Trustworthy Operations Management Solutions

I asked Stan to contribute a blog on a topic that he and I are asked, that of "trust worth systems/ data" this is an incredible critical item as we move to "actionable decisions"

Blog by Stan DeVries.

When younger workers are asked about how “trustworthy” solutions should perform, a common response is “it just works”.  This is a reasonable but demanding expectation, and it is a combination of availability, accuracy and acceptable user experience in all facets.  One aspect of operations management solutions which makes this expectation more challenging is that these solutions are inherently more complex – they include at least 2 software applications, sometimes 15 or more.  And complexity tends to reduce availability.

Several customers have asked how to practically achieve and sustain “trustworthy” operations management solutions.  An appropriate analogy is a fuel gauge in a car; if it is functioning less than 100% of the time, users won’t trust it at all.  The following are best practices:

• Design the solution to automatically handle many failure modes, including user error.  Most of the design of automatic teller machines (ATM’s) is handling failure modes.  Methods include automated workflow for missing or grossly erroneous data, software and machine health, network outages etc.

• Design the solution for some redundancy, including “store and forward” of data to withstand network outages and other failures.  Note that this technique is only usable when the software applications can rapidly process the restored data while processing “new” data.

•  Design the calculations for sufficient accuracy and availability.  Simple mathematics is much more available, but much less accurate, than complex mathematics.  Technology is available that delivers high accuracy and has built-in logic and knowledge to overcome many failure modes including “solver” errors, sensitivity to missing or inaccurate input data etc.

• Design the solution’s outputs using the “4 rights” instead of the “4 anys”:

1.  Information should be delivered at the “right” time (which might be earlier than “real time”) depending upon the operations management conditions.
2.   Information should be delivered to the “right” persons.  Operations management solutions tend to broadcast information including undesired performance and tend to broadcast information which is irrelevant to most users, which means that users must filter out information that seems like “spam” and users must learn to trust the solution.
3.    Information should be delivered in the “right” context.  There is an analogy which characterizes “data”, “information”, “knowledge” and “wisdom”, where “data” is raw data, “information” is trustworthy data (may include substitutions and reconciliation), “knowledge” presents a comparison of information to targets, constraints and similar information, and “wisdom” is prescriptive instructions to exploit desired opportunities and to prevent or minimize undesired conditions.

An operation management solution evolves technology is introduced, the operation evolves and as users increase their dependency and trust in the solution; the above methods are good fundamentals for the solution’s lifecycle.

“Operations leaders know they have a problem but aren’t quite sure what the solution is.”

This statement continues to echo around the meetings I attend, the challenge is there are many parts to the dynamic situation we find ourselves and they are all converging at the same time.

The Top 3 operational challenges faced by executives tells us that functional silos of people and systems continue to frustrate them and they need help justifying potential solutions to address these challenges.

It also explains why software categories like Manufacturing Execution Systems has limited awareness outside the plant. Also the growing discussion around platforms to accommodate the variety and provide basis for absorption of differences, while applying consistent changes.

A big discussion last week with 3 different groups, but it all came back to trusted , validate data that decisions can be made on. It was clear that much of the recorded data when actually taken and moved to a basis for business decisions, that people had to stop with grand plans of information and knowledge, they had to go back to getting basics sorted with validate, trusted data.

While the diagram above indicates 48% had issues with collaboration across departments, (very true, just transparency and communication is an issue) but in two sessions it was clear terminology and alignment for these conversations was a basis for significant part of the problem. Between systems/ applications, and people.

What shocked me in the conversations was how people were taking a very pointed (local) approach to solving the issues of terminology and structure, and not looking at how to make it “sustainable innovation”. The models and approaches must not be “band aids” they must structured and sustainable, avoiding anything that is not “managed”.

The process of delivering goods and services better, faster and cheaper sounds simple but can sometimes be unpredictable and lead to shortages or surpluses. Over the past two decades, the supply chain journey has evolved through a number of distinct phases along with a shift in power from suppliers to customers. Over the course of this evolution, operations professionals have expanded their perspective and philosophy from an inventory-centric view in the 1980s to an order-centric view in the ’90s to a product-centric view today. As product lifecycles shrink, innovation has risen to the top of the CEO agenda. But product innovation cannot meet the business objectives of lifecycle profitability without supply chain process considerations.

Future operations professionals need to get involved in the product development process to enable both product and process innovation. The product lifecycle perspective becomes more important as it provides a holistic view across disparate enterprise silos to provide a coordinated response to the end-customer — who is the ultimate driver of demand. Integration of product lifecycle and supply chain management can provide fresh perspectives and critical insights that are often missed due to the extreme fragmentation of functions within the enterprise and across supply chains. This is the new frontier for value creation, an untapped area of opportunity to create competitive differentiation and growth for businesses

Making money is no longer from a transaction. It is from a customer experience for a lifetime.

As companies grapple with their own journey to “Operational Excellence” they must gain control on their information and data, otherwise the alignment and collberation, across teams, for actionable decisions will fail.

More and more of the problems we face today don’t have easy answers. Solving these hard problems require “integrative thinking”, a concept put forward by Roger Martin in his book, The Opposable Mind. Martin defines the term as follows: “The ability to face constructively the tension of opposing ideas and, instead of choosing one at the expense of the other, generate a creative resolution of the tension in the form of a new idea that contains elements of the opposing ideas but is superior to each”. Rather than accepting conventional tradeoffs where you choose either X OR Y, integrative thinking is about pushing the boundaries and searching for creative resolutions which give you X AND Y.

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.