Showing posts with label Pattern modeling. Show all posts
Showing posts with label Pattern modeling. Show all posts

Sunday, November 29, 2015

Forecasting and Predicting, Must be a Cornerstone of the Modern Operational System

For the last couple of weeks Stan and I have been working with a number of leading companies in Oil and Gas, Mining, and F & B around their Operational Landscape or experience of the future.
Too often the conversations start off from a technology point, and we spend the initial couple of days trying to swing the conversation to the way in which they need to operate in the future and what their plans are around operations.

It becomes clear very quickly that there is allot of good intent, but real thought into how they need to operate in order to meet production expectations both in products and margin has not been worked through.

Over and over again we see the need for faster decisions, in a changing agile world, and this requires an "understanding of the future" this maybe only 1/2 hour. The time span of future required for decisions depends on role, (same as history) but it is clear that modeling of future is not just something for the planner, it is will become a native part of all operational systems.

This blog from Stan captures some of the necessary concepts.
  
Operations management systems must deliver better orientation than traditional reporting or decision support systems.  One important aspect of operations is the dynamic nature – there will be a journey of changing schedules, changing raw material capabilities, changing product requirements and changing equipment or process capabilities.


It might be helpful to consider desired and undesired conditions, using the analogy of driving a car on a long trip.  The planned route has turns, and it may involve traffic jams, detours, poor visibility due to heavy rain or fog; the driver and the car must stop periodically; and the driver may receive a telephone call to modify the route.  The following diagram is a sketch which displays how an integrated view might appear:

In the above example, the actual performance is at the upper limit for the target, and the scheduled target and constraints will shift upward in the near future.  The constraint is currently much higher than the scheduled target limits, but it is forecast-ed to change so that in some conditions in the future, the constraint will not allow some ranges of targets and limits.  This simple view shows a single operations measure with its associated constraints and target.
  • At this stage, we propose a definition of “forecasting”: a future trend which is a series of pairs of information, where the pairs include a value and a time.  The accuracy of the values will be poorer as the time increases, but the direction of the trend (trending down or up, or cycling) and the values in the near future are sufficiently useful.
  • In contrast, “predicting” is an estimate that a recognized event will likely happen in the future, but the timing is uncertain.  This is useful for understanding “imminent” failures.

The following diagram shows an example of estimating the probabilities of 5 failure categories, where the first (rotor thermal expansion) is the most likely.


Given these two definitions, it is helpful to consider industrial equipment behaviors.
  • Several types of equipment, especially fixed equipment such as heat exchangers, chillers, fired heaters etc. exhibit a gradual reduction in efficiency or capacity, or exhibit varying capability depending upon ambient temperature and the temperature of the heat transfer fluid (e.g. steam, hot oil, chilled water).  While the performance is changing, the equipment hasn’t failed, although its performance might reach a level which justifies an overhaul.  In extreme cases, sudden failures can occur, such as tube rupture or complete blockage.  These benefit from “forecasting”.
  • Other types of equipment, such as agitators, pumps, turbines, compressors etc. exhibit sudden failures.  These benefit from “predicting”.

One analogy of incorporating both “forecasting” and “predicting” is that it is like driving a car without looking forward through the windshield/windscreen, such as shown in the following sketch:


In the above sketch, the road behind the car is clear, but ahead, a potential collision will occur.  High-performance operations requires that teams prevent unplanned shutdowns or other events.

Monday, March 31, 2014

Predictive analysis is Key to Effective Decision Making, and Future of Industrial Operations

Last week I toured the east coast of Australia, engaging with Food and Beverage customers about opportunity. In Australia like US, the focus is on becoming competitive and effective to deliver high value products, in a more timely manner.
At the ARC conference in Orlando in Feb, we saw increased discussion around predictive analyst ices.
The whole objective is to move from the "as is" (which is traditional alarms) to the "to be" state, so the impact of time to detect, time to react to a conditions, has less impact on the situation, reducing cost, risk.

The diagram below I have shown before but is still one examples of why we must start a shift to predict through patterns, and relationship with between variables to see conditions developing before they develop into risk conditions.
As can be seen from the ARC diagram below most of the systems we build today are on now (dashboards) and past (Reports), but the edge is going come from the future, often learning from past to predict the future. The user must be looking to the near future to allow decisions this could minutes to 12 hours into the future with high predictability so he can ask “what ifs” and help make rapid decisions.

 All the areas in the orange provide new insights into the direction of the operations or their situation so they can predict, what you they do now. This requires lots of information, and analytics to predict models, and patterns.
Good example is the partner company Asset Insights from Pattern Discoveries Technologies.:

Taking known events around assets, combining these as context to determine patterns from within the historian data to bring value a repetitive pattern that can now be used to “shift to the To Be” state. So decisions are made faster and in more confidence, with insight into what to do next.
Software like this will grow fast around the core big data of such items as historians, alarms, and asset, energy systems.