Queueing quandary

And the air, obviously.

Queueing quandary

That is how you would describe your job at the centralized records and benefits administration center for Cutting Edge, a large company manufacturing computers and computer peripherals.

Queueing quandary

The company provides a call center at the administration center for the 60, Cutting Edge employees throughout the United States. Employees contact the call center to obtain information about dental plans and pension options, to change tax forms and personal information, and to process leaves of absence and retirements.

The centralized records and benefits administration center and its call center opened just six months ago to replace 35 small regional administration centers across the country.

Since this recent opening, you and Mark Lawrence, the director of human resources, have endured one long roller coaster ride. Receiving the go-ahead from corporate head- quarters to establish the centralized records and benefits ad- ministration center was definitely an up.

Getting caught in the crossfire of angry customers all employees of Cutting Edge because of demand overload for the records and benefits call center was definitely a down. Accurately forecasting the demand for the call center provided another up. And today you are faced with another down. Mark approaches your desk with a not altogether attractive frown on his face.

The forecasting job you did for us two months ago re- ally allowed us to understand the weekly demand for the center, but we still have not been able to get a grasp on the staffing problem.

We used both historical data and your forecasts to calculate the average weekly demand for the call center. We transformed this average weekly demand into average hourly demand by dividing the weekly demand by the number of hours in the workweek.

We then staffed the center to meet this average hourly demand by taking into account the average number of calls a representative is able to handle per hour. But something is horribly wrong. Operational data records show that over thirty percent of the customers wait over four minutes for a representative to answer the call!

Customers are still sending me numerous complaints, and executives from corporate headquarters are still breathing down my neck! In addition, the number of calls a representative is able to handle per hour can be much less or much greater than the average depending upon the types of calls received.

You then tell him to have no fear, you have the problem under control. You have been reading about the successful application of queuing theory to the operation of call centers, and you decide that the queuing models you learned in school will help you determine the appropriate staffing level.

Personal Data Collected

He tells you that calls are randomly received by the call center and that the center receives an average of 70 calls per hour.

The computer system installed to answer and hold the calls is so advanced that its capacity far exceeds the demand.

Queueing quandary

Because the nature of a call is random, the time required to process a call is random, where the time frequently is small but occasionally can be much longer. On average, however, representatives can handle 6 calls per hour.

Which queuing model seems appropriate for this situation? Given that slightly more than 35 per- cent of customers wait over 4 minutes before a representative answers the call, use this model to estimate how many representatives Mark currently employs.

SCADA is not a middleware

Given this customer service level and the average arrival rates and service rates from part ahow many representatives should Mark employ? He asks you to perform sensitivity analysis. How many representatives would he need to employ to ensure that 80 percent of customers wait 1 minute or less?

How many would he need to employ to en- sure that 95 percent of customers wait 90 seconds or less? How would you recommend Mark choose a customer service level? He therefore wants to explore alternatives to simply hiring additional representatives.

The alternative he considers is instituting a training program that will teach representatives to more efficiently use computer tools to answer calls. He believes that this alternative will increase the average number of calls a representative is able to handle per hour from 6 calls to 8 calls.

How many representatives will Mark have to employ and train to achieve the customer service level desired in part b? Do you prefer this alternative to simply hiring additional representatives? Why or why not? He realizes that the queuing models will not provide accurate answers if the inputs used in the models are inaccurate.

What inputs do you think need revaluation? How would you go about estimating these inputs?Article: The Queuing Quandary: Benefits Of Virtual Queuing.

The Queuing Quandary No one likes a queue. Queues are a waste of contact centers' resources and customers' precious time. A Mintel study found that 90% of contact center users are angry and frustrated with the system – particularly with long wait time.

This section describes how to implement the Loan Broker example (see Introduction to Composed Messaging Examples) using lausannecongress2018.com, C# and lausannecongress2018.com lausannecongress2018.com framework includes the lausannecongress2018.coming namespace that lausannecongress2018.com programs access to the Microsoft Message Queuing Service that is included in the recent versions of the Windows operating system (Windows , .

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Striking a balance among conflicting targets such as maximizing system throughput and system utilization while minimizing application response times is a quandary for system administrators.

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