Final Post: The Journey of Data Transformation to Wisdom to Enable Smart Work

This post will be my final one in this blog, as forum does not seem to be working as it once did, and we exploring new approaches to getting thought leadership out.
Thank you for your interest if you feel like we should continue in some form, please post a comment.

This final post I will post a paper Stan DeVries and I have just completed that summaries the journey, and pitfalls on that path we see leading companies following to achieve Operational Excellence.

In today’s “flat world” of demand-driven supply, the need for agility is only going to move faster in the next 10 years.  This is driving leading companies to transform their operational landscape in systems, assets and culture in a shift to “smart work.”  Agility transforms their thinking from a process-centric to a product and production focus, which requires a dynamic, agile work environment between assets/machines, applications and people.  Aligned decisions and actions across a multi-site product value/supply chain is crucial, and  requires the ongoing push of a combination of automated (embedded) wisdom and augmented Intelligence (human wisdom).

The paradigm shift from the traditional “lights out manufacturing concept” of fully automated systems  in an agile world  shifting to scheduled work that can be dynamically planned requires both:

• Automated embedded Intelligence and knowledge
• Augmented Intelligence using humans to address dynamic change

Foundational to this shift is an environment where a worker can have the mind space to understand the larger changing situation and make augmented intelligent decisions and actions.  To provide this, data is transformed naturally into operationalized information upon which decisions can made, then extended to be combined with “tacit, applied knowledge;” it can provide incredible value when taking operational actions.

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  – prescriptive advice and procedures to help achieve targets such as safety, health, environment, quality, schedule, throughput, efficiency, yields, profits etc.

An example of this transformation: imagine driving along a freeway in California which you do not know.  You rent a car with a GPS.  

• Data: the GPS knows that I am on a freeway, at 80 mph.
• Information: it is “situationally aware” that I am heading south, on the I-405 freeway
• Knowledge: it works with other services to determine that 10 miles ahead the traffic is stopped, and provides me with a warning that I will be delayed due to a traffic hazard. It has combined traffic knowledge with my location, speed, and destination to provide timely, advanced decision support knowledge that I can use to potentially take an action.
• Wisdom: the GPS provides two alternative routes on the display giving me time and characteristics of the two alternatives. Without me having to take my eyes of road and use an A-Z directory, I have been:
• warned ahead of time of a potential issue in achieving my destination on time
• given two actions at my fingertips based upon route characteristics I desire, to guide me through a neighborhood and route I have never been on.

There is no reason why this same journey of data transformation to wisdom cannot apply to manufacturing operations.

Avoiding the Pitfalls Going to Wisdom

Many companies stand on the edge of a data swamp that is growing quickly, with the Industrial Internet of Things and Smart Manufacturing providing access to an exponential level of additional data from their industrial value chain.  This data influx can either “bog them down” on growth, or if leveraged to achieve proportional growth in “knowledge and wisdom” could propel a new level of operational agility.  The Fourth Industrial Revolution (Industrie 4.0) provides a potential framework for leading this ubiquitous transformation.  Major industrial organizations are now realizing the incredible value that can be extracted from data and are applying the time, resources and new technologies such as big data and machine learning, combined with a new evolution in operational culture to leverage this potential.

Those operating in manufacturing have been living for decades with vast amounts of data located in historians, equipment logs and across their extended supply chain network. Data, in and of itself, is not of much value.  The same can be said for reams of paperwork that document best practices-- it isn’t of much value sitting on a desk or in a document management system.

The same discussion could be used for companies running on paper-based operations and paper “black book” operations (as one operations department mentioned), where the knowledge and wisdom to take correct actions based upon experience or “instinct” also becomes an unsustainable model.

The diagram below shows the quadrants of potential situations where companies can find themselves with “experience vs data.”  Today, many companies find themselves on one of two ends of the spectrum of knowledge and data, and both ends leave companies in a state of limited agility to change to required speeds.

Summary

Manufacturing is in a constant drive to improve performance, and transformation of work has become the main method to achieve and sustain this.  Higher capacity or more efficient machinery and processes aren’t sufficient any more.  Manufacturers with agile and cyclical operations need a method to remain cost competitive during the lower throughput periods, yet remain responsive enough to take full advantage of high throughput or high margin conditions.

Implementing systems which transform work using higher value information and reliability change when, where  and/or how users make decisions, and is the foundation for this next level of improvement.

Operational Transformation through Smart Work is a journey, and technology is only one of the key elements.  The user culture must adapt, similar to the previous waves of quality, safety, health and environmental improvements.  The journey advances with work process improvements, as applied to sections of a site or an entire site.  Existing software must be assessed in terms of delivering knowledge and wisdom, supporting mobile and traveling workers, with the goal of significantly reducing the skill and effort to maintain them.  The journey is worthwhile, practical and essential for manufacturers not only to stay competitive, but thrive.  

A Culture of Empowerment: Problem Solving at Source

 As we observe the world around us change, and more and more the focus shifts to a “new way to operationally work”, we see leading thought companies start introducing innovations in their culture.

One of these we now seen in two leading manufacturing companies who are shifting to the “New World of Work” and introducing not just technology but key cultural changes. One of these is the “Empowerment Culture” one of the strategies these companies is shifting the problem solving to the source.

This overcomes the delays in decisions through having decisions go up the tree by increasing the skill of the workers closer to action, and empowering them to be able to make decisions. This means skill development, embedding knowledge and experience in the system to help facilitate the correct timely decision.

The diagram below shows the structure

This allows the different levels, roles, and time horizons to focus on their problem solving optimizing the overall operational agility.

The “edge” worker on the front line is performing situational problem solving relative to production and asset at the NOW.

The plant supervisor have a wider time horizon of hours, and is looking at the plan, and adjusting thru “Systemic” thinking.

While management is then left with the bigger picture of strategic thinking, and problem solving, setting the overall direction.

Now if all decisions came to management they would be ‘firefighting” not free to think strategically and often the decisions would be delayed effecting production, efficiency and safety.

Getting out from behind the desk/ control terminal requires aligned work

The above comment was made to me by an oil and gas company, and when I enquired to their expectation, it was clear that they wanted to empower "controllers" (operators) to be responsible for more. Enable decisions to be made faster while also gaining consistency across teams and shifts.

Transforming the traditional role of sitting in a control room behind a terminal, to a dynamic role, where the operator can get up and roam to investigate, make decisions and take actions in a timelier manner. This concept embraces all of the concepts this blog has raised over the last couple of months and shows the transition in the industrial sector to focusing operational empowerment.  

By unleashing the controller from their seat, they are free to investigate, see for themselves, and interact with others with experience to make informed decisions faster.

What is the reason why users have been locked to the desk/ control room, why has this transition not happened successfully before?

It is simple, the requirement to be monitoring the plant. A traditional control room is the central place alarms, notifications were traditionally piped.

So to free up the user, to leave the control room, the user needs to empowered with situational plant awareness, freed from monitoring, shifting to the experience of exception based notification. As the user roams the plant, the user is still responsible, and aware, and able to make decisions across the plant he is responsible for even if he not in view of that a particular piece of equipment. Driving the requirement for the mobile device the user carries to allow notification, drill thru access to information and ability to collaborate so the dependency to sit in the control room has lifted.

The second key part of the transformation/ enablement of a edge worker is that their work, tasks and associated materials can transfer with them. As the world moves to planned work, a user may start work task on a PC in the control room, but now move out to execute close action. As the user goes through the different steps, the associated material and actions are at his finders tips. The operational work can be generated , assigned and re directed from all terminal.s and devices.

So freeing up people is not just about providing mobile data taking the HMI Mobile. It requires the transformation to task based integrated operational environment where the "edge worker" is free to move.

System will notify him of situations, and he can take action, whilst also providing him with the opportunity to execute tasks. The information must free them to navigate with freedom, no matter the format, no matter where they start an activity, and have access to everything.   Why now? The technology is here with wireless plants, commodity devices to suite different applications/ roles, and the desire and culture to enable the flexible team, and design to enable exception based notification. The information, to knowledge systems are here to enable navigation through the required information while his situation changes. No longer are clients specific applications, they are role and situational based.

For all situations, freeing the anchors that hold people to the desk, and enable the reality of a flexible , empowered edge team. 

  

Reduction technical skilled capacity at site, accessing new sites, equipment with limited technical skills “calls for the Industrial Internet of Things”

For the past couple of weeks I have been engaged in opportunities / projects from Coal Seam Gas fields where the expectation is to plug in a well, and the system must self-discover and set up, to farms where the fields are being monitored, the solos and storage vessels being monitors, to cattle waring health senses. With many other applications in between, but all came with two major requirements:

1/ No dependency on technical skilled people on site or close to site, so the system must be plug and play, at a commodity “throw away” price.

2/ Transparency to operational information, and asset health awareness across the value chain, (more than often across different legal companies contributing to product value).

The Industrial Internet of Things, provides the opportunity, as long as it thought through, not just thrown out there. Considerations on:

• Cost and sustainability of devices e.g. the tendency is to go for throw away but with that comes the need to characterize the devices for the situation
• How do you configure and set up the devices with no skill on site, but do it in a sustainable way
• How do you manage 1000s of devices so they are operationally coordinated?
• How do you understand health and availability of devices so you can take action to maintain operational continuity
• That is before you get to the data / information getting to a location that can leverage it.

But this is classic Industrial Internet of Things where we are seeing tiny, simple devices with connectivity, but with a processor with Linux operating system which we can down load and engine provide character for the role, at a cost of less $50, often less $20. No site skill needed to “plug and Play” and enable the device now the opportunities are coming real and immense.

I found this diagram summed up the opportunity and obstacles well:

What hit me was the two obstacle of:

• ·         Insufficient skills of technology staff.
• ·         Use of Legacy Systems

But the IIOT must resolve these by providing edge devices that do not need technical skills on site, and can connect and enable legacy equipment to integrate. But the IIOT service fabric must provide the expertise and capability to remotely enable devices with the required character.

The more and more I look places that where not enabled at all, transforming to agile efficient business through IIOT being applied in Industrie 4.0 logic.

Perfect example was this week when I visit a person who had 2 dairy farms that he has to transform the value he is delivering beyond just milk to the service he can deliver and good “gate margin”. He has now got his paddocks with moisture senses in them using devices that last 10 years and cost less than $10 to connect a year, to trucks, and vehicles all being monitored. All the cows are being wired up for health awareness, and the milk capture and processing is being monitored. But he does not have a PC on plant, all the data goes to the cloud and he can have eyes for decisions across the two dairies as if they were one. He had a strategy to align two properties look for the “edge” but now you have typical location that was not automated now Information driven. But not with more people, and the cost in opex so he can budget it into his “gate Margin”.

But he has a strategy and took and an opportunity, which he is still on journey. The fact that only 25% of companies in the above survey had an IIOT is indicative of the market not really understanding the opportunity, which will become a requirement to survive in the future. The key is it is not about IIOT it is just one of the enablers, it is the requirement to become “hyper agile” in a dynamic world.   

Industry 4.0 Provides a Framework for Agile Manufacturing/ both Discrete and Process

Industry 4.0 is a relevant and significant shift in how industrial automation and operations management software is implemented and used to deliver significant business improvements.  However, it is too simplistic to directly apply the Industry 4.0 concepts to all manufacturing industries, or without considering industrial requirements which are not addressed by this activity.  The following is a set of viewpoints which are discussed in this white paper:

• Industry 4.0 is about the transformation from controlling focusing on process to “controlling the product/ order” and the “product/ order being self aware”.
• Industry 4.0 is about operations transformation, not about technology.
• Industry 4.0 provides a practical strategic framework for “lean” and “agile” industrial operations.
• Industry 4.0 addresses the needs of discrete and batch manufacturing, but it needs some adaptation for the heavy process and infrastructure industries.

Cloud computing and IT/OT convergence are often linked to implementing Industry 4.0, but these needs some adaptation to address “trustworthiness” of the architectures.  One emerging topic is Fog computing.

The functions and requirements of automation and operation management technology are even more relevant than before (contrary to some of the recent vendor claims).

Information standards such as IEC 61850/ISO9506, ISA-95/ISO62264, PRODML etc. are even more relevant than before .

Industry 4.0 is about the transformation from controlling focusing on process to “controlling the product/ order” and the “product/ order being self-aware”

Industry solutions and products have traditionally been focused on controlling the “process” in order to deliver the product.  But as the agility in the market changes, “new product introductions” and increasing, product lifetimes are shortening and becoming for individualized even to a point per order or customer.  This is driving the operational world towards “controlling the product/ Order” vs the process as the process will naturally be controlled if the equipment, process is set up for the order.  This requires the “order” to be “self-aware” of its required characteristics to satisfy the order/ customer.  So that it can make the system is set up/ and process runs according to the order. Driving “first run” success, and that the product/ order will create warnings, alarms, and enforce required actions to maintain it road to success.

Even in heavy process industries such as oil and gas, the requirement to develop more individualized products/ orders is key.  Scheduling is becoming a part of everyone’s role relative to their time slice/ span of awareness.

So the ability to track an order through the process, read and understand current condition of the product/ order, simulate the characteristics of the order, now and into the future based on the current condition and environment of the process, and equipment is a corner stone.

Industry 4.0 is about operations transformation

One of the central thoughts of this activity is transforming how work gets done.  Transformation of work often includes combinations of the following:

• When work gets done – proactive decision-making before desired changes and undesired conditions occur.  This is very different than “reacting better”.  This isn’t about “real time” work – it’s about “right time” work, which means early enough to make informed, accurate decisions, even no change.
• Where work gets done – local workers are essential, but an increasing use of traveling experts is essential.  These experts require standardized, trustworthy, and actionable information in order to add value.  Many experts spend more than 50% of their time in unplanned work and processing data to produce trustworthy “wisdom”.
• How work gets done – lean operations depends upon lean work; automation of information processing and distribution is essential.  The expert’s private spreadsheets are necessary for testing and innovation, but operations teams can only use and trust standardized, easily accessible and actionable information.
• Who does the work – a central thought in industrial operations is work simplification.  Fewer experts are available within the industrial enterprise, and in many locations around the world, younger technical workers refuse to live near the industrial facilities.  So much of the knowledge and wisdom must be embedded in the operations management architecture.

This transformation is essential; if there isn’t sufficient transformation, then Industry 4.0 is wasted.

Industry 4.0 is a practical strategic framework

One way of summarizing the central thoughts of this activity is that wasted work, cost, time and energy can be eliminated, and the product lifecycle can be optimized, by exploiting information about product design, manufacturing, distribution and use throughout the “supply chain”.  In order to exploit this information, much more data is necessary, and it must be transformed with appropriate context much earlier and to a much greater extent.  The enterprise which can constantly exploit this information has a sustainable competitive advantage.  This exploiting generates a terminology for the higher value information, such as the following:

• Data – raw values, automatically sensed and manually entered, including geographical/altitude location, time, and if possible, with asset identification (machine, material, vehicle etc.)
• Information – cleaned and reconciled data and derived information, such as efficiency and statistics
• Knowledge – information in context, including comparisons to targets and constraints, estimated times to thresholds, predicted failures
• Wisdom – optimal targets, prescriptive procedures to address desired and undesired situations

The above terminology is only one of several alternatives, but all of these address a foundational requirement that Industry 4.0 requires better information, not just more data.

Industry examples of transformation include:

Discrete and batch industries – accelerated “new product introduction”.  New products can be introduced earlier, manufacturability effort can be accelerated, and new learnings can be distributed across multiple manufacturing lines and facilities much faster.

Mining industry – higher “sales value of product”.  Exploiting the detailed knowledge of the metallurgy in each group of material in each location of the mines, the stockpiles, processing and transportation can significantly raise shipment prices and increase revenue.

Petroleum refining and petrochemicals industries – increased margins.  Exploiting the detailed knowledge of the chemistry of the fluids and the capabilities of the processing equipment can significantly reduce the cost of raw material purchases and processing costs for the same output.

Power generation and transmission industries – exploiting the detailed knowledge of current and upcoming power demand, and knowledge about current and upcoming capabilities of generation and transmission can significantly increase reliability, increase revenue and reduce emissions.

Regulation Drives Companies to “Paperless” system, but they Need to Integrated

Regulation is a growing principle required in solutions. The growing demand is coming from increased government regulations on industry around environmental impact, or trace-ability, really driving “Accountability” for actions and being able to determine history fast. Not all the drive is coming from government regulation requirements, in many industries it is being driven by the consumer as a value differentiator for the products.

So we are seeing investments in serialization systems in Life Science and Food and Bev, which extend the existing packaging lines, and are integrated to MES and ERP systems.

But the area that is interesting to me is not this section it is the implementation of “paperless strategies” removing the “clipboard” and replacing it with some forms based software. I have reviewed 3 of these systems, that are driving consistency in capturing of information. Within built rules that drive the user to capture the required points, take the required actions in order to satisfy the particular regulation or combination of regulations required for shipping that product.

While the shift to “paperless” has been a goal for a long while and people have headed down this when implementing a MES (Manufacturing Execution System), this is logical as the drive to paper less in integrated to operational events and practices. Providing an end to end trace-ability, and view into the manufacturing history of the solution.  

What concerns me what I started to see in Oil and Gas, Mining, F & B is these isolated event/ forms capturing systems that are put in isolated from the production/ operations system. Yes they may have some events and points integrated but the procedures are not an extension of the operational system. The captured data is in isolation of from operational/ production data, it should be captured along with the production records of what materials were consumed, who was operating the machine, and what product was going thru the system.

We seeing in the integrated solutions, the tradition operational/ mes system, being extended with a Business Process Modeling system, introducing workflows that can be configured to be triggered from the real time operational systems at Scada/ DCS or at the operational levels. Driving the capture, check, validation of process data boundary violation, or required capture to align with “inspection requirements “ from the ERP. The workflows natively interact between the systems, just extending the process operational processes but provide forms to drive intuitive actions, and capture. 

The most important part is that these are extendable and customization so that your companies capabilities, uniqueness can be captured as “best practices”. Most of all these are not separate systems they native extensions of the MES/ Supervisory automation, Quality applications driving the results and information back into the consistent repository.

So as you look going paperless, avoid doing it in isolation of the other systems, it must be extension, so that the operational process and practices from execution, to quality process, and process management, and extended with regulation requirement in a native way both from an operational experience process as well as data / information integration.

ISA-95 “Vertical” and “Horizontal” Integration

ISA-95 is an international standard which has been used for 20 years; recent transformations in operations management and computing technology has caused some to question its importance.

ISA-95 has now become extremely important – it is necessary to understand what this standard does before we can understand its importance.  This standard is an “information exchange model” focused on “level 3” (operations management functions), with specifications for information exchanges between level 4 (enterprise software) and level 3, and level 3 to level 3.

Before 2010, many operations management implementations were relatively simple, using what the author calls “vertical” integration – the dominant pattern was exchanges between level 3 and level 4, such as the following example:

In recent years, more industrial facilities have adopted the use of operations management software, including many more software components, and more customer industries, such as petroleum refining, mining etc.  For many implementations, the dominant pattern is exchanges between level 3, such as the following example:

In the above diagram, several of these activities are often implemented with multiple applications, such as work management, electronic logbook, laboratory information management, material movement tracking, data reconciliation etc. – some implementations have more than 20 applications for a single site, with multiple sites (a few dozen in a large petroleum company, several hundred in a large food and beverage company).  Much of the information exchange is level 3 to level 3 or what the author calls “vertical” integration.

So, why has ISA-95 become very important now?  One of the main reasons is that information is exchanged 10-100 times more frequently, with 10-100 times more detail.  Materials have sub materials – even mining, petroleum refining and petrochemicals deal with molecules or groups of like molecules.  Lots have sub-lots; work has much greater detail.  Determining the suitability of a previously trusted software application (including the ubiquitous Excel files) for this information exchange becomes easier when assessed against this standard.