There are a number of modeling issues especially important for the achievement of accurate and valid Simulation models of manufacturing and material-handling systems.
Two of these issues are the proper modeling of downtimes and whether, for some inputs, to use actual system data or a statistical model of those inputs.
- Unscheduled random downtimes can have a major effect on the performance of manufacturing systems.
This discusses the problems that can arise when downtime is modeled incorrectly and suggests a number of ways to model machine and system downtimes correctly.
Scheduled downtime, such as for preventive maintenance, or periodic downtime, such as for tool replacement, also can have a major effect on system performance. But these downtimes are usually (or should be) predictable and can be scheduled to minimize disruptions. In addition, engineering efforts or new technology might be able to reduce their duration.
There are a number of alternatives for modeling random unscheduled downtime, some better than
Others:
- Ignore it.
- Do not model it explicitly, but increase processing times in appropriate proportion.
- Use constant values for time to failure and time to repair.
- Use statistical distributions for time to failure and time to repair.
Of course, alternative (I) generally is not the suggested approach. This is certainly an irresponsible modeling technique if downtimes have an impact on the results, as they do in almost all situations.
One situation in which ignoring downtimes could be appropriate, with the full knowledge of the customer, is to leave out those catastrophic downtimes that occur rarely and leave a production line or plant down for a long period of time.
The second possibility, to factor into the model the effect of downtimes by adjusting processing times applied to each job or part, might be an acceptable approximation under limited circumstances.
If each job or part is subject to a large number of small delays associated with downtime of equipment or tools, then the total of such delays may be added to the pure processing time to arrive at an adjusted processing time.
If total delay time and pure processing time are random in nature, then an appropriate statistical distribution should be used for the total adjusted processing time.
If the pure processing time is constant while the total delay time in one cycle is random and variable, it is almost never accurate to adjust the processing time by a constant factor.
The third possibility, using constant durations for time to failure and time to repair, might be appropriate when, for example, the downtime is actually due to preventive maintenance that is on a fixed schedule.
In almost all other circumstances, the fourth possibility, modeling time to failure and time to repair by appropriate statistical distributions, is the appropriate technique. This requires either actual data for choosing a statistical distribution.
The nature of time to failure is also important. Are times to failure completely random in nature, a situation due typically to a large number of possible causes of failure?
In this case, exponential distribution might provide a good statistical model. Or are times to failure, rather, more regular-typically, due to some major component-say, a tool-wearing out? In this case, a uniform or (truncated) normal distribution could be more nearly appropriate.
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