westh final paper

INTERNATIONAL MINE MANAGEMENT CONFERENCE / MELBOURNE, VIC, 20 - 21 NOVEMBER 2012 1

INTRODUCTION

The skills dimension

The mining industry occupies an important place in the nation’s economy despite accounting for only two per cent of the Australian workforce. The growth rate in employment within the industry over the fi ve years leading up to November 2011 has been 75.5 per cent (Department of Education, Employment and Workplace Relations, 2012); far higher than any other industry in the country.

The Minerals Council of Australia predicts that if the mining industry sustains its market share for the next decade it will require the addition of 156 000 employees to the workforce. This will include skilled tradespeople, operators and industry professionals (Fraser, 2010).

Australia’s unemployment rate has continued to remain low (5.2 per cent in March 2012 – which is considered full employment in economic terms) and amongst the lowest in the industrialised world (Department of Education, Employment and Workplace Relations, 2012).

Organisations planning the development of new mines in Queensland are predicting they will need to hire up to 75 per cent of their workforce from other industries. ‘Cleanskins’ – those people new to mining – are already fl ocking to mining companies in an attempt to capitalise on mining opportunities, lured by infl ated salaries and attractive rosters.

1. General Manager, Confi ance, Level 2, 11 Lang Parade, Milton Qld 4064. Email: jo.westh@confi ance.com.au

2. Senior Performance Analyst, Confi ance, Level 2, 11 Lang Parade, Milton Qld 4064. Email: luke.gallagher@confi ance.com.au

3. Graduate Consultant, People Management, Confi ance, Level 2, 11 Lang Parade, Milton Qld 4064. Email: stephen.rashleigh@confi ance.com.au

Improving Safety and Mine Productivity Using Real-Time Video Technology

J Westh1, L Gallagher2 and S Rashleigh3

ABSTRACT

Mining companies are moving towards automated haul trucks and other equipment. One reason for this is that if automated trucks can operate in a standard pattern over and over again without variation, then human error is reduced, if not eliminated.

The challenge for mining production leaders/managers is to ensure operators follow standard operating procedures for load and dump operations, with each operator doing the same thing, every time. However, operators are only human and inconsistent performance is rife at every mine site. The fi nger may be pointed at the operators for not doing as instructed. The clarity and variability of trainer instructions may be considered causal to the problem. Herein lies the problem. Almost all operators believe they are following the procedure very closely; moreover, they all demonstrate a strong desire to conform to instruction.

Using video analysis and feedback, each operator can be shown where and by how much they differ from the benchmark set by their company. Operators, often for the fi rst time, can see defi nitively what they are doing, compared to industry best practice.

The technology utilised by real-time video (RTV) crews measures time, distance and angles. It can overlay the video of the operator at the load site, with video of the perfect pass. Operators are amazed when they see how near to, or how far they are from best practice.

The footage and data gathered by RTV assists operators in understanding exactly what adjustments are required to increase profi ciency. It also assists load and dump superintendents to identify where in the process they are losing time. When translated to overburden removal or mineral/resource extraction, the result is large production economies without the need for large capital investment in new automated technology.

INTERNATIONAL MINE MANAGEMENT CONFERENCE / MELBOURNE, VIC, 20 - 21 NOVEMBER 2012

J WESTH, L GALLAGHER AND S RASHLEIGH

2

There has also been an ongoing trend towards reducing tenure in employment in Australia. For example, in 1959 the average tenure was 15 years, which has signifi cantly reduced in today’s climate to an average tenure of just four years. This is supported in the Queensland Government’s Skills and Workforce Development Priorities 2011 - 2012 (2012), which states that the mining industry for that period had an average length of service of three to four years and a turnover rate of 24.4 per cent.

In summary, the rapidly expanding mining sector is already plagued by a defi cit of skills, which will worsen over the next decade. Australia’s continued low unemployment rate, coupled with high turnover rates in mining, further exacerbates the problem. Recruiting ‘cleanskins’ is a strategy being implemented by many companies, which brings a new set of problems associated with adequate recruitment and training methods to ensure workers’ safety and the mines’ productivity.

The skills issues for mining companies have an enormous impact on the productivity and profi tability of the industry today and this will continue into the foreseeable future.

Can the issues facing the mining industry be solved by moving toward fewer workers on mine sites and greater automation? How will organisations tackle the issues around attracting and retaining mine site workers so that they are able to develop and retain the right skills sets for their workforce? Or is the answer a combination of both? Embracing automation is a given, but it would be foolish to assume that automation will be the panacea to all the ‘people issues’ present in the mining industry. How can the industry growth projects be achieved with a dearth of adequately trained competent people? How can mining companies bring in large numbers of unskilled and inexperienced people and ensure their safety and competence? How can the mining industry improve productivity without impacting negatively on safety? These are just a few of the questions facing the industry, which needs to fi nd resolution in 21st century training solutions.

The automation dimension

According to the Mining Industry Skills Centre Inc (2010), the resources industry is ‘on an inescapable and unstoppable march towards automation’. Given this emerging trend, there is a signifi cant amount of research devoted to the concept of developing automation equipment for use within mine sites. Such equipment may be remote controlled trucks, more advanced collision avoidance systems, key hole mining technologies, blast hole drilling rigs, excavators that can differentiate ore and waste and potentially even a fully automated mine (Robins, 2012; Lilly, 2009). The Mining Industry Skills Centre Inc (2010) defi nes automation as the ‘intelligent management of a system using appropriate technology so that its operation can occur without human involvement’. The report also describes the emerging role of the ‘automation technician’. Assuming 50 per cent of all 500 resource industry-related sites Australia wide (which include a combination of mines, processing plants and ports) move toward automation, it is predicted that 1500 ‘automation technicians’ will be required across mine sites in Australia within the next ten years. Given this ‘unstoppable march towards automation’, this number will most defi nitely increase as automation technology further advances.

The remoteness and extreme conditions experienced in mining areas is often a contributing factor to the mining industry’s ability to attract and retain workers. However, with the continued implementation of automation, mining companies will be placing more emphasis on recruiting roles designed for controlling machinery from kilometres away, as highlighted by the Rio Tinto Operations Centre in Perth. This offi ce, located up to 1300 km from the Pilbara region, is the primary control centre for Rio Tinto’s vast network of rail systems, port operations, infrastructure and mines (Spooner, 2012).

As this trend continues, companies may no longer have to search for employees to drive dump trucks for 12-hour shifts in remote locations, or work underground in dirty conditions on a continuous miner. Instead companies will be looking for people who are information technology (IT) qualifi ed, who can quickly navigate between four computer screens at once, or who can manipulate a joystick with the ease of a teenager on a PlayStation. All of a sudden the strategy to attract skilled personnel into the industry changes. Generation Y candidates who can put their technological prowess to good use and continue enjoying their inner city lifestyles will apply. The spiralling salaries that we are witnessing now, because of the diffi culties the industry has in attracting and keeping experienced workers, may well be pulled back into line with other industries.

In the future mines will need to expand on the surface and deeper underground in order to access ore deposits in deeper, and increasingly complex, environments. As a result, mining is continually


IMPROVING SAFETY AND MINE PRODUCTIVITY USING REAL-TIME VIDEO TECHNOLOGY

3

driven by the need to increase personnel safety, improve productivity and secure environmental sustainability (Reid et al, 2010).

This paper acknowledges the industry’s inevitable move toward greater automation. It is unclear what proportion of a workforce may in fact be replaced by automation and it is not the intention of this paper to debate the displacement of mine workers by machines operated remotely by a greatly reduced, but potentially more specialised, workforce. The relevance of automation to this discussion is that mining leaders seek ways to enable processes to be replicated consistently with reduced error. By replacing numbers of people involved in a process with a machine, human error can be signifi cantly reduced. RTV provides a methodology to enable processes to be replicated consistently with reduced error, without replacing people with machines.

All mines cite at least one highly profi cient employee, a person who can demonstrate a skill over and over again to the same consistently high standard. Describing why this person is so profi cient is diffi cult, almost impossible. If this can be done, then replicating this degree of profi ciency in other employees is made possible.

RTV is a process which helps upskill employees to a similar, and consistent, level of performance excellence and delivers many of the key advantages that automation brings. The most effective way to improve a skill is to capture video footage of a person undertaking that skill, and then provide immediate feedback on what he/she is doing well and how performance can be improved. The combination of visual, auditory, text and statistical information at the one time, is the fastest way to train a learner, and to up-skill those who are already competent. The RTV concept is also based on the premise that the most effective learning is undertaken in the same context in which the task will be completed, that is, in the workplace. The knock-on effect is obvious – safety is improved, productivity is increased and cost effi ciencies are obtained.

LEARNING AND DEVELOPMENT

The report produced by the Mining Industry Skills Centre Inc (2010) clearly illustrates the importance of learning and development in the context of engaging and retaining employees.

The aim of using engagement initiatives associated with training is to create:... the right set of work and employment conditions so that talented people are encouraged to stay, develop their ability and perform at high levels ...

The engagement drivers relating to training include: learning and development – professional development and training leadership – senior leadership support and direct supervisor quality reward and recognition – performance management, career opportunities, promotion and

remuneration job design – meaningfulness of work work environment – communication and feedback, the quality of co-workers, safety and risk

management.In relation to learning and development, it was noted that consistency and quality in the way the

leadership team implements, develops, coaches, mentors and reviews output are critical strategies.

Technology and changes to learning methods

Human memory systems have limitations, and it is almost impossible to remember accurately the meaningful events that take place over time, even for the most experienced personnel. As an example, a study of international level soccer coaches by Franks (2008) found that they could only recollect 30 per cent of the key factors that determined successful soccer performance. Further, they were less than 45 per cent correct in the post-game assessment of what occurred during a game. Committing data to memory and then retrieving it at a later time is a complex process with many opportunities for interference. As a result there is a need to use modern technology methods to assist with the learning and recall process.

Exposure to visually stimulating learning materials, demonstration of techniques, hands-on-experiences and virtual reality training all provide a faster and more effective means of acquiring, maintaining and enhancing knowledge and skills.



4

Generations X, Y and Z expect stimulating training environments in order to be engaged in the learning process. They no longer wish to be passive learners. Advances in learning techniques that have been shown to assist with this process of engagement and inclusion include: the use of technological aids (eg smartboards, multi-media devices, interactive gaming experiences

and applications for laptops/phones etc) the creation of interactive learning environments (eg virtual reality, scenario and role modelling

activities) involving the learner in the information gathering and feedback process (eg RTV analysis, online

information sharing experiences).

Real-time video analysis

One of the key elements of training for high performance is the provision of real-time feedback and coaching to increase the speed at which skills are acquired and enhancement of skills and processes over time.

RTV analysis techniques allow trainers to embrace the innovations technology provides. This overcomes the serious limitations of traditional training techniques by using RTV feedback and software analysis programs that include the following functions: video of the skill or process – analysis possible in real-time or post-activity audio of responses and noises in the environment real-time skill analysis conducted by the expert trainer graphs and statistics – speed, reaction time, timing, angles etc real-time feedback – trainer/coach working with person undertaking the skills reporting on key performance indicators – annotated video benchmarking of skills – allows for comparison for personal development over time, expert

operator against developing operators, evaluation of processes for innovation use of video for creating training resources enhancement of recruitment strategies evidence of quality control of training methods for regulatory compliance and audits (Franks,

2008).The performance analysis techniques include the use of the following technology: performance analysis software performance analysis equipment (eg intrinsically safe video cameras and PDAs) dedicated web channel for information sharing biofeedback equipment (eg heart rate monitors). As these techniques become more widely accepted, the industry is beginning to take advantage of

the direct benefi ts in productivity that can be gained from such techniques. As an example, a $2 B automotive parts manufacturing company improved manufacturing cycle times by up to 40 per cent through the use of video performance analysis in combination with their Lean systems (Heston, 2010).

Ongoing training programs can be developed, and targeted, more effectively with a combination of standardised training packages and in-house strategies developed from performance analysis techniques.

RTV analysis has been used by trainers and elite athletes all over the world for years to extract the full potential from their bodies, to minimise injuries and to reduce recovery periods. This type of technology is currently used by some of the best teams and athletes in the world. In this country, after every NRL or AFL game, teams and coaches will replay video recordings of the game to learn, and constantly improve on, individual and team performances.

RTV analysis utilises video technology in order to detect and determine strengths and weaknesses in the production cycle. The video software is then manipulated to compare an individual’s successive performances, performances between team members, or high performers with lower performers. Trainers use the analysis to determine the most effi cient, and productive, ways to carry out a task.

The relevance, and applications, within the mining industry are enormous. Many scenarios can be analysed from something simple such as the distance from which dump trucks queue from an



5

excavator, to more complex issues such as the optimum angle and most effi cient route that the excavator’s bucket will travel to load the dump truck. The analysis will quickly highlight ineffi ciencies and point to required changes. The video analysis can be used in educating the operators, which may result in improved safety, enhanced productivity and cost effi ciencies. The knock-on effect is to increase the mine’s overall productivity and profi tability, with no corresponding increase in the workforce and/or capital equipment.

Why real-time video worksThe University of Leicester (2010) explains that:

Feedback should be the means by which learners – with the assistance of experts and peers, and through their own refl ection – forge a deeper understanding of the knowledge and skills associated with a subject discipline.

This is precisely what RTV analysis facilitates. The use of video delivers undisputable evidence of performance, along with the ability to compare and/or benchmark operator performance. It allows the trainer assessor to have a rich, positive and constructive conversation about what’s working and where improvements can be made. This greatly increases the learnings from a feedback session.

According to Steve Draper from the University of Glasgow (Draper, 2007), good feedback: helps clarify what good performance is (goals, criteria, expected standards) facilitates the development of self-assessment (refl ection) in learning delivers high-quality information to students about their learning encourages teacher and peer dialogue around learning encourages positive motivational beliefs and self-esteem provides opportunities to close the gap between current, and desired, performance provides information to teachers that can be used to help shape the teaching.RTV analysis facilitates all of these. Furthermore, Hofmann and Otto (2008) explain that video

feedback can be an effective way of correcting a distorted self-perception. The effect can be further enhanced by asking people to describe how they performed, prior to viewing their videoed performance. In other words, operators, whose view of their performance is under or overestimated, can be critiqued and trained in a non-threatening manner and learning can occur in a positive environment. A 2012 fi eld study by Confi ance shows that this approach works, and works extremely well. Details of the study will be discussed later in this paper.

The following sections discuss the mine-critical issues of safety and productivity and how RTV analysis can positively impact these issues.

SAFETY

According to the Notifi ed Fatalities Statistical Report (Safe Work Australia, 2012), there were 138 notifi ed work-related fatalities between 1 July 2010 and 30 June 2011. Agriculture, forestry and fi shing industry workplaces recorded 33 notifi ed worker fatalities, the highest of all industries when assessed on the basis of workplace. Other industry workplaces with relatively high numbers of worker fatalities were construction (27 fatalities); manufacturing (22 fatalities); transport and storage (ten fatalities); and mining (nine fatalities).

Just over one-quarter (28 per cent) of all notifi ed worker fatalities involved workers in the occupation group called intermediate production and transport workers (34 fatalities). Tradespersons and related workers accounted for one-quarter (30 fatalities) and labourers and related workers 18 per cent (21 fatalities). Farmers and farm managers alone accounted for 17 per cent (20 fatalities) of all notifi ed worker fatalities.

The mining industry recorded nine fatalities; however, since the industry has a relatively small work force, the industry recorded the second highest fatality rate of 3.9/100 000 workers, as shown in Figure 1.

Over the past four years the most common causes of fatality in the mining industry were: vehicle accidents accounted for 28 per cent of fatalities falls from heights accounted for 17 per cent of fatalities.



6

Serious claims

In 2009 - 2010, the mining industry accounted for two per cent of all serious workers’ compensation claims (2557 claims). This equates to seven employees each day requiring one or more weeks off work because of work-related injury or disease.

The incidence rate of serious claims in the mining industry has fallen 47 per cent, from 30 claims per 1000 employees in 2000 - 2001, to 16 in 2008 - 2009 (see Figure 2); still higher than the rate for all industries in 2008 - 2009.

The most common causes of compensated injury and disease in the mining industry in 2009 - 2010 were: muscular stress (due to manual handling or repetitive movement) – 36 per cent of claims falls, trips and slips of a person – 20 per cent of claims being hit by moving objects – 17 per cent of claims.

2003-4

2004-5

2005-6

2006-7

2007-8

2008-9

2009-10

2010-11

Agriculture, forestry & fishing 10.2 9.6 8.8 6.7 7.5 12 6.9 8.6Mining 8.3 5.7 7.7 8.8 4.1 8.3 3.5 3.9Construction 4 2.3 3.5 3.4 3.4 2.9 2.8 2.8Transport & storage 1.9 3.1 3 4.9 4.4 4 2.7 2.5Manufacturing 0.8 0.7 1.9 1.7 1.3 1 1.4 1.7

0

2

4

6

8

10

12

14

Fata

litie

s as

pe

r 100

,000

wor

kers

Worker fatality rate by industry

Agriculture, forestry & fishingMiningConstructionTransport & storageManufacturing

FIG 1 - Worker fatality rate a by selected industry of employer, 2003/2004 to 2010/2011 (Safe Work Australia, 2012). Note: calculated using annual averages of the number of employed people in each quarter (ABS Data Cube STE06Aug94). The number of employed people for the years 2008 - 2009 and 2010 - 2011

are concorded to ANZSIC1993 industry categories.

2000-1 2001-2 2002-3 2003-4 2004-5 2005-6 2006-7 2007-8 2008-9 2009-10

All Industries 17.5 16.9 16.5 16.4 16 14.9 14.3 14 13.5 12.6Mining 29.8 30.6 27.5 26.5 25 20.4 20.9 19.7 15.9 15

0

5

10

15

20

25

30

35

40

45

50

Cla

ims

per 1

000

empl

oyee

s

Incidence of serious compensated claims

All Industries

Mining

FIG 2 - Incidence rate of serious compensated claims (Safe Work Australia, 2012).



7

In the mining sector safety measures have increased, safety legislation has toughened and compliance with legislation has been mandated and is now closely monitored. Safety behaviours have improved and education and focus on safety have become paramount for all mining industry leaders. Safety has risen in everyone’s consciousness. Safe work procedures (SWPs) and standard operating procedures (SOPs) at any mine site are important documents that must be read, understood and rigidly adhered to.

Mining companies looking at automation refer to the benefi ts automation will bring from a safety perspective. Removing people from hazardous workplaces and replacing them with remotely controlled equipment will allow the industry to continue the downward trend in serious incidents and fatalities; however, automation isn’t the only answer.

At the Flight International Crew Management Conference in London in early December 2009 (Learmont, 2010), delegates debated whether deterioration in pilot performance was a symptom of the long-term effects of fl ight crews operating highly automated aircraft. Loss of control has been proportionately increasing as a serious accident cause. Translate this experience to a mine site. What are the consequences of an operator losing control of a vehicle being operated remotely? Serious accidents, involving millions of dollars of equipment, serious impact on productivity and possible injury to other personnel on site. There are no guarantees of ‘zero harm’ being realised, no matter what the investment in sophisticated technology. Human error will still be evident. The need to continually search for ways to reduce potential human error remains, regardless of the level of automation.

Although it has been acknowledged by industry that training needs to meet the demands of the task, there has been a distinct lack of focus on analysis of the real demands of the tasks and structuring the training accordingly. Human error will always be a major risk to any mining operation, but in many instances the training and processes are not aligned with the task demands, or do not cater for adaptability to changing task demands (Simpson, Horberry and Joy, 2009).

A review of fatality and injury patterns in mining in Australia, conducted by the Minerals Industry Safety and Health Centre, found that human error is generally unintentional, but companies should be strengthening operator skills to cope with non-normal operations such as changes to environmental conditions, changes to the mine as production progresses and responding proactively at times when an event is approaching loss of control (Minerals Industry Safety and Health Centre, 2005).

With the growing problem of skill shortages, initial training programs implemented by mining organisations should incorporate training that enhances the capacity of an individual to deal with the specifi c aspects of the task, including adaptability and recovery. The following provides some further details as to the arguments for such a decision.

Many of the problems associated with repetitive, physically or mentally demanding work occur because of the mismatch between an individual’s physical and mental health state and the demands of the task. It also includes poor design and processes, failure to provide training that focuses on expert skill execution and failure to revisit and maintain core skills in an expertly competent manner.

Upon commencement of duties, each role can be audited on a regular basis to ensure that each person undertaking the role is maintaining expertly competent skills and the physical and mental capacity to meet the task (incorporated into the probationary review).

More and more organisations are using software and hardware technologies to shorten training times, improve training effectiveness and to reduce the cost of education.

Real-time video analysis and safety

Nexteer Automotive is a US-based automotive supplier whose success is based on continuous improvement in the areas of quality, safety and productivity. One of the directors of the fi rm introduced RTV analysis to assist. He discovered that the technology has a solid reputation in the sports community; in fact, 95 per cent of medals won by the United States during the Vancouver Olympics were won by athletes and teams who trained with performance analysis software (Heston, 2010).

Nexteer Automotive experienced some safety issues when it came to one metal cutting operation. Some workers received lacerations after pulling a razor-sharp chip off the tool. Of course, workers were told to use caution and wear personal protective equipment where applicable, but safety managers wanted to prevent the hazard from occurring in the fi rst place. Nexteer videotaped the operation and analysed the video using the RTV process.



8

We could slow the video down to the point where we actually could see that chip forming at the end of the tool. We were able to match up that data with the machine G code. We put in a few hesitations, changing the feeds and speeds, and we ended up dropping the [cutting tool] rake angle a little bit. This formed the chip differently, which prevented the hazard. The result was we went from multiple lacerations during a time period – to zero (Heston, 2010).

The senior site executive of a coal mine has responsibility for the development of SOPs for managing, and controlling, hazards at the mine. SOPs are critical mine site documents, which stipulate agreed procedures. These documents can be numerous and are frequently wordy and usually paper-based. Rigorous induction and subsequent training and refresher training around SOPs are critical at any mine. Compliance to SOPs is vital. Digesting and understanding the contents of SOPs can be arduous. Most people retain learning better visually, yet many of these SOPs, which help shape the safety performance of the mine, rely on the transfer of information by text.

RTV analysis can be used to develop multimedia SOPs. Video-based learning delivers greater retention of learned knowledge when compared to both spoken words, as in a lecture, or written words, as in a book or pamphlet. In their paper: A cognitive theory of multimedia learning: Implications for design principles, Mayer and Moreno (1998) state that the ‘multimedia principle’ is that ‘people learn more deeply from words and pictures than from words alone’.

According to Mayer and Moreno (1998), separate channels of working memory are utilised to process auditory and visual information. RTV depictions of SOPs utilise multimedia technology to draw on dual-coding theory (Paivio, 1986). This allows the learner to employ more cognitive processing capability to study the material than by using material that combines printed (visual) text with visual graphical information. Studies into Paivio’s dual-coding theory clearly show that learners perform signifi cantly better when applying what they had learned after receiving instructions via multimedia channels rather than mono-media (visual only) instructions. RTV multimedia SOPs provide the learner with a reference library of multimedia clips that can replace wordy paper-based SOPs or SWPs. Employees are able to use these clips as references, templates and guidelines in induction, training or retraining processes.

Fast-tracking the learning process is critical for all operations. Learning style differences should be considered in the development of any training program. Research has demonstrated that despite a person’s intellectual capacity, if new information about a skill is presented to an employee in their preferred learning style, then reinforced using the remaining preferences, this will enhance learning and increase motivation (Farrow, Hall and Diment, 2011). Additionally RTV SOPs: Show demonstrations – RTV SOPs demonstrate how a particular process occurs. Rather than the

trainee having to read or hear about it, they get to see what happens. This is really important for procedures that are diffi cult to show in a training room environment.

Are quick – RTV multimedia videos will explain information in about half the time as words alone. Using visuals helps people instantly understand information and results in less confusion.

Provides consistency – a multimedia SOP is not open to interpretation – it can be shared across multiple mine sites to ensure consistency of application.

Video-based SOPs and training material can be created specifi cally for any organisation, using footage and scenarios from a company’s own workplace or performance situation (see Figure 3).

PRODUCTIVITY

Productivity is one of the primary objectives for the implementation of more sophisticated automation within the mining industry. To equalise the performance of each and every truck, without the reliance on the individual capability of each operator, is a great objective. To ensure the excavator’s bucket and dump truck trays are fi lled to optimum capacity, maximising productivity in each and every circuit is incredibly appealing to the industry.

A signifi cant advantage of automation is that it provides a consistency in the execution of a skill, a standard procedure, replicated over and over again, with relatively low variation. Potentially companies will be able to have machinery operated in the most effective, and productive, manner for every operation. This may be as simple as a dump truck driving on the most effi cient path towards an excavator, or to a more advanced process such as the calculation of the optimal depth and gradient for an underground longwall shearer to dig into the coal face.



9

Fisher and Schnittger (2012) report that:… remote controlled and automated equipment operates in a more predictable, controlled and precise manner. This eliminates a range of ineffi ciencies …

By 2015 Rio Tinto will have 150 automated trucks in their fl eet - that will be approximately 15 per cent of the total fl eet in that year. For the world leader in this technology, this means that 85 per cent of their operations will still use humans to operate haul trucks. Understanding the specifi c impact on productivity using more and more sophisticated technologies is still to be determined. What isn’t in dispute is that more sophisticated technologies require more training, which is itself more sophisticated.

It will be many years, maybe decades, before autonomous mine sites are industry standard. Even so, it is hard to predict from this far out whether automation will be suitable in every mining operation. Automation will inevitably reduce operator numbers, but it is not clear at this stage by what percentage. Operator numbers will decrease, but automation technician numbers in central service centre locations will increase. Automated equipment will still require maintenance, therefore,

Share video filesby Enhanced CDor Web Platformwith additional

notes andcustomisation

…which canbe printedas a report

A completeanalysis saved

as aMultimedia

SOP

Instant‘snapshots’of screendisplay

Print orsave KeyPositionPictures

FIG 3 - Multimedia standard operating procedures.



10

maintenance personnel will still be needed. It is unlikely they will need the same skills as current maintenance personnel, but even this is diffi cult to predict.

The installation of automated machinery can bring with it an array of complications. The requirements of every mine will vary to say the least, resulting in a highly customised system to fi t each individual operation. Mines are also dynamic in their design and are consistently being moved, redesigned, or diverted. Each move creates the need for considerable testing, redeployment of equipment and reprograming. This testing and investment can severely diminish an operation’s productivity during these periods, potentially costing the company signifi cant time and resources (Hopewell, 2012).

Moreover system failures in an autonomous mine could bring the entire mine to a standstill. Automated take-off and landing systems on airlines – complex computer systems to monitor every

single part of an aircraft’s operation – have failed to deliver systems that are error free. A problem with the system that collects airlines’ fl ight plans is causing widespread cancellations and delays across the US ...Passengers were stranded at airports across the country Friday night after a failure in United Airlines’ computer system... United relies on the computer that broke down today for everything needed to dispatch fl ights, including managing crew scheduling and measuring planes’ weight and balance …Air New Zealand … System failures within the structure of the Flight Operations Division was the originating and decisive origin of this disaster ...NASA has delayed the fi nal launch of space shuttle Endeavour for the third time since its failed take-off attempt last month. Engineers are still trying to fi x an electronics problem which scuttled the spacecraft just moments before it was due to blast off from the Kennedy Space Center, Florida (Daily Mail, 2011).

The Daily Mail (2011) published that NASA, Airline companies, and aircraft manufacturing companies have a long history of experience with automation and have made the investment over many years and still system failures occur over and over again. Mining companies will be dealing with the same issues; people will always run mine sites. How many people, and with what skill sets, are the crucial questions facing managers with training responsibilities/portfolios.

Real-time video analysis and productivity

Human error and differences in style and competence between one performer and another cannot be fully eliminated. However, human performance can be signifi cantly enhanced through effective skill acquisition.

By adopting RTV techniques it is possible to create a valid, and reliable, record of skill execution that can be viewed and analysed, with the aim of providing performance enhancing coaching and feedback.

RTV analysis incorporates many features to compare and contrast operators with themselves, or with other operators and/or trainers. Video overlay enables the video image of one operator to be directly compared to the video of another operator or trainer. This enables the operator to see exactly what he/she is doing, compared to a benchmark (eg a trainer).

Let’s take an example of spot times using RTV analysis. For the purpose of this example the defi nition of spot times is as follows: Truck A: truck currently under the excavator being loaded. Truck B: next truck staging/queuing for the excavator waits for truck A to be loaded.(Therefore,

no rolling starts for this example). Spot-time: when truck A commences motion from under the excavator after being signalled to

exit, to when truck B that was queuing is stationary under the excavator. (The spot time includes reaction time (RT), which is the time taken for Truck B to react to the Truck A motion from the excavator).

So, if an operator has consistent spot-times of say 55 seconds and the trainer has a consistent spot-time of 35 seconds, the RTV analysis and subsequent coaching aims to reduce the operator’s spot-times from 55 seconds, down to as close as possible to the optimum spot-times demonstrated by the trainer. In order to do this, both the trainer and operator are video-taped using the RTV software. This video is then analysed and annotated to determine where the differences between



11

the performance of both the operator and the trainer lie. The analysed annotated video is then used in a coaching session with the operator so that he/she can now see their queuing distance, reaction time, route taken and reversing time in comparison to the trainer. This overlay enables the operator to realise what he/she is doing well and not so well.

Figures 4 and 5 demonstrate some features of RTV analysis, particularly how the reduction of queuing distance can impact spot-times and productivity.

The video is analysed and annotated and then presented to the operator in a training session. The split screen feature, using examples of the operator performances contrasted to those of the trainer, is very effective in identifying where changes are required.

Figure 5 shows the queuing distance of the same operator, pre- and post-training. By queuing closer to the previous truck, the operator is able to follow the previous truck out to improve their alignment under the excavator, allowing the operator to reverse into position more quickly and reduce the overall spot time. By queuing closer to the previous truck, travel distance is reduced, as is travel time. The cumulative effect to productivity, if this reduction is replicated for every circuit, can be huge.

Figure 6 shows another feature of the RTV software, called a tracking tool. This can be used on any moving object on the video; no physical markers are needed on the equipment. Again this can be used in training scenarios to improve operator performance eg by reducing swing times.

FIG 4 - Comparison of an operator’s two performances pretraining and post-training.

FIG 5 - Example of an operator’s queuing/staging distance before, and after, real-time video training.



12

Real-time video analysis fi eld study

The most recent fi eld study targeting spot-times was conducted on a central Queensland coal mine. The results have shown that operators can improve their spot-times signifi cantly after feedback and training, using RTV analysis and coaching. The objective of the study was to target a variety of truck operators to see if their average spot times could be reduced signifi cantly. Variables considered were: operator experience (variety of experience levels) age (using various aged operators) environment (double benching) weather (clear and sunny) truck type and size all operators on same circuit training sessions were delivered by the same trainer.The study was conducted in three parts.

1. pretraining capture video footage of operators individual data analysis for each operator develop training sessions

2. training run individual training sessions with each operator (one hour duration)

3. post-training capture video footage of operators analyse footage compare time one with time two.It should be noted that as part of the agreement between the mining company and Confi ance (the

RTV analysis service provider), the footage collected may not be used in the performance reviews of any of the operators. At the end of fi eld study all trainers, managers and operators were extremely positive about the experience and volunteered to be part of any further programs that involved RTV analysis.

The fi eld study results, shown in Figure 7, were better than anticipated. Outstanding results, as depicted in the chart below, were obtained.

The red line at the top of the chart depicts the average spot-time for the group before the one-hour training was conducted. The red line toward the middle of the chart depicts the average spot-time for the group subsequent to the one-hour training.

Each set of two bars represent one operator. The blue bars show the operator’s average spot-time measured pretraining, where the red bars show the operator’s average spot-time post-training. Therefore, the graph shows a 22.56 second reduction (37.6 per cent) in average spot-times across the group.

FIG 6 - Example of use of the tracking tool on an excavator.



13

Additionally, all operators moved signifi cantly closer to the trainer’s (control) average spot-time. No dangerous operations were witnessed in the course of this study.

CONCLUSION

Automation technology is clearly a big part of the future of the mining industry. The time frame, the scope and the impact of automation on the people and skill requirements are uncertain. Rio Tinto – arguably the leader in technology such as fully automated haul trucks – is still conducting extensive research into this type of technology (Duffy, 2012). There are reasons, other than safety and productivity gains, for investing in automation. Being seen as the pioneer and the world leader brings considerable benefi ts for Rio Tinto as an organisation, not the least of which include their attractiveness as an employer (both from an attraction and a retention point of view).

The journey is a long one and with escalating production costs and increasing labour shortages mining companies need to be considering a range of alternatives. So what can mining companies do in the short- to medium-term, and potentially long term, to enhance safety and improve productivity? One simple, yet effective, way this can be achieved is through the use of RTV analysis and coaching. The cost of labour turnover to mining companies is massive. These training strategies will not only enhance the safe and productive performance of personnel, but will certainly add to the attractiveness of the employer, thereby reducing turnover.

RTV technology and training enables users to benefi t from the following outcomes: a video-based safety management system, with annotated video at points of task for easy and fast

reminders of safe work procedures increased productivity and safety outcomes due to more effective skill execution reduced costs associated with the learning phase of a trainee (eg become more productive quicker,

less accidents/incidents) reduction in accident/incidents when used in conjunction with effective coaching techniques reduction in costs associated with poor skill execution, errors and accidents enhances the trainer’s/leader’s capacity to provide effective feedback to their trainees/team

members removes confl ict over errors because the person is able to see the error rather than relying on just

being told about their mistake provides an auditable record of training company, and site-specifi c, learning and training resources capacity to up-date learning and training resources at any time when organisational needs change increased capacity to develop innovations in skills and practises in order to improve productivity

and safety culture of collaboration within teams and between teams/sites uniformity of operating procedures within one site and across multiple sites

63.7959.15 61.1

58.3252.96

32.15

38.21 36.4333.38

35.0739.36

27.24

0

10

20

30

40

50

60

70

1 2 3 4 5 Control

Second

s

Group

Spot Time Averages: Pre-training vs post-training

Pre Training

Post Training

Pre-trainingaverage spot time

Post-training average spot time

FIG 7 - Results pre- and post-training for fi ve operators, contrasted with a trainer (control) showing average spot times.



14

enhance the skills of employees, thereby enhancing the company’s reputation as an employer committed to attracting, and retaining, good people.

Using RTV technology, athletes and coaches have eked out performance improvements from many of the world’s best performers for many years. Coaches can watch a diver’s angle into a swimming pool, or a golfer’s range of motion when swinging a golf club, or any other physical motion, and fi nd areas for improvement. Considering that 80 per cent of learning is visually-based, it is obvious how video analysis can help. Watching yourself perform a task that you have performed over and over again fl awlessly in your own mind can lead to an epiphany. Mining companies should embrace this simple, but powerful, tool to achieve greater insight into their operations and staggering improvements in performance.

REFERENCES

Daily Mail, 2011. NASA delays fi nal endeavour launch for THIRD time as engineers work to fi x shuttle’s heating glitch [online]. Available from: <http://www.dailymail.co.uk/sciencetech/article-1384774/Nasa-delays-fi nal-Endeavour-launch-THIRD-time-engineers-work-fi x-shuttles-heating-glitch.html> [Accessed: 10 March 2012].

Department of Education, Employment and Workplace Relations, 2012. Australian jobs 2012 [online]. Available from: <http://www.deewr.gov.au/Employment/ResearchStatistics/Documents/AustralianJobs.pdf> [Accessed: 14 June 2012].

Draper, S, 2007. Seven principles of good feedback [online]. Available from: <http://www.psy.gla.ac.uk/~steve/rap/nicol7.html> [Accessed: 2 March 2012].

Duffy, A, 2012. Unions vs Rio: The automation battle [online], Australian Mining. Available from: <http://www.miningaustralia.com.au/features/unions-vs-rio--the-automation-battle> [Accessed: 23 March 2012].

Farrow, D, Hall, E and Diment, G, 2011. Maximising skill learning through identifi cation of athlete learning styles [online], Sports Coach Magazine, 27(2). Available from: <http://www.ausport.gov.au> [Accessed: 23 March 2012].

Fisher, B and Schnittger, S, 2012. Autonomous and remote operation technologies in the mining industry, BA economics research report, 12.1.

Franks, I, 2008. The need for feedback, in The Essentials of Performance Analysis an Introduction (eds: M Hughes and I Franks), pp 1-4 (Routlege: Canada).

Fraser, C, 2010. MCA welcomes the Skills Australia report, Minerals Council of Australia, media release 5 March, 2010.

Heston, T, 2010. Lights, camera, lean-recording manufacturing effi ciency [online], The Fabricator. Available from: <http://www.thefabricator.com/article/fabstories/lights-camera-lean--recording-manufacturing-effi ciency> [Accessed: 23 March 2012].

Hofmann, S and Otto, M, 2008. Cognitive Behavioral Therapy for Social Anxiety Disorder: Evidence-based and Disorder-specifi c Treatment (Taylor and Francis Group: New York).

Hopewell, L, 2012. Robotic mining worth its high cost: Rio [online], ZDNet. Available from: <http://www.zdnet.com.au/robotic-mining-worth-its-high-cost-rio-339331176.htm> [Accessed: 23 March 2012].

Learmont, D, 2010. Global airline accident review of 2009 [online]. Available from: <http://www.fl ightglobal.com/news/articles/global-airline-accident-review-of-2009-336920> [Accessed: 13 February 2012].

Lilly, P, 2009. The next mining boom starts here, CSIRO Earthmatters – CSIRO Exploration and Mining Magazine, 19:Mar/Apr.

Mayer, R E and Moreno, R, 1998. A cognitive theory of multimedia learning: Implications for design principles [online]. Available from: <http://www.unm.edu/~moreno/PDFS/chi.pdf> [Accessed: 10 March 2012].

Minerals Industry Safety and Health Centre, 2005. Final report to Queensland Resources Council on underlying causes of fatalities and signifi cant injuries in the Australian Mining Industry, Minerals Industry Safety and Health Centre, University of Queensland.

Mining Industry Skills Centre Inc, 2010. Automation for success, Brisbane. Paivio, A, 1986. Mental Representations: A Dual Coding Approach (Oxford University Press: Oxford).Queensland Government, 2012. Skills and workforce development priorities 2011-12 [online]. Available from:

<http://www.skills.qld.gov.au/Functions/Industry-engagement/industry_skills_bodies/industry_skills_bodies_list.aspx> [Accessed: 1 March 2012].



15

Reid, P B, Dunn, M T, Reid, D C and Ralston, J C, 2010. Real-world automation: New capabilities for underground longwall mining [online], in Proceedings Australasian Conference on Robotics & Automation 2010 (Australian Robotics & Automation Association: Brisbane).

Robins, B, 2012. Automation ‘key’ to mining’s future [online], The Sydney Morning Herald. Available from: <http://www.smh.com.au/business/automation-key-to-minings-future-20120206-1r1sc.html> [Accessed: 2 March 2012].

Safe Work Australia, 2012. Notifi ed fatalities statistical report 2010-11 [online]. Available from: <http://www.safeworkaustralia.gov.au/sites/SWA/AboutSafeWorkAustralia/WhatWeDo/Publications/Documents/686/AnnualNotifi edFatalitiesReport2010-11.pdf> [Accessed: 16 June 2012].

Simpson, G, Horberry, T and Joy, J, 2009. Understanding Human Error in Mine Safety (Ashgate Publishing: United Kingdom).

Spooner, R, 2012. Are robots the future of the WA mining industry? [online], WA Today. Available from: <http://www.watoday.com.au/technology/sci-tech/are-robots-the-future-of-the-wa-mining-industry-20120206-1r1nd.html> [Accessed: 2 March 2012].

University of Leicester, 2010. Case study 6: Enhancing the experience of feedback [online]. Available from: <http://www.jisc.ac.uk/media/documents/programmes/elearning/digiassess_enhancingfeedbk.pdf> [Accessed: 20 February 2012].

INTERNATIONAL MINE MANAGEMENT CONFERENCE / MELBOURNE, VIC, 20 - 21 NOVEMBER 2012 16

westh final paper

Documents

rtv assists operators

video analysis

industry best practice

new automated technology

video technology j westh1

automated trucks

realtime video rtv crews

load site