GEOG 000

Lesson 5.3: The Quest for Continuous


Lesson 5.3: The Quest for Continuous

Near the end of Lesson 5.2, I introduced you to the concept of availability in the context of equipment maintenance and unexpected breakdowns of equipment. We saw that even a seemingly high availability in the 90s for the equipment could result in a shockingly low availability for the overall system. In our example, the availability at that production face was barely over 50%! We focused our discussion on delays created by equipment that was out of service due to a breakdown. The same concept can be applied to other, non-maintenance delays. For the purpose of this discussion, assume that we have the following scenario:

An underground limestone has eight different working faces, but normally only works five of those faces in a given shift. The sequence of operations is:

  1. Five different working faces were shot (blasted) at the end of the shift yesterday.
  2. These faces are inspected to ensure that it is safe to load out the blasted ore.
  3. Loader #1 trams to Working Face A, and waits for Haul Truck #1. Loader #1 begins to load the pile into Haul Truck #1. It takes an average of 75 seconds to load and dump one-bucket load into the truck, and it takes 4-bucket loads to fill the truck.
  4. Truck #1 trams to the dump point, and it takes an average of 12 minutes to make the trip, dump, and return to the working face.
  5. Truck #2 was waiting nearby, and maneuvers into position at Working Face A as soon as Truck #1 departs. The loading cycle begins again.
  6. Truck #3 arrives and waits to maneuver into position as soon as Truck #2 departs.
  7. Working Face A will be loaded out, after three truckloads, and Loader #1 will move to the next working face.
  8. Scaler #1 trams to Working Face A, and scales the roof. This takes 45 min. on average.
  9. Bolter #2 trams to Working Face A, and installs 8 rock bolts. This takes 35 min on average.
  10. Drill #1 trams to Working Face A, and drills blast holes. It takes 2 hr. on average to set up and drill the pattern.
  11. The blaster(s) will load the holes. This takes an average of 1 hr. All faces will be loaded, but not shot until the end of the shift.

Finally, let’s assume that we’ve engineered our system perfectly. The equipment moving in and out of each face, and among the faces, resembles a beautiful symphony. All of the pieces come together at exactly the right time for the right duration. It’s a sight to behold! And if you believe this, you probably believe in the Easter Bunny and Santa Claus!

But why wouldn’t or couldn’t this be true? To use yet another metaphor, why can’t it operate like a well-oiled machine?

In a word –variability: the normal variations associated with each operation or piece of equipment. Consider the following list of normal variations:

  • The pile of shot rock is compacted, and the loader operator has difficulty loading the bucket in one pass.
  • The operator for Haul Truck #2 is not feeling well, is taking more time than usual to make the round trip back to the face, and is delayed in arriving by four minutes. The loader operator is idled until the truck arrives.
  • Ground water inflow from an overnight storm has washed out part of the haul road, increasing the travel time for the haul trucks by 20%.
  • Difficult ground conditions required substantially more time to scale the working face, which idled the bolter operator.

Honestly, we could go on and on with delays that have nothing to do with equipment maintenance. So, how do we deal with this? We apply industrial engineering techniques: we conduct time studies and develop statistical distributions of the times that it takes to conduct all the specific tasks for each machine. We study and document delays, and of course we attempt to correct the situations underlying the delays. However, our goal is to run production simulations.

Once we know something about the statistical behavior of the unit and auxiliary operations, we can execute Monte Carlo simulations for example, and we can study various options. We can add a truck or increase the size of the loader, for example, and predict how this will affect our production and productivity. We examine the sensitivity of the result for various parameters. For example, we could identify the piece of equipment whose availability has the greatest impact on production. Armed with this information, we can consider improvements to the system, and we can “test” the improvement for making an investment in time or money.

Before continuing with this discussion on production simulations, I want to explain the difference between two words that we just used: production and productivity. Both are important metrics, but the terms are not interchangeable. Production is the amount of material that we have mined. If we say that we mined 20,000 raw tons yesterday, it means that is the tonnage of rock and ore that went out of the mine to the plant. If we say that our production was 15,000 clean tons, we mean that we produced 15,000 tons for sale. Productivity on the other hand, is indicating how efficiently we mine with our labor force. If we required 12 people working for two shifts to produce those 20,000 tons, then our productivity would be 833 tons per man-shift. It’s important to remember this difference. Ok, now back to our discussion on the production simulations.

There is a fundamental weakness in typical production systems that these simulations highlight. As the number of individual operations in a sequence increases, so does the likelihood of more delays. This is intuitive: as you increase the number of people and pieces of equipment required to complete a cycle, you are more likely to experience a delay, whether it is an equipment breakdown or other factor. A practical consequence of this inherent characteristic of production systems is the quest to reduce the number of operations and/or pieces of equipment required to complete a cycle.

As a good example, let’s consider a conventional cycle in an underground coal mine. The following equipment is required:

  • Drill: to drill holes for explosives.
  • Cutting bar: to cut a kerf at the bottom of the coal face, which serves to create a free face (this improves the performance of the blast).
  • Loading machine: to load the blasted coal into shuttle cars.
  • Shuttle cars (2): to transport the coal to an intermediate dumping point.
  • Roof bolter: to place bolts into the newly exposed roof.

The sequence of operation is in the order of this list. I have not listed the loading of the explosives, nor have I shown that a gas check is required prior to each operation and ventilation curtains will need to be advanced. These are of no consequence for the purpose of this discussion.

In addition to the time required to perform the indicated operation, there is a significant amount of time in the place change, i.e., when the piece of equipment leaves one face, travels to another, and then sets up to begin work at the next face. In this example, the production simulations highlighted what miners knew from experience: the place changes and other delays associated with all this equipment were “killing” production. The solution? Let’s eliminate some of these pieces of equipment and the associated place changes. How? The development of a new piece of technology: the continuous miner.