Listen-Up.Com Case-Operations Management

13) Refer to the table. What is the average number of customers in the queue plus the number being served?

William Alfred Massey is an African American mathematician and operations researcher. Massey is an expert in queueing theory. He was born in 1956 in Jefferson City, Missouri. Massey was the youngest son of Richard A. Massey SR. and Juliette Massey. His family moved to St. Louis, Missouri when he was only four years old. Massey’s favorite color when he was younger was blue. One of his favorite foods that he enjoyed eating was steamed crab legs. Also his favorite season is late spring. William Massey went to the public schools of St. Louis, Missouri and high school in University City. After Massey graduated from University City High School he received a Harvard book award and a

Now we can apply Little’s Law to calculate the throughput time which is equal to the manufacturing lead time in this case.

Customers must use the internet to fill out an online form to address their complaints or service needs. These forms are processed by employees in your department. Currently the turnaround time on any given form is between four to eight hours. This creates a number of other customer complaints. Project Call Center is designed to reduce this turnaround time by 75% by creating and staffing a call center in Tampa. Building acquisition, building renovations, building fit out, IT system upgrades, and hiring and training of staff are estimated to cost $8.5 million dollars. This $8.5 million dollars can be paid evenly in any two quarters in the next year. In addition, seven new employees will need to be hired at $40,000 burdened labor costs per year to staff the call center. Management of this project could easily be done with the current in-house staff. Most of the work of this project would be outsourced and will have minimal impact on day-to-operations.

Answered and screened two or more phone calls at a time using the multi-line switchboard, effectively directing the customer to and from the department of their asking

Using the appropriate queuing model, compute the server utilization (probability that the server is busy) and the waiting time W (known as the response time in this application), as the number of clients M varies from 1 to 20. (Use a data table). Plot W against M to show the effect of the number of clients on the system response. At high server utilization the system is congested and each additional client increases the response time by its service time and the plot of W against M becomes linear. From your computed results calculate the change in W as M increases from 19 to 20.

Describe the methods that will be used to sell and distribute the products or services

The phone system is currently set up with two lines: if the customer service representative is on one line, incoming calls are routed to voice mail where the customer can leave a message. The representative listens to the messages and returns calls as time permits. This is an inefficient system as the customer service representative has difficulty taking calls directly on busy days. Instead, the representative ends one call, listens to the next voice mail, returns that call, and so forth.

Once the client is able to access the account, prior to ending the call, she inquires if there are any further questions.

Hence, the bottleneck is due to high variability in order arrival rate and order processing time. Hence, we need to analyse the quarterly utilization level.

It assumes every request to be of longer duration (95th percentile with respect to request duration) and does not take into account the time saved while executing smaller requests. Mean on the other hand takes into account that fact that time consumed on longer jobs is compensated by the time saved on smaller jobs.

The current average utilization rate of the call centre is 30.48% (see appendix XXX). The average arrival rate, rate at which the patients call, is lower than the average service rate, rate at which the patients are serviced. However, both the arrival time and the service time contain moderate variability (see appendix XXX), negatively impacting the flow time during peak hours. There are two arrival rate variability issues: variability amongst the different days the calls are received and variability amongst the hours the calls are received. The problem is bigger than Laura anticipated. As per the ‘Appendix 5’ of the case, the average daily abandoned calls are 338 and not 35. This does not include the patients receiving a busy signal, therefore becoming lost throughputs. Thus given the low utilization rate it is clear that the problem the call centre faces is in managing variability and not capacity.

3.   The travel time is 1 hour.  While this is considered part of the service time it actually means that the customer will be waiting during the first hour of the service time.  Thus, travel time must be added to the time spent in line as predicted model in order to determine the total customer waiting time.

In this essay, two companies will be identified and described on how they utilize a queuing system. Only two of the four most basic waiting line structures will be discussed: single-server and multiple-server waiting lines. Since waiting is an integral part of many service related operations, it is an important area of analysis. Each queue system has its advantages and disadvantages, but with no doubt each company’s goal is to cut down on the waiting time and that customer returns. In particular, we examine their implementation of both processes and try to find solutions to improve the waiting line process.

In an ideal situation, customers would not have to wait for the delivery of products and services. However, in the real world, organizations cannot always match exact capability and demand; therefore, waiting is frequently inevitable while purchasing, especially in service marketing, as service firms can barely inventory their “stock” for sale at a later date (Lovelock, 1992, p.154). In general, waiting in lines – known as “queuing”, happens when the number of customers arrive at a facility exceeds the capability of the system to serve them (Lovelock & Wirtz, 2011, p.260). Basically, this essay will state the relationship between queuing and customer satisfaction, as well as relationship between customer satisfaction and