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.

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.

http://businessfuturist.com/chances-are-you-wont-have-your-job-in-2025-abc-local/

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.

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.”

http://www.wired.com/2014/11/iot-wont-work-without-artificial-intelligence/

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.”