Why is the (Level 2-3) platform key to the future state?

I seem to end up in many discussions between IT/OT, the convergence that is required in order to achieve today’s agility. It is really is the transition of existing operational / business systems from “open Loop” to “Closed Loop”. For many of us from the control world this is just extending the “closed loop” control approaches to the supervisory/ operational architectures, but with longer periods.

As stated many times we have 4 pillars of change occurring:

•        Demand Driven supply driving agility
•        Changing work force to a Dynamic/ collaborative workforce
•        Changing Workspace and process “way of working”
•        Changing technologies with Big data/ Internet of things

Driving the creation of integrated, flexible platform that bridges knowledge management, and information / decision support, while naturally absorbing change through Model Driven Architectures.

The key is applications, and capabilities will constantly evolve, change, this should not restrict the agility of the system to adopt through a platform allows new processes and loops to be put in place, but easily adopted.

The shift to closed loops where a “process” is applied to reducing variation in the speed a decision is made vs the action that is taken, and that the execution of these processes can be monitored and tuned even if it just executed across humans. Correct action maybe improving the transfer of “knowledge” so people or systems involved in the process execution have the knowledge to make the decision and act in a timely manner.  

The required platform between automation/ and business must eliminate:

•        Silos of information and control/ elimination of disconnects.
•        Elimination of disconnects thru contextualization across systems so transparency of understanding can achieved across systems.
•        Elimination of delays thru aligned kpis and targets and decision across the different levels of business control. Removing isolation of decisions and actions through integrated bossiness and control loops.

This does not mean we replace existing automation and operational systems, it means we applying new technologies to federate systems where possible, into a hybrid solution thru standards, and managed processes that can be evolved over time. 

Operational Continuity Foundation is Rapid / Early Decisions by Systems and People.

I was involved in an interview last week for an article in a magazine, like so many of these we had an expectation of the subject the editor wanted but as started it was clear that subject and expectations where different. Basically the editor wanted to understand about “big data” being applied in a particular industry, again it was someone with a technology concept the market is throwing about vs really understanding the business / operational challenge the industry is facing.

It did not take long for us to evolve the conversation into the number the main things companies must harness:

• Operational Continuity: Maintaining their producing plants at the maximum output, with greatest efficiency, and best product margin
• Agility: to supply the market with the correct product at the right quality, and right price and the right time in an every dynamic market
• Asset Management/ Utilization: This is both fixed, mobile capital assets (non breathing assets, such as plants, trucks, ships) and the human assets (breathing assets). We find that as globalization increases the buying and selling of capital assets increasingly happen, introducing of challenge of  how do incorporate existing systems, automation, and practices into your overall value chain to provide the above “Operational Continuity” and “Agility”. Same when the asset is sold how you dis engage it cleanly especially with IP in the products and process. Combine this with the dynamic Human Asset landscape where human assets are moving regularly between plants and locations. Causing on a site not to have the required experience to make decisions, but people are in a role of having to make the decisions. YES the asset world for both capital assets and human assets is shifting form traditional stability in both classes for the last 20 years to one of both dynamic.

This comes back to conversation I started last week around “Achieving Consistent and Right Results in an Agile World” and the need for systems that have both:

• Embedded knowledgeand experience: key in this system is have a culture and system that naturally enables knowledge to captured and arranged, managed to be current and while native consumption of this knowledge is simple.
• Augmented Collaboration of Experience: That in this dynamic world the need for a community of human assets of different experience in role and locations to contribute naturally to a decision in real time, augmenting the embedded knowledge. 

This calls for a system that enables people and systems, all of different levels of capability, and experience to work in coordinated way to reach a decision and act. The above diagram is a simple one of the core items of Action orientated Augmentation system.

Unconditional to success is:

• Detect the situation requiring attention as early as possible, pin pointing the location and cause as best as we can. The traditional alarm, “as is” is too late. This does not apply just to process situations but also operational/ supply chain situations that effect “Operational Continuity”.
• Understanding:This means the right person, or people, or system is notified with the required context of the situation. To often transitional systems are application based, so you alarm to control room, or SCADA system. There needs to shift to notify to people and roles, with acknowledgement of acceptance so that correct people are investigating and acting. Yes this will mean systems require unique logons, and shift to named users as people will be connected more, across applications and devices, and accountability in a team situation is fundamental so tasks do not get dropped. But core to understanding is the ability not to be just notified but to investigate, from the provided context the situation based on the workers experience, to collaborate fast with peers, and contribute an opinion.
• Decide: Based on the incoming opinions from systems, and people, and raid decision can be determined based upon experience, knowledge, and technology. The decision could be made by a system or person based upon the inputs.
• Actions: The appropriate actions and process to resolution can then taken across people (one or many) and across systems.

All the way along the system will rapidly manage the process of detect, understand, decide and act, and then track the success and increase the embedded knowledge. 

This is not a question of a new technology like "big data" it is about having a framework that works across your operational landscape to empower people, and systems to leverage existing knowledge and experience to evolve the Operational Success of the company.

How Real are we Treating the State of Connectivity in Your Operations relative to Success of the Business?

Control over your business across different sites, the supply chain, and targeted markets is key to survival. Control ensures that you are delivering the correct “Rights” in order to maintain the “shelf space” therefore access to the customers and market position and potential growth.

But as time moves forward the requirement for control over a wider value chain, and tighter control is key, and becoming critical. This directly relates to the “connectivity” within your business especially over the “value chain” including supply chain, manufacturing/ production, and then distribution. With increased regulations set by government, or the public/ market, brand integrity is core as brand loyalty has gone.

Consistency in decisions, consistency in actions, real-time awareness drives the operational world towards “self aware” production, and “self Aware” products that enable the timely awareness and action.

Connectivity means real-time alignment between People at all levels, with focus, and the value chain assets relative to their current production/ operation. The diagram below shows how the world is changing how control is becoming key, yet if connectivity and that means not data but “knowledge” and "Wisdom" is key.

Will you let opportunities pass, due to un-awareness, or the inability to be agile even if you do have the data, can you act on it with the current operational systems or operational culture?

In mining, oil and Gas we seeing strategies with the “integrated operational centers” to take a paradigm shift in the operational connectivity between the key functions like planning, experts and operational control over multiple assets by putting them in a common room. No longer is it a call, or meeting, in real-time people can cross the room and talk, review each others situation, call small adhoc decision meetings. The returns have been significant, when you then combine this with “trusted data” and total transparency in real-time in context across the Value Chain decisions can be made.

Take that one step further with a “self aware” system, that knows what it is meant to be doing, at what efficiency and safety level, and can see into immediate operational future through embedded simulation, that the system can draw awareness to critical decisions that can be acted on. If the system knows the collaboration it requires to move to a resolution and maintain operational continuity then it can interact with other systems, key people in real time with the correct context of their contribution.

Can we not deliver this, can we competitive without connectivity?

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.”

How will we work in 2025?

During the holiday break I was catching up on reading, validating ideas, and directions, and I found this article on "Why we would work in 2020? from NASA IT Talk.
http://www.nasa.gov/sites/default/files/files/IT-Talk_July2014.pdf

What interested me was you had a big semi government organization often not know for agility, talking agility of missions, of different sizes, and also the workspace transformation technologies and experiences were the same as we are predicting in Industrial/ manufacturing operational space.
The link above takes you to the article and here is an extraction: The targeted outcomes are aligned as well, with agility, collaboration, understanding the future,

"As IT professionals, we are used to rapid changes. But compared to what’s coming, we ain’t seen nothin’yet. Of course, no one actually knows the future, but by predicting it, we can make better decisions today that will help us become more effective tomorrow. The purpose of this article is to start a discussion so we can innovate together to help NASA IT lead the way and prepare for how NASA employees will work effectively in 2025. It has been said that the best way to predict the future is to create it. While we may not be able to create the IT future by ourselves, we can certainly in‑fluence it. A good way to accomplish this is to:

(1) collaboratively predict the future;

(2) test it together now with leading industry innovators by creating meaningful and evocative prototypes that provide high value for our constituents in the NASA environment;

(3) measure the results

(4) communicate the results as visibly and loudly as we can.

So, what will the technology environment look like in 2025, you ask? OK, here’s a prediction at a subset of the new normal in 2025:

• ·         3D printing / scanning / faxing is mainstream.
• ·         Consumer robotics is everywhere and really cheap.
• ·         All data is accessible, searchable and usable from any device.
• ·         We can use unlimited computing and storage through cloud computing.
• ·         Computing is wearable with any data accessible at any time.
• ·         Reality is augmented via modeling by default through our mobile apps and wearable computing.
• ·         Space is partly commercialized and NASA routinely partners with commercial and nontraditional
• ·         entities.
• ·         Over 10% of cars are self-driving.
• ·         More than 50% of  employees are Millennials.
• ·         NASA looks and feels much more like a startup than we did in 2014 and we use
• ·         crowd sourcing routinely.
• ·         Projects are accomplished in months, not years.

How will we work?

·         We will routinely use effective, rapid prototyping with faster, lighter, cheaper, and more effective infusion of the latest technologies into the NASA missions. Agile development will seem cumbersome in comparison.

·         We will evaluate and use the most effective emerging tools as part of our normal work. Visual programming and modeling will be expected and NASA will show visible leadership to industry.

Where will we work?

Simply put, NASA will be the workplace of choice. We will have a balanced, “startup-like” environment with mobile, reconfigurable, ­t-to-purpose workspace that enhances personal productivity and job satisfaction.

Working from anywhere with any data and any device will be the new normal.

Who will perform the work? NASA will be the employer of choice and the

partner of choice for the next generation of startups, industry, partners, and competitors. What about “the crowd” you say? Bring it on! Crowd ideation / development / funding will be commonplace and highly effective.

What will we work on? We will be equally adept at small and large missions, for both wild and feasible ideas. We will use industry for transportation. We will be on our way to 3D printing on Mars in preparation for sending humans to Mars. Asteroids will be within our grasp (literally). Submarining under the ice of Europa will be imminent.

We will monitor and protect our planet with millions of sensors composed of official NASA instruments and crowd-sourced wearable computing and nanosats. And that’s just a start.

Here is a sampling of predicted changes and prime candidates for prototyping that will show us the way to taste test this future now across NASA Centers and with leading industry innovators:

• By taking advantage of Big Data and Analytics, we can easily ­nd, store, share, and update all relevant information when we need it. We will provide self-service analytics to all who need it, so our decisions are based on data, not anecdotes. Robotic devices and scripts will collect valuable data for us 24 hours per day, every day.

• The Internet of Things and Wearable Computing will help us to have instant access to all this information at our fi­ngertips, on our wrists, in our glasses, via hand gestures, and by simply speaking the questions.

• We will use just-in-time training through videos created by current NASA specialists, and through specialized Massive Open Online Courses, all available from anywhere and any device via on-demand video snippets delivered directly to our favorite devices, such as smart glasses.

• 3D Printing/Scanning/Copying/Faxing will be mainstream and will allow us to hold effective

brainstorming sessions where we mix virtual and physical models regardless of where we are located.

Is this too Pollyanna’ish for you? Too conservative? Either way, please participate in the conversation and help us steer this train in the right direction, because it is already moving and speeding up, with or without us. Our destination is exciting indeed. And it’s all enabled by IT. "

We should not be surprised, but it is good to see validation of our thoughts.

Asset Management / Optimization Stands to Take Significant Leaps of Value with the Internet of Things

Last week I talked about the “smart plant”, one of the key areas that is changing and opportunity for a step level of output value is in the “Asset Reliability” / “Operational Continuity”.

The real opportunity in is increasing capacity of through :

• Increased flexibility in the existing assets to run more products, and we understand asset condition through pattern recognition
•  Improved preventive, and “awareness” of asset condition and capability of performing at optimum. The devices / assets are “self aware” and self learning on improvement and conditions so early detection of conditions are seen and corrected in a timely manner.
•  Improved planning and asset utilization through transparency across assets on a site and across sites,

There is a lot of talk around the internet of things (IoT) in the general world but in the industrial world there is huge opportunity just due to the significant number of devices.
Industry pundits predict that by 2020 over 50 billion everyday objects will be connected to the Internet. This does not even include the Industrial IoT and the entire M2M environment, much of which is already in place in our factories, plants, and infrastructure.The initial trend will be to establish one-way communication, ultimately migrating to "many-to-many" communications as more physical objects be-come connected. Connecting all the assets and devices in communities of active tuning, decisions and optimization, requiring a significant rethink and change to current operational management/ supervisory systems and information systems to take advantage, but it aligns with the workforce operational transformation.So if we look at the clear steps that can happen in Asset Efficiency:

1/ Increased information, data in a one way capture of asset information.
This step is the first one and is well under way where increased intelligent devices are monitoring / calculating their performance and the information is logged to an historian. As stated in past blogs we seeing the I/O count between control systems and historians increasing by over a factor of 10. (example a pump use to be 5 to 10 variables, now is 120 to 200, a well head was planned to 50 points now logging 690 points).
Once you have this data companies like Pattern Discovery Technologies (http://www.patterndiscovery.com/) produce solutions that used defined events to investigate through Big Data Techniques asset condition patterns, from this vast historical data, so that better prediction is possible of conditions earlier.

2/ Is by direction, and communities of devices “learning” together and tuning their performance.

So instead of a device/ asset just learning on it’s own, imagine a community or similar type devices learning and sharing their learnings between them. This is not a linear learning of optimization but an exponential learning. So the conditions for a type can be immediately picked up and used by a new device / asset of the same type. Machine learning and community “hub” learning is a powerful predictive capability coming into the market.

Companies like MTELL (http://www.mtell.com/) have introduced some powerful “Machine Learning” capabilities, that combine with their “Transfer Learning” capability. Key is this does not have to wait to new devices/ assets on the plant it can be applied to existing assets, and the “learning” will begin.

The concept of going to “Smart Machines/ assets” that are:

• Self-Aware”
•  Self and Tribal Learning, so improving in predictive understanding of behavior

  Notification and increased analysis capability through powerful tools for asset analysis from the dramatic increase in data available.Now that devices can be connected through wireless to internet, and therefore a “cloud historian that is managed” and these analysis tools can executed centrally, or devices discover each other and learn together provides the breakthrough in the Asset world from predictive to prescriptive.

Decisions in the NOW, increases the desire for analytics as a key component of an Information Driven Enterprise

Again on a flight to Europe this week, I struck up a discussion with a fellow traveler who is out the oil and Gas industry while sitting in Abu Dhabi on a layover. It was around the transformation of decisions support from reports, to dashboards, and now for a need for more real time decision support.  This discussion aligns with the Information Driven concepts and the transformation from reports to predictive and decision dashboards.

Information Driven Companies are looking for more than what has happened they driving to understand what will happen?
It is crucial to note Excel, BI and EMI tools provide extremely solid basis for analysis in the past and now, and in a focused area, but as decisions become more predictive the way to sure up the prediction is to start looking across significantly bigger data to see common patterns. EMI provides dashboards and alerts based upon basic rules and KPIs this is still required.

Source ARC

The above diagram shows the evolution of Enterprise Intelligence and Business Intelligence from the understanding of today to a more predictive requirement, this is from ARC. When I talk with customers, I use the Operational Excellence Journey diagram below to describe the evolution to real time, agile decisions. It is vital that companies accept that achieving operational excellence is a journey not a one off project as it evolves as the company learns and tunes.

As you move to right the analysis and analytics start looking for patterns, and relationships across data sources, and linking causes to a set of conditions. BI and EMI remain indispensable tools of information driven companies, but they do have limitations. For example, BI involves the IT and runs in batches of set time breaks while information driven companies are requiring broad access to analytical information and they need it continuously in real time. Finally to be able to adapt quickly to market place changes, information driven companies need to look forward, predicting what will happen next. Traditionally BI/ EMI systems have not incorporated predictive analytics tools to apply pattern matching rule and algorithms to historical data.

These requirements combine with the new technologies that are now coming common place and transforming the capabilities of large data analysis. Four overarching trends are transforming the industry: e.g.| Data, predictive analytics, self-service/ embedded analytics and cloud based analytics. Advance techniques such as data mining, predictive analytics, statistical analysis, data visualization, text analytics/ natural language processing can all be applied with e.g.| Data to discover new patterns and relationships opening new understanding and potentially operational advantage.. This significant trend, reinforced by the fact that modern predictive analytics tools do not necessarily require advanced skills, and thus overcome many of restrictions of traditional predictive tools. Many of us are evolving technologies and tools, that will analytics and simulation module as part of a supervisory/ operational experience (similar to alarming). Enabling small forward-looking models to run off existing  systems and history to allow a forward look based on the situation today. Combine this with users now getting information for decisions via advanced analytics tools on top of traditional data sources that they can use themselves(self-service) and immediate value. Next week I will expand on some of key transformations in the Intelligence BI worlds that apply in operations.
 

Time for Information Driven Manufacturing!

Information Driven Manufacturing concept is starting gain traction in the thought leaders. Information Driven Manufacturing is a manufacturing strategy that combines the concepts of collaboration and value network manufacturing, building on the newer technologies to achieve and sustain a agile competitive multi plant business. A key concept to this strategy is that explicitly recognizes that avoiding change, while comfortable, may represent a bigger risk for the organization that the risk associated with introducing new solutions where appropriate.  There is a different culture not taking technology for the sake of it, but an attitude that understands the need for alignment of people, value asset network (multi-plants) and business and operational processes, to reduce cost, but most of all provide a flexible manufacturing base that can adjust with market providing the necessary agility to absorb market change, acquisitions, and new products rapidly and in a cost effective manner.

 

 

Source ARC Feb 2013

Information Driven manufacturers take a holistic view of manufacturing and the production plant’s role within the extended value network. They apply information technology broadly  to improve or replace business process. With the maturity of internet, workflow, databases, and other technologies there is a host of possibilities that can be applied in a program to improve the dynamic nature of the whole manufacturing from people to assets, and processes, to enable consistency of execution and therefore the opportunity to be dynamic to absorb, evolve to change.

The cornerstone of  information driven company is the empowerment of all people in their roles, to make decisions and act as an aligned team, based upon process and business information, provided in a holistic view (across assets E.g.| unified model despite the underlying sources), contextualized, visualized so that it can be analyzed easily relative to their roles. Key is making sure this information and core data are a “Trusted system” and the leading companies are now applying consistent embedded actions to go with the information decision so that consistency in action, and reduction in skill experience are needed to achieve a consistent, timely result.

The culture in an information driven manufacturing company, is understood the value and need of change/ evolution in a constructive way, but executing this change on proven technologies but not just used in their business, but looking outside their business and asking “why cannot we apply that for this____”. They have active investigations through internal, and external looking at:

 

More and more I am engaging with leading companies who are looking at open minded people to make a team who can constructively develop a value program. It is important to recognize this is more than one program, it is alignment of programs, technologies and cultural journey which leading companies are on, and potential benefits are significant in agility (market share) and long term cost, through staying 'ever green" and aligned.

 

 

Fast Data, data in Context is as Critical as Big Data

I was listening to M2M (Machine to Machine conference sessions), and this interview of Chris Baker from Oracle was fascinating as it echoed what we seeing in the industrial / operational world.

Design the systems assuming massive amounts of data, for not just today but also the future. This means the architecture must be able to access the different data, put it in context, and provide patterns and exception based analysis so decisions and then associated action can be taken.

Data without the ability to take action is of no value, and it is key we move to “intelligent work” concept where information is delivered in context of role, and situation, and associated actions with it. Independent of location, device and architecture, as these will change.

Interview with Chris Baker, SVP, Oracle at M2M WORLD CONGRESS 2013 - YouTube

http://www.youtube.com/watch?v=oqzam6aWqHk&feature=player_embedded

Big Data requires Pattern Awareness to Provide Situational Awareness

2012 saw the rise in what I call “Industrial Information Systems, Projects”. You may say “rubbish”, the whole historian, and information business has been around for years, and the answer is true. Today these projects are different dealing with a “lake of data”, delivering to more people, of different roles typically not even aware of what an historian is drawing data from many different sources including historians, xml files, transactional data sources such as MES and Batch systems, alarm, event systems, MS Excel and customer odd databases, as well real-time data. There is no one supplier, one source, or structure to this data. The challenge is when the context and knowledge of the data is retiring from the companies, but the size of internal community of roles and workers requiring access is increasing. How often I have been asked and discussed the issue of data validation and data awareness, vs architecture, and technologies at tossed into conversation hoping for a “silver bullet”, but I believe the solution comes with new capabilities like “Big Data” but also evolutions in existing industrial implementations, with a more holistic design!

I believe the growth will accelerate in “Industrial Information Systems, Projects” during 2013, and beyond, but this is not about delivering reports and information, it is about “empowering” the increased community in  business in making real-time decisions, based on real-time trustworthy, effective industrial information, no matter their location. I continue to get surprised by the notion that the solution is an Enterprise Historian on top of the existing system, acting as a data warehouse. I ‘Scratch my head” and usually “ask how to you know the data is valid, in context, and comparable. Too often it is a blank look they were lead to believe the data in or supplied from their SCADA, lower level historians, etc. is the only thing they need to access. Key to understanding is the ability to detect patterns, across data, but there needs to be enough context to allow the evolving big data tools to enable detection of patterns.

 

Last week this blog discussed exception based “self aware” models required in today’s proactive operational/ supervisory systems, especially as devices grow in intelligence capability. This model is also key to putting things in context enough to enable this analysis and patterns to be seen way further than process analysis. Big data concepts of pattern analysis, save that pattern, and now have it as auto detect on a similar pattern happening again, triggering an operational process that will continue a proven procedure to resolution, guiding the workers involved interacting in a consistent and pro active manner with the objective for early detection and fast resolution. This automatic pattern recognition, detection, and embedded procedure are one key aspect of the modern situational awareness concept. Building on last week’s blog concepts of the “self aware” model, if these smart devices and processes include a pattern recognition capability as part of the “self” intelligence, the shift is a response from the “as is” status to the ‘to be”. The diagram below shows a significant opportunity for improvement with the two “value of early corrective action” lines  effectively illustrating the value gain in early detection and pro active correct action.

The diagram illustrates how a condition over time “x axis” changes in cost/ value through time, and how the traditional alarm systems are in the “as is” state, and the whole objective to “SITUATIONAL AWARENESS” is to shift to the “to be “ state.

Self Reliance/ Self Servicing are Key Concept as the Hunger for Real Time Data Grows

2013 will see another leap in the amount of people, and different roles, accessing industrial data, and there will be another quantum leap in the amount of data people accessing. The concept of Self Reliance and Self Servicing of the user to empower himself will be key!

“Self-service BI is the idea that any business user can analyze the data they need to make a better decision. Self-reliance is the coming of age of that concept: it means business users have access to the right data that the data is in a place and format that they can use and that they have solutions that enable self-service analytics. When all this happens, people become self-reliant with their business questions and IT can focus on providing the secure data and solutions to get them there.”

“Source Top 10 Business Intelligence Trend for 2013” Tableau software

I have spent a number of hours this week discussing this concept and determining in the industrial sector how to make this a reality as we believe it is key to unlocking the full potential of the industrial landscape.

 

“Self service is the practice of serving oneself, usually when purchasing items, examples self service gas stations, and ATMs which have transformed banking.” Wikipedia

Self Reliance is a concept from the 1830s

“To accomplish one’s assigned task in an independent, resourceful, self-sufficient manner; to do one’s job without making a fuss. Reliance on one's own capabilities, judgment, or resources; independence.”Wikipedia

Both these concepts align with the “what If and “why not” culture where information is presented to ask questions, make decisions.  Key is trusted information that is effective for decisions. No longer can people request a report, they must have the ability to access, browse and navigate an information model that abstracts the industrial data source landscape, ask questions as they see fit, answers returned in a format that can be review for further analysis.

So in the industrial/operational world we will see a significant shift towards “operational” analysis, the ability to ask and search answers by asking questions like in “google” but with graphical/ process analysis feedback in narrower time span of interest. This is different to a printable report or dashboard due to the requirement for discovery, investigation mode. Users understand the basic relationships, but are looking for patterns. The plant data landscape is a “lake of unstructured, related data” that needs to be in context, and trusted, often abstracted from the traditional data source like a historian, alarm DB, or batch database. More and more tools will be introduced to help in determining “pattern recognition” through the use of complex event analysis, and “Big Data” tools and concepts will be adopted. Already tools like the “Overview” capability in the latest Release of Wonderware Information Server (released dec 2012) provides a new paradigm in process analysis out of the box across multiple data sources.

Why is operational information relevant vs reporting, it comes down to 2 factors:·        

·     More people needing access to real time data to make decisions and the majority of these people do not understand the automation layer, naming and model.

·     The Dynamic, and speed of decisions is critical to success time analysis and the decision is shorter while the breadth of information and responsibility increases.

The day of requesting and waiting for reports is over, the role of reports is reducing significantly. We are use to the internet, using “Search”, “Wikipedia” to ask and investigate, so why not for the Industrial information, it can no longer be isolated and difficult!   

Big Data will Play in 2013, and Reality on an Information System could be in 2013

“The staid old world of databases is developing faster and faster, with startups addressing new data problems and established companies innovating on their platforms. Web-based analytics tools are connecting to web-based data. And everything’s mobile.”

I read a good article whitepaper from a our Partner Tableau for intelligence software they outlined the 10 concepts that are becoming reality in 2012/3.

·         10 Intelligence Concepts :

·         Proliferation of data stores.

·         Forecasting and predictive analytics become common.

·         Mobile BI moves up a weight class.

·         Visual analytics wins Best Picture

·         Cloud BI grows up.

·         The value of text and other unstructured data is (finally!) recognized.

·         Self-reliance is the new self-service.

·         Hadoop is Real

·         Collaboration is not a feature, it’s a reality.

“2013 is the year we will recognize this story as a fairy tale.

The organization that has all its data in one place does not exist. Moreover, why would you want to do it? Big data could be in places like Teradata and Hadoop. Transactional data might be in Oracle or SQL Server. The right data stores for the right data and workload will be seen as one of the hallmarks of a great IT organization, not a problem to be fixed.”

All these concepts align with what we seeing and I will expand on but I thought this could be a good read to many of you, even though it is focused on Business Intelligence this applies to Industrial Infomration Systems:

http://cdnlarge.tableausoftware.com/sites/default/files/whitepapers/top-10-business-intelligence-trends-2013.pdf

Big Data Requires a Big, New Architecture

“The potential of “big data,” the massive explosion of sources of information from sensors, smart devices, and all other devices connected to the Internet, is probably under-appreciated in terms of its eventual business impact. However, to take maximum advantage of big data, IT is going to have to press the re-start button on its architecture for acquiring and understanding information. IT will need to construct a new way of capturing, organizing and analyzing data, because big data stands no chance of being useful if people attempt to process it using the traditional mechanisms of business intelligence, such as a data warehouses and traditional data-analysis techniques.” Dan Woods; Forbes

So does this apply to Industrial Area, I was heading through Terminal 5 in Heathrow this week, and articles banners around Big Data were all around me, and yes it is the latest “train” for people to board, but is it real in the Industrial Space? As I boarded a train, sat doing a mind thinking moment looking at the industrial operations/ automation landscape I realized why there is confusion is that in the industrial space,  we talk about Enterprise Historians, and one person said to me that is big data! I do not think so, it is just one aspect of the growing industrial information dilemma facing all us over the next 5 years.

When I look at the predictions of Big Data by Industry from Gartner:

The column for “Manufacturing and Natural Resources” which has every row in “Hot” or greater and points to “Volume of data”, “Velocity of data” and especially “Underutilized Dark Data” as Very Hot. This is should not be a surprise to anyone with the historians out there with 10000s of tags soaking up the data at second intervals. In the last 7 years,  Invensys Wonderware has installed 128 million I/O in historian points. Another point not brought out here is the need to make the data “trust worthy” and auditable so business decisions can depend upon it, much of the industrial data is just captured today, not validated against the current state of the process etc.

Now lets understand the “Jobs People want to do today” has there been a change? Yes there has been around the responsibility scope increase. This is both in making decisions and more business impactive decisions, as well as the increase in breadth e.g. Area that a person has to manage.

Initially this seems okay, but  now consider  the devices in the field today, and the amount of data coming from a device that traditionally would have 2 to 3 points, can have 400 points. Is this exaggeration, lets look at an example of a pump.

In the old days,  a pump would have:

• Speed
• Pressure

 Today:

• Speed
• Temperature
• Pressure on incoming and outgoing
• Vibration
• Energy calculations (many variables)
• Number of starts
• Volume
• On goes the list

The reason is that today devices are much smarter this to improve performance, efficiency, maintenance lifetime, and energy consumption management as well as predicting the operational reliability of  the pump. Compared with the old requirement of turning it on and making sure it is pumping to make sure it does not run dry.

Now take one device and put it in a plant context where it is one of 1000s, we have effectively increased the volume of data by 100000s and it will not stop growing. So the ability to capture this data as close to local data source, validating the data, but accessing the data, understanding events, patterns, and relationships across devices, plants, and device types etc required for this ever increasing drive to lower the OPEX costs, through increased efficiency and lower maintenance lower energy consumption  etc.  Again review this data historised for  a pump, the data falls under multiple categories:

·         Operational

·         Energy

·         Maintenance

·         Efficiency

Different roles within the “day to day” running of the industrial operations will analysis the data in different ways, to draw different conclusions. Examples are some people will want to look across multiple pumps and compare efficiency, energy etc vs the Operator who is just look at the current status and availability.

Will the traditional industrial tools be good enough?  I do not think so as all data is not in one form, one data source, take the above time series historian data, combine this alarming, events, and operational data. The introduction of new architectures, “Information Models” and analysis tools which will enable a view across large amounts of data, put this data in the context (this does not mean a data warehouse) and analysis tools quickly bring out trends/ relationships between data from different sources over large areas. All with the simple objective of enable more “real time decisions support”. An example of this is in the latest Wonderware Information Server 2012 R2 (released this month) with a new operational analysis capability. Seen below this capability is out of the box across, MES, Batch, Time series historian data and alarms data sources, providing an immediate view into a trend with a “halo” to show the shift, or batch or phase of operations the process was in, and associated alarm data, all at the operators finger tips.

This is the first step as Invensys will be expanding this capability through the next few years across the Enterprise Control Solution. I will expand on this Big Data in Industry and Decision Support concepts over the next couple of weeks.