Achieving Consistent and Right Results in an Agile World

As Sunday draws to a close, I sit out looking over to pacific the waves crash, and birds fly, it is my time of reflection.

Last week I talked about some of the observations from the last month on the road. But this week I debated with a number of thought leaders and we all aligned on the challenge of a dynamic workforce, and dynamic operational/ business environment, means that chance of “Lights out Manufacturing “ are slim.

One company I was engaged with last week their thought pattern was still about replacing the personal on the plant, going to total automation. While I agree with automation, it is required for consistently and velocity of production. But I struggle with agility.

Two days latter I was at another company and they were all about empowerment of people, they wanted to automate process, and operations to free up people to add complimentary agility and “out of the box” thinking.  As one C level said to me, our market is changing as fast as we ever seen, and

Stepping back and looking at both these companies the second company was more automated than the first and the second was investing in automation more than the first. But their attitude was to gain consistently and free up people from repeatable tasks, and increase the responsibility of people, and empower people to make decisions fast.  

The diagram below really depicts what I started to introduce last week, and what this second company believed in.

That they needed to design their systems and people to play “natively in the dynamic world”, and they have realized that “embedded Intelligence and knowledge” will be key and must grow proportionally with increasing data. They also realized that with agility comes the changing operational environment and situations which will require “augmented intelligence” with the human brain can work out. The key thinkers in the industry are not looking to dependency on 1 to 2 people, they are leveraging the concept of “crowd sourcing” thru a active community of people. As we look at the operational/ automation world of the future the key pillars will be:

•     Ability to capture knowledge and intelligence into the system to automate process, and operations. Key is this is not just traditional automation in PLCs/ DCS etc, it is capturing repeatable knowledge and decisions. So the system must bread a culture of contribution and use natively.
•    Ability to have a community of workers who can share collaborated “naturally” with ease, no matter the location of the users and state. Foundational to this is  the ability trust the information, the measures so a common understanding of the situation, and basis for decision can be made.

Leading to natural decisions, and action paradigm across the team as seen below. I will expand on this more next week.

Taking a Lead from the Human Body with Reducing risk through an Enterprise Nervous System for Industrial Architectures

For the last couple of weeks I have been travelling in what seems hundreds of meetings with many people. However, last week I had a number of presentations on the direction of Operational/Automation systems, and challengers of the 7 to 10 years.

Twice a question was asked around flat vs. layered architecture, similar question around one platform vs. multiple platforms.

•       Layers allow me to contain change

•       Layers allow me to manage complexity, divide and conquer

•       Inter-operable layers reduce technology lock-in and increase options for clients

•       Federated means lower level has autonomy but cannot violate higher level rules and principles.

Too often people put forward ONE platform/layer, but actually the world is made up of layers of information, interaction, and decisions. It is important to optimize across a layer, so interaction with the “things” at that layer is focused, efficient, and in context of that layer in content and time. As you transverse layers so does the context of information, the interaction between different “things” and complexity or focus change.

In the industrial automation/ operations control has it’s layers of executing with the different equipment components in the process unit, requiring speed and tight coupling. As we go up the layers to supervisory then MES and Information, the context changes, responsibility for decisions increases, but time context changes. The “things” interacting change, combined with more complex messages with more context.  

The diagram above illustrates some of the concepts between the natural layering in humans, and the corresponding concepts in industrial information systems, and the required isolation, focus of sectors is key.    

Bio-mimicry is an emerging science that adopts nature’s designs to solve human problems. Moreover, the concept of federation, which enables central coordination but local autonomy, is part of nature’s design for information management.

If we think about it – the human nervous system has over a billion neurons spread throughout the body to help control its various functions. If the brain had to deal constantly with a billion signals, it would “crash” the system. Thus, nature has designed a system where functions are layered in an architecture that helps create a robust sense-and-response mechanism.

Autonomous functions (layers) which have Interoperability is key for fast relevant actionable decisions to take place with the most efficiency. So why do we ask about one, when we should design in layers but understand the layers the context, things, and actions. But understand how the layers must be “loosely coupled but aligned” so that operational execution aligns with business strategy in near real time.    

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.

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.