6.5: Summary

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This chapter discusses a beyond lean analysis of the operation of a single workstation, both with and without operations detractors: breakdowns, part reworking, as well as setup and batching. An analytic model is used to compute the workstation utilization as well as the average time and number of parts in the buffer of the workstation for the case of no detractors. This model provides validation evidence for a simulation model of the workstation which estimates the same quantities plus the maximum lead time. The different replications of the simulation experiment show a wide range of different system behavior possibilities and the corresponding performance measure values. Details of system behavior could be extracted from the simulation as well.

Simulation models and experiments were conducted individually for each detractor. Results were compared to the no detractors case. An analytic model was used to set the best batch size given a utilization of 95%.

Problems

Perform a complete comparison of the breakdowns case to the no detractors case using paired-t statistical tests.
Perform a complete comparison of the part reworking case to the no detractors case using paired-t statistical tests.
Perform a complete comparison of the setup and batching case to the no detractors case using paired-t statistical tests.
Find the best batch size for a target utilization of 95% for a workstation with average time between arrivals of 10 minutes, cycle time of 9 minutes, and setup time of 1 hour. Production is 1000 parts.

Based on the simulation results that follow, provide validation evidence for a model of a single workstation with utilization of 80%

Replicate	Utilization
1	80.2%
2	79.5%
3	80.4%
4	80.6%
5	79.2%

Based on the simulation results that follow, provide verification evidence for a model of a single workstation.

Initial items:	10
Items remaining at the end of the simulation:	15
Arriving items:	150
Departing items:	145

Consider a single server workstation for which the average time between arrivals is 10 minutes and the average processing time is 9 minutes. Suppose a group modeling the workstation is trying to determine the distributions for the time between arrivals and the processing time in absence of data. Use the VUT equation to determine the average waiting time in the queue for the following possibilities.

	Time Between Arrivals	Processing Time
a.	Exponential	Exponential
b.	Constant	Exponential
c.	Exponential	Uniform (6, 12)
d.	Constant	Uniform (6, 12)
e.	Exponential	Triangular (6, 9, 12)
g.	Constant	Triangular (6, 9, 12)
h.	Exponential	Triangular (6, 7, 14)
i.	Constant	Triangular (6, 7, 14)

Case Problem

A new workstation is being designed and a complete analysis is needed as described in this chapter. The workstation operates 168 hours per month. Parts are modeled as arriving according to an exponential distribution with mean 10 minutes. Processing time is uniformly distributed between 6 and 9 minutes.

Detractors are as follows.

Breakdowns: The average time between breakdowns is 40 hours. Repair time is uniformly distributed between 30 and 150 minutes.

Defective parts: Five percent of parts are defective and require rework.

Setup and batching: The setup time is 45 minutes. A utilization of 95% is targeted. The best batch size should be determined.

First perform a complete study of the new workstation with no detractors. Use an analytic model as well as a simulation model and experiment. Part lead time is the primary performance measure. Verification and validation evidence for the simulation model must be obtained.

Second, use a simulation model and experiment to assess the joint effect of all three detractors. Verification and validation evidence should be obtained.

How to do this case study will be described in tutorial style for the simulation environment that you are using in a separate document.