Thursday, 17 October 2019
Innovative Tool to Assess the Value of New Technologies
Austmine Limited
/ Categories: Press Releases

Innovative Tool to Assess the Value of New Technologies

Isaac Dzakpata, Senior Research Engineer and Kristyn Zoschke, Mechanical Engineer at Mining3

As new and emerging technologies and methodologies are developed to meet pain points within the mining industry, the task of identifying solutions that provide value to one’s own operation has become a challenging task. Mining3 is working on a new solution that forecasts the value, risk and business case of new technologies, which could speed up the adoption of new technologies by the mining industry.

Current evaluation methods

When assessing the future value of investment in a new technology, for example, a fleet of autonomous load haul dump machines, several value and cost drivers emerge. These include increased productivity, improved safety and lower equipment maintenance costs.
 
Based on light historical data and assumptions, mining companies utilise traditional spreadsheet tools to establish a high-level business case for the new technology. These traditional spreadsheet tools tend to be large, uncontrolled and static, and are built with complex macros that only the developer and owner of the model truly understands.
 
A recent example in a 2013 report from the JPMorgan Chase Management Task Force, showed that reliance on the work of one “over-worked spreadsheet modeller” impacted the company in at least an AUD$400 million of financial losses in 2012.  
 
It is for this reason that Mining3, a leader in cutting-edge research and development for the mining industry, is developing a new dynamic modelling solution that offers a far more effective method for assessing disruptive technologies.
 
Modelling the impact of new technology

Dynamic modelling is an alternative method for accurately assessing a novel technology or methodology. However, we must first consider the uncertainties and variabilities of model inputs and perform a comprehensive sensitivity and attribution analysis.  This analysis covers not just the “most likely” scenario but also the “best case” scenario (assuming the solution outperforms its targets). As well as the “worst case” scenario, where the solution grossly underperforms and we must understand any negative implications of the implementation of the technologies.
 
Second, it is critical that the downstream and upstream effects of a solution be considered. By effectively integrating the model with the appropriate lower level process levers, sensitivities of worst-case downstream or upstream impacts can be adequately explored.
 
This means understanding capacity limits, bottlenecks, and new opportunities that could be unlocked by an alternative solution. And, by developing a dynamic model that mimics the entire operational value chain, several solutions can be tested independently or together, to arrive at an optimised system.
 
The third factor – and the most complex of the three – is the incorporation of risks into a model. Whether this means understanding the risks of the new system failing, or assessing the value of the risk that the new alternative system is mitigating. This step is often left out when it comes to developing a value model. However, how can one properly assess the potential value of an autonomous truck system if we haven’t quantified the liability of a scenario in which an autonomous truck is involved in an accident?
 
The outcome of this type of modelling approach would be a range of values based on multi-factor decision criterion and not just a single central value expressed as a net present value (NPV), as is the case with most deterministic spreadsheet-based valuation models.
 
The whole of business value driver tree modelling (Wob-VDT)
 
The challenge is understanding the impact (economic and non-economic) to the business as a whole and assessing the impact dynamically. The ability to use a whole of business (WoB) value modelling approach to understand issues like operational debottlenecking, life of mine opportunity trade-offs, and change management implications is critical to whether the proposed value is overstated.
 
Mining3 has taken a WoB-VDT modelling approach for assessing new (and potentially disruptive) technology and solutions. Utilising a top-down approach (with adequate levels of lower-level high-impact), we have developed a plug-and-play modular modelling structure, comprised of four main modules:

1.      Financial model
2.      Production model
3.      Task allocation model
4.      Utilisation model

In a hierarchical structure, in which the utilisation model – the lowest-level set of inputs – flows into the financial model – the highest-level set of inputs – which contains familiar financial metrics including cash flow, net present value and internal rate of return.
 
By building out the model from the top down, a certain customisation is possible, allowing a generic model can be applied to any mine with swift and minimal adaptation by simply plugging in and plugging out the relevant modules.
 
Once a modular based model is built, data at the lowest level of the value drivers, such as equipment capacity, utilisation, and unit costs, will flow up to the high-level business case.
 
By utilising ranges of inputs rather than static inputs, the capability of sensitivity and attribution analyses is unlocked, where small variances in a low-level input can be tracked through the model in a series of scenarios where the impact of the change of an input is quantified using stochastic or Monte Carlo style analyses. 
 
Remaining challenges
 
Several challenges remain to be solved when it comes to value driver tree modelling of mining innovations. Firstly, the complexity of many mining task level relationships makes it difficult to build a dynamic model that effectively aggregates the task outputs into a total system output, and then replicate it for other processes.
 
Secondly, while a model must be structured initially from the top level, the model and its modules must be adaptable to allow for future modelling of lower level inputs.
 
Finally, successfully performing stochastic analyses on low-level inputs, while essential for the accuracy of technology assessment, is an onerous challenge. This requires significant statistics expertise with a sound understanding of the interactions between low-level inputs that may alter their output values in a mining environment.
 
It’s a big task and  Mining3 and its industry members, are working to complete the development of a one-stop-shop, plug-and-play innovation value modelling tool that the mining industry can trust to assess new technologies and methodologies.
 
Significant strides have been made in developing this capability but there is still work to be done before it will be available to the mining industry.
 
The project is open for mining companies to get involved and help progress the whole of the business value driver modelling system. For more information or expressions of interest please contact Mining3 at info@mining3.com.

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