As stated by Ajak et al. (2018), decision-makers should imagine the unimaginable. The world is unreliable, and it cannot be precisely predicted. The business environment has become so volatile, and uncertainty is higher than ever (Ajak et al. 2018). Thus, it is much better to build a system that can be adapted to change than trying to predict the unpredictable and assumed the future to be constant as per the present conventional models. The traditional valuation models such as DCF do not trigger the possibilities of the future decision. However, RO asks what could happen in the future since the model revolves around uncertainty and emergence of new information in the future. In the next subsections, uncertainty and flexibility, which are very central to the application of RO, are explained in detail.
6.3.1 Uncertainty
Brammer and Smithson (2008) put forward a taxonomy for uncertainty by distinguishing the known from the unknown (Table 6.2) and acknowledged the difficulty of integrating uncertainty into existing structures. Therefore, having a framework for uncertainty identification can enhance the operational ability to identify existing options.
Table 6.2. Uncertainty taxonomy as presented by Brammer and Smithson (2008).
Known Unknown
Primary level
Known Known knowns Unknown knowns (tacit knowledge)
Unknown Known unknowns
(conscious ignorance)
Unknown unknowns
(meta-ignorance)
Known knowns involve situations where information is known with certainty and strategies can be put in place to deal with the problem.
Known unknowns (conscious ignorance) is a situation where organisations focus effort and resources on finding out what has been recognised as being unknown (e.g, a drop in commodity prices is known but the amount cannot be reasonably approximated).
Unknown knowns (tacit knowledge) involve situations where there is uncertainty identified via experience or intuition, but no one can pinpoint the details (e.g, when and how an event will occur). In operations and industry, unknown knowns are common in the fields of technological innovation and data analytics. It is unknown what future innovation will look like, but it is known that technology presents uncertainty.
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6.3.2 Can uncertainty be reduced?
‘Managing unknowns is just as important as making maximum use of what is known when responding to real world problems’ (Brammer and Smithson 2008). Conventional wisdom is that uncertainty is reduced. However, this notion is being debated across various disciplines and sectors such as in economics, where there is an argument that certain knowledge is possible given enough time and effort (Brammer and Smithson 2008; Kasperson 2008). In this research, there is partial agreement with the economic notion of possible knowledge. It is postulated that there is value in unknown future information by creating RO in paying costs (effort) and to have the necessary needed flexibility ahead of time such that it can be exercised when needed.
Since uncertainty cannot be eliminated, there will always be uncertainty during the life of a mining project, but knowledge of the uncertain variable can be gained through further learning or inquisition. However, it is possible for one to argue that decision makers, particularly in mining operations, deploy a process which this research calls the ‘transferential approach’. The transferential approach is a situation where decision makers can either change the sequence of activities to avoid dealing with an uncertainty now or delay knowledge acquisition. This approach disintegrates a complex state into small management events. For instance, complex geological information is simplified by starting mine development based on the assay data of an interim pit while the resource block model is continually updated as the information emerges from the exposed dig faces (Ajak et al. 2017). This implies that problems change as processes are fine-tuned and researched. In essence, information is utilized as it emerges and the knowledge of the situation is gained gradually.
6.3.3 Flexibility
Flexibility has its roots in manufacturing, where the concept was referred to as the flexible manufacturing system (FMS). This concept was brought in at a time when manufacturing was expanding, new markets were emerging and volatility was equally increasing. Thus, companies looked for a system that could allow them to increase productivity and lower operating costs (Singh and Skibniewski 1991). This required reductions in rework, elimination of processes that did not add value, avoidance of unnecessary delays and increased equipment utilisation.
Processes involved in mining production share similarities with those of manufacturing. The mining industry saw a huge expansion in the 21st century, especially from 2004 to 2015 when capital investment
in mines was very high, new mines commenced, and production rates increased (Commonwealth of Australia 2015). This boom was driven by the expansion of the Chinese economy, which grew at a rate
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of more than 7% per year during that period (World Bank 2016). As a consequence, mining companies were caught off-guard; those whose production systems were rigid could not rapidly respond to the changing demands for bulk commodities like iron ore and coal. By the time that the mining companies responded to this demand by investing in the development of new mines and starting to produce, the coal and iron ore markets had already flattened-out, economic growth in China had started to slow down and, as a result of these two factors, iron ore and coal prices dropped sharply. These caused companies to pursue aggressive cost-cutting, and many projects were either suspended or collapsed.
Flexibility is defined as being more concerned with the ability of a system to sustain performance, preserve a particular cost structure, adapt to internal or external changes in operating conditions, or take advantage of new opportunities that develop during a mine’s life cycle by modifying operational parameters. The ability to respond has to be rapid and cost-effective in order for its impact to be felt. This illustrates the point that current production processes and practices in the mining industry are well rooted in traditional methods that are very rigid and not easily adaptable to changing operational environments.
Even though the mining industry has adopted manufacturing processes that promote efficiency, such as lean methodology, with the hope that these processes will provide competitive advantages, many operations do succumb to volatility caused by external factors (Kazakidis and Scoble 2003). This is the main reason why operations collapse. Although they strive to be efficient, they fail to be agile, which comes from flexibility. Moreover, creating flexibility that leads to agility comes at a cost; however, it is where operational managers should focus.
The current trending topic in the mining industry is productivity improvement, which is tied to eliminating wastage and increasing efficiency. Management tends to narrow this down in terms of cutting costs. The main idea of this research is not to minimize the importance of managing costs. It must be emphasiszed that managing operational costs is very important and always will be, but it should not be the only thing that managers consider and focus on. The moment that cost-cutting takes the central stage, operations reduce their possibilities for creating RO in uncertain conditions and miss opportunities to mitigate losses and increase or create project value.
However, the ‘key to improving productivity is being flexible and being able to adapt to changing conditions’ (Honeywell Analytic 2015). Decision makers are very much aware that the world is uncertain, but there is a debate on how operations can best adapt to new environments. Adapting to change is critical for operational success in a tough business environment. The necessary ingredients required to succeed, which this research propagates as being applicable to creating RO in mining operations, are listed below:
• The ability to renew, adapt, change quickly and succeed in a rapidly changing, ambiguous, turbulent environment is referred to as agility (McKinsey & Company 2015). For organizations to succeed, they
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require speed and flexibility to respond and adapt rapidly (EY 2018). Thus, flexibility is the real source of business agility.
• Agility and capabilities that come from RO: Capabilities are essential for competing effectively in a given position, and agility is essential for making shifts in that position in response to a changing environment. Therefore, any option under consideration must meet both capability and agility tests if it is to add value to a mining operation. This means that mine planners and managers may need to do more than simply produce mine plans and aspire to achieve targets in these uncertain times.
• Hyperawareness that RO must provide: A company’s ability to identify and monitor changes in its business environment and digital disruption that is marked by high market turbulence and shifting industry boundaries (McKinsey & Company 2015; Wade 2015; Deloitte 2016). This can be achieved through the use of data-mining methods. Organizations can deploy predictive data mining techniques where elements of mining operations, like maintenance and geological changes, can be predicted and options put in place to respond to help in making an informed decision (Ajak et al. 2017). For instance, if prices changes, organisations can use predictive data mining technique as a way of reducing cost by being proactive in controlling unscheduled breakdowns in mining equipment and processing plant. Such unplanned losses can be predicted in time, and RO can be created to mitigate losses.
• Informed decision making: A company’s ability to make the most appropriate decision in a given situation (Wade 2015).
• Fast execution: A company’s ability to execute its plans quickly and effectively (Wade 2015). Therefore, mining operations must view RO not a simply a strategy but also an instrument for gaining the ability to be agile and are a key driver of future success.