At the recent Digitalization in Mining North America conference in Toronto, two themes kept coming up: 1) data digitalization is the key to long-term industry success, and 2) people’s involvement with, and benefits from, digitalizing mining must be our focus.
Whether addressing hiring and retention challenges through diversity initiatives or reimagining how people work with data and why, DMNA 2022 featured speakers and participants from around the world. Most agreed that building or sustaining a successful mining company in a tech-driven world requires putting people first.
As Peta Chirgwin said, people are the common denominator in every company, regardless of differences across the sector; people determine if corporate tech initiatives succeed. Further, psychological hazards at work are predicted to be the biggest issues with hiring and retention going forward, so we must always ensure we put “people before technology.”
According to Lynessa Moodley, “Mining innovation is at a turning point. The industry is always looking to do things better, cheaper, faster, safer, etc. The tech is there, it’s all about how we execute.”
Yes, the tech is here, has been so for sometime, and continues to evolve. IoT, ML, AI, and other technologies are readily available to make mining more profitable, efficient, and safe, while also helping organizations prepare to meet Canada’s ESG targets for 2030.
Moodley suggested organizations have been slow to digitize due to mining value chain siloes. From our perspective, this results from smaller working siloes within companies and leads to employees at all organizational levels having access to only limited, duplicated, out of date, or incorrect data.
Working with problematic data may sometimes be required in even the most data-centric businesses; however, having to do so all the time—with all the miscommunication and extra effort it creates—is bad for both business and employees.
The benefits are myriad, well-communicated, extensively documented, and widely understood, at least theoretically. What mining operation wouldn’t want to know that:
The roadblock here, we believe, is companies not finding the right balance between immediate needs and long-term plans.
In a difficult and sometimes bumpy industry, mines can set themselves apart with tech solutions that, as Justin Simkins said, significantly increase production speed, quality of results, and overall employee safety.
Speeding processes up means “less time in mines, which are dangerous.” Quality data means “not having to re-collect it” and, we would add, not wasting time in less profitable parts of the mine. Safety is, of course, “the most important factor, because the less time people are in danger, the better.” We really appreciate this perspective: pretending mines are not hazardous will only contribute to the sector’s ongoing hiring, retaining, and reputation troubles.
While most conference attendees agreed that hiring and retaining great people is a big problem in mining, there was an interesting divide: Some asserted tech adoption readiness requires a complete handle on company data; others advised companies to start with what they have now.
Mariana Sandin noted, “mining has lots of data collections, historians, etc that are decades old—now we’re seeing the gaps. But you don’t need a full data set to start; just get going and worry about completeness later.” Jake McGregor added, “We don’t necessarily need all the data we’re collecting now; 5-50 years down the road, we might.”
Conversely, Ewan Botterill asserted, “Companies need an enterprise-class library, even if it’s not used across the whole company. This includes establishing a shared language in an enterprise schema so everyone knows what data is being examined. You have to know who owns the data or you won’t know whether or not it’s trustworthy.”
We agree with Prashanth Southekal, who pointed out, “There is no such thing as 100% quality data. Find the balance between quality vs. cost. According to IBM and Carnegie Mellon U, 90% of businesses’ data is not used.” Tapping into even 10% more of the data you already have can disproportionately improve business outcomes.
Achieving balance can be tricky, and not just with data collection and integrity. As Simkins pointed out, many mines operate 24 hours per day; this makes downtime a big problem for everyone involved. Joseph Amankwah agreed, pointing out that production interruptions can negatively impact safety and may be why the industry hesitates to make significant workflow changes.
Further, younger workers are not pursuing mining careers as their parents’ and grandparents’ generations did. According to Amankwah, the industry’s difficulties in hiring and retaining skilled workers is amplified for very remote sites.
Still, as Michael Roy argued, finding the right people is essential. He sees part of the solution to this staffing issue in industry rebranding, in better “telling the mining story and our purpose around it.”
But it’s not enough simply to hire the right people, according to Roy; companies need to work on keeping them—and part of doing so is giving people autonomy to grow and learn. He quoted Steve Jobs who famously said, “It doesn't make sense to hire smart people and tell them what to do. We hire smart people so they can tell us what to do.”
Jake McGregor echoed this, saying, “Mining companies are failing by not tailoring HR job descriptions to actual data jobs.” He also pointed out that retaining good people shouldn’t be limited to keeping them in their current jobs; mining organizations should “train up the people they already have” to fill positions instead of always looking elsewhere.
For an industry so “full of uncertainty and high dynamics,” Roy said, it’s not surprising that it moves slowly when it comes to big, often expensive, changes. Moodley agreed: “Economies of scale are important, especially in remote areas.”
Even simple internet connectivity can be a huge challenge in remote locations; workers might not be willing to work very far afield without worthwhile compensation. Such factors can make decision-makers wonder if tech is worth the time and money.
It does take time and money to digitize an operation; but it doesn’t need to take up all the time and money available. This is where companies should clearly determine how tech can help them in the short term, as well as far down the road. Roy suggested that since the world is moving away from fossil fuels anyway, this trend can and should influence mine design and redesign now.
ML, AI, IoT, and other tech tools can keep mining relevant and sustainable; however, as Roy added, “We can’t take humans out of the loop completely. Machines can make dumb decisions; we need people as reality checks.”
People should, in fact, be at the heart of every major decision. Amankwah suggested consulting employees to “develop an implementation plan with them, understand how it will impact every section and department. Get their consent, first during implementation, and reach out constantly on how they’re managing.” We couldn’t agree more.
It all comes down to what Rachel Wallace so eloquently noted: “In a world becoming increasingly more digital, it’s the human touch that matters most” and it’s our “collective human intelligence” that will help us succeed. Digital technologies in mining are important precisely because they “give people time, better data, so we can think more about the unknowns, the future, higher order, and more strategic questions.”
We’ve already made it clear that we think offering employees more satisfying mental engagement is key to attracting and retaining the best people, and we’ll continue to shout this from the proverbial rooftops.
Saif Shaikh discussed how mining organizations need to involve their people if they want to successfully digitize.
To begin, stakeholders and decision-makers need to understand when their organization is truly ready; "it just can’t be FOMO (fear of missing out)” prompting them to adopt new tech tools. Sandin underscored this too, saying, “There are pitfalls in using ML”—or any other tech tool, we would add—“for things that could be solved with first engineering principles on the floor.”
Organizational readiness is more about people than about machines; companies need active buy-in from data engineers and ML Ops; senior management; the right partners; SMEs—without whom, all the fancy tech in the world won’t do much.
Guiding an organization from within requires taking the time to present a plan to different business units, separately. It involves asking decision-makers and site workers a lot of questions. It depends on offering learning opportunities for staff, managers, and executives. And it involves widely sharing results in simplified ways, so end users can easily integrate them into existing tools.
In other words, according to Southekal, it requires, “treating everyone in your company as a customer, adopting a service mindset.”
Stephen Mellor asked The State of Machine Learning Technology and Innovation panelists how organizations know if they’re ready.
Sandin summed it up nicely: “Vision and strategy have to be in place first; skills and willingness, too—you need a culture willing to try things, fail, and try again." We believe this kind of culture is only possible in environments where workers know that their well-being and success sit firmly at the forefront of all company initiatives.
Khushaal Popli added, “Be transparent, let site workers know you’re testing. Ask site workers about sensor accuracy. Machine Learning is meant to make human jobs better, not replace them.” In parallel, getting Ops to see you’re helping them is key, as is making sure you have skilled and happy SMEs.
We agree with Southekal's observation that “All companies are already data analytics companies. For example, TD Bank says 91% of the money used to run their business goes to data management;” even companies still relying on paper-based record-keeping are constantly collecting invaluable data, whether or not they realize it.
Such data may be as important in 50 years as it is today, so it’s crucial that mining companies ensure they're collecting, storing, and managing data in the best way.
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