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.  

Data Diodes for Levels 2-3 and 3-4 Integration

Blog entry by Stan DeVries.

Data diodes are network devices which increase security by enforcing one-direction information flow.  Owl Computing Technologies’ data diodes hide information about the data sources, such as network addresses.  Data diodes are in increasing demand in industrial automation, especially for critical infrastructure such as power generation, oil & gas production, water and wastewater treatment and distribution, and other industries.  The term “diode” is derived from electronics, which refers to a component that allows current to flow in only one direction.

The most common implementation of data diodes is “read only”, from the industrial automation systems to the other systems, such as operations management and enterprise systems.

This method is not intended to establish what has been called an “air gap” cybersecurity defense, where there is an unreasonable expectation that no incoming data path will exist.  An “air-gap” is when there is no physical connection between two networks.  Information does not flow in any direction.  Instead, the data diode method is used as part of a “defense in depth” cybersecurity defense, such as the NIST 800-82 and IEC 62443 standards.  It is applied to network connections which have greater impact on the integrity of the industrial automation system.

One-way information flow frustrates the use of industrial protocols which use the reverse direction to assure that the data was successfully received, and subsequently triggers failsafe and recovery mechanisms when information flow is interrupted.  A data diode can pass files of any format and streaming data such as videos and an effective file transfer, vendor neutral approach, in industrial automation is to use the CSV file format.  The acronym CSV stands for comma-separated values, and there are many tools available that quickly format these files on the industrial automation system side of the data diode, and then “parse” or extract data on the other side of the data diode.

There are 2 architectures which are feasible with data diodes, as shown in the diagrams below.

The single-tier historian architecture uses the industrial automation system’s gateway, which is typically connected to batch management, operations management and advanced process control applications.  This gateway is sometimes called a “server”, and it is often an accessory to a process historian.  A small software application is added which either subscribes to or polls information from the gateway, and this application periodically formats the files and sends them to the data diode.  Another small application receives the files, “parses” the data, and writes the data into the historian.

The Wonderware Historian version 2014 R2 and later versions can efficiently receive constant streams of bulk information, and then correctly insert this information, while continuing to perform the other historian functions.  This function is called fast load.

For L2-L3 integration, the two-tier historian architecture also uses the industrial automation system’s gateway.  The lower tier historian often uses popular protocols such as OPC.  This historian is used for data processing within the critical infrastructure zone, and it is often configured to produce basic statistics on some of the data (totals, counts, averages etc.)  A small software application is added which either subscribes to or polls information from the lower tier historian, and this application periodically formats the files and sends them to the data diode.  Another small application receives the files, “parses” the data, and writes the data into the upper tier historian.

The Wonderware Historian has been tested with a market-leading data diode product from Owl Computing Industries, called OPDS, or Owl Perimeter Defense System.  It uses a data diode to transfer files, TCP data packets, and UDP data packets from one network (the source network 1) to a second, separate network (the destination network 2) in one direction (from source to destination), without transferring information about the data sources.  The OPDS is composed of two Linux servers running a hardened CentOS 6.4 operating system.  In the diagram below, the left Linux server (Linux Blue / L1) is the sending server, which sends data from the secure, source network (N1) to the at-risk, destination network (N2). The right Linux server (Linux Red / L2) is the receiving server, which receives data from Linux Blue (L1).

The electronics inside OPDS are intentionally physically separated, color-coded, and manufactured so that it is impossible to modify either the sending or the receiving subassemblies to become bi-directional.  In addition, the two subassemblies communicate through a rear optic fiber cable assembly which makes it easy for inspectors to disconnect to verify its functionality.  The Linux Blue (L1) server does not need to be configured, as it accepts connections from any IP address. The Linux Red (L2) server, however, must be configured to pass files onto the Windows Red (W2) machine.  This procedure is discussed in section 8.2.2.6 of the OPDS-MP Family Version 1.3.0.0 Software Installation Guide.  The 2 approaches can be combined across multiple sites, as shown in the diagram below.  Portions of the data available in the industrial automation systems are replicated in the upper tier historian.

Predictive analysis is Key to Effective Decision Making, and Future of Industrial Operations

Last week I toured the east coast of Australia, engaging with Food and Beverage customers about opportunity. In Australia like US, the focus is on becoming competitive and effective to deliver high value products, in a more timely manner.

At the ARC conference in Orlando in Feb, we saw increased discussion around predictive analyst ices.

The whole objective is to move from the "as is" (which is traditional alarms) to the "to be" state, so the impact of time to detect, time to react to a conditions, has less impact on the situation, reducing cost, risk.

The diagram below I have shown before but is still one examples of why we must start a shift to predict through patterns, and relationship with between variables to see conditions developing before they develop into risk conditions.

As can be seen from the ARC diagram below most of the systems we build today are on now (dashboards) and past (Reports), but the edge is going come from the future, often learning from past to predict the future. The user must be looking to the near future to allow decisions this could minutes to 12 hours into the future with high predictability so he can ask “what ifs” and help make rapid decisions.

 All the areas in the orange provide new insights into the direction of the operations or their situation so they can predict, what you they do now. This requires lots of information, and analytics to predict models, and patterns.

Good example is the partner company Asset Insights from Pattern Discoveries Technologies.:

Taking known events around assets, combining these as context to determine patterns from within the historian data to bring value a repetitive pattern that can now be used to “shift to the To Be” state. So decisions are made faster and in more confidence, with insight into what to do next.

Software like this will grow fast around the core big data of such items as historians, alarms, and asset, energy systems.

The Change Landscape of business Intelligence, EMI and Analytics driven by Information Driven Companies

All through this week in Europe I have spoken with customers looking for decisions in the NOW, and more people empowered to make these decisions. This does mean the traditional worker is evolving to knowledge worker, and this is across the different roles in the operational plant. But following on from last week’s blog the drive for realtime decisions is driving up the requirement for more advanced analytics to enable that decision, especially as the experience and time in the role of the decision maker reducers.

This drive for information and decisions, is causing significant other transformations in intelligence segment, and these are captured in a recent ARC table:

Some of the fundamental concepts it points out:

• The shift in time focus from Past to Future: in the industrial world I would put that as truly an expansion from the past and current to now past, current and future.
• From a performance to a predictive view that enables that decision
• Move from Batch data to realtime, this is key as we move from reports to dashboards that are dynamic with small trend tails showing now and immediate past easy to understand from a glance.
• Move from IT intensive creation to Self service: this is even more apparent in the industrial space as operations want to be self empowered without the complexity and delay in engaging either engineering or IT.
• Users move from a few Gurus to a collective team of people what I referred to as the flexible operational team (many blogs on this) where experience is shared to achieve realtime decisions. Virtual experts in an active community either from within a company across sites, and subcontractors/ suppliers can now be in the realtime decision with the on plant person.
• Deployment will shift from “on Premise” to a “Managed set of on demand services” this while only just starting in the industrial space makes logical and even more sense in the industrial space. Because the information and these virtual communities will live outside of plants and across the world. Companies are talking of Asset Facebook concepts to where a community of peers and experts across plants working on similar equipment and processes can interact share and make informed decisions together. The concept of a hosted set of services for an Information environment will be foundational for this to work.
• Another comment is the automated actions, I see this more in the industrial space as the shift from just supplying information to having embedded operational procedures to guide users through consistent actions.

Yes,  the traditional EMI (Enterprise Manufacturing Intelligence) system will transform dramatically, and it will need to work well with the I tools which are also transforming, as well as provide “preconfigured experiences” to enable users to answer known decisions and enable “self Service” for rapid adoption.   

 

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

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

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

Source ARC

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

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

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

Time for Information Driven Manufacturing!

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

 

 

Source ARC Feb 2013

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

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

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

 

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

 

 

Product as a Service adds Spice to Operational Landscape!

Last week on the flight across the pacific, I had an engaging discussion on the effect of “disruptive technologies” not just on efficiency, but on the business models available and are expected. This lines up with a discussion on one of “think tank groups” I am involved on looking at the future, where a thread of discussion was around innovation, and much of the focus on technology but the most effective thread was around “operational, business innovation” enabled by the latest technologies.

In the discussion on the plane we went through the new business models due to cloud e.g “on premise”, “infrastructure as a service”, “Platform as a service” and “Software was a service”. These are related software, but now the conversation started to shift to rise in “product as a service”. Now this is nothing new with rental systems, example is the car rental business, but there is evolution happening with companies looking to enter the market or more importantly capture market share by people avoiding buying or leasing products. Example would be kitchen manufacturer supplies all the equipment to the kitchen or more likely and property development with 100s of kitchens. The contract is not for equipment but it is for “kitchen functional capacity”. Another example would a jet engine supplier supplying not engines but “power as a service”. We have already seen this concept with EPCs supplying a turn key plant, and then an operational contract for 5 years, with performance criteria in place.

                                                                  Source ARC

Another example is the growing “contract manufacturing” where companies out source a section of their value network to another party, (not new) but what is new is the tight alignment of this contract manufacturing to the whole value network in order to enable agility. Again we have seen this in the Toyota models in car manufacturing, but the supplier partnership is key, and this is going beyond the relationship, but the direct linking of the information systems. Requiring a value network to be a federation of value assets, which tightly aligned, but loosely coupled. The blogs on the third generation of MES align to this thinking, where value network of a brand becomes a “virtual manufacturing network” across the different value assets no matter who is executing.

 

Key is now stepping back and understanding what does this mean, what is required to make these models work. Importantly the design and manufacturing expertise of products is maintained but the new business is the service side business which requires linking into the full lifecycle management of the product during usage, so manufacturing does not stop at the day of shipping. Why this is intriguing is that I find myself engaged in a number of companies, not in the traditional manufacturing ut in this service business, which is a new business. The engagement has been around enabling central operational centers for monitoring and providing guaranteed levels of service, across many of the products in usage.

This is demanding some re thinking of the product design to provide usage information that can used to provide the levels of service to acceptable. On the contract manufacturing is driving people to “Information Driven Manufacturing” in a manufacturing 2.0 model, where that the facility can easily couple of another companies value networks, accepting actions/ tasks and providing information in real time exception manner.

The acceptance of Service Orientated Architectures and the “Internet of Things” will only accelerate the ability to provide “product as a service” with the leading supplies actually shifting to information in context and enabling a rapid and natural “plug and play” of plants, assets into a customers value network. Food for thought, when we designing the operational systems going forward.