to become a Natural Feature of the Industrial Operational Landscape

To empower operational teams, workforce, each role must understand the context of the future, as well as current state and history. In the traditional industrial solutions, the worker is provided with tools for understanding history and NOW. This is the same as driving a car by only looking at the dashboard of the car, and rear vision mirror. Can you drive the car without looking out 100 meters or longer into the future the answer is NO, you would miss judging the next corner?

So why do we think we can run plants in this same manner?

The leading companies are defining the operational experience of empowering the different roles in the plant operations with the ability to “understand the future”. This will then empower the user with:

·         Understanding of the NOW situation

·         Understand the History

·         An expectation of the future based on NOW situation

·         Access to Experience

The concept or words used to describe this capability is “What If” capability. The ability to have at the finger tips of the worker the ability to ask “what if” question and the system will provide the best estimate based upon the current state of the process/ plant, and history. This provides maybe a “times 10” in time look forward so that it provides another reference point for the worker to base their decision on.

An example of how the general day to day market has come to accept using simulation models as day to day is in the weather. I do a lot of ocean sailing; today a natural tool I use on the boat is weather simulation. On my Ipad,  I have two applications, which connect to a cloud application that will allow me to select a location, and run a number of weather simulation models, based upon the current condition. There are a few models with different characteristics that I use to 2 to 3 models and compare feedback, combine this with reading the weather maps, and do a “gut validation”. It is this future, plus my experience from the weather map reading plus applying local conditions such as mountains that allows me to gather enough information to make decisions on what to do. This effects safety, comfort etc, there were examples of friends on other boats that just listened to general weather broadcast especially in remote places, and they made decisions based on this, but these forecasts were over 1000s of miles and extremely general, and open for interpretation as they were line item. In the same situation,  I ran the models; play off scenarios “what if” based upon local navigation, mountains, and my boat. On at least 3 occasions,  these friends left and where caught with young families in life threatening situations. This is decidedly different to when I was a teenager sailing the Australian coast, where we only had the weather maps, and a number of peoples opinions, we got caught a couple of times in particularly severe weather especially 5 hours out. Today I am able to use the models to look 7 days into the future for a trend, and greater accuracy at 12 and 24 hours. Today these models are a natural part of my navigation tools, having on board capability to download the results of the model, and then tools to ask questions based upon routes I want to go, easy to interpret graphic experiences to see results. (As seen below the weather is overlayed on the charts and uses colors to show intensity, I can work through time forward 5 days by hour).

It is easy to receive model results even at sea by sending an email of current locations, areas I am interested by a text email and the results come back in a data file that I load into my local system with my boat parameters, navigation charts, and I can now easily see graphically the results layered on the navigation charts and ask questions, because of this simplicity to use I now use this many times a day to make and validate decisions.

So why, not on the plant? No matter the role, it could be maintenance, performance engineer, safety, operations, where decisions are having to be made, and experience varies, the ability to get these reference points is key.

This must use high fidelity simulation models to predict the future, but the capability of adding this to a solution must be small. Traditionally simulation systems have been offline, and large after thoughts, especially outside of the oil and gas industry. “Why cannot we just add this future capability similar to adding alarm or historic capability to a device supervisory control?”. The answer should be yes. This will require smaller foot print simulation, dynamic and libraries of simulation models for different devices/ processes etc. Again in the oil and gas world this has been done for years, and companies such as Exxon and others have built libraries of optimization, and simulation capability. All industries require this no matter the complexity, but to achieve reality it must be easy (a natural act) and rich this library of simulation models must be rich, and easy to apply, plus domain experts must be able to develop these models easily, these could be device / process vendors, domain experts, and companies. With the models naturally able to be applied to the automation/ operational platform as just another function.

Yes, dynamic simulation is one of fastest growing aspects of Industrial Operational Systems, as we design systems we must assume that this capability should be included.