Matt Schneider, Optika Solutions: Extracting More Out Of Existing Assets: Capacity and Utilisation

1,028 views
715 views

Published on

Matt Schneider, Managing Director, Optika Solutions delivered this presentation at the 2013 FE Tech Conference. The event focussed on the economics of processing and the beneficiation of iron ore. In light of the slowdown in demand for iron ore and pricing decreases, the need to process more efficiently and cost effectively is a challenge. The conference examined on how we can achieve greater value from the iron ore supply chain, with topics addressing optimisation and streamlining processes, applying improved technologies, understanding the ore body and how to properly characterise it, knowing the steel makers needs. For more information please visit the conference website: http://www.informa.com.au/fe-tech

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,028
On SlideShare
0
From Embeds
0
Number of Embeds
8
Actions
Shares
0
Downloads
36
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Matt Schneider, Optika Solutions: Extracting More Out Of Existing Assets: Capacity and Utilisation

  1. 1. Optika Solutions Extracting More Out of Existing Assets: Understanding Capacity and Utilisation By Matthew Schneider
  2. 2. Introduction Iron ore mining supply chains in Australia are complex, involving everything from pit to port: open pit digging, haul trucks, crushing, rail and port facilities. In some cases they can have over 500k elements and behaviors. They are also capital intensive, driving the desire to seek out any improvements and get the most out of every asset, whether at the planning stage or for existing operations. They have “personality” and are “temperamental”.
  3. 3. Introduction • To extract more from your assets one must understand some different analytical techniques and that you are dealing with a complex system. • This talk will describe and compare some example Analytic Methods – Value Driver Tree (static) – Monte Carlo (statistical) – Discrete Event Modeling (dynamic) • But one must also know that an opportunity exists and we will cover this using Benchmarking and Operational Performance.
  4. 4. A Supply Chain (Complex System) Key sensitivity: Key sensitivity: Mine Variability Product blending Mine + Plant Product Stockpiles Mining Constraints Key sensitivity: Rail scheduling Rail Key sensitivity: Port Stockpile Port Stockyard Logistics Queue and Buffer Key sensitivity: Key sensitivity: Ship Grade Variability Shiploader Port Outload Shipping Customers Policies and Rules 4
  5. 5. Metrics • The first question is, what are we measuring? Since we are ultimately concerned with total system throughput, we look at capacity constraints or bottlenecks . • For example, the crusher rate for a day, the size of a port stockpile, haul truck cycle times – any one of these can form a bottleneck to throughput. • Making a change to the system may fix one bottleneck, only to have it reappear elsewhere.
  6. 6. Metrics • A second measure is utilisation, the percentage of time that a piece of equipment is being used. This can indicate either a choke point or an underperforming area. • A crusher, for example, may have a very high rate of 85% (good) or it may have a low rate of 40% (Bad). • The important aspect is to understand the context in which the metric is being applied and to understand its drivers.
  7. 7. Methods – Value Driver Tree • Value Driver Tree – Visual Tool that describes the linkages between different elements within a business/operation. Nominal production rate (Mt/a) 10.0 Mean time to failure (hrs) 400.0 Mean breakdown (hrs) Availability 89% Crusher Production (Mt/a) 8.01 Maintenance duration (hrs) 36.7 Planned maintenance (hrs) 144.0 Utilisation No feed (hrs) 90% 500.0 Crushed ore stockpile full (hrs) 300.0 12 Maintenance Frequency (no/year) 12
  8. 8. Methods – Monte Carlo Analysis • Monte Carlo analysis is used to calculate a statistical forecast of the decision variable. Iterations Name Class Mark Frequency Acum. Freq. Frequency % Acum.Freq.% 7.48 1 1 0.20% 0.20% 7.51 0 1 0.00% 0.20% 7.55 0 1 0.00% 0.20% 7.59 1 2 0.20% 0.40% 7.62 1 3 0.20% 0.60% 7.66 2 5 0.40% 1.00% 7.69 3 8 0.60% 1.60% 7.73 4 12 0.80% 2.40% 7.76 6 18 1.20% 3.60% 7.80 6 24 1.20% 4.80% 7.84 18 42 3.60% 8.40% 7.87 24 66 4.80% 13.20% 7.91 30 96 6.00% 19.20% 7.94 32 128 6.40% 25.60% 7.98 42 170 8.40% 34.00% 8.01 34 204 6.80% 40.80% 8.05 39 243 7.80% 48.60% 8.09 44 287 8.80% 57.40% 8.12 42 329 8.40% 65.80% 8.16 43 372 8.60% 74.40% 8.19 32 404 6.40% 80.80% 500 Maximum Minimum Mean Variance Std. Dev. Dev./Mean Availability 94% 84% 89% 0% 2% 1.79% Production 8.50 7.46 8.03 0.03 0.16 1.99% MTTR 57.2 17.8 35.7 39.7 6.3 17.68% MTTF 424.9 375.0 401.4 210.1 14.5 3.61% 50 45 40 35 30 25 20 15 10 5 8.48 8.41 8.34 8.27 8.19 8.12 8.05 7.98 7.91 7.84 7.76 7.69 7.62 7.55 7.48 0
  9. 9. Method – Discrete Event Modelling • Still retains the randomness of real life but it adds the element of time. Specifically it adds the dimension of time that allows actions/events to occur so we can study their impact. • This provides insight into the overall system that otherwise would be hidden. • Example it allows you to test the effect of a change at the crusher to the impact it will have on ship loading.
  10. 10. Discrete Event Model Variable lump% 18 Calendar Days to Load Capes (NOR to Finish Loading) 16 14 12 10 8 6 4 2 0 6.75 6.8 6.85 6.9 6.95 7 7.05 7.1 7.15 7.2 Railed Tonnes 47% IK 47% IK - 30% lump 75% IK 75% IK - 30% lump 7.25 7.3 7.35
  11. 11. Analytics Method Comparison Method Pro Cons Value Driver Tree Simple, visual, quick to put together and widely used. Static analysis, does not capture variability or time. Monte Carlo A little more work, gives a better view of uncertainty. Includes variability but not time. Discrete Event More involved, gives whole of system behaviour. Includes both variability and time.
  12. 12. Ideal Supply Chain • In an ideal world a supply chain system would be completely balanced. This situation is very rare. It implies that all latent capacity in the system has been utilized. • The next most favourable situation is where the limiting process is the most difficult or expensive to improve. This means the system is at a natural boundary and lower cost options have been exhausted (basically Theory of Constraint). • The worst case situation (but best opportunity) is one where a low cost process or a policy is limiting the entire system capacity.
  13. 13. Supply Chain Elements & Personality Assets Determine unit capacity and cost Buffers upstream and downstream operations Make decisions (within policy) People Stockpiles Customers and Revenue Supply Chain Supports decision-making Systems Market Generates uncertainty Limits discretion Policies Risk Optika Solutions Pty Ltd - Proprietary and Confidential 13
  14. 14. Benchmarks- Motivate the Need • Benchmarking is useful for two reasons; it tells you to what extent unit operations could be improved and acts as a catalyst to shake complacency and drive improvement. – – – – What is the real world experience with similar unit operations? Are there explanations for any divergences from our experience? Do theoretical limits exceed benchmark data? Why is this? What is a reasonable/realistic improvement target for individual operational units?
  15. 15. Operational Performance • Another way to assess achievable improvement is to look at the instances of short term outperformance for particular items of equipment (Best Day, Best Performance) : • Why was performance so good? What distinguishes these periods from other less impressive periods? List the adverse factors at play during the latter. • Are there any patterns? What are the top 2 or 3 constraining factors which distinguish poor performance periods from high performance periods? Perform a root cause analysis on each. • What would it take to extend these periods of over performance?
  16. 16. Rail Simulation Analysis Mode Analysis Drum Beat Run when Ready Static (DT) 560 560 Monte Carlo (Avg) 558 557 Simulation (DES) 513 554
  17. 17. Understanding True Capability • • • • • • Are unit operations consistently operating at maximum achievable levels? Are work-in–progress stockpiles sufficient? Too large? Are the implications of system shocks fully appreciated and mitigated? Are there hidden feed-forward and feed-back loops? Do system harmonics degrade capacity? What is the impact of variability? Can this be reduced and what is the cost/benefit? • Where is the greatest leverage for improvement – ie rate limiting step in supply chain ?
  18. 18. Summary • To improve the Asset you must know where your rate limiting steps are. • To find this a clear understanding of supply chain and its personality is required ie total system . • This can best be defined, analysed and understood through the lens of simulation modeling.
  19. 19. Questions ? Thanks for your time.

×