Operations of Surface Mines. An insight

Contents

Introduction

1.1 Problems faced by mining business
1.1.1 Commodity super-cycle scenario in Mining Industry
1.1.2 Aftermath of commodity super-cycle in Mining Industry
1.2 Significance of the book
1.3 Direction of the book
1.4 Chapter summary
1.5 Chapters of book

References

Studies carried out around the world

2.1 Introduction
2.2 Studies carried by researchers/ experts
2.3 Attributes from studies of researchers/ experts
2.4 Gaps of previous studies
2.4.1 Statutory Clearances
2.4.2 Political Influence
2.4.3 Dispatching Systems
2.5 Operational definitions of influencing variables identified
2.6 Chapter summary

References

Indian Mining and minerals at a glance

3.1 Introduction
3.2 Indian Mining Industry at a glance
3.2.1 Average Daily Employment and Value of Output
3.3 Chapter Summary

Reference

Surface mining process and productivity

4.1 Introduction
4.2 Type of mining methods
4.3 Massively deposited surface mining processes
4.3.1 Mineral Prospecting and Exploration
4.3.2 Excavation
4.3.3 Loading and Hauling/ Transporting
4.3.4 Calibration/ Crushing and Sizing
4.3.5 Beneficiation/Washing
4.3.6 Dispatching
4.3.7 Reclamation
4.4 Productivity
4.4.1 Labour Productivity
4.4.2 Multi Factor Productivity or Total Factor Productivity
4.4.3 MineLens Productivity Index
4.5 Chapter Summary

Reference

Business influencing factors and interpretations

5.1 Introduction
5.2 Five-Factor Structure of Mining business
5.3 Explanation to the five principal factors
5.4 Ranking of attributes based on impact factor
5.5 Cluster of attributes based on impact factor
5.6 Colour coding of influencing attributes
5.7 Chapter Summary

References

Conclusion

6.1 Introduction
6.2 Summary of important factors
6.3 Ranking of influencing attributes and their clusters
6.4 Managerial and academic implications

Operations of Surface mining – An insight

Dr. P. C. Mishra

Dr. M. K. Mohanty

Dr. M. Mall

Authors

1. Dr. P. C. Mishra is an Engineer born and brought up in a large mining area and has 17-years of working experience in surface mining area as an Electrical Engineer. He did his MBA in Operations Management and subsequently PhD on topic concerned with surface mining management. He has published many research articles in different international Scopus indexed journals.
2. Dr. Manoj Kumar Mohanty is a Sr. Manager (Supply Chain) at Larsen Toubro Ltd. He did his MBA Degree in Operations Management and MS degree in Business Management from University of Warwick and subsequently did his PhD and D.Litt. (Continuing). He has published many research articles in different international Scopus indexed journals.
3. Dr. Manmohan Mall is an Assistant Professor at NERIST, Nirjuli, Arunachal Pradesh. He did his PGDBM and subsequently PhD in Management. He has published many research articles in different international Scopus indexed journals.

Tables

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ABBREVIATIONS

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INTRODUCTION

Mining and history of civilization move in parallel to each other. After agriculture, mining is the next endeavour created by humankind. The pathway of mining culture is Stone Age, Bronze Age, Iron Age, Steel age and Nuclear age which is still continuing. The probable timeframe this sector has witnessed is from 4000 BC to as of now. Mining is considered a strong method to create wealth and this statement is supported by the countries rich in minerals and metals. Those countries have enriched civilization too. Mining not only creates wealth, it creates employment, drives the economy, contributes to GDP and also creates socio economic balance. In each step of our day to day life, we are directly or indirectly linked with the product extracted by mining. In a country like India, mining has its own importance. Like some other countries, mining is one of the backbones of Indian economy and there are enormous mining opportunities available across the country. Mining sector contributes 2.6% to Indian GDP and government has kept a target to increase the same to 3.6% within one to two years’ of timeframe. GDP from mining in India increased to 988.17 Billion (In Indian Rupees) in the first quarter of 2017 from 798.16 Billion (In Indian Rupees) in the fourth quarter of 2016. GDP from mining in India averaged 723.83 Billion from 2011 until 2017, reaching an all-time high of 988.17 Billion in the first quarter of 2017 (https://tradingeconomics.com/india/gdp-from-mining). This calls for a special focus on mining sector.

Extraction of valuable ore/ mineral from earth is mining. It provides vital inputs to numerous industries, ranging from food supplements, clothes to shelters of human civilization, minerals or ores from mines plays their part as an integral ingredient. From common salt, rock salt, baking soda consumption in food to complicated robots, usages of ore/ mineral for various purposes cannot be ignored. The ores/ minerals are the basic raw materials for almost all necessities of human consumptions. Telephones, computers, televisions, eyeglasses, medicines and vitamins etc. all contain minerals or processed mineral compositions. Ores recovered by mining comprise of Iron, Aluminium, Chromium, Manganese, Tungsten, Copper etc. and minerals such as coal, limestone, dolomite, chalk, rock salt, potash, common salt, stones, gravels and clays etc. Mining is required to achieve any material that can’t be developed by agricultural processes or formed artificially in laboratory or factory. In a wider sense, mining is extraction of non-renewable source including natural gas, petroleum, ores, and minerals.

In 2010, the nominal value of world mineral production was nearly four times higher than it had been in 2002. This increase has in large part been driven by the unprecedented growth in China, India and other emerging economies coupled with the associated sharp rise in commodity prices (International Council on Mining Metals, UK, October, 2012). The top-20 mineral producing countries produced valued US $ 417,867 million in 2010 (World Bank, 2015). The GDP contribution of Indian Mining Industry was 1.5 %, USA 0.2%, Canada 0.9%, Australia 7.8%, and South Africa 7.5% in 2010 (World Bank, 2015). The mining sector generates notable foreign exchange earnings for nations export minerals. Mining develops an economic-chain along with capital market, equipment manufacturers, human force, suppliers of goods, suppliers of services, loan providers etc. Mine employment on its own is usually small relative to the total national labour force i.e., 1–2% of total employment (International Council on Mining Metals, UK, October, 2012).

This book puts emphasis on identifying the problems faced by the mining industry, particularly massively deposited surface mines of India, on issues related to surface mining business.

1.1 Problems faced by mining business

The problems of the industry were visible on studying the comparative situations witnessed by the mining industry during the super-cycle scenario witnessed from 2002-2012 and aftermath. India’s multibillion mining industry was undergoing tough challenges amid the weak global commodity markets, falling prices, devaluation of Yuan, and a strong US Dollar. Base metals have continued a declining trend to nearly 8-years low. The International Monetary Fund in its World Economic outlook has also cut the global growth forecast for the third time in less than a year. It has indicated that world economy would expand by 3.4%, down from the figure of 3.6% projected in October, 2015. (Ministry of Mines, Govt. of India, 2016)

1.1.1Commodity super-cycle scenario in Mining Industry:

During the period of 1992 to 2002, China’s average annual real GDP growth rate was 9.8% and India’s 5.8% (World Bank Data). For the last many years, China has been biggest single influence of global mining. The surge in demand for metals and minerals during the 2000s quickly translated into much higher prices and profitability for mining companies. Boosting production volumes became the industry’s top priority (AusIMM Bulletin). Between 2002 and 2012, China experienced an annual average real gross domestic product (GDP) growth rate of 10.4% compared with India’s 7.6%, the UK’s 1.3%, Germany’s 1.2%, France’s 1.0 and Japan’s 0.8%. The commodity super-cycle was a global mining boom (World Bank Data).

1.1.2 Aftermath of commodity super-cycle in Mining Industry

After 2012, Chinese economic growth rate decelerated significantly. In 2015, Chinese GDP was 6.9% and in 2016 it went further down to 6.7%, whereas Indian GDP growth rate was 6.6% in 2016 (World Bank Data). With the end of the super-cycle and collapse in profitability, there is intense interest across the industry in reversing the excesses of the 2000s. CEOs have been acknowledging to investors that poor productivity performance must be addressed (AusIMM Bulletin). The mining industry started to struggle with volatility and market uncertainty. Higher costs combined with low demand threatened capital project delivery. Typically lower cost jurisdictions remained no longer lower cost. Demanding rising taxes price, mounting government interference, growing community expectations and risk of corruption became common. To counter these pressures, companies started giving ground, postponed/ cancelled projects, halted construction in certain regions; to secure financing and searching started seeking out pre-emptive mergers for more effective ways to realize short-term investor returns (Deloitte, 2012).

Bearman, (2013) and Prior et al. (2012) wrote, “ The mining industry is facing a range of economical, technological, social, and environmental challenges all impacting on productivity and sustainability”. Chris Thomas, (Partner, Energy Resources, Deloitte, UK, 2013) said, “While much of the world has focused on the global financial crisis in recent years, mining companies have been sheltered somewhat due to the prolonged commodity price super-cycle. Now that commodity prices have come off their highs, margins are getting much tighter and cost rationalization is once again becoming a critical issue”. “But that does not mean companies can afford to get complacent about cost control. Depressed commodity prices continue to threaten corporate profits, impel mine closure, put shareholders return in peril and undermine capital budgets. This is forcing companies to consider how to both sustain their cost take-outs and drive ongoing productivity improvements” (Deloitte, 2016).

Paul Mitchell John Steen (2014) emphasised, “productivity boost is required to regain ground lost over the super cycle or activities of mining, to continue to innovate to recover lost competitive advantage and to counteract rising real wages”. “The need for sustainable and enduring productivity improvements remains vital for survival and prosperity and, even though some work has been done on it, there is still sizeable scope for improvement” (Ernst and Young Global Limited, 2015). Andy Clay (Venmyn Deloitte, South Africa, 2014) advises, “Reducing costs over the long term requires mining companies to prepare for a hard campaign of changing the way they do work. This, in turn, should spur them to look closely at their culture to determine if that needs changing too”.

Despite these pressures, companies understand the imperative that succeed over the long haul to maintain corporate resolve. Mining companies are better known for taking long-term view of market. Without much change in corporate strategy, it is time for companies to hang tough to shift industry dynamics. During volatile times, the key is to determine how and where to focus. Some companies focused on improved industry collaboration, some focused on corporate social responsibility, some on sustainable operations earning an operating license, and some on more synchronized negotiation with government think tanks and regulators. Others remained involved with cost containment programs, improved technology management, intensive analyzing demand forecasting, identifying optimal projects or recruiting more skilled labour etc. Regardless of the route, future seemed secure for those who set solid strategic directions. In 2015, Deloitte observed, “mining companies are simplifying their portfolios, divesting non-core assets, renegotiating debt and shutting down marginal operations. Now, they are turning their attention to wringing more productivity from their organizations by heightening their focus on operational excellence”.

While volatility is expected to continue over a short term period but long-term industry fundamentals will remain positive. As global resources demand grows over time, those mining companies which lay their groundwork today will be positioned well to seize tomorrow’s opportunities. This will encourage stronger industry profits. Also, it will position leading companies to play an instrumental role in advancement of local communities, will support undeveloped economies and help growth of jobs and nurture skilled talent around the world.

1.2 Significance of the book

This book is an attempt to explore the attributes influencing mining business in massively deposited Indian surface Mines. The gap between academics and field execution and its approach has been simplified by linking with a colour coding system previously used by Homeland Security Advisory Council, USA. The executives/ management people working at field can have an idea regarding the high impacting attributes playing against or in favor of their industry. The survival or sustainability of the industry lies at the disposal of those attributes of importance. Finding solutions to those miseries and adding inertia to those favorable attributes can propel productivity of the industry and can help in maintaining a balance of societal needs and the socio-economic life of workmen and workwomen of this particular industry. Economy and GDP get boosted with the uplift and sustenance of industry and its direct indirect dependants.

1.3 Direction of the book

Considering the global scenario, the mining industry in India in particular requires attention on various productivity improvement angles for its sustenance. The latent and exposed attributes are exposed and analysed at management level. The objectives of this book are:

a. Various attributes influencing surface mining business have been exposed through field interactions and various previous studies
b. The business influencing factors have been summarised
c. A colour coding system has been recommended to codify the business influencing attributes of mining.

1.4 Chapter summary

The mining industry always passes through challenges and strives for improvement with various measures. The industry necessitates innovative approaches to address the issues responsible for its misery. The sustainability of this industry depends largely on how the think-tanks deal with the impacting attributes of business. Various efforts in this direction are the need of the hour. This book is aiming at giving an alternative direction to the mining experts trying at their level. This book may guide the top level operational level management to strategically attend the production and productivity impacting attributes based on their financial impacts/ importance by colour coding them as recommended in this book.

1.5 Chapters of book

Chapter-1 : Introduction: This 1st chapter of this study deals with introduction to the mining industry followed by problems of industry, wherein commodity super-cycle scenario mining industry and aftermath of super-cycle is discussed; significance of the book; objective of the book; and chapter summary in brief. The chapter creates a background of this study in a systematic manner.

Chapter-2 : Studies carried out around the world: This chapter starts with an introduction followed by findings of studies carried out by researchers/ experts in sequence, summary of raw attributes in tabular form, refined attributes collected from previous studies, gaps of previous studies, summary of attributes influence surface mines business, operational definitions of influencing variables identified and chapter summary. This section deals with reviewing of previous researches around the globe, discussing related researches and studies of previous authors. The reviews are presented in a sequential manner covering research articles, trade journals, technical papers, expert blogs, discussion papers, websites etc.

Chapter-3 : Indian Mining and Minerals at a glance: This chapter consists of an introduction part reflecting positions of mineral rankings of India in world context and the financial position etc. It also mentions the future of Indian mining Industry, mining belts in India, location-wise mining belts and minerals found in each mineral belt. Different maps such as geological map of India, Mineral map of India (Metallic or Ore), Mineral map of India (Non-metallic or mineral) and ores and mineral maps of the field of study has been mentioned for an overall concept. Indian mining employment and value of output has been presented in tabular form followed by a table of value of mineral production (State-wise). The chapter concludes with a chapter summary.

Chapter-4 : Surface mining processes and productivity: This chapter starts with an introduction followed by the type of mining methods prevalent in mining industry, details of surface mining processes in sequence, definition of productivity and types of productivity used in mining industry and lastly summary of this chapter. The surface mining processes have been briefly explained giving suitable figures for better understanding of the concerned field operations. The core issue of productivity and evaluation of productivity techniques have been explained in brief in later part of this section.

Chapter-5 : Business influencing factors and interpretations: This important chapter starts with an introduction followed by details of analysis with tables of findings. Important tables such as 5-factor structure and Colour coding of influencing attributes is explained with linking with suitable colours of importance. The chapter ends with a brief summary part.

Chapter-5 : Conclusion: The last chapter of this book starts with a brief introduction followed by summary of findings. The last two parts of conclusion are ranking of the business influencing attributes and managerial academic implications.

References

1. Bearman, R. A. (2013). Step change in the context of comminution. Minerals Engineering, 43-44, 2–11. doi:10.1016/j.mineng.2012.06.010
2. Deloitte (2012), Tracking the trends 2012- The top 10 issues mining companies will face in the coming year
3. Deloitte (2013), Tracking the trends 2013- The top 10 issues mining companies will face in the coming year
4. Deloitte (2014), Tracking the trends 2014- The top 10 issues mining companies will face in the coming year
5. Deloitte (2015), Tracking the trends 2015- The top 10 issues mining companies will face in the coming year
6. Deloitte (2016), Tracking the trends 2016- The top 10 issues mining companies will face in the coming year
7. Ernst and Young Global Limited, 2015. Business risks facing mining and metals 2015–2016, Ernst and Young.
8. GDP growth (annual %) | Data- World Bank Data- World Bank Group
9. Indian Minerals Year Book, 2012
10. International Council on Mining Metals, UK, October, 2012
11. Lala, A., Moyo, M., Stefan Rehbach, S. and Sellschop, R., McKinsey Company.
12. M M Monthly (2016), A Monthly Newsletter from Ministry of Mines, January, 2016, Vol.1, No. 1.
13. Mitchell P Steen J (2014), Productivity in mining- a case for broad transformation, Ernst Young.
14. Prior, T., Giurco, D., Mudd, G., Mason, L., Behrisch, J. (2012). Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change, 22(3), 577–587. doi:10.1016/j.gloenvcha.2011.08.009
15. GDP growth contribution and inflation: India. (n.d.). doi:10.1787/888932836563

STUDIES CARRIED OUT AROUND THE WORLD

2.1 Introduction

The background of this section considers studies from research journals, staff working papers, trade journals, technical papers, conference papers, featured articles, expert reports, as well as joint study reports, etc. from various fields, like engineering, economics, human resource, psychology and others studying, discussing or analyzing attributes accountable for production and productivity in surface mining fields around the globe. Mining companies are reshaping their operational events to be more flexible, agile and reactive to the changing operational challenges worldwide. Most mining companies are reducing operational wastes, inflexibility and variability to work upon productivity and enhance production. A range of attributes are being continuously recognized, and efficiency improvement measures are being implemented. Mission of the companies is being aligned with requirements of mining field. The bigger challenge is to create an agile chain of operational activities in the affected mining sector. By distinction, it is difficult to identify waste and inefficiency often in mining operations. Management seems to be aware of operational inconveniences but, not being able to characterize them in detail. Sixty studies closely linked with production productivity issues of surface mines from 1974 to 2016 were analyzed in this chapter. This section has been dealt in chronological order.

2.2 Studies carried by researchers/ experts

This chapter discusses and analyses different works carried out at different parts of the world. Prominent countries having established mining business are all concerned of production and productivity at their respective industries and working for the survival and sustenance of the industry. The researches concerned with surface mining field around the globe were analyzed and 60-such articles related to this field of study were specifically reviewed. Among those articles reviewed, 16-articles were from Australia, 09-articles were from United States of America, 07-articles were from Canada, 05-each articles were from India and South Africa and the rest were from other countries as mention in table. 2.1.

Table 2.1: Country-wise articles studied

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Lawrence (1974) conducted research on South African mining industry to identify the human factors named CORE (C- Competence, O-Opportunity, R-Recognition and E-Expectations/Enrichment) that has certain impact on productivity. He summarized views of different research that has causal link and relation between work attitudes and performance those can have impact on productivity. He analyzed different human hypothesis models such as Satisfaction-Performance Model, Performance-Satisfaction Model, Pressure-for-Production Model and Performance-Rewards Cyclic Model to suggest that the CORE has impact on productivity and requires to be addressed in the right perspective for enhanced productivity in mining industry.

Byrnes et al (1988) researched on surface coal mines of the United States of America to investigate productivity comparison between mines having trade union and without union. They used two techniques such as nonparametric tests and econometric approach to compare productivity differential in those two sets of mines. The period of research was from 1975 to 1978 and they took a sample size of 113. The nonparametric test result found 33 out of 35 unionized mines had higher productivity than without union mines and the test of econometric approach showed positive effect of unionization on productivity. The overall study indicates role of union or presence of union had positive influence on productivity.

Brodzikowski and van Loon (1990) studied Belchatow surface mine in Poland. The geological structure of that area was very complex in lithology and post-depositional deformation and hence was unsafe for exploitation. The overburden removal in that mine was associated with problems such as dewatering, slope stability, in-homogeneity, and transport of the overburden from the outcrop and dumping of the removed overburden material. The unpredictable conditions might have frequent breakdowns of equipment and other interruptions. To have accurate sequence on the structural development, clear insight of characteristics of the hard-rock substratum was to be judged into. The Palaeogeographic reconstruction methodology was used with detailed geological inventory to have a clear structural styles appearance to carter with safety and cost effectiveness in that mining operation.

Goodman and Leyden (1991) used data collected by The Carnegie Mellon Coal Research Project, USA. They collected samples from two mines to check the effect of familiarity on group productivity. Familiarity is the precise work knowledge within co-workers and their work environment configurations. Every workplace is unique in terms of physical environment, people, performance strategies, configuration of machinery and jobs assigned. They observed, during absenteeism of few workmen led to staffing change and the level of familiarity within a new set up of workmen was getting affected. Their methodology was Random Assignment Process of Construction of Familiarity Variables. Finding of the study was increase of 11 % production level that was possible through increases in familiarity particularly on the labour variables.

Kumar and Huang (1993) conducted research at Kiruna Iron Ore Mine of Sweden to find bottlenecks of critical units of operations. They chose simulation program SIMURES as methodology to analyze number of critical factors within various units and operations. The duration of their application was 1720 hours within 100 days. The findings of the study were: for the system availability of 76 %, the associated factors with their availability have to be load-haul-dump machine (LHD) 90%, operators 95%, geo-mechanical factors 96%, ventilation and illumination 97%, ore and access 97% and borehole stability 98%. Further they suggested the ore shortage can be predicted to prepare mine production plans accordingly by using SIMURES program.

Krishna Sundar and Acharya (1995) conducted research in one of the captive iron ore mines of Steel Authority of India Limited (SAIL), government public sector of India. The objective of the research was to provide a method for improvement in blasting, better production schedules and resource allocation for extracting produced iron ore and waste from the mine. They used a computer integrated planning system in the form of linear programming and stochastic programming model as methodology to choose blocks and area were to be blasted and the resources to be employed for the excavation of ores and wastages from the blasted area. Through the implementation of the software planning model resource allocation i.e. dumpers, shovels and crushing mill, operational time reduced substantially and the production also got improved. The specific result they had observed was 4- shovels were required in place of 6 and 6-dumpers against 8, production of ore improved from 52,500 tons to 69, 000 tons and better waste removal was observed from 5,400 tons to 9,650 tons. Better waste removal led to improved quality of iron ore i.e. from 62.18 % iron content of ore to 62.4 %.

Tilton and Landsberg (1997) in their discussion paper mentioned, United States Copper Mines revived remarkably in the 80’s. The same industry was in a very bad position to survive during the 70s and early 80s. Many companies introduced cost-reducing measures for their survival. But, in the 80s the scene was completely different. The factor responsible for growth was found to be dramatic improvement in labour productivity. The sub-factors were increase in capital and inputs per worker, improved quality of copper mined, adaptation of new technologies and innovative activities etc.

Brown et al (2000) conducted research on mining machinery to find out the demerits of Direct Current (DC) machineries in comparison to Alternating Current (AC) Machineries used in mining operation. The method used was technical comparison with DC and AC mining machineries. They suggested replacing existing Direct Current (DC) drives to Alternating Currents for mining machineries such as shovels/ drag-lines and haul trucks to increase productivity. Their focus was on the factors such as rate of production, reliability, maintenance cost, compatibility with supply power grid, intelligence system and efficiency of the equipment. The DC traction systems had their limitations to carter to larger equipment because of the problems associated with maintenance cost, power convertibility, speed and space. AC drives were found to perform more efficiently after eliminating all the drawbacks of DC drives to go for higher capacity. The findings were, shovel production increased 20%, and hauler capacity increased about 50% (AC hauler capacity of 327 tons compared DC machine with 218 tons).

Galiyev et al (2000) conducted research on transportation system of a surface mine at Kazakhstan with an objective to find out the scope of productivity. They used a methodology of simulation logic-statistical modeling based on software. The technique enabled in sorting most of the variants in an automated mode and chose the most rational variants amid a high degree of reliability. They considered a working shift as the study period. The significant findings of their research by applying simulation technique were improvement in total mass of rock transportation from 20,094 m[3] to 20,988 m[3], improvement in ore transportation from 13,457 tons to 16,769 tons and decrease in current expenditures on total rock mass from 22.63 Tenge to 21.80 Tenge per m[3].

Roman Daneshmend (2000) used the simulator Rapid Availability Prototype Operational Readiness (RAPTOR) in estimating lost haul capacity in deciding to increase haul truck capacity from 240-ton to 360-ton for a period of 1-year in an surface mines of Canada. Production cost was less with higher capacity equipments so also for shovels. The lacunae with higher capacity equipments were: reduced flexibility, sensible downtime cost and utilization linked with single piece equipment. The problem could have been addressed with higher reliability and maintenance characteristics of larger trucks. The cost benefit analysis was an integral part in taking decision for larger capacity equipments.

Macfarlane (2001) attempted in his paper to illustrate a holistic approach in transferring and implementation of technology to capitalize on opportunities available by new technology in South African mines. Introduction of new technology in mining field was a strategy required for improved safety and operational efficiencies. In the past, many attempts had been made for the same but with dual results. The process of identifying suitable technology lied in balancing among business objective, strategic intent, purpose and operational effectiveness in mind. He recommended a process to ensure right new technology implementation in South African mines. The processes he recommended were, identifying a strategic component, developing feasibility study, adopting a change management process, understanding the suitability of technology, assessing risk and risk management program and evaluating performance management system.

Edwards et al (2002) conducted research in UK surface mining industry to predict cost of downtime per hour of tracked hydraulic excavators. They used regression models as methodology in three different stages, viz. cycle time (machine productivity), hire cost of machine and downtime cost prediction from cycle time and hire cost of machine. The findings of this study was downtime cost of tracked hydraulic excavators used in UK surface mining industry found to be 85.6% (£24.84) of the basic operating cost (£29.02).

Shebeb (2002) in his study in Australian Gold Mining Industry examined to measure and analyze contribution of technological change, scale of economies, and capacity utilization toward productivity growth from 1968-69 to 1994-95. He used Short-Run Cost Analysis as methodology to analyze the historical data collected from the said gold industry. The findings were after 1989-90, the industry experienced negative productivity growth due to negative technological change. The negative technological change during the period 1989-90 to 1994-95, lead to increase in production cost on an average of 0.17 % a year. Again, average productivity growth experienced a favorable trend of 0.47% a year over the time period 1969-70 to 1989-90.

Alarie and Gamache (2002) reviewed previous researches find many authors indicated transportation cost was around 50-60 % of total operational cost. Reduction of a few percent would result in noteworthy savings. The transportation management systems and dispatching systems were to be developed for this potential savings. The goals revolved around reduction of operational cost and improvement in productivity. The paper described different dispatching problems within and outside mining industry and the specific peculiarities there in. The paper illustrated, strategies existed in dispatching problem solving and their pros and cons. They proposed dispatching systems should be of multistage approach rather single stage.

Bartley and McClure (2003) of USA conducted an experimental research with a series of test blasts detonated for a period of time at the quarry site of Rich Hill Quarry, Better Materials Inc. The test blasts were symmetrical in all respect. Two types of blasting, such as conventional non-electric system and Daveytronic programmable blasting system, were compared. The fragmentation performance found increased (43% decrease in mean size of rock) with Deveytronic system using chemical energy of explosives more efficiently since the detonators were of high accuracy. The crusher throughput or the productivity level found increased by 17% with Daveytronic blast. Increased fragmentation level improved excavation rates, increased bucket fill factors and more efficient excavation cycle were observed.

Thompson and Visser (2003) observed South African mine haul road set-up typically constituted between 10–40 km comprising number of road segments with variable traffic volumes, type of construction and material qualities. Truck haulage costs amounted up to 50% of the total operating costs incurred by any surface mine. Some savings if generated either from improved road design or the management, the mining company was directly benefited from reduced cost per ton ore hauled. They proposed MMS (Maintenance management systems) applicable to a typical haul road network of surface mine to demonstrate interactions and influences of different models proposed representing road maintenance costs, vehicle operating costs and road roughness. The lowest road use cost was found by modeling rate of change of RDS (roughness defect score) for the road segments under study. They opined cost savings were linked with adaptation of a healthy MMS model programmed approach depended on vehicle types, traffic volumes, road geometry, hauling operation and tonnages hauled, etc.

De Jagar et al (2004) chose Kumba Resources, South Africa as a case study. They observed Kumba Resources was using Total Operations Performance (TOP) for reduction of compressible or controllable costs by re-engineering and continuous improvement (CI) programs. The improvements, they observed, were 3% of real cost on a year. They implanted a tool named Four Bubbles Notion to focus on stabilizing environment and developing facilitative behaviors and measured the result on a five point scale. They found sustainable results started to materialize. They named the model as CI maturity model recommended using as a methodology for analysis and enhancement of CI capability.

Sarin West-Hansen (2005) in their paper addressed the problems associated with scheduling of miners in different sections in USA coal mines using a mixed-binary programming model, in order to maximize net present value of sections of a mine exploited for mineral extraction. The importance of the problem was associated with productivity and scarcity of financial resources. Scheduling miners to different sections in a coal mine was complicated due to types of mining methods, order of execution and variations in coal quality found in different sections. The programming developed for this problem included coal quality requirement and production smoothing. Benders’ decomposition solution methodology addressed special nature of other associated sub-problems. The proposed computational approach effectively solved the on hand problems.

Equipment size evaluation in surface mining to attain economies of scale was associated with the identification of variables influenced by size. Bozorgebrahimi et al (2005) of Canada worked upon Equipment Size Sensitive Variables or say, ESSVs that were directly or indirectly subjective to equipment size. The research used simulation models to analyze the complexity of whole mining systems and their relationships including ESSVs. The observations and findings of the study implied, (a) for use of larger equipment, mining bench heights should have been high. For non-homogenous deposits, selectivity would be reduced along with poor dilution control. (b) Larger trucks result in higher stripping ratio and lower overall pit slope with wider ramp requirements. (c) Mining equipment size directly influenced milling cost. Larger the truck coarser was the ore fragmentation. The milling process got costlier with increase in ore size reduction ratio because of coarser ore sizes. (d) Any machine failed, opportunity cost was found more with higher size of trucks. The optimal size of equipments and fleet size were to be selected considering all risks and costs associated with.

Leedal (2006) conducted research in British coal sector to explore uncertainty over need and scarcity of information that hampered forward planning to development control. In his study, he interviewed officers of planning authority of Government recognized mineral organizations. The paper had identified restrictions of the planning system concerned with the public, resolving conflicts at the forward planning stage in building consent for decisions next to the application stage. Consensus building efforts drained on resources and were often unattainable at the development control stage, compromising between time limits and controversy.

Akcakoca et al (2006) at Lignite Mining Company, Garp Lignite Enterprise, Turkey established effects of legislation, labour responsibility, management and supervision as the sub-factors associated with labour productivity. They used the Labour Productivity Management by Ratio (WPMR) model as methodology to evaluate labour productivity in their chosen field. They emphasized labour-intensive industries should have been time consciousness as a fundamental element in improving labour productivity and in terms of raw materials as well as other capital goods also. They concluded, man-hours (time losses) governed or controlled by the legislative and administrative system was the main cause of lower productivity. The other associated sub-factors for low productivity were low capacity machines use and excessive manpower employment.

Rennel and Beck (2008) in their feature article mentioned the mining industry was relatively low-tech industry. The surge in demand for raw materials by the developing nations became high. The gain in production was observed by the use of larger equipment, long hour operation and maintenance efforts. The recommendations were using variety of mining methods, collection of real time data, implanting Reliability Cantered Maintenance (RCM) programs and employing highly trained work-force. Skill training, collaborative work environment, decision making authority and communication were vital in attaining higher production and productivity. Utilization of condition monitoring, overall component life will be increased to 50% from 20% and fleet availability will be around 93%, they predicted.

Akcakoca et al (2008) conducted research on Western Lignite Cooperation (WLC) of The Turkish National Coal Board (TNCB of Turkey. The period of research/analysis was between 1991 and 2002. The objective of the study was to analyze the econometric productivity such as total factor productivity, partial factor productivity (like, material energy cost, investment cost, labour), value added and profit, so that the factors affecting productivity could be identified. The methodology used in this paper was linear regression–correlation analysis between production factor values and productivity index values to determine the most effective parameters on the productivity indexes. The findings from this study were the high operating cost, low sale income, low technology, inadequate training and low labour productivity (high employment) etc. were responsible for reduced productivity and profit of the company was affected badly.

Topp et al (2008) in their working paper for Australian Government researched upon the multifactor productivity issues in Australian mines. The team used neoclassical growth model to estimate multifactor productivity of eight mining sub-industries. The data sources were from Australian Bureau of Statistics. The factors associated with multifactor productivity were resource inputs, nature of mining capital, optimal quantity extraction, depletion of deposits, purchased inputs, technology changes, work practices, poor weather, and infrastructure constraints and putting the pieces together.

Afeni (2009) researched on Uranium surface mines of Somair in the Niger Republic in comparing the efficacies of two types of drilling equipment and two types of blasting explosives in an experimental methodology for a time span of 3-months (June to August 2003). The drilling machines used were Down the Hole Hammer Drilling Rig (DM No. 406) machine and a Drill Master (DM405) machine. To evaluate and optimize drilling operation, the time was analyzed statistically for those two drilling machines to carry out the same task. The findings of drilling were cumulative time spent by DM No. 406 were between 169 min and 198 min whereas cumulative time spent by DM405 was within 260 min and 413 min. DM No. 406 was found to be more efficient than DM405. The explosives used were Explus, Nitram-9. Findings for explosive result indicated that the unit consumption of Explus was among 0.150 gram per ton of blasted ore material (g/t) to 0.183 g/t and Nitram-9 among 0.190 g/t to 0.240 g/t.

Okely (2009) in his article explored the attributes which could affect productivity in mining operation. The attributes/factors were wearable items, impact process deficiencies, quality distribution of ore, price-change of purchase items, spare part management, training, technology and panicking situations. His recommendations were, controlling and analyzing the mining supply chain, minimizing unnecessary costs, implementation of latest technology, purchase of original equipment manufacturer (OEM) spare parts, continuous improvement programs, review of quality distribution of deposit, up to date training, replacement of worn spare parts in a timely manner, tapping expertise and knowledge and consultation with sources of available information in a panicking situation. Maintaining proper equipment would have potential power savings up to 20%, he stressed.

Bradley and Sharpe (2009) researched on Canadian Mining industry to prepare a research report regarding the productivity performance analysis by comparing data collected from Centre for the Study of Living Standards Research (CSLS) productivity database. The research was a comparative study between period 1989-2000 and 2000-2009. Tang and Wang (2004) s’ methodology was used to analyze the data. The attributes responsible for mining productivity were found as declining capital intensity (Capital-Labour ratio), lack of innovation, technological progress, quality deterioration in workforce, quality deterioration of resources, labour relations, taxation, high prices for energy and minerals and greater environmental regulations. The research team observed labour productivity directly proportional to capital intensity which improved by 3.66% from 1989 to 2000 and fell by 1.93% from 2000 to 2007. Evidence showed the industry lacked innovation and was adhering to adopt “off the self” technologies rather developing their own. The quality of workforce was poor due to influx of untrained new workers and poor education of mining workers at that period. The workforces were observed to be less unionized from 1997 to 2007. The influx of investments reflected in profits from 2000 to 2006. Higher Prices of energy and minerals almost doubled during 2000-2006, which increased profitability and productivity. Environmental regulation and protection expenditures were higher in mining industries of Canada.

Ercelebi and Bascetin (2009) of Turkey observed operational cost of shovels and trucks was huge and application of liner programming model methodologies had probable chances for substantial savings. The first stage was determining the optimal number of trucks functioning with each shovel in the system under observation using a closed queuing network theory based liner programming model. The optimum truck number got reduced significantly minimizing loading and hauling costs.

Parreira and Meech (2010) conducted research by analyzing the secondary data provided by BHP-Billiton Nickel West Division, one of the multinational mining giants of the world. The objective was to find out the factor associated with autonomous haulage system like, consistency, efficiency, safer practice, environment friendly and economical mode in maximizing production and reduced cost of production. They had used EXTENDSIM software as methodology to predict the key performance indicators (KPI) such as cost, equipment failure, wear and tear, fuel consumption, safety as well as productivity. The result of their research were reduction in Truck haulage cycle times by 7%, Fuel consumption by 10%, Tire wear by 12%, Maintenance costs by 14% and Labour costs up to 5%. They suggested developing simulation software tools separately for each mine to assist project managers in optimizing projects, since each mine was different from other.

Sahoo et al. (2010) observed d ump trucks used worldwide for the handling ore waste were consuming 32% of total energy requirement in almost all of the surface mines. They developed a model based on steady flow of trucks at constant speed for their field study at Nimbeti surface mines, Rajastan, India. They used 10-sample trucks for 1-hour experimental study. The model was meant for predicting minimum fuel consumption with multiple trucks. The model was used for recommendation of empty speed, loaded speed, optimal allocation and optimal trip frequency of trucks. With their model, they observed potential of fuel saving was to be 15%.

Groeneveld and Topal (2011) conducted research in copper and gold mines of Australia to explore the factors / attributes responsible for uncertainties in surface mines which affected business outcomes. In their study, they took samples from gold and copper mines of Australia and used Monte Carlo Simulation (MCS) and Mixed Integer Programming (MIP) as methodology to evaluate the flexibility of strategic mine design under uncertainty. This research work found that market prices, distribution of grade of mineral deposit, ground conditions, equipment reliability, recovery of ore, human capital and legislative change in inflexible mine design were the uncertainties in open cast mine and affected the business. The researchers also found that the global financial crisis led to rapid decline in expansion activities and affected the mining business considerably. The yield of their application was expected in increasing NPV (Net Present Value) by 11% with flexible mine design in comparison to inflexible.

Takahashi (2011) conducted research in Australian coal mining industry to explore the impact of multitasking on productivity. He studied 21-surface coal mines during 1985 to 2005. His study was on elimination of task demarcations on production. This research found elimination of task demarcation between productions and maintenance can improve productivity through multi-tasking and the same logic within the production groups had no impact on productivity. The research witnessed an improvement of productivity from 27% to 33% in the area of study. He further suggested coal mines should design jobs keeping multitasking as an objective.

Mineral fragmentation in blasting process is termed as gradation of material feed at primary crusher. Cost of crushing is dependent on feed material size. Improvement in primary rock fragmentation can decrease the cost of calibration and cost of secondary breaking. Hence, the importance of predictions and analysis on blasted rock mass figured prominently. Strelec et al. (2011) worked on this aspect at Croatia using Kuz-Ram fragmentation prediction model to measure the prediction with actual fragmentation. Improved fragmentation led to increase in specific consumption of explosives, but significant cost reduction was observed in overall process. A good after blast fragmentation was favorably influenced profitability on account of raw material extraction process. Optimal fragmentation achieved by blasting was one of the most sought cost effective steps of the whole production process.

Liu and Kozan (2012) researched on the sites of Australian iron ore mines to analyze operational problems. The paper identified factors such as mining design, production sequencing, problems associated with transporting those could impact production and productivity. They used a linear programming model as methodology and suggested a direct network flow graph to optimize the sequence of mining operations. The framework suggested in their paper assured to be helpful in integrating and interacting within the operation systems and optimizing the identified factors of activities. They assessed by implementing their model, the equipment cost would reduce, production efficiency would improve and net present value could be maximized.

Ramulu et al (2012) conducted research at an surface mine of Coal India Limited (CIL). The research team experimented blast optimization with respect to different types of rocks by choosing 3-mining benches, two types of explosives and two types of blasting techniques. They collected primary data using an instrument called Geode and analyzed those data using Seismic imager software. The findings of optimization study observed improvement in fragmentation of rock size by 15-47% and reduced vibration intensity by 14-45% in one technique and 30% improvement in fragmentation of rock size with reduced vibration intensity by 15-20%. Improvement in fragmentation led to improvement in efficiency of drilling, blasting, loading and dispatching operations.

Clayton (2012) an expert in Russia put emphasis on the reliable and effective maintenance of electrical and mechanical equipment such as pumps, compressors, fans and conveyors, used in coal mines. He stressed to implement effective monitoring system that could communicate the data to the management system, so that the efficacy of preventive maintenance would be strengthened. He also stressed that sound condition of both electrical and mechanical equipment would maximize production eradicating the root causes of all faults.

Cooper (2012) opined, the shortage of right skill in Australian mining industry owed mainly due to the remotely located mines. Mining jobs remained less lucrative to Australians in the 80’s and 90’s. Monitoring the operations from a far-away place with plant experts would negate employing experts all the time on sites to assess the efficient running. The cost of employment would be reduced drastically. By employing CCTV network, a maintenance expert could solve the fault remotely and would make an assessment of the situation to decide the course of action. He observed, due to stiff competition among the mineral countries and shortage of skilled hand in Australia, the lacunae could be well compensated by employing remote controlled gadgets.

The GlobalData (2012), an expert panel conducted research in the South African Mining Industry. The fields covered in their research were platinum, gold, copper, manganese, nickel and chromium. They collected secondary data from internal and external reliable sources and primary data by interviewing industry participants and commentators and validating the secondary research findings. They found labour unrest in platinum and gold mining industry cost around $518 million and the coal, manganese, nickel and chromium industries lost around $13 million in FT-2012.

Logan and Krishnan (2012) in their conference paper presented an out of league work attitude of Newcrest Mining Company of Australia that operated at Asia Pacific regions and beyond like, Australia, Indonesia, Papua New Guinea and West Africa. The USP of the company was to adopt many innovative tricks to succeed. Beliefs such as, what ‘good’ looks like in a situation, open innovative approaches, leap frogging leverages of making old new again, adopt-adapt-develop methodology implementation, make your hand dirt with test work plant analysis and staying focused on important high business area. The working approach of Newcrest to establish renewed metallurgical processes aimed at innovation, like early waste rejection avoiding high energy processing cost, broader gold product channels, lower energy processing and geo-metallurgy approaches. The operating mines situated at Telfer in Western Australia Cadia in New South Wales though being of largely low grade ore bodies with copper less than 0.3% and gold less than 1 g/t were mined efficiently. Newcrest had developed mining and process technology over the years for efficient drawing out of copper as concentrate and gold as bullion.

Evatt, Soltan and Johnson (2012) formulated a mathematical model to understand precisely the reserve risks lied associated with price uncertainties. The mine owner operating a mine responded to price fluctuations and the risks surrounds it. The decision to continue extraction prior to closure was highly dependent on the reserve price fluctuations. The methodology prescribed enabled the modifying factor of price uncertainty calculable within a reserve estimate. The methodology seemed bridged the gap between regulatory standards and industrial practice for mineral reserve reporting.

Minerals Council of Australia (2012) in their working paper analyzed their opportunities, challenges, and their failures, and needs to address them. The greatest opportunity was to capitalize on the requirements of developing and transforming world. The greatest challenge they observed was presence of multiple rivals in market. Identification of failures was response to places of enormous benefit. The need to address was a co-ordinate approach for the lost ground and to regain their competitive edge. The attributes responsible for productivity enhancement they pointed out were, skill building with technical training, maximizing innovation, reorienting workplace relation framework, optimizing infrastructure investment and reforming taxation for low government risk.

Epstein et al (2012) presented an extended network-flow formulation methodology in Chile surface copper mines for long term mine planning. The team used a network flow formulation based on general capacitated multi-commodity. They had presented an approach for optimize long-term production plans for Chilean copper mines considering those multiple deposits, multiple products and their multiple processing plants. There was significant impact at Codelco mines on implementation of this model decision process. Use of this model, NPV (Net present value) increased by 8% (3% due to mine integration and 5% due to optimized plan).

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