Business Improvement...on a Budget
Mining companies want what they have always wanted: uncompromised value. For most mining companies, value means sustainable growth, low-cost operations, optimal capital allocation, optimal product mix, and maximum cash.
While businesses share similar value drivers, traditional value creation and optimisation approaches have mainly been fragmented and ineffective – at least in the medium to long term. Internal realities impede companies from getting to that next level of productivity or increment in marginal value of throughput.
Complexity can lead to liabilities
What's true of many mining businesses is that they are complex, with too much to track – expensive equipment; production, maintenance and safety; systems and processes; people and behaviours/culture as well as volatile commodity markets. With so much to track, it's no wonder managers keep chasing their tails.
Mining companies are also plagued with exponential increases in data volumes – an effect of digitisation and IoT. Although large data volumes can be an asset, more often than not, it can be a liability. This is because all this data comes from many disparate systems (e.g., ERPs, telematics, Excel, etc.), and this data is often dirty, unstructured, and arduous to integrate and analyse.
Apart from the sheer volume of data, access to consistent operating and financial assumptions across business units is also a challenge. Everyone has Excel-based models on their computers, resulting in replicating data and business logic, duplicating effort, and painfully wasting time. Some have analytical and predictive tools but many complain of their complexity, requirement for specialised skillsets or lengthy implementations. So, it is no surprise that optimisation occurs in silos, and the impact on the end-to-end value chain is often overlooked.
Accessing advanced decision support
The current emphasis on achieving that next step in productivity means the time is ripe for mining companies to increase the number and scope of analytic and optimisation resources. Larger mining houses have deployed Business Improvement (BI) Teams, analytics, and optimisations tools. Whilst many have seen value in this approach, even BI teams are not immune to the challenges mentioned above. They, too, are unable to adequately leverage current analytical and predictive analysis tools to improve data interpretation and influence key business decisions. Smaller mining houses, or many contract miners, have not even been lucky enough to have access to BI capabilities, whether from specialised teams or tools.
The good news is that there is hope. Recent breakthroughs in machine learning and AI make it possible for any mining company, irrespective of size, to access deep analytics and advanced decision support systems without the need for specialised teams or capital outlay. For companies with BI teams, this means all analytics and decision support can be done in-house. For those without BI teams, frontline managers have access to self-service BI capabilities. Over the last few years, self-service BI tools such as Cliqview, Tableau, and Power BI have certainly assisted business users and executives in visualising their data to derive insights.
With machine learning and AI adoption, evolved BI tools have also come on the market. These tools enable mining companies to optimise complex value chains through deep data analytics, simulations, and what-if and predictive analyses tools whilst maintaining the 'visualisation appeal' of the introductory self-service tools. In addition, these evolved tools successfully use real-time data and complex event processing in modelling and analytics.
It’s about outcomes, not ownership
Although many of the new age BI tools have started to address this new expectation of model-drive BI, they have also become complex, and more often than not, become less ’self-service’ and more ‘assisted service’ tools. It also meant time to implement became longer, and the tech company's level of support required increased tremendously.
Where many mining companies are getting it wrong is that the focus should ultimately be on outcomes, not ownership.
Realising the highest value opportunities
Fortunately, solutions, such as the ManagedAnalytics platform, provide the best of both worlds. These are not just modelling, simulation, and analytics solutions as it also allows users to develop, deploy, share, and extend business models quickly. Consequently, specific, high-value insights and analytics are provided to support frontline managers, optimisation teams, and decision-makers at every level, then integrate these insights and actions into customer's management operating systems.
The benefits of this are that it allows mining companies to quickly identify and analyse the highest value opportunities across the end-to-end value chain and realise the full potential of their assets. All this without the need to build teams with specialist skills or the need for capital outlay