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.

Sunday, June 7, 2015

Can we have the internet of things with operational data management?

We all talk about data from different devices etc. This is well and good but can you really have effective information if the data is not in context?

The challenge is how you gain this context and then sustain this context over many devices (things) without significant impact on the devices, how do add, remove and evolve devices (things). The role of an operational data management system that is a “yellow pages” of the system, providing the context, and relationship between devices and the operations.

Providing the ability to register new devices and associated data, input the associated context, while maintaining the detail in the device, but provide the bigger operational process alignment. This will also provide the association, other naming of that device so other applications, roles can find and interact. Often other systems, machines have a different outlook on the process and will use different naming and referring for the device. The Operational Data Management capability provides this association and ability to align many devices without having change the underlying applications or devices.

From a data to information point of view it provides the contact to gathering of data to shift it to information, so that big data analysis and other tools can be applied transforming that information into “knowledge”. Providing a pattern for contextualized operational data (e.g.: production, quality, machine status, etc.) integrated to templated collaboration activities (ODM) and ultimately broader supply chain management.

Without this companies have a real opportunity of just gathering significant more data without creating or having the ability to create the associated proportion of Information, knowledge and eventually wisdom. The diagram above shows the knowledge management pyramid and how on the right hand side companies have not go the top one which is blow out in data without the associated knowledge. The leaders will put architectures and systems into place which enable them to gain the contextualization while providing the “plug and Play” ability for devices and things to be added to the solution.
Which path are you on, how are you addressing this ODM concept?

Thursday, June 4, 2015

Monday, June 1, 2015

Industrial Internet of Things, enables going beyond the 4 walls of a Plant, to Mobile Plant Supply Chain

The last two blogs on the “Cyber Physical and Operational Management Evolution” and “How do you Achieve Orchestration in Industrial Internet of Things without Managed Configurations and Standards?” have created a lot of activity in hits but also email discussions.
This is good to see as two years ago these subjects would have hardly moved the needle, and they real opportunities for leading companies to embrace to expand their capabilities beyond the four walls of their plant.

Again last week I was involved in a number of discussions around these topics and liked one of my South African collaborators discussing how we have all these rich applications and capabilities for the fixed process plants, WHY cannot we apply these same tools to “MOBILE PLANT”?  Now he was from mining and was launching into the extraction side of mining and how to optimize the asset utilization, but he really wanted to go beyond that “Operational Optimization”.
The targets are not about data, what I like he is putting real operational goals in place:
  • Operational Processes optimization, understand operational times vs expected times and analysis of areas to improve
  •  Asset utilization / optimization
  •  Energy and Fuel optimization

As he put we have platforms in the plants that abstract equipment below, and model these equipment so we can record, track their operations, and then apply operational process improvements, and built in operational process rules for the fixed plant. Now taking this to mobile these same platforms could be used but now across mobile equipment, so now we must record geographical data as core as location is key when using fuel, doing operational routes, and time is of essence. But the Delay accounting applications of today could be applied to these mobile equipment and we could then move beyond that to embedding operational best practices and operational behavior in the devices, and equipment to guide the operations to work within the “operational windows” of optimized performance.  

The diagram below shows a more detailed chart of maturities I mentioned last week. The concept of smart, to optimized and autonomous can only come with inbuilt operational strategies and practices that enable the orchestration I talked about last week.

Source ARC (

The key is most of the mining extraction/ mobile plant is isolated, and I would say siloed even when connected between applications.

Remember the Industrial Internet of things is not about data, it is about “actionable decisions” in the NOW, by either machines, applications or people, and this will require embedded operational strategies/ processes that coordinate the mobile equipment to align with the overall business strategies.

If you are looking in the plant / fixed process world to apply IOT and gain significant value, you should think again and “open the door” and look outside the plant to mobile plant, or mobile supply chain, and extend the richness of operational applications to these traditionally isolated equipment and processes!