Benchmarking for Operational Improvement

Transcription

1 Benchmarking for Operational Improvement Leo Dawson, Principal Mining Engineer AMC Consultants Pty Ltd AusIMM Cobar Mining Seminar 6 August

2 Introduction Wikipedia definition: Benchmarking is the process of comparing one's business processes and performance metrics to industry bests and/or best practices from other industries. There are many types of benchmarking: Internal, Competitive, Industry, and Generic. Financial, Performance, Process, Functional, Operational, and Strategic. Be clear why you are benchmarking and use a customer specific model and collect and analyse the data in a very consistent way. 2

3 Costs are interesting But there s more to it 300 Mining Cost ($/t) Mine Services Materials Handling Development Backfill Production Supervision & Control 3

4 Underground Mining How does it apply to underground mining? Most underground mining operations conduct the same (or similar) activities. It is the way in which the activities are performed that makes the mine unique. By analysing mining activities at a fundamental level it is possible to compare different operations that utilise similar activities but in different ways. Mining can be regarded as a process which transfers a mineral resource from the ground into a product such as ROM ore, concentrate or metal. The process is made up of a number of sequential process steps which transfer ore from one stock type to another. 4

5 The Mining Process 5

6 Start t with the monthly report: Variability & Volatility 6

7 Problem 1 High Variability & Volatility Variability is the difference in the month-to-month planned to actual performance. Variability is a measure of how effective the operation is at achieving its business plan. Actual Plan ined (kt) Ore Mi Month 7

8 Problem 1 High Variability & Volatility Volatility is the month-to-month fluctuation in actual or planned performance. Volatility is a measure of how efficient the operation is in utilising its capacity. Actual Plan Ore Mined (kt) Month 8

9 Case Study (1) Addressing Bottlenecks and Ore Starvation Ore tonnes mined average monthly variability / volatility 50% 45% 40% Variability (%) 35% 30% 25% 20% 15% 10% 5% 0% Study B Study A Ore Stock ks (weeks) Ore Stocks Study A Study B ROM Broken Drilled Lateral Ore Lateral Waste Decline 9

10 Result (1) - Increase Development Capacity Two Benchmarking studies conducted on the same mine two years apart. Study A represents the earlier study whilst Study B represents the latter. Significantly lower variability and volatility was observed at the mine during Study B. This improved performance can be linked to the increase in ore stocks and available working areas. This has enabled better utilisation of people and mining equipment, and greater quality control resulting in an improvement in overall mine performance. A 40 per cent reduction in the underground mining cost per tonne of ore mined resulted while the mine simultaneously achieved planned metal production. 10

11 Working through h the data: KPI s and Driver Trees 11

12 Problem (2) Identifying Poor Performers Driver trees are a simple tool to visualise and model how physical processes relate to their cost outcomes. The objective of using a driver tree as an analysis tool is to evaluate the interrelationships between physical activities, costs and value added within a mining system. Properly structured, driver trees drill down from high-level key performance indicators (KPI s) to detailed operational cost drivers. High level KPI s are important as they explain what is going on with the business, while lower-level KPI s explain why those things are happening. Being able to relate the different levels l of KPI is a fundamental first step in extracting value for money from a cost structure. 12

13 Driver Tree High level (overall mining) Asset Base ($) Return on Assets Revenue (%) ($pa) Cash ($/metal unit) Annual Margin Production ($ pa) (t ore pa) ($/metal unit) Cost to (metal units pa) Metal ($pa) ($/metal unit) Cost to Ore ($ pa) ($/t ore) 13

14 Driver Tree Lower level (mine development) Jumbo Drilling ($ pa) Supervision & Control ($/bcm dev) ($ pa) Development ($/m dev) ($/t ore) Activity (bcm dev pa) Blasting (m dev pa) ($ pa) Development ($/bcm dev) ($ pa) ($/m dev) ($/t ore) Lateral Development Loading ($ pa) ($ pa) Production ($/bcm dev) ($/bcm dev) ($ pa) ($/m dev) ($/m dev) ($/t ore) Mining Ground Support ($ pa) ($ pa) ($/t ore) Materials Handling ($/bcm dev) ($ pa) ($/m dev) ($/t ore) Cost to Ore Processing Development Services ($ pa) ($ pa) ($ pa) ($/t ore) ($/t ore) Backfil ($/bcm dev) ($ pa) ($/m dev) ($/t ore) General & Admin ($ pa) ($/t ore) Mine Services ($ pa) ($/t ore) 14

15 Driver Tree Detail (jumbo drilling) Labour ($pa) ($/m drilled) ($/hr) Consumables ($pa) ($/m drilled) ($/hr) Drilling Activity (m drilled pa) Operations Energy (hrs pa) ($ pa) ($pa) Jumbo Drilling ($/m drilled) ($/m drilled) ($ pa) ($/hr) ($/hr) ($/m developed) Jumbo Drilling Maintenance Contracts ($ pa) ($ pa) ($pa) ($/m drilled) ($/m drilled) ($/m drilled) ($/hr) ($/hr) ($/hr) Capital Other ($ pa) ($pa) ($/m drilled) d) ($/m drilled) d) ($/hr) ($/hr) 15

16 Case Study (2) Addressing the Poor Performer The case study relates to restructuring of jumbo development for a medium to large underground mine in Australia. It examines the use of driver trees to isolate problems with the productivity and cost structure of jumbo development. Restructuring the development to reduce the component of ground support drilling unlocked the capacity of the jumbo. 16

17 Result (2) - Replacement of Jumbo Meshing The operational impact of the decision was as follows: Reduction in jumbo ground support cycle time from 4-5 hours down to hours. This effectively doubled the time available for face drilling. Elimination of jumbo scaling and rock fall during the ground support component of the cycle. This in turn eliminated the need to re-bog the heading prior to face drilling. Rock fall damage to the jumbo was reduced, lowering maintenance costs and increasing availability. Shotcrete reduced rock surface deterioration, lowering re-support requirements, increasing i the amount of time available for face drilling. A predictive study was undertaken prior to implementation and a repeated benchmarking was undertaken some time after implementation. The direct result was better than predicted, with a cost reduction of almost $600 per metre, and jumbo productivity increase to 250 metres per month. 17

18 Wrap Up: Conclusions and Discussion 18

19 Conclusions The use of benchmarking in mining is a practical and effective method of measuring operational performance and processes, identifying performance gaps, and prioritising improvement opportunities. A holistic and systematic approach is recommended, using operational benchmarking against industry peers. Address performance and processes. Consider efficiency and capacity. A change to efficiency and/or capacity will have an impact on value. To maximise the potential benefits of benchmarking, follow the cycle of: Measure - Assess - Plan - Act - Do Review. Once you ve identified d an opportunity to improve, a process of modelling and analysis is required. Failure to complete a thorough analysis before embarking on change can lead to unexpected outcomes, and potentially destroy value. There are always opportunities to improve, even in a mature mine of any scale. 19

20 References 1. AMC Consultants Benchmarking Database 2. Benchmarking A Practical Technique for Measuring and Improving Operational Performance 1. Hall A J & Harper P J, in Proceedings 9th AusIMM Underground Operators Conference. Perth March Why Cost Cutting Fails to Deliver 1. De Vries J, in Proceedings 8th AusIMM Underground Operators Conference. Townsville July

21 Thankyou to the Cobar branch of the AusIMM, AMC Consultants & the Benchmark sites