Cyber Physical and Operational Management Evolution

In recent months Stan DeVries and I as part of Common Architecture Team, and also investigating large opportunities have spent many hours discussing the internet of things, Industrie 4.0, and shift to Cyber Physical architectures. It is fundamental for the rapid innovation businesses will need in order to stay competitive, both delivering products, but evolving efficiency and leveraging an effective "operational team", Stan submitted this blog on the subject.

Recently the academic phrase “cyber-physical systems” has appeared in presentations and articles on smart manufacturing and Industry 4.0.  Much of the emphasis has been on the “cyber” element, with frequent example of automation.  This may imply “lights out” operations, which might be achievable and desirable in some operations, but unnecessary, in-feasible and undesirable in most.  It should be helpful to consider one of the models of cyber-physical systems, which is called the Boyd OODA Loop, as shown in the following diagram:

Colonel Boyd was an excellent fighter pilot and military strategist.  The key elements of his decision model are:

• Observation: the collection of data by means of the senses
• Orientation: the analysis and synthesis of data to form one's current mental perspective
• Decision: the determination of a course of action based on one's current mental perspective
• Action: the physical playing-out of decisions

Using this model, automation improves the Observation and Orientation so that users engage with only the “right” information, at the “right” time (often earlier than real-time) in the”right” context – for the “right” results.

While the Boyd decision model is excellent for one or a few workers, another model is necessary for considering an entire operation, such as a manufacturing plant, a power generation station, petroleum refinery, oilfield etc.

If we accept that the main value of the automation is to improve the Observation and Orientation, then the above diagram implements these 2 important steps in what can be called the Smart Solution Center, which is a combination of a technology/data center and specialists who are providing support, improvement and instruction to other workers.  One of the key outputs of the Smart Solution Center, so that most of the work performed by knowledge workers within that Center and other workers spend the majority of their time on planned work, instead of being consumed with reactive work.

But automation of Observation and Orientation must be extremely accurate and trustworthy.  To achieve and sustain these attributes, we recommend a “Virtual Smart Plant” which is used to design, modify, test and train workers.  This is also key to sustaining behavior change and if possible, culture change.  Best practices have shown that workers change their performance in lasting ways if they experience the change for themselves, and especially if they can experience new learning in a “safe” environment.

In the above diagram, the “work orders” are more than task lists, but a combination of recipes, KPI targets, instructions, handover/turnover actions etc.  The black rectangle at the center bottom of the diagram is focused on people, who are doing what humans do best: dealing with new knowledge, managing complexity, and navigating change.

So the key to applying “cyber-physical systems” is optimizing the use of the workers, not eliminating them, and this optimization requires using technology in a “smart” manner, such as focusing on Observation and Orientation.

An increasing amount of leading companies are developing the "Smart Solution Centers" (often reference to as Centers of Excellence) where they can physical one location or a "virtual smart center" maximizing the leverage of key thought leaders in the analysis and development of operational/ process innovations. A good example of this is Rio Tinto's Process Excellence Center for mining in Brisbane (plenty of write up on this) where data is analysed converted into effective knowledge through simulation, analysis models, to improve operational running of mine process.