Queue time analysis

Items with a total cycle time higher than 40 hours through the whole five operations were identified. They represent 1.5% of the item‘s population in the data. For each of those items, the queue time at each operation was calculated. Therefore, the operation where each item spent the most time was identified. For each operation, the number of lots spending the longest time could be counted.

data points 179 data points 179

ks stat 5.38E-02 ad stat 0.581

alpha 5.00E-02 alpha 5.00E-02

ks stat (179,5.e-002) 0.101 as stat (5.e-002) 2.49

p-Value 0.654 p-Value 0.666

result Do Not Reject result Do Not Reject

Kolmogorov-Smirnov Anderson-Darling

Exponential

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Figure 3.16: Number of lots having the longest queue at the operation

As can be seen in Figure 3.16, 70% of the items spent the longest time queuing in operation A. 9% of the items spent the longest time queuing in Operation B. No items spent the longest time queuing in operation C. 4% of the items spent the longest time queuing in Operation D and finally 17 % of the items spent the longest time queuing in operation E.

Let‘s explain why Operation A generates so many high cycle time lots by considering

the relationship between tool availability, average Process Time (PT), Queue Time (QT) plus Transport Time (TT).

As can be seen from the highlighted area on Figure 3.17, availability seems to impact queuing. In the period from period 112 to 120 (Figure 3.17) queue time shows a sharp peak. In this situation, one would expect utilization to increase reasonably high as well to compensate. But that cannot be seen in the Figure 3.18. The maximum of the utilization is 0.5. So why does queuing not impact utilization? Furthermore, why does availability not always have the same impact?

0 10 20 30 40 50 60 70 80

Op.A Op.B Op.C Op.D Op.E

P e rc e nt ag e (% ) Operation

CHAPTER - 3 PRE-STUDY: REAL PRODUCTION LINE DATA SAMPLE

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Figure 3.17: Operation A tool availability analysis

Figure 3.18: Operation A utilization

The data have been reviewed again and particularly, the period between period 112 and 120. There was no downtime or high utilization involved. So, the high queue during this period was not caused by machines downtime or an arrival of lots exceeding the capacity of the operation.

3.5 Conclusion

A real production system was considered. Six months data were collected on one product route. Five operations were observed and their inter-departure times were monitored. Several metrics to measure variability and more importantly to determine the

0 0.2 0.4 0.6 0.8 1 0 8 _{16 24 32 40 48 56 64 72 80 88 96} 104 112 120 128 136 144 152 160 168 176 N or m al is e d un it 48 hours periods

Operation A average queue time and tool availability Average QT Availability 0 0.2 0.4 0.6 0.8 1 0 8 _{16 24 32 40 48 56 64 72 80 88 96} 104 112 120 128 136 144 152 160 168 176 N or m al is e d un it 48 hours periods Operation A Utilization

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origins of variability have been developed. When operations have a high variability, it could be caused by the previous operation transferring its own variability or it could be caused by the operation itself creating variability. Therefore, propagation of variability from one operation to the next has been illustrated and a metric, the coefficient of correlation, has been exposed to evaluate it. Using a 48 hours period as the base for the calculation was found to be the best compromise to evaluate the data. Overall, Inter- arrival time and Inter-departure time correlation is quite strong for each operation. Nevertheless some phenomena observed in the line remain unexplained. Some periods showed particularly high queuing, which could not be related to any downtime or utilization peak.

One issue is the lack of accuracy of the lot monitoring. For example, lot departure corresponds to the time the operator logs the departure in the monitoring system. Actually this lot might have been completed several hours ago, but no operator was available to unload it from the operation and log its completion. No explanations are recorded in the system for the delays seen on the lots. Therefore it is hard to follow the progress of the lots through the line. Lack of information impedes learning from real system data and limits the generalization of findings. Another solution has to be found to understand the behavior of the production line. In particular, each parameter needs to be isolated, their influence analyzed and optimum configurations devised.

This can only be achieved in a controlled environment, in other words using simulations. Simple simulation models are needed to gain insights and then conduct further analyses on more complex model.

CHAPTER - 4 METHODOLOGY

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CHAPTER - 4 METHODOLOGY

4.1 Introduction

The concept of using a simulation model was based on the questions raised from the real data analysis in Chapter - 3. Various approaches can be used to study a production line: experimental, theoretical modeling using the queuing theory (APPENDIX - B) and simulation modeling (APPENDIX - D). It has been shown in the pre-study that an experimental approach in a semiconductor production line is too complex to isolate the influence of all the parameters.

Theoretical modeling provides insights and understandings of production line behaviors; nevertheless, it is insufficient to predict with accuracy the response to modifications brought to the line (APPENDIX - B). Therefore, this study was undertaken using simulation modeling.

Simulation is a sound approach to gain insights of the dynamics of complex systems without costly physical experiments. The advantages of simulation include: identification; incorporation or elimination of system parameters; fast experiments and what-if analysis; low cost and low risk [13]. The simulation models considered in this research are discrete, dynamic, and stochastic and will henceforth be called discrete- event simulation model.

 A discrete system is one for which the state variables change instantaneously at separated point in time [95].

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 Dynamic simulation models: A dynamic simulation model represents a system as it evolves over time [95].

 Stochastic simulation models: If a simulation model contain probabilistic (i.e., random) components, it is called stochastic. Stochastic simulation models produce output that is itself random, and must therefore be treated as only an estimate of the true characteristics of the model [95].

Discrete-event simulation concerns the modelling of a system as it evolves over time by a representation in which the state variables change instantaneously at separate points in time. These points in time are the ones at which an event occurs, where an event is defined as an instantaneous occurrence that may change the state of the system.

A process approach to simulation modelling was adopted. It views the simulation in terms of the individual entities involved and describes the ―experience‖ of a ―typical‖ entity as it ―flows‖ through the system [95]. It requires the use of special-purpose

simulation software (Extend). A production line was modeled using ExtendTM v6 simulation software (APPENDIX - D).