Pw C Value Driver Modelling Feb 2009 Email Final
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Pw C Value Driver Modelling Feb 2009 Email Final

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Understanding the complex linkages between operational variables at a mine site and financial performance of that mine is now more critical than ever as operators deal with the slump in commodity......

Understanding the complex linkages between operational variables at a mine site and financial performance of that mine is now more critical than ever as operators deal with the slump in commodity prices.

Even before the downturn, many of Australia’s leading mining companies had started to implement a more structured approach to cost effective decision making across all areas of mine production.

This paper highlights Australian coal mining best practice in both operations cost management and production value maximisation through robust modelling of operational value drivers.

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  • 1. Finding cost efficiencies in mining operations through effective value driver modelling Aaron Carter, Brian Gillespie and Chris Gilbert Performance Improvement Group, Brisbane February 2009
  • 2. © 2009 PricewaterhouseCoopers. All rights reserved. “PricewaterhouseCoopers” refers to PricewaterhouseCoopers, a partnership formed in Australia or, as the context requires, the PricewaterhouseCoopers global network or other member firms of the network, each of which is a separate and independent legal entity.
  • 3. Introduction Understanding the complex linkages between operational variables at a mine site and the financial performance of that mine is now more critical than ever as operators deal with the slump in commodity prices. Even before the downturn, many of Australia’s leading mining companies had started to implement a more structured approach to cost effective decision making across all areas of mine production. This paper highlights Australian mining best practice in both operations cost management and production value maximisation through robust modelling of operational value drivers.
  • 4. A. Return to cost efficiency For the five years until mid 2008, most major mining However, prior to the credit crunch, and probably as companies in Australia emphasised cost effectiveness early as January 2008, some of the more forward thinking over cost efficiency, particularly in the areas of Australian mining companies were already preparing for maintenance and transportation. Mining the largest an expected change in market conditions. At the mine possible quantities of minerals as quickly as possible site level, this involved a change in emphasis towards has been more important than minimising the cost of maximising the profitability per tonne of product with key maintenance or production activities due to the high an increasing focus on reducing costs rather than just prices available. Operational efficiency had in effect maximising the total tonnage mined and shipped at any been compromised to varying degrees in the quest for cost. This renewed focus on achieving acceptable return production volume to take advantage of high prices. on investment per individual mining asset has now taken hold across the sector as mining companies once again Large capital projects have the potential to destroy begin to take a closer look at the cost of capital items and substantial shareholder value during extended periods of their operational and maintenance practices. low prices. Anticipating an extended price slump, almost all of the major mining companies around the world took a critical look at their major capital expenditure plans towards the end of 2008, clearly demonstrated by a significant lessening of lead times for many categories of major capital items. As commodity prices continue to slump and demand scales back from the growth markets of India and China, many mining companies have already deferred specific major development projects and many more have announced non specific scale back of aggregate capex projects over the next five years (Aeppel 2008). Figure 1: Buying power is moving back to mining companies as demonstrated by the significant decreases in asset lead times over the past 12 months Tyres Wagons Locomotives Draglines Power generators Crushers Grinding mills 0 5 10 15 20 25 30 35 40 45 50 Source: The Australian Financial Review and PricewaterhouseCoopers Average delivery time Jan 2008 Average delivery time Jan 2009 2 Finding cost efficiencies in mining operations through effective value-driver modelling
  • 5. B. Barriers to finding cost efficiencies Understanding how operational levers drive the financial have been determined for one mine site, the cost drivers performance of an individual mine is the key to cost for the same process may vary considerably at another. efficiency and value optimisation. There are a number of Even the simplest mine operations will have unique aspects barriers that will typically compromise the many different of their operation that must be taken into account when types of projects that in some way are geared towards estimating costs. improving cost efficiency. Very often the first symptoms of project failure occur when target operational metrics Lack of tools have been achieved with smaller than expected financial improvement. Typically this situation will arise when the Another barrier to finding cost efficiencies is that the relationships between operational metrics and financial mining industry traditionally has underinvested in tools to results are not sufficiently understood. The primary barriers quickly and reliably assess the financial impact of potential to finding cost efficiencies are that: mine improvement ideas. Although many mines have mine planning, process optimisation and financial modelling 1. There is little understanding of the relationship tools, they tend to be non integrated or receive limited between operational metrics and financial results data feeds from each other and are therefore used in 2. There is a lack of tools available to assist this isolation for scenario modelling purposes. Often the sheer understanding quantity of operational data and the linkages between data contained in these stand-alone tools can give a false 3. Operating performance and financial results are not sense of reliance on the output even when the hierarchy of sufficiently disaggregated operational effect and financial result is incomplete. 4. There is a lack of accountability for financial performance below senior management level. These barriers will be discussed further in the context Figure 2: An integrated approach to operational modelling of the Australian mining industry. links all key aspects of a mine Little understanding of the relationship between operational metrics and financial results It is now almost 60 years since mining companies started Financial Refining using the same accounting principles that were used to Operations measure the financial performance of the company to help measure the performance of equipment and operational processes (Hoyt 1950). However, understandably, few mining companies expect their trained engineers to be able to apply cost accounting principles to each of the possibly hundreds of operational decisions they make every week. Mining Geology Operations This lack of understanding can take a number of forms. At the most basic level, process controllers with minimal understanding of component costs may be given the task of optimising a part of the production process. Even when the cost components of the production process are well understood over a certain range of variability, a lack of understanding of the various inter-relationships between cost components will usually lead to simplistic Supply Maintenance & Availability assumptions about the drivers of value. Most difficult of all is that each mine site is unique in its combination of factors such as plant layout, mineral ore body and proximity to rail, road or port. Even if the cost drivers of a particular process Finding cost efficiencies in mining operations through effective value-driver modelling 3
  • 6. Many major mining companies have also attempted to In addition to poor disaggregation of financial information, use data provided by their financial reporting system often operational drivers and financial outcomes will not for mine planning purposes and the large Enterprise be included within the same reporting framework, or if Resource Planning (ERP) vendors now all offer mine they are, the linkages will not be clear. Without appropriate planning modules to help integrate financial information reporting of both financial and operational information, at the mine site. The advantage of major ERP systems it can be difficult to understand why performance has can be the depth of the financial data and the ability to been tracked in a particular manner. In many cases, provide simultaneous multi-user capability. Many mining operators and superintendents do not have the ability to companies are using their historical financial data from report which operational metrics drove throughput and an ERP system to assist annual budgeting and annual financial performance. Without obtaining a full and accurate production planning. However the standardisation understanding of operational performance and the resulting required from financial reporting systems is a significant financial implications from lower level staff, it is very difficult limitation when it comes to modelling the uniqueness of the for senior management to effect cost reduction initiatives operational set up at a mine within the ERP system. within an operation with any degree of certainty. This creates a problem for mining companies seeking to prioritise cost saving initiatives from a portfolio of possible Lack of accountability for performance projects. Many opportunities are not explored properly and The final impediment to achieving cost efficiency is a lack are accepted or rejected on the basis of weak logic based of appropriate accountability for those operational metrics financial modelling. Inevitably many such cost reduction that have the greatest impact on financial outcomes. A lack initiatives fail to deliver the expected financial results due to of visibility of the linkages between operational metrics the impact of other parts of the mining operation not being and a significant negative or positive financial variance adequately taken into account. Even more worrying is that means that an operator will not be required to provide some value optimisation activities are never considered an explanation of the variance appropriate to the level of due to the time and effort required to evaluate financial impact. Accordingly, the operational staff that them properly. may have the ability to heavily influence financial outcomes through the way they conduct their day-to-day operations, Reported operating performance is not are not being measured in a manner likely to change sufficiently disaggregated behaviour to improve financial performance. In some mining companies, another impediment Conversely, senior personnel that are being measured to sufficiently understanding costs is that financial against financial outcomes may have little influence on performance is often only reported at a consolidated (or sufficient understanding of) how their operational staff level, or at a lower level based on the cost hierarchy and can assist them to improve performance. If the inter- cost elements as defined in the chart of accounts. In relationships and linkages between lower level operational many instances, the cost hierarchy does not sufficiently metrics and higher level financial indicators were clearer, disaggregate costs in a manner capable of accurate cost then the organisation would have a better chance of reporting across operational processes, ie by activity. developing a set of useful metrics for all staff to reward Even if there is a close alignment between operations and successful financial performance. cost centre reporting, this reporting often provides little consideration to the level of value created from incurring these costs. 4 Finding cost efficiencies in mining operations through effective value-driver modelling
  • 7. C. Linking operations and finance Extracting minerals from the ground and then selling those Mining companies must have a solid understanding of the minerals in a global market may be a simple business operational levers that drive financial performance if they model, but the cost components of that business are want to be able to quickly and cost effectively configure huge, complex and inter-related. Additional value adding for required production. Building an accurate operational processes such as even basic refining further confuse the model where all components of that model link to the components of cost. predicted production cost is the most straightforward way to combine operations and finance. Many mining engineers still believe that the process of extracting minerals from the ground is straightforward and The most useful operational models are those that replicate that the fundamental quality of the mine determines that the full structure of operations and process logic at a mine mine’s position on the cost curve. site, or extended operation. The best models provide a cascading top down view of operations, linking high-level This assumption fails to understand the scope for financial outputs to the key operational drivers of those optimisation in even the most basic of mine operations. outputs such as production performance metrics and the For example, even financially aware senior operations disaggregated operating costs of each major process staff will struggle to optimise single large basic cost or asset. components such as the effective manpower cost of a changing shift pattern. How then can they be expected to minimise the combined production unit cost of hundreds of equipment assets over an extended time period in a dynamic production environment? Figure 3: High-level value driver logic for development activities at an underground coal operation Financial Mechanical Parts ($) Continuous Miners ($) + + Shuttle Cars ($) + Electrical Parts ($) Development ($) + Breaker Feeders ($) + General Consumables ($) + Labour ($) + Other ($) Development ($/metre) + Production Speed (metres/hr) Calendar Time (hours) x - Calendar Hours (hours) Development Scheduled Time (hours) Unscheduled Time x (hours) (metres) Calendar Availability (%) / - x Calendar Time (hours) Idle Time (hours) Production Time (hours) Utilisation (%) / Scheduled Time (hours) Operational Finding cost efficiencies in mining operations through effective value-driver modelling 5
  • 8. These tools are primarily implemented to provide an return that capital expenditure will yield through improving accurate and reliable insight into the key elements of operating performance. Proposed cost and productivity value creation at the mine being modelled. They are used improvements are entered into the model for comparison for predictive modelling, sensitivity analysis and variance against a baseline operating scenario. Based on these reporting purposes. Over the longer term, they will start inputs, the value driver model can calculate expected to educate, then influence management thinking and operating performance under different scenarios, and can encourage a sharp focus on the key metrics that have the highlight the source of key performance variances. biggest impact on the performance of the mine. Predictive value driver models can become a valuable tool for the quick evaluation and prioritisation of improvement The power of predictive models opportunities. For example, questions that are often tested Predictive value driver models are focused on evaluating through a predictive value driver model include: the impact that alternative operating scenarios will have • Which capital investment options will have the biggest on performance and modelling the core value drivers of impact on operational performance? a mining operation. They show how changes in capacity, leverage points and process inputs can influence • How will improving the reliability and availability of key operational and financial results. The design of a predictive plant items impact the performance of the mine? value driver model must allow the model, when populated, • What operational improvement initiatives will have the to replicate the true factors underpinning the economics biggest financial impact? of a mine. Production constraints, mine geology, mine planning data, and the operating performance and Another common use for predictive value driver models maintenance constraints of key assets are combined is to conduct comprehensive sensitivity analysis. The with precise financial data to create a model capable of sensitivity of financial performance and mine production mirroring mine performance. They differ to traditional mine volumes to each driver in the model is calculated and planning, scheduling and optimisation tools due to the prioritised to highlight those elements of the mine that emphasis placed on the financial implications of different create and destroy value. operational scenarios. This knowledge empowers management and staff to Predictive value driver models can be used to assess focus their time and resources on ‘where the money is’ to the likely benefit of proposed operational improvement improve the performance of their mining operation. and cost reduction opportunities, or predict the level of Figure 4: Example sensitivity analysis highlighting the key operational drivers of financial performance % change to EBIT Value Drivers -1.00% -0.50% 0% +0.50% +1.00% Longwall Idle Time Longwall Operating Delays Conveyor Maintenance Delays Longwall Change - Out Time Development Unit Cut Rate EBIT impact of + 5% change CPP Unscheduled Time in operational value driver EBIT impact of -5% change Development Idle Time in operational value driver 6 Finding cost efficiencies in mining operations through effective value-driver modelling
  • 9. Jointly reporting finance and operations operating delays and unplanned maintenance of key assets representing the lower operational levels of the value Value driver models can also be used to report a driver model. A well constructed value driver model can be combination of historical operational and resulting used as the basis of an accountability framework that can financial performance data covering all aspects of a mining embed key performance metrics across an organisation. operation. The key point of difference, from conventional reporting mechanisms, is that the value driver model can One of Australia’s largest mining companies has be used to present operating performance in a logical implemented such frameworks in a number of its mining cascading model structure, disaggregating and refinery assets in Western Australia and Queensland, high-level reported financial performance into the lower linking value driver models to business intelligence. Senior level operational elements driving that performance. management meets with plant superintendents on a monthly basis to examine a variance report, which requires Reporting in this way can enhance the level of control input from all key areas of the operation. A negative that management has over operations by providing variance on the model can be tracked to its source transparency of the key drivers of monthly results. operational driver(s). Managers can understand exactly which elements of the mine have positively and negatively impacted reported results, and the extent of this impact. Figure 5: Example value driver reporting tool and accountability framework Continuous Miners ($) Mechanical Parts ($/ROM t) Cost ($) 754,201 738,722 Cost ($/ROM t) 0.762 0.754 Variance (15,479) -2.1% + Variance (0.008) -1.0% + Accountability Darryl Keating Shuttle Cars ($/ROM t) Accountability Mike Stapleton Cost ($) 1.885 2.174 Shuttle Cars ($) x Variance 0.289 15.3% Electrical Parts ($/ROM t) Cost ($) 420,715 487,002 Accountability Jason Stubbs Cost ($/ROM t) 0.459 0.443 + Variance 66,287 15.8% + Variance (0.016) -3.5% Accountability Geoff Price Development Production ($/ROM t) Accountability Daneil Cotters Mine Development ($) Cost ($) 2,141,219 2,160,346 ROM t 223,191 224,012 Variance 19,127 0.9% Breaker Feeders ($) Variance 821 0.4% General Consumables ($/ROM t) Accountability Jason Stubbs Cost ($) 243,202 236,299 Accountability Jason Stubbs Cost ($/ROM t) 0.192 0.422 Variance (6,903) -2.8% Variance 0.230 119.8% + Accountability Sarah Smith + Accountability David Stanton Labour ($) Lubricants ($/ROM t) Cost ($) 723,101 698,323 Cost ($/ROM t) 0.218 0.212 Variance (24,778) -3.4% Variance (0.006) -2.8% Accountability Jason Stubbs + Accountability David Stanton Other ($/ROM t) Cost ($/ROM t) 0.254 0.343 Variance 0.089 35.0% Accountability Geoff Price Developing an accountability framework Key measures in the regular reporting pack can be Personnel accountable for negative variances must provide assigned to appropriate personnel to create accountability an explanation and rectification plan for variances below for performance. Management level personnel, such certain tolerances. There are two clear benefits to this as superintendents, are typically made accountable for approach: first, operators and superintendents clearly performance metrics higher up on the value driver model, understand the economic impacts of their operational area, such as plant or major asset availability. and second, this granular level of visibility can be used to motivate individual operators to improve the priority Operators can be held directly accountable for the specific operational metrics that they control. metrics particular to their part of the process, such as Finding cost efficiencies in mining operations through effective value-driver modelling 7
  • 10. D. Modelling cost reduction opportunities in turbulent times In the current market conditions, many companies are reduction during a period of continued low commodity undertaking urgent cost reduction programs to counter prices. A true cost improvement program for reduced significant shortfalls in revenue due to price slumps and production levels requires sustainable cost reduction slackening demand. Mines have already been closed over a longer period. This is particularly the case where in Queensland and Western Australia where the cost of production levels may be substantially reduced for an extracting the reserves significantly exceeds revenue extended period requiring a significantly altered cost available under the forecasted commodity price. structure for the operation. For many more mines in Australia, there will still be a lag Modelling scenarios of significantly lower production between the drop in the market price available for near levels than recent levels is not straightforward. Production term production and the input costs of that production. constraints can change significantly when the requirement For some operators, there will be a transition period lasting for the number of major capital plant items such as power well into 2009 of considerable reductions in revenue with generators, draglines or crushers is reduced in number little drop in input costs under existing contracts. When but give rise to significantly higher asset utilisation. A presented with shrinking or even negative margins, the flexible value driver model can calculate expected costs options of implementing immediate measures such as under different production level and operating performance turning off production, reducing headcount or delaying scenarios, even when historical cost data is not available major capital expenditure must be considered. for the particular mine capacity configuration being considered. Predictive value driver models can become Whilst these measures are clearly necessary for some significantly more valuable than mine planning tools using mining operations, for other mines it will be important to ERP cost data in such circumstances. understand which levers will have the most impact on cost 8 Finding cost efficiencies in mining operations through effective value-driver modelling
  • 11. E. Conclusion Understanding the complex linkages between operational References variables and the financial performance of a mine site is now more critical than ever as operators deal with Aeppel, Timothy, December 2008. “Miners Cut Spending the slump in commodity prices. This paper has sought in Half” Wall Street Journal Vol. 252 to highlight the importance of finding greater cost Charlton, S, May 2007. “Mining sector has to formalise efficiencies by modelling the operational drivers of financial processes and systems to improve productivity” Mining performance. Weekly Vol. 142 There are currently four barriers to finding greater cost Fordham, P, Jan 2004. “Mining Company efficiencies through use of such initiatives. Performance Improvement Programs and 1. Little understanding of the relationships between Results — Summary of Benchmarking Study” operational metrics and financial results. Plant Operators Forum 2004, Colorado 2. Lack of tools available. Hoyt, Charles D, September 1950. “Time Studies and Cost Accounting increase efficiency at Titania” 3. Operating performance and financial results Mining Engineering Vol. 187 are not sufficiently disaggregated. PricewaterhouseCoopers, 2008. “Aussie Mine* Reaping 4. Lack of accountability for financial performance below the rewards. A review of trends in the Australian mid-tier senior management level. mining industry” Global Energy, Utilities and Mining Several leading Australian mining companies have PricewaterhouseCoopers, 2008. “Global Mine* implemented value driver models linking operations and Bulletin — May 2008: Cascading KPIs” Global finance. Value driver models provide mining companies Energy Utilities and Mining with four significant capabilities: PricewaterhouseCoopers, 2008. “Mine* as good as it gets? 1. An understanding of the operational levers Review of global trends in the mining industry” Global that drive financial performance. Energy, Utilities and Mining 2. The ability to jointly report on financial results and the operational drivers of those results. 3. The ability to identify and prioritise cost reduction opportunities. 4. An accountability framework to drive financial performance. Acknowledgements This paper has been developed following insights gained by PricewaterhouseCoopers while working on operational improvement projects with Anglo Coal Australia, BHP Billiton, Newcrest, Rio Tinto and Xstrata Coal. Our special thanks to Xstrata Coal, Newcrest and BHP Billiton who recently engaged PricewaterhouseCoopers to work with them to develop value driver models at mine sites and refineries in the Australian states of Queensland, New South Wales and Western Australia. Finding cost efficiencies in mining operations through effective value-driver modelling 9
  • 12. About the authors Brian Gillespie Chris Gilbert Aaron Carter Partner Director Senior Consultant Performance Improvement Performance Improvement Performance Improvement Brisbane Brisbane Brisbane T: +61 7 3257 5656 T: +61 7 3257 8126 T: +61 7 3257 8679 E: brian.gillespie@au.pwc.com E: chris.gilbert@au.pwc.com E: aaron.carter@au.pwc.com Brian is a Partner with the Performance Chris is a Director with the Performance Aaron is a Senior Consultant in the Brisbane Improvement Group in Brisbane, leading Improvement Group in Brisbane. He specialises Performance Improvement Group. He has Strategy and Operation Improvement in operational improvement and cost reduction experience across a number of industries assignments. In recent years, he has worked and has led multiple value driver modelling including resources, transport and logistics and on large projects with organisations such as assignments. In recent years Chris has played utilities, with a specific focus on operational Anglo Coal Australia, BHP Mitsubishi Alliance, a lead role on assignments with Anglo Coal, modelling, cost and revenue analysis and Rio Tinto, Queensland Resources Council, the BHP Billiton, Dalrymple Bay Coal Terminal, operational improvement. Queensland Rail Coal Division, Dalrymple Bay Queensland Rail Coal, Bulk and General Freight Divisions, Queensland Resources Council, Rio Aaron has recently been heavily involved in Coal Terminal and Xstrata Coal. Tinto and Xstrata Coal. He has experience in a number of operational modelling projects, Brian holds the degrees of BSc and MBA and coal, aluminium (bauxite mining and alumina with his experience including a value driver is a Chartered Engineer with the Institute of refining), copper and iron ore. modelling engagement with Xstrata Coal and the Engineering and Technology in the UK. development of financial forecasting and retail Chris holds a Bachelor of Mechanical pricing models for Queensland Rail. He has also He also sits on the Advisory Board of the Engineering from the University of Queensland recently delivered a project to identify the core Brisbane Graduate School of Management at and an MBA from the Australian Graduate drivers of cost and value in the newly formed the Queensland University of Technology and on School of Management, which he completed on South East Queensland bulk water sector. the National Executive of the Chartered Institute exchange at the University of Chicago. of Logistics and Transport, Australia. Aaron holds a Bachelor of Accounting and Bachelor of Business (Information Systems) from Central Queensland University where he was awarded the Business and Law Faculty Medal on graduation. Australian Resources Team Resources Industry Leader South Australia PricewaterhouseCoopers, Michael Happell, Melbourne Andrew Forman, Adelaide Riverside Centre, T: +61 3 8603 6016 T: +61 8 8218 7401 123 Eagle Street, Brisbane QLD 4000 E: michael.happell@au.pwc.com E: andrew.forman@au.pwc.com GPO Box 150, Brisbane QLD 4001 Australia New South Wales Western Australia Marc Upcroft, Sydney Mark Bosnich, Perth Office: +61 7 3257 8995 T: +61 2 8266 1333 T: +61 8 9238 3376 Facsimile: +61 7 3023 0936 E: marc.upcroft@au.pwc.com E: mark.bosnich@au.pwc.com Website: www.pwc.com.au Queensland Victoria Brian Gillespie, Brisbane Tim Goldsmith, Melbourne T: +61 7 3257 5656 T: +61 3 8603 2016 E: brian.gillespie@au.pwc.com E: tim.goldsmith@au.pwc.com