Data reliability is the cornerstone of any operation to measure, analyse and ultimately improve performance. To see a clear picture of the future, you must first have a clear picture of the present. To this end, data quality and consistency are essential to effective forecasting.
Luke:
Welcome to Episode three of the Ramesys Global Podcast. My name is Luke Wundke, and I'm back with Ramesys Global Managing Director John Goodman. Welcome back, John.
John:
Thanks, Luke. Great to be back.
Luke:
Okay, today, John, we're going to follow on from our last podcast. We were talking about the reliability of the data that mining companies work with and how this affects their ability to produce reliable forecasts and costed what-if scenarios. Now we all understand, John, that data, information, and statistics have become the veritable lifeblood that modern-day organisations thrive on and prosper.
During our last podcast, John, we quoted some highlights from a KPMG International Survey about how organisations enhance the reliability and confidence of their forecasts and, as a result, create measurable business value for their shareholders. Now, here are a couple of additional snippets to support this. Gartner measures the average financial impact of poor data on business at 9.7 million per year. And these costs are not solely financial but also loss of reputation, missed business opportunities, and higher risk decision-making because of their low confidence that they have in their data.
Also, a global survey of C-suite executives and finance and accounting professionals commissioned by BlackLine has revealed less than a third, in fact, 29% of respondents, are confident that the financial data they use for analysis and forecasting is accurate. So only a third feel confident in the data that they're using.
Look, what's your feeling on this, John? Is this the case with our anecdotal evidence in discussions with our clients?
John:
Luke, to me, this is probably the most significant financial issue facing today's junior and midcap mining companies. I'd really love to measure the value of missed opportunities within the industry simply because reliable financial information is unavailable to executives when needed.
From our experience with hundreds of miners, we see two key issues compounding this problem. Firstly, the inability of their current reporting to provide management with an understanding of what's going on in the operations. They don't know what their problems are, or they can't see what is causing those problems. How do you improve something when you can't see what's wrong with it in the first place? And then significantly, they want to try different operational plans, but they can't get a reliable understanding of the cost outcomes. It's like driving on a wet road at night with mud all over your windscreen.
Luke:
Okay, hang on here. So, you're telling me that many decisions impacting the bottom line are made on gut feel because that seems more reliable than the available financial info. Really, how does it come to this, John? What exactly is the problem here?
John:
I hate to say it, Luke, but mostly Excel is the problem. Now, I'm an accountant, and I love Excel. I live on Excel. But really, it has critical limitations. Ok, simple changes, you want to apply a ballpark inflation factor across all costs—easy breezy in Excel. But management isn't really looking at those kinds of generic decisions. Flowing five mine plan scenarios through an Excel budget model, adding a new pit, and changing from owner-operator to contract mining method. These are complex models; you've got to have software that can manage this. Our clients can typically turn around two to three costed mine plan options daily. Other mining operators using Excel struggle to turn around one financial model a month.
Luke:
Yeah ok. So, there is massive difference. And it seems entirely logical for me, John. So why don't mining companies introduce better software?
John:
Yeah, really good question, Luke, and it's not a straightforward answer. But I think perhaps two key elements. Firstly, there's just the effort and the cost involved. Miners have invested heavily in their Excel models, and it does require a commitment to move. The problem is, there's always so many competing priorities that this never gets to the front of the pile. It's easy to leave because the systems in place work, sort of, and it's always harder to make an expenditure justification supporting opportunity cost and opportunity savings.
And secondly, I guess many people in the financial team don't know what software solutions are out there, and they don't always need that much effort to implement. So, for instance, we recently implemented a mid-size gold miner. They had one month to convert the budget from Excel to our software. Now, there were some long hours put in to achieve this, but they got there, and the immediate benefit the client flagged once this was complete was that running the raft of subsequent budget iterations became much easier. They were running mine plan options, manning options, and numerous other changes within a few hours and presenting the results back to management.
Luke:
Yeah, well, that is significant. So, what you're suggesting that a digital leap is no longer optional if miners want to be at the forefront of cost competitiveness. Is that fair to say, John?
John:
Absolutely, Luke. And look, although it seems scary, costly, or time-consuming, it doesn't have to be any of these.
Luke:
Yeah. Thanks once again, John, for your time and valuable insights. I look forward to next week.
As a recap, Ramesys Global goal is to make it easier for mining companies to achieve a transparent understanding of their cost performance, develop a cost-conscious culture, and create a single source of truth that helps senior leadership make better decisions faster.
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