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Managing data deluge in mining amid digital transformation

Aug. 18, 2020

Too many risks, too little time (and money).

Industry 4.0 has the chance to make mining safer, more cost effective and efficient. We all know this.

The development of inexpensive sensor and communications equipment and analytics software has

Hitachi ABB Power Grids' Bryan Friehauf

accelerated the pace of innovation in predictive forecasting. Most equipment operators today are deploying a range of predictive solutions from various providers, such as self-diagnosing equipment, condition-monitoring process-automation software, and advanced pattern-recognition software. Condition-monitoring systems alone include oil analysis, thermography, mobile-asset health data, plant data historians, vibration, ultrasound, operator inspections and PM inspections.

As the rapid pace of technological innovation continues, however, progress is not guaranteed.

Digital transformation in mining has created more data and information than was conceivable just a few decades ago. And it just keeps coming. Take new, cost-effective predictive models, for example, which use more data, collected automatically and at higher frequencies, to monitor equipment more effectively.

Techniques like this are flooding mining companies with more risk alerts than ever before, overwhelming the people in charge of risk mitigation. No organization has the budget or time to respond to every risk produced by these systems as they occur. The innovative technology of Industry 4.0 is creating information overload and organizational attention deficit. The mining industry has to tackle the challenge of balancing technology and human thinking head on if it wants to reap all the benefits of Industry 4.0. 

A system perspective

So how do we do that?

The systems involved in mining are almost innumerable, and data exists in silos. For best results, all the information needs to be freed and aggregated in a centralized system to pull insights that simply cannot be garnered from single data sources on their own.

This system perspective depends on more than an aggregation of direct condition data. It also requires a review of indirect business-process trends. Trends in preventive-maintenance compliance, backlog size, obsolescence and spare-parts availability, operator workarounds, near misses, and operating experience can complement direct information on current equipment condition and historic performance for a comprehensive, aggregate view of health on a medium to long-term horizon. To understand and manage complex work like mining, a system perspective is a necessity.

Asset-performance management (APM) programs with a holistic, systematic perspective allow the user to see all aspects of the business and the industry in an accessible, user-friendly, centralized layout. Having all the information in one place to enable a big-picture view of all the systems eases and clarifies the processes of shifting through multiplying data sources and deciding where and when to invest.  

Collaborative action

No matter how sophisticated the predictive analytics and system health perspective, it means nothing if action isn’t taken. Many times these new systems produce more risks than organizations can handle. It can get overwhelming if there isn’t a system that prioritizes and organizes risks and helps manage which of those risks to address now and which to defer. Insufficient prioritization of work has a negative impact on process efficiency and, thus, reduces the capacity to remove risk.

This workflow-risk identification and task prioritization typically spans various organizational silos. The key to realizing the value of APM is to break down those silos and enable various groups to contribute to the full picture by connecting APM, work, and operations-management tools. This collaborative process produces better decisions by encouraging individuals to take action based on insights from data, and peer reviews and other safeguards support those actions. Plus, this collective operation creates cross-team ownership that discourages decision procrastination. Consistent application of these collaborative decision-making processes pays off in higher reliability and lower costs.

Looking ahead

A well-implemented APM strategy relies on technology being fitted to human thinking, not humans acquiescing to technology. Halfhearted implementations of new technology lead organizations to becoming overwhelmed by the data, which results in decision-making paralysis. The best APM systems help break down barriers between data and people and make the entire mining enterprise more collaborative.

By pulling complete information and expertise into one system, we can better manage risk and investment. This can transform mining operations by increasing efficiency, reducing costs, and getting the most out of the big data emanating from smart sensors. 

Bryan Friehauf is EVP and GM of enterprise software solutions at Hitachi ABB Power Grids