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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.

What is productivity?


A very basic definition of productivity – the average measure for the efficiency of a production process – would just be the ratio between process output units and process input units. The labor productivity might, for example, be four output units (such as car or netbook parts) per labor hour.

If one does not focus on a specific form of process input units (such as labor hours), but instead takes all kinds of input units into account (such as materials, energy, labor etc.), we are talking about the so-called multifactor productivity. Since both process input units and process output units are often difficult to measure and compare, fiscal measurements such as revenue and costs can be used instead. Fiscal measurements are also needed for comparing different input units (work time, materials) in the already mentioned multifactor scenarios.

These considerations lead to some basic mathematical expressions:

productivity = output / input
labor productivity = output / labor time
transformation efficiency = output / capacity
multifactor productivity = output (in $) / (capital + labor + materials + services + energy) (in $)

The two main drivers that reduce productivity are waste and inefficiencies. Inefficiencies and waste can be seen as the distance between a company and the efficiency frontier.

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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