Tuesday, July 28, 2015

User Experience Convergence between Personal/ and Industrial is Accelerating

Every day you walk around plants and operational centers and you see a growing acceptance of “bring my own device” and use of these personal devices in the day to day industrial life. This is just natural, and good, but it is not just the devices that come into play it is the expectation of the similar user experience, and applications.

The next generation workforce has grown up as digital aware, and with the expectation to always be connected and able to access information and people. Sharing is a natural act, and texting, the "now " situation is expected. But as the personal world becomes totally wearable and in the "Now", when you on the plant, doing your jobs, you expect the same interaction, and experience.

It is not the industrial user experience that will dominate it is the personal one coming into the industrial experience. The industrial solutions and interfaces will evolve to leverage the same:

·       Applications
·       Sharing
·       Access
·       Immediate awareness
·       Location awareness

The day in the life of a worker on a plant will not be in a control room, or human machine interface, to be more responsive the worker will adopt the wearable devices, and expect the industrial information, and interaction to mimic the personal one but in the industrial context.

The challenge comes in developing the industrial user experience in a way that interact with the existing industrial back end applications, but enabling the collaborative experience, and a user to transverse across a series of devices and experiences to execute a job. Common information, tasks and actions will be required to be performed from each of the devices in a industrially safe and reliable way.

This evolution is happening at an accelerated pace. The diagram below illustrates the convergence.

Sunday, July 19, 2015

Will you still have a job in 2025, or will a robot be doing it instead?

Found this blog fun and topical, with reality
"I’m pretty certain that in 10 years you won’t have the job you have today, and why would you want to?
In 2005 most people were using a Nokia phone, handling emails at their desk and believed social media, Facebook and LinkedIn were just a fad and of no possible use.
Switch to 2015, Nokia is out of the phone business, emails find us wherever we are 24/7 and social media has evolved into a multi-trillion dollar industry complete with new jobs, professions and services.
Fast forward to 2025 and who knows what we will be doing, thinking and working at and on, but the thing I’m certain about is that it will not just be what it is today.


Saturday, July 18, 2015

What are the hurdles to Real-Time Operational Excellence?

I see a significant increase in “operational Transformable projects” , but too often it is talk, or dreaming, and when we discuss the ideas people like, but they really miss the challenge. Too often they fall back into the traditional approaches can we get access to information, through reports and dashboards. 
Born out of the frustration to gain the transparency to “what is going on NOW”. Yes it is a journey for “operational excellence “ and it will not be done once or ever over in this ever “speeding , agile world”.

Taking a step back and understanding the hurdles to getting to “Managing by Exception”. I thought the image below simplified the discussion.

Understand where you are, and set a vision of where you want to be, and this goes back to shift towards “activities” design vs application or even role.

Above you can see how not having the data in context, or even accessible is key, this is seen in the two bottom challengers. As one customer said last week, how do eliminate cleaning data every 3 months. The answer is simple, capture data as close to the source, validate and structure it as close to source as possible, so now you are storing valuable, trusted information, and you can depend upon it.
But now you have the information people put it into reports, and dashboards, for decisions to be made, but did it get to correct person, did it get decided upon in timely manner, why it did not escalated, or collaborated to accelerate the decision. The system must provide this framework for escalation, and ability ask/ share.

With the changing roles, and people on plants, and the horizontal structure, do we know the decision was made, “accountability” is important when something is sent. Too often tradition alarms, notifications have no accountability, the only way a team works is that they understand their role, and responsibility for decisions.

Then you come to final hurdle “what do I do having made the decision”? This needs to consistent processes across different workers of different experience. Also the system has to shift to a “crowd sourcing” culture of continuous improvement and everyone is empowered to contribute.
This may seem so simple but it is fundamental to the “transformation in Work” yet so many programs are missing these basics.

I will follow this up next week again on why “People and Processes” are key to take the automation to the next level.

Sunday, July 5, 2015

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.”

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.”

Sunday, June 28, 2015

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

Sunday, June 21, 2015

Why this time round the Smart Strategies (plants, airports etc.) have a chance to succeed!

Too often as a technologist and presenter of where operational systems are going, people come up to me afterwards and ask are you grounded? We have seen people talk about Intelligent, smart manufacturing strategies before but they have failed.

Yes we have had a number of significant attempts with technology and “lights out manufacturing” and various levels of MES/ operational systems, including the rollout of significant ERP programs. But there are some key walk away learnings. 20 / 20 vision is a good reflection:

Since 1995, projects have failed because:
  • They started as technology projects
  • They were implemented as technology roadmaps
  •  Insufficient organization change management
  •  Insufficient innovation
  • Insufficient integration of people, process, strategy and technology
  •  Innovation fatigue

The key with above observation is that too often these programs have been lead from within and based upon applying a technology, expecting the technology to transform the company. The technology goes in often not driven from operations but from automation engineering, or IT, and really it does not have buy in from the plant or “edge” operational people.

Since 1995, projects have succeeded because:
  • They started as work transformation projects
  • They were implemented as holistic combinations of people, process, strategy and technology
  • Senior management actively sponsors the “new way of doing work”
  • The Covey “high performing organization” based on their “4 disciplines of execution” are applied

The success of these programs is that they “operational improvement/ transformation driven” but leverage the latest technologies and processes to implement. With this comes the cultural evolution, the buy in from all levels especially “edge operational workers”. There are clear “wild” goals on a direction improvement, and measures put in place to make sure the direction is moving correctly. These are also not projects they are recognized as programs and journeys where assessment, tuning and cultural shift must happen.

 Is there a “silver Bullet” of technology that will take you through this transformation, I say NO, but due to many “planets lining up at the moment” around operational agility, the flat world and transformation of both the workforce and workspace. The operational transformation journey companies are on is been driven by operational requirement to change, and everyone recognizes this, and there are significant technologies with mobility, IOT, and cloud that enable different architectures and solutions to generated faster and aligned.

It is an exciting time of transformation, but always understand why you doing something, and you understand the measures to make sure you are on the correct path. 

Monday, June 15, 2015

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.