By: Nigel Bailey, Richard Helm, and Grant McCabe from The Boston Consulting Group.

The scope of automation

Integrated systems can help mining companies coordinate far-flung operations, anticipate and avoid potential problems, and respond faster to disruptions. That’s powerful stuff. But it can also seem overwhelming. What tools and processes—in what places, in what order, in what time frame—are needed?

Eat the elephant one bite at a time. End-to-end integration isn’t going to happen overnight; it will need to be developed piece by piece. So companies will want to start with the pieces that offer the greatest rewards—the parts of the value chain in which automation will have the biggest impact, such as by increasing utilisation at bottlenecks.

At the same time, it’s crucial to think about how these initial projects will interact with what’s coming down the road. This is what makes the holistic approach to automation different from the ad hoc approach: You’re always thinking about how any given project fits into the big picture. To this end, a roadmap is essential. So, too, is an awareness of technical standards. You want to be sure that the technology you’re putting in the field will be able to integrate with the technology you’re putting on the agenda.

Laying the technical groundwork, however, is just the beginning. Companies also must champion integrated automation to break through any cultural and organisational resistance. This means clearly defining and articulating outcomes and potential benefits. And while a long-term goal, such as a fundamental reset of the cost base, may be the real prize, companies will need to identify short-term benefits, too, such as more localised asset productivity. Early tangible benefits will be instrumental in sparking the funding that the overall effort will require.

The implications of more data

Automated equipment with on-board sensors will generate a great deal of data. Thanks to increasingly fast networks and less-expensive storage, this information can be readily transmitted to, and retained by, a mining company. Big-data tools and analytics provide powerful new ways to make use of this data; for example, by identifying patterns in a machine’s performance that signal an impending catastrophic failure. (See The Industrial Internet: Six Critical Questions for Equipment Suppliers, BCG article, October 2014.)

But analysing data and drawing insights can be complex tasks that often call for new skills. In many cases, these skills will be unfamiliar to mining companies, and the required talent will have to be actively developed internally or attracted from outside. Either way, mining companies must determine how to support and nurture these professionals, and that can be trickier than they might expect.

Experts brought in from outside, particularly from the digital-savvy startups that excel in data science and analytics, may be used to working in a faster-paced, more entrepreneurial environment. They may also be accustomed to having leeway to experiment and to do things in new and unproven ways. Mining companies will need to minimize culture clash and spur innovation. One way to do both is to embrace more of the agile ways of software development, which is built around experimentation, an acceptance of failure—because it happens early, doesn’t cost much, and offers lessons to draw on—and short, iterative cycles.

The opportunities that big data presents to mining companies will be extensive. But realizing them will require investments in time, effort, and resources, as well as a willingness to try novel and untested approaches. Companies will have to decide just how big an investment they should make. They’ll have to weigh the potential benefits of using data against the costs of doing so. And they’ll have to assess the price of notusing data, too.

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