University researchers examining the behavior of local bank teller lin

Question

University researchers examining the behavior of local bank teller lines have developed a stochastic model that integrates pattern analysis of most popular services by hour, and general customer traffic averages. When implemented, despite the significant and random fluctuations in actual line length, including even longer lines at moments, the model was able to better process peak crowds with the same number of tellers by shortening average time at the window. Accordingly, the stochastic model is likely to provide a higher quality of service for the customer.
 
Which of the following, if true, most strengthens the argument?
 
Ⓐ The model can also predict local traffic patterns during rush hour as well as quieter hours.

Ⓑ A similar study by a local bank also improve line flow by adding a ‘flex’ employee to supply additional teller capacity at peak times.

Ⓒ Extreme crowding at bank teller lines remains relatively rare at most local banks.

Ⓓ The risk that long teller lines could anger customers is markedly intensified when lines are excessively long.

Ⓔ Customer aversion to significant spikes in line length is mitigated if customers perceive the line to move quickly.

Nevernevergiveup · Accepted Answer

I got this one incorrect for the first and then understood while trying to express the analysis.

Additional info: A bank teller (often abbreviated to simply Teller) is an employee of a bank who deals directly with most customers.

University researchers  examining the behavior of local bank teller lines  have developed a stochastic model that integrates pattern analysis of most popular services by hour, and general customer traffic averages .

When implemented, despite the significant and random fluctuations in actual line length, including even longer lines at moments, the  model was able to better process peak crowds  with the same number of tellers by shortening average time at the window.

Accordingly, the stochastic model is likely to provide a higher quality of service for the customer.

Which of the following, if true, most strengthens the argument?
 
Ⓐ The model can also predict local traffic patterns during rush hour as well as quieter hours.............. model needs to process/manage crowds and there is no point of discussion regarding predictions. Keep it a side even if it looks like an advantage.

Ⓑ A similar study by a local bank also improve line flow by adding a ‘flex’ employee to supply additional teller capacity at peak times............... similar study need not give similar performance.

Ⓒ Extreme crowding at bank teller lines remains relatively rare at most local banks............. .this is mentioned as "longer lines at moments" but extreme and rare words indicate extreme meaning.

Ⓓ The risk that long teller lines could anger customers is markedly intensified when lines are excessively long............. true but this cannot explain why model would become successful.

Ⓔ Customer aversion to significant spikes in line length is mitigated if customers perceive the line to move quickly............ If this is true, as model processes lengthy line scenario's well, customers will have less anger and be more happy thus the model achieves its purpose and is successful.

prashantrchawla · Answer

According to me, 
 
Ⓐ The model can also predict local traffic patterns during rush hour as well as quieter hours. - Already mentioned in the passage.

Ⓑ A similar study by a local bank also improve line flow by adding a ‘flex’ employee to supply additional teller capacity at peak times. - We are talking about a teller system here and not improving employee for additional teller

Ⓓ The risk that long teller lines could anger customers is markedly intensified when lines are excessively long. - This is showing that the teller is not working as per expectation.

Ⓔ Customer aversion to significant spikes in line length is mitigated if customers perceive the line to move quickly - not relevant.

I guess i am going at right direction.

prashant212 · Answer

My Understanding:

Schotastic model uses various techniques to speed up the processing of customers at the local banks.

Conclusion: Model is likely to provide a  higher quality of service  for the customer.

So the question we must ask is: Higher processing speed = higher quality of service?

Option A is already mentioned in argument so doesn't answer our quesiton. Option B is talking about the case of a different strategy so not related. Option C says there are no cases of extreme crowd but we are talking about providing relatively high quality of services so option C doesn't help.

Option D weakens the conclusion, since schotastic model doesnt eliminate the queues. It just processes the queues faster and if queues make customer angry then there is no question customer satisfaction which is directly related to quality of service.

Option E says that even if the queues are long, quick processing of customers will not allow the customers to get angry which is good for customer satisfaction hence the quality of service. Option E strengthens.

warriorguy · Answer

I was not able to understand as to why E is correct.

My understanding:

The stochastic model is likely to provide a higher quality of service for the customer because the model was able to better process peak crowds with the same number of tellers by shortening average time at the window.

D - The risk that long teller lines could anger customers is markedly intensified when lines are excessively long.

Since the model reduces the average time at the window and is able to better process peak crowds then customer won't be angry. Should translate to higher quality of service than traditional bank tellers.

E - Customer aversion to significant spikes in line length is mitigated if customers perceive the line to move quickly.

Average time is reduced, customers perceive the lines to move quickly and they are OK with significant spikes in line length. Should translate to higher quality of service than traditional bank tellers.

NikGujju · Answer

gmatexam439 · Answer

University researchers examining the behavior of local bank teller lines have developed a stochastic model that integrates pattern analysis of most popular services by hour, and general customer traffic averages. When implemented, despite the significant and random fluctuations in actual line length, including even longer lines at moments, the model was able to better process peak crowds with the same number of tellers by shortening average time at the window. Accordingly, the stochastic model is likely to provide a higher quality of service for the customer.
 
Which of the following, if true, most strengthens the argument?
 
Ⓐ The model can also predict local traffic patterns during rush hour as well as quieter hours. - Traffic pattern? Out of scope

Ⓑ A similar study by a local bank also improve line flow by adding a ‘flex’ employee to supply additional teller capacity at peak times. - We don't have to add any extra helping hands.

Ⓒ Extreme crowding at bank teller lines remains relatively rare at most local banks. - This is a fact statement, not related to the argument at hand

Ⓓ The risk that long teller lines could anger customers is markedly intensified when lines are excessively long. - Out of scope

Ⓔ Customer aversion to significant spikes in line length is mitigated if customers perceive the line to move quickly. - Correct. Supports the conclusion that the customer service would be improved

vineethshankar · Answer

Hello warriorguy,

Being a strengthen question, we are looking for an option that indicates that implementation of new analysis method led to better quality of customer service
Option D:  The risk that long teller lines could anger customers is markedly intensified when lines are excessively long.  To put it simply, longer lines make customers angry. Now, the implementation of analysis did not eliminate long lines (please see passage - "including even longer lines..") Therefore, option D is at best a weakener of the conclusion.

Option E is almost like a counterpoint for Option D. Customer anger/aversion is mitigated if the lines are moving quickly. This is exactly what is accomplished with the new analysis.

Hope that is clear to you.

VerbalBot · Answer

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University researchers examining the behavior of local bank teller lin

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