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Scrapping or reworking?


Should a damaged unit be dropped from the process or should it be reworked? In order to answer that question it has to be noted, that reworking defects can turn a process step into a bottleneck, which has not been the bottleneck before. Reworking defects (and thus, defects themselves) can have a significant impact on the process flow and on the location of the bottleneck. The bottleneck can therefore not longer be determined by just looking at the capacity of the process steps. Instead, one has to take into account the capacity changes in relation to the scrap and reworking rates.

To figure out where the new bottleneck is, we have to assume that the process as a whole will be executed in a way in which the demand is met, so that there is a match between the process output and the demand at the end of the process. The process therefore needs to start with more flow units then actually needed, so that enough flow units will be left over to satisfy demand. By working the process diagram backwards and determining the new demand for each process step, we can then discover where the new bottleneck will be located.

Instead of completely scrapping a flow unit, flow units can also be reworked, meaning that they can be re-introduced to the process and given a work-over to get rid of defects. This must also be taken into account when trying to figure out whether the location of the bottleneck changes, because some of the process steps will now have to process the same flow unit twice in rework, which will have an impact on their implied utilization. The location of the bottleneck can be determined by finding the process step with the highest implied utilization.

If the demand is unknown, the bottleneck can be located through four simple steps:

(1) Assume that the flow rate is an unknown demand D (e.g. 100 flow units).
(2) Figure out the demand D_x for each process step if D is to be reached.
(3) Divide D_x by the capacity of the process step to get the implied utilization.
(4) Identify the process step with the highest implied utilization. This step is the bottleneck.

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.

Reducing waiting time by pooling demand


By pooling demand, the inter-arrival times are shortened and thus the specific demand goes up (which is intuitive, since pooling demand basically means combining different demand streams). While the utilization rate is not effected by demand pooling, the waiting time is shortened because some inefficiencies (idle time at station A while station B is overwhelmed) are eradicated. However, pooling more and more resources together also decreases the overall efficiency once the demand is met. Therefore, companies need to find a viable balance between efficiency and responsiveness.

What main benefits and costs are connected with pooling in the context of waiting time?

  • Pooling assumes total flexibility (Spanish call center agents will not be able to answer to German customers, even if the call center company decided to pool all calls together).
  • Pooling increases the complexity of the workflow, since demands needs to be shifted between resources who might be locally apart (e.g. two hospitals or two plants).
  • Pooling interrupts the continuity of interaction between the flow unit (customer) and the resource (worker) and can thus hurt the customer experience because customers will not want to see a different physician or a different financial consultant on every separate visit.
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.

Seasonal demand


In practice, demand sometimes exhibits seasonal ups and downs with spikes in demand at certain busy hours, days or weeks. It would be misleading to just ignore those spikes and assume that the demand during the spikes is drawn from the same statistical function as the rest of the demand. In those cases, the analysed timeframe has to be sliced up into equal time intervals and every interval has to be taken as the basis for a separate calculation.

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.

The concept of responsiveness


Responsiveness is the ability of a system or process to complete tasks within a given time frame. E.g. how quick can a business respond to customer demands? If customers are made to wait, they are turned into inventory, potentially resulting in a unpleasant customer experience. Any customer waiting time is also an indicator of a mismatch between supply and demand.

Concepts for solving waiting time problems can include increasing the capacity of the resource at the bottleneck as well as increasing process flexibility in order to ensure, that capacity is available at the right time. It has, however, to be kept in mind that waiting times are most often not driven by either the capacity or the flexibility of a process but rather by variability. Variability in the process flow (e.g. customers arriving at random) can lead to unwanted waiting times even when the implied utilization is clearly below 100%. If analysis builds solely on averages and fails to consider process variability, it can thus be wrongfully concluded that there is no waiting time, when, in fact, there is.

To solve this problem, new analysis methods are needed when dealing with process variability. It is noteworthy, that those methods are only requisite when a process has more capacity than demand – if demand exceeds capacity, it can be safely concluded that there will be waiting time even without looking at the process variability.

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.

Re-definition of the batch size in accordance with demand


The batch size was previously defined as the number of flow units that are produced between two set-ups. While this definition is correct, it does not take into account the actual demand for the flow units. If a process is able to produce multiple flow units (e.g. cheeseburgers and veggie sandwiches) with one set-up time in between, a batch in a mixed-model production is re-defined as a number of mixed flow units produced during a certain amount of time (before the used pattern of production is repeated). The additional set-up times for switching between the flow units during the production of the batch have, of course, to be recognized.

This brings us to the following formula:

target flow = batch size / (set-up time + batch size * processing rate)

Here, the target flow is defined as the number of flow units needed per time frame in order to stay on top of the demand (e.g. 100 units per hour). The processing rate is determined by the bottleneck of the process or by the demand while set-up time and batch size have previously been defined.

If the goal is determining the ideal batch size, the formula can be resolved for the batch size. The result has to be set in ratio to the demand for the various flow units within the batch in order to find out, how many flow units of each type are produced within the ideal batch size. Note, that the set-up time needed to start the production pattern at the beginning is part of the overall set-up time and thus needs to be included in the total sum of set-up times needed for this calculation.

Obviously, the batches will become larger and the inventory will become bigger the more set-ups are necessary as long as the overall demand does not change (but is simply spread out over more offered product choices). Variety thus leads to more set-ups and thus to more inventory, which is one of the biggest problems associated with offering more variety.

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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