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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.

How does product variety affect distribution systems?


The more the demand is divided into smaller and smaller segments, the harder it is to predict. Once there is more product variety, demand is going to become variable as well. Thus, statistical indicators such as mean and standard deviation are needed to cope with the so-called variability of demand. If demand streams are combined, the standard deviation of the combined demand goes up slower than the standard deviation of the previously uncombined demands. Such an aggregation of demand is called demand pooling and is an important method for reducing statistical uncertainty. Reductions in uncertainty can also be achieved through variance reduction or stock building.

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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