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Shortening set-up times in order to increase flexibility


Every set-up process can be broken up in external and internal activities. External activities can be completed while the station in need of the set-up is still running. Such activities can be moved up front, e.g. preparing patients outside of the operating room while another patient is still being operated on inside. If set-up times can also be improved through other activities (standardization, process optimisation etc.), flow unit types can be changed more often and flexibility is increased. This idea lies at the heart of the mixed-model production, which also profits from configuring a network of production facilities for pooling in order to be more flexible in catering to demand.

These lecture notes were taken during 2013 installment of the MOOC “An Introduction to Operations Management” taught by Prof. Dr. Christian Terwiesch of the Wharton Business School of the University of Pennsylvania at Coursera.org.
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How to pick the optimal batch size?


If the batch size is so small, that the process step with the set-up time (assuming, that there is only one) becomes the bottleneck of the process, the process looses on overall efficiency. Thus, the batch size needs to be chosen in a way that assures, that it will not generate a new bottleneck.

If, however, the batch size is too big, any increase in capacity at the station with the set-up time (assuming, again, that there is only one) is ultimately of no use, because it will only lead to inventory piling up somewhere else in the process – wherever the bottleneck may be.

This goes to show, that the ideal batch size is one, in which the station with the set-up time has a processing time which is just identical to the process bottleneck. Only then will the batch size not lead to the creation of a new bottleneck or additional inventory pile-up. This is calculated as:

capacity determined by the batch size = capacity of the bottleneck

b / (s + b * p) = m / p

with:

b = batch size
s = set-up time
p = processing time
m = number of resources

These lecture notes were taken during 2013 installment of the MOOC “An Introduction to Operations Management” taught by Prof. Dr. Christian Terwiesch of the Wharton Business School of the University of Pennsylvania at Coursera.org.
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