What did 2015 mean to Operational System?

As we enter the final weeks of 2015, did it live up to what we expected, what trends did it star to cement into Operational System design.

These are just some of observations I have seen:

1/ Shift to significant operational transformation programs, vs just projects, as accelerated in the second half of 2015. Certainly we have seen a lot of projects initially started or investigated as projects in 2014, in 2015 reemerge as multi-site, multiyear transformation programs. With the understanding that these programs are on journey both in technology, but also operational goals/ outcomes, and culture.

Certainly a couple of us have seen a significant amount of time allocated to evolving these opportunities working with the customer to help define their outcomes, the approaches, this has been and is still continuing as educational process for all involved. This is fundamentally changing the engagement models between end user vendors and engineering houses as a partnership, requiring changes on both sides.

2/ Cyber Security/ Application Security: This continues to grow as a huge area of interest, but this year it shifted not only how to secure, but how to maintain successfully, evolve their business and agile operations in a tighter security model. Realization that cost is not just in setting up a secure operational environment, but the cost of evolving and sustaining it while maintaining an agile business requires a strategy on it’s own.

3/ Operational Awareness/ Effectiveness: Understanding, not the “aging workforce” but the transformation in both “workforce culture/ approach” and transformation in “Workspace”  are real. That today's and last ten years of operational systems will not satisfy the agile decisions that required, but also the changing workspace culture and methods. The amount of workshops and strategies sessions I have asked to be involved in 2015 was three times that of 2014, and they were clear strategic discussions around people and how people will operate in the future.

4/ Understanding and reality of Internet of Things: The hype has been here and continues around IOT. But there has been some real sole searching in many industrial companies to understanding what it means to them. Many it dawned as the operational alignment end efficiencies they have in the “walls of the plant” now can extend to the “mobile plant”. In Oil and Gas, and Mining moving to include “extraction” wells, equipment in the operational process in real-time. In many other industries, it moved the mobile receivables plants, distribution trucks and then the distribution centers, etc. to be included in the “end to end” operational control.

5/ Realization that the Operational architecture of the future near and long term will have Internet and “cloud” as a natural part of it, and we must design the security, and systems assuming on premise and off premise architecture.

All of the above does surprise us, based on the trends, but it is good to see the shift from talk to reality. I would expect that 2016 this strategic journey programs to increase. Certainly the scope of operational responsibility is changing include a end to end supply chain, that means move outside the plant walls with the traditional systems, and we will see the alignment of Process operations and utility operations (power) into one operational strategy and control.   

Have a very happy holiday season and may 2016 continue the momentum to deliver operational solutions that will handle the "operational transformation" happening around us.

Will Data Historians Die in a Wave of IIoT Disruption? A transformation in data historian thinking will happen!

A group of us were asked to comment on this article by Matthew Littlefield , President and Principal Analyst, LNS Research, on October 22, 2015. It certainly is an integrating questions, and valid question in the current industrial , operational transformation that is happening around us. As we answered it on email, I thought it is a valid topic for blog discussion.

http://www.automationworld.com/databases-historians/will-data-historians-die-wave-iiot-disruption

My immediate first response is “that the traditional thinking of industrial data historians will transform”. Actually it is already transforming, due to type , volume, and required access to the data. It is important to not look at the situation as a problem, but as a real opportunity to transform your operational effectiveness through increased embedded “knowledge and wisdom”:

The article raises the question of how or is this a disruptive point in the industrial data landscape, I would argue that is a “transformation point”.

Mathew states in the article:

“Even so, one area of the industrial software landscape that many believe is ripe for disruption is the data historian. The data historian emerged out of the process industries in the early 1980s as an efficient way to collect and store time-series data from production. Traditionally, values like temperature, pressure and flow were associated with physical assets, time stamped, compressed, and stored as tags. This data was then available for analysis, reporting and regulatory purposes.
Given the amount of data generated, a modest 5,000-tag installation that captures data on a per-second basis can generate 1 TB per year. Proprietary systems have proven superior to open relational databases, and the data historian market has grown continually over the past 35+ years.
The future may seem very bright for the data historian market, but there is disruption coming in the form of IIoT and industrial Big Data analytics.
As these systems have been rolled up from asset or plant-specific applications to enterprise applications, the main use cases have slightly expanded, but generally remained the same. Although there is undisputed incremental value associated with enterprise-level data historians, it is well short of the promise of IIoT.

In our recent post on Big Data analytics in manufacturing, I argued that Big Data is just one component of the IIoT Platform, and that volume and velocity are just two components of Big Data. The other (and most important) component of Big Data is variety, making the three types structured, unstructured and semi-structured. In this view of the world, data historians provide volume and velocity, but not variety.

If data historian vendors want to avoid disruption, expand the user base, and deliver on the promise of IIoT use cases, solutions must bring together all three types of data into a single environment that can drive next-generation applications that span the value chain.

It is unlikely that the data historian will die any time soon. It is, however, highly likely that disruption is coming, making the real question twofold: Will the data historian be a central component of the IIoT and Big Data story? Which type of vendor is best positioned to capture future growth—traditional pure-play data historian provider, traditional automation provider with data historian offerings, or disruptive IIoT provider?

If the data historian is going to take a leadership role in the IIoT platform and meet the needs of end users, providers in the space will have to develop next-generation solutions that address the following:

·         How to provide a Big Data solution that goes beyond semi-structured time-series data and includes structured transactional system data and unstructured web and machine data.

·         How to transition to a business/pricing model that is viable in a cheap sensor, ubiquitous connectivity, and cheap storage world.

·         How to enable next-generation enterprise applications that expand the user base from process engineers.”

The comments are very valid, that the data we now capturing is increased in both volume and variety, but I would argue that it needs to transformed into contextualized information, to knowledge so that  proportional wisdom growth can occur. The diagram below shows the potential direction many companies can go, of blowing out on data and not gaining the significant advantage of wisdom for operational efficiency from the increased data in the Industrial “sea”.

The way in which people will access and use data is transforming, they not using it just for analysis on traditional trends etc. They are applying big data tools, and modeling environments to understand situations early in assets condition, operational practices, and process behavior.

They are expecting to leverage this past history to predict the future through models that “what ifs” can applied. They are expecting access to their answers from people who with limited experience, in role or location (site/ plant awareness). They will not use traditional tools, they will expect “natural langue search” to transverse the information, and knowledge “ no matter where the location.

The article took me back to a body of work I collaborated on with one of the leading Oil and Gas companies around “Smart Fields” and in those conversations we talked about the end of the historian as we know it, due to the distributed nature of data capture, and the availability of memory, why would historise to disk vs leave the history in the device in memory.

I think this really drives the thought pattern around how the data is used, and the key 3 are:

• Operational “actionable decisions”
• Operational/ process improvements, through analysis and understanding to build models that transform situations in history to knowledge about the future.
• Operational, process records archiving.

The future is federated history that partitions the “load” between most-recent transient fast history in the device itself (introducing a concept of  “aggregators”) with periodic as-available uploads to more permanent storage. These local devices will have their own memory storage and can “aggregate” the data to central long term storage.

But when you are access information in the now you will not go to historian, you will go to the information model, that will navigate across this “industrial sea” of data and information, delivering it fast, and in a knowledge form.

So is the end of historian here, I would say no, but certainly as the article points out the transformation of the enterprise information system is happening, so are the models you will buy, manage, access the data.  

Manufacturing Industry Leads Cloud Adoption

It was good to see a blog by Gary Mintchell revealing that the industry sector is leading the adoption of Cloud, yet so often I hear the words that “it will not happen in our company or industry for years!”.

Gary writes a great set of blogs always worth having a link to it.

http://t.co/LIPLIc6SgV

Some Quotes from this blog:

“global study that indicates cloud is moving into a second wave of adoption, with companies no longer focusing just on efficiency and reduced costs, but rather looking to cloud as a platform to fuel innovation, growth and disruption.

The study finds that 53 percent of companies expect cloud to drive increased revenue over the next two years. Unfortunately, this will be challenging for many companies as only 1 percent of organizations have optimized cloud strategies in place while 32 percent have no cloud strategy at all.”

So often I have sales and people saying that cloud is driven by a change in cost model, but also in all my interviews with customers and strategic thinkers it has been a platform for addressing the “changing speed of change and flexibility needed today” that is driving it. The world is changing faster and faster, and the ability to deliver the RIGHTS:

• Right Product
• Right Price
• Right Cost
• Right Time
• Right Location

Is key and this means rolling increased new numbers of products across a distribution of value assets (plants) that will produce smaller lots (production runs), at less cost.

Understanding NOW what the state of Inventory, Work in Progress, and equipment to get to market is key.

Also we seeing the “walls of a plant” expand, beyond the manufacturing plant to now treat the whole manufacturing, and distribution supply chain as part of manufacturing. So the traditional MES (manufacturing Execution System ) is expanding, to offer the ability model the plant to store as operations, where product must be tracked to compliance, and work items distributed to workers and assets in that distribution chain.

“In the study IDC identifies five levels of cloud maturity: ad hoc, opportunistic, repeatable, managed and optimized. The study found that organizations elevating cloud maturity from the ad hoc, the lowest level to optimized, the highest, results dramatic business benefits, including:

•        revenue growth of 10.4 percent
•         reduction of IT costs by 77 percent
•         shrinking time to provision IT services and applications by 99 percent
•         boosting IT department’s ability to meet SLAs by 72 percent
•         doubling IT department’s ability to invest in new projects to drive innovation.”

Cloud Adoption by Industry

By industry, manufacturing has the largest percentage of companies in one of the top three adoption categories at 33 percent, followed by IT (30 percent), finance (29 percent), and healthcare (28 percent). The lowest adoption levels by industry were found to be government/education and professional services (at 22 percent each) and retail/wholesale (at 20 percent). By industry, professional services, technology, and transportation, communications, and utilities expected the greatest impact on key performance indicators (KPIs) across the board.”

The above learnings and results do not surprise me, based upon my own engagements in the field, and observing the increased realization that speed of change is important, and tradition large projects are going out the door. Replaced by rapid projects leveraging existing expertise in the industry and adding through own operational process value to differentiate.  

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.”

Can we achieve the last mile of operational Excellence without IOT?

This question was posed to me last week, and it is a good one. The critical items is to understand what is operational excellence is trying to achieve to realized that it is journey and moving goal of effectiveness pushed by the market and technology. Like when you are riding a wave, you staying in front, and leveraging the wave to excel, otherwise it swallows you up.

Operational excellence is about:

• Agility to deliver products/ services to Customer/ market at the correct price, time and location
• The ability to rapidly introduce new innovation value to lead the market and open new markets
• The ability to enable sustainable innovation and value through effectively leveraging people, and technology.

The diagram below illustrates this, and I am sure some people will have different angles, but it is about leading the competitive edge.

But can you achieve this with the traditional approaches? I believe you can get to 60/ 70 % of the way with traditional approaches and current technologies, but that last mile needs a paradigm shift in “actionable decisions”. Agility requires timely decisions across a team, and consistency and timely actions associated with the decision across teams, roles etc.

A core concept of Internet of Things (IoT) is teams of things (devices, and people) interacting in an orchestrated manner to achieve an operational timely result. With devices being more “self-aware”, empowered to take actions, interacting with workers or other devices to move “work “to the next step.

This foundation of IoT and the orchestration of devices /people, timely knowledge, provides that much needed paradigm shift to enable that last mile on the above operational excellence journey. The constant discovery of new capabilities, and knowledge through big data techniques, the ever increasing lake of embedded knowledge lends it as the basis for companies to go on this Operational excellence journey, but with this is the required cultural evolution to continuous improvement and knowledge/ wisdom.

                                       Source ARC

The above IOT maturity model matches to Operational Excellence journey, especially on the stages of “smart, and autonomous” linking to the Operational Excellence stages of “Driving Business and Driving the Market”). Foundational to Operational Excellence is timely knowledge and procedures being delivered so actionable decisions can be taken in a consistent manner across plants, assets and people. The IoT principles provides the opportunity to deliver this knowledge, while abstracting the variability in plant, assets and experience levels of people.

To me the desire and programs being enabled at companies to take them down the operational excellence journey provides the cultural evolution needed combined with IoT to succeed and make IoT effective not just from technology but most of all business side

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?

Industrial Internet of Things, enables going beyond the 4 walls of a Plant, to Mobile Plant Supply Chain

The last two blogs on the “Cyber Physical and Operational Management Evolution” and “How do you Achieve Orchestration in Industrial Internet of Things without Managed Configurations and Standards?” have created a lot of activity in hits but also email discussions.

This is good to see as two years ago these subjects would have hardly moved the needle, and they real opportunities for leading companies to embrace to expand their capabilities beyond the four walls of their plant.

Again last week I was involved in a number of discussions around these topics and liked one of my South African collaborators discussing how we have all these rich applications and capabilities for the fixed process plants, WHY cannot we apply these same tools to “MOBILE PLANT”?  Now he was from mining and was launching into the extraction side of mining and how to optimize the asset utilization, but he really wanted to go beyond that “Operational Optimization”.

The targets are not about data, what I like he is putting real operational goals in place:

• Operational Processes optimization, understand operational times vs expected times and analysis of areas to improve
•  Asset utilization / optimization
•  Energy and Fuel optimization

As he put we have platforms in the plants that abstract equipment below, and model these equipment so we can record, track their operations, and then apply operational process improvements, and built in operational process rules for the fixed plant. Now taking this to mobile these same platforms could be used but now across mobile equipment, so now we must record geographical data as core as location is key when using fuel, doing operational routes, and time is of essence. But the Delay accounting applications of today could be applied to these mobile equipment and we could then move beyond that to embedding operational best practices and operational behavior in the devices, and equipment to guide the operations to work within the “operational windows” of optimized performance.  

The diagram below shows a more detailed chart of maturities I mentioned last week. The concept of smart, to optimized and autonomous can only come with inbuilt operational strategies and practices that enable the orchestration I talked about last week.

Source ARC (arcweb.com).

The key is most of the mining extraction/ mobile plant is isolated, and I would say siloed even when connected between applications.

Remember the Industrial Internet of things is not about data, it is about “actionable decisions” in the NOW, by either machines, applications or people, and this will require embedded operational strategies/ processes that coordinate the mobile equipment to align with the overall business strategies.

If you are looking in the plant / fixed process world to apply IOT and gain significant value, you should think again and “open the door” and look outside the plant to mobile plant, or mobile supply chain, and extend the richness of operational applications to these traditionally isolated equipment and processes! 

How do you Achieve Orchestration in Industrial Internet of Things without Managed Configurations and Standards?

Last week I was at mining conference and had a rare chance to sit back and listen to people’s thoughts on innovation, and the future. It was good to hear the topics of partnership are key to innovation, (relating to my blog of a month “Participation architecture and culture key to Innovation”).

As expected the “internet of things” came up a lot, in many contexts, like it did at the Dairy conference the week before. With this cam the usual many definitions of IOT and the impact it will have on the mining industry. I just wondered how many people really comprehend the value, and complexity that it brings?

One evening I was on call with France with a partner discussing smart cities and IOT and he made the interesting comment:

“The Internet of Things has moved beyond big data and analysis to how will we align the devices and people into an orchestrated operational strategy that achieves a repeatable agile outcomes.”

I sat back with a big smile as he had articulated the change I had been seeing. As decisions and data is nice but it must go from data, information, knowledge to wisdom where actions can be taken, no matter if that action is taken by a device, or human.

Then I saw this categories of maturity in the internet of things, I had seen something similar but in a week of much discussion on this topic I thought this one would do. It shows devices going from a data sources with intelligent data / I hope actually Information. Evolving to control of devices in orchestrated way, no matter if the control is in the thing or in cloud the things know how to work together in a coordinated strategy. Once you have all the things working together you can move to tuning their operational behavior and effectiveness. This seems simple but things require access to control strategies, and orchestrations that guide these things, now we talking 100s to 1000s of things in this coordinated community. Eventually you end up autonomy or semi autonomy “managed by exception”.

In another discussion with a large network hardware supplier we were discussing a mining extraction alignment solution that could be enabled by IOT unlike today. So we took a practical look at the application, and saw 10s of like machines and a few classes of machines. Then you look at the operational processes they executing and again see repetition, but we are now talking 1000s look at devices.  Yet we had a customer wanting achieve level 3 in the above model “Optimization”. I thought back to many industrial sites I have been on in the last few years where there are 10s of PLCs programmed with larger control strategies but programmed at different times and by different people (even if from the same vendor) and how customers were having a significant cost of ownership in evolving these strategies. This why organizations like OMAC and PACKML have come about defining standard control strategies for operations/ devices that could span vendors.

So I ended back at my conflict, as we move to landscape where we will have 1000s of devices often smaller than traditional PLCs but each with their own monitoring, or control strategies, and then high level strategies that enable the orchestration of these devices/ things into a an effective operational strategy.

I asked how are we going sustain and evolve these strategies without having an “Enterprise Standards Management Framework” that enable standards to built for an operation? These are then deployed over 100s of similar operations on different devices. Now we shifted to managed, agile and sustainable solution.  

The thought of 100s of people programming 1000s of devices and then trying tune and evolve these seems un practical, plus if we enable standards management the reuse of IP and rapid rollout is achieved, while leveraging the revolution to smart devices and lower cost devices that execute these strategies.

A food for thought!!!!!

Smart XXXX: What does it mean!!!

So often today you hear the word “smart” put on the front of a segment describing the transformational program encompassing many of the Internet of Things concepts.

Smart Cities, Smart Farms/ Agriculture, Smart Airports, Smart Plants, Smart Fields etc.

Are they different or do they all come down to a basic set of concepts, transformations that are applied to that industry to significantly shift the needle in operational efficiency?

 Fair question, and so often lately I am being asked what is the difference between IT/OT, IOT, and Smart xxx? So I thought it was worth a discussion, as I suspect there different interactions.

To me the discussion of “smart/intelligent” industrial it is all about achieving “operational Optimization/ Excellence”, to suite the required production at the most effective time, cost. This is a shift from time based production and managing the process to managing the production of product/service. Driving the optimized execution of work / actions on operational processes for that product/service delivery.

At the core it is about changing the way in which we manage and execute work tasks, either automated or actions with human intervention so that only required work is performed at the correct time.  

“Smart Strategies” are fundamentally different from current IoT, Big Data etc. thinking:

• The IoT, Big Data etc. Initiatives/trends can be characterized as offering the 5 “any’s” – any information, in any context, at any time, to any user, for any action
• “Smart” products and operations can be characterized as offering the 5 “right’s” – the right information, in the right context (operations situation), at the right time (which is often earlier than “real-time”), to the right users for the right actions (which are often preventative and at best prescriptive).

All fundamental on the journey towards “operational excellence.”

That said “Smart Strategies” will employ the services of IOT, and big data, but the key is “Smart” is about tightening the execution of an operation process relative to the current product delivery expectations. A key concept is that the Operational Process, (no matter if it is in a city, airport, or production line) understands:

•        What it is expected to deliver in characteristics of product or service, and when
•        It is “self-aware” of it’s condition and ability to deliver that product/ service, due to capability, materials and the situation it is in.
•        It is able to then request and interact with other process, applications, assets and people to gain the required actions needed to succeed and when. 

This is a transformation from just understanding it is taking control of the process, as opposed to time schedule actions.

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.

Top 10 Manufacturing Operations Blogs for 2014

Many of you probably already are subscribed to MOM blogs, but just in case you not I though these top 10 showed some interesting thoughts.


http://cerasis.com/2014/12/22/top-10-manufacturing-blog-posts-2014/


On my blog the two topics which had a number of blog posts, but had highest hits were around:

1/ Situational Awareness , and change in the way we must design based upon impact
2/ Internet of Things and Cloud.

Interesting the year before cloud and internet of things created very little interest, but this aligns with significant growth in interest in these topics in 2014, and will only grow further into solid reality in 2015.
Enjoy 

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.

Real time information Platform vs. traditional historian, Why it is Key to Pushing “Actionable Decisions”, foundational to the “Industrial Internet of Things” and Empowering the Teams.

Again last week I was presenting to a set industrial companies in water, food, and mining, and the topic of a "real time information platform" many questions.

My immediate answer is “what are you trying to do? " " who are the users targeted to interact with the system, and what decisions and actions are they expected to take?". These last two questions usually leave a complex blank expression on people's faces.

Many are engineers who have been asked to investigate, and they centered on the traditional approach of a "data centric" historian centered  world, leading with a technology strategy. The question of what people will use the data for, what roles and actions to be taken are secondary in their minds! 

Why is this when if someone had wanted a "historian" they would have asked for it. So why a platform, what does real time mean, and key is information.

It all comes back to one of the quadrants we talk about in the "operational transformation" around networking a series of assets, plants into a a "trusted" information system. That "actionable decisions" can be taken by a ever increasing community of operational people across the operational landscape.

To me it is understanding this community of consumers and what their requirements, uses are is key:

1. What activities, decisions, and actions they are expected to take?
2.  Their roles, skills, and approach is their time frame, location relative to the data
3. Their context and understanding of the plant, asset or process in question, as their is a growing trend of highly educated skilled people on assets, process. With little or no practical experience on the asset, and more than likely will not have visited site.

On investigations you find you have the traditional process engineers, who need the trend analysis and discovery of potential improvements. 

However, there is a growing tribe of people who need to make actionable operational decisions. They will not monitor the system must best "self-aware, and living" (exception based) capture the data, transformation  it into information.  Apply experience and knowledge, clear understanding of the situation, and what are typical actions with "best operational process" provided to take action.

This is very different to everything getting data stored and then extracted, yes in this new world there is history as it provides the history for reliable knowledge and basis for wisdom or " application knowledge".

The real key is the change in approach from “predictive to prescriptive” which embeds the “actionable decisions into the model. Empowering the operational team, no matter the location or experience with decisions and associated actions.

Understanding this maturity curve and evolution is what we see as foundational to the success of “industrial Internet of Things”. Through the embedded practices provides a basis for the changing workforce to act and make decisions in a timely manner.

However, these two communities in the industrial landscape are interlocked for success. The two communities are:

• Community 1: Process, performance, optimization team that accesses the data with trending, analysis, and predictive tools. Identifying the trends, conditions by applying their experience combined with “big data” techniques allows these conditions, to be seen in the “to be state”. If captured in a managed configuration framework, that will allow roll-out over sites and sustainable evolution. These become embedded into the system, for adoption by the operational team.

• Community 2: Operational Team: This is the dynamic team, from roaming people on the plant to central operational teams, to virtual expert teams, collaborating together in real time to enable “actionable decisions” no matter role, location, and experience.

The diagram below shows the this maturity of capturing this “applied knowledge” as Managed “Actionable Discussions” that interact with people, assets and process as key, very different a traditional historian approach.

The “Real Time Information Platform” provides a real-time "living" model that is self-aware that captures validates the data with rules aware of it is current state. Storing this data in context and rules and calculations in that provide motivation, embedded operational process, and awareness to correct people. Fundamental is the "trust" worthiness of the information, without impacting current automation systems. The ability to have sustainable evolution and scalability, through managed components that represent the assets and processes (actionable decisions) to the model is available on storage side in history and real-time.

You cannot do this with Historian (data centric) architecture and solution. Make sure you looked at who the communities of users you are satisfying now and in the immediate future?