Information Driven Operational/Process Excellence Set Drive Next Wave in Mining but with a Twist

As I toured a number of the leading mining companies this week, the conversation showed a significant shift from last year from "greenfield" to “brownfield" discussions. Shifting from new plant implementation and speed to full production to how they draw the most efficiency from existing assets. The interesting twist was that the discussion of what was an existing asset:

1/ Fixed assets such as equipment

2/ plant ore assets

3/ mobile assets like trucks, digging equipment

4/ human assets, operators, maintenance and experts

So the strategy was how to tap existing information more than often locked within SCADA trend systems, and other data stores, it was key to extract this data and align these records into effective information. The driving forces are :

1/ minimal impact on the existing systems

2/ speed of delivery of the value

3/ expertise to understand and interpret the value

4/ predictive awareness, pattern recognition

The diagram below again resounded in the discussions.

The key for Industrial Analytic s is the trusted data, and just a historian will not achieve this, the model and validation must be done as close to the source.

The information needs delivery in many cases outside the automation landscape, often in the corporate networks. The key is to use the not APIs but make the connection through an SOA architecture. The service sits on the data source, with configuration, and data delivery built in, but key is low impact and effort.

This is not new, as the enterprise historian has been around for years, but the real difference is the need not just gather data, but to capture the data in a structure,  context, and validation of data that makes sure all stored data through resulting information is in trusted.

You are probably sitting there and saying nothing new! Fair, but the key was how are they going get this structured trusted data, that the concept was to do this as close to the source as possible, and then send through. This means the underlying systems do not change, minimize risk, maximize Lifecycle managed to enable evolution which will happen. Why is this not an IOT service, local and pushing vs polling, “self configuring” ?

Remembering the performance team of experts can be anywhere, and will probably virtual, where sharing, analysis and Modeling is done in offline mode looking for patterns.

As the discussions evolved the architecture evolved, and again the "cloud" came into play, why because the data size will grow, the users are everywhere, and the infrastructure of delivering is now there.

Why not?

The collaborative information, industrial analytics, is going to be foundational for the future of Gen Y teams of analysts experts from different locations and outside the companies.

Standby, as we see some of the optimization learnings from Oil and Gas come over into mining.