| Course | MGT 240 Operations Management |
|---|---|
| Module | Module 4 |
| Paper type | Waiting line analysis |
| Length | About 1,056 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Business Administration |
| Updated | October 2026 |
Free sample paper for MGT 240 Module 4
Ninety Arrivals, Seventy-Five Services: Variability, Utilization and the Evening Line at an Airport Rental Car Counter
Student Name
Business Administration Program, Aspen University
MGT 240: Operations Management
Instructor Name
Month Day, Year
Ninety Arrivals, Seventy-Five Services: Variability, Utilization and the Evening Line at an Airport Rental Car Counter
Sunport Rent-A-Car, a composite company, operates a counter in the rental car center at the Albuquerque International Sunport in New Mexico. Most of the day, its agents keep up easily. But between 6 and 8 p.m., several flights land within an hour of each other, and customers wait up to half an hour. Online reviews mention the evening line more than anything else, and the regional manager has proposed adding two agents to the evening shift at a cost of about $56,000 a year. This paper analyzes the evening line and compares options for reducing waits.
Arrivals and Service
Counts over four weeks show that about 90 customers arrive at the counter per hour between 6 and 8 p.m., though arrivals bunch after each flight. Five agents work the counter. Each transaction, including checking a license, explaining insurance options, taking payment and assigning a car, averages four minutes, but ranges from about two minutes to more than eight for customers with questions or payment problems. Each agent can serve about 15 customers an hour.
Utilization
Utilization is demand divided by capacity. Five agents can serve 75 customers an hour, while 90 arrive, so utilization is 120%. When utilization exceeds 100%, the line grows continuously: here by about 15 customers an hour, reaching roughly 30 people by 8 p.m. Even with seven agents, utilization would be about 86%, high enough that bursts of arrivals would still create lines.
Little's Law
Little (1961) proved that, on average, the count of customers present equals the arrival rate multiplied by the average time each spends there. Applied to the line, 30 waiting customers served at 75 an hour means a customer joining at 8 p.m. waits about 24 minutes. The relationship also works in reverse: if the target is a wait of three minutes at 90 arrivals an hour, the average line should hold no more than about four or five customers.
Why Variability Creates Waiting
Operations textbooks use a common approximation for multi-server lines in which the expected wait depends on service time divided by the number of servers, on utilization and on the variability of arrivals and service. The utilization term rises steeply as utilization approaches 100%, which is why the last few percentage points of capacity matter so much. Assuming arrivals vary about as much as a random process and service times vary by about three-quarters of their average, the approximation gives the expected waits in the table.
| Option | Agents | Average service | Utilization | Expected wait | Added cost per year |
|---|---|---|---|---|---|
| Current | 5 | 4.0 minutes | 120% | Grows to about 24 minutes | None |
| Add one agent | 6 | 4.0 minutes | 100% | Still grows | About $28,000 |
| Add two agents | 7 | 4.0 minutes | 86% | About 2 minutes | About $56,000 |
| Express pickup, current agents | 5 | 3.0 minutes average | 90% | About 4 minutes | About $15,000 |
| Express pickup, one added agent | 6 | 3.0 minutes average | 75% | Under 1 minute | About $43,000 |
The Peak Within the Peak
Hourly averages hide the sharpest bursts. When two flights land within ten minutes of each other, as happens twice most evenings, arrivals reach a rate of about 130 an hour for twenty minutes. No realistic staffing level can serve that burst without a line; the question is how quickly the line clears afterward. With current staffing, it never clears during the peak. With express pickup or seven agents, a line of fifteen to twenty customers forms after each burst but clears within about fifteen minutes. Sunport can also use flight arrival information, which is public, to have the manager at the counter just before each burst rather than after the line has formed.
Express Pickup
About 40% of evening customers book through the company's website. If they complete license and payment details online, their counter transaction falls to about 90 seconds, mostly checking the license and handing over keys. The average service time drops to three minutes, raising five agents' capacity to 100 customers an hour. The website changes and a separate express lane cost about $15,000 a year.
How Waiting Feels
Taylor (1994) studied airline passengers whose flights were delayed and found that delays lowered their evaluations of service, largely because delays produced anger and uncertainty. Customers who believed the company could have prevented the delay were angrier. Larson (1987) argued that the psychology of queueing matters as much as its mathematics, and that perceived fairness, especially being served in order of arrival, strongly affects how customers feel. At Sunport, customers can see agents idle in other areas and lines at competitors moving faster, which feels unfair. A screen showing the expected wait, a single line feeding all agents and visible express service would reduce uncertainty and the sense of unfairness.
The Agents' Side
Agents working a line that never clears face frustrated customers for two hours straight, and the location has lost three evening agents in the past year. Shorter lines are likely to improve retention of staff as well as customers. Agents asked for two changes: a printed sheet explaining insurance options that customers can read while waiting, which shortens the longest transactions, and a supervisor who handles disputes away from the counter.
Recommendation
Sunport should introduce express pickup and keep five agents in the evening, with one manager trained to join the counter when the line exceeds eight people. The expected wait falls to about four minutes at a cost of about $15,000, roughly a quarter of the proposed two agents. If express adoption stays below 30%, the company should add one evening agent.
Measuring Results
Sunport will measure waits by recording when each customer joins the line and when service begins, using the queue screen's ticket system. Targets are an average evening wait under five minutes, no customer waiting more than fifteen and express use above 35% of evening customers within three months. Review scores mentioning the line will be tracked monthly.
Conclusion
The evening line at Sunport forms because demand exceeds capacity during peaks, and variability makes waits sensitive to every minute of service time. Little's Law and a waiting-time approximation show that reducing service time through express pickup achieves nearly the same result as adding two agents at a fraction of the cost, while research on how waiting feels points to information and fairness as further improvements.
References
Larson, R. C. (1987). Perspectives on queues: Social justice and the psychology of queueing. Operations Research, 35(6), 895-905. https://doi.org/10.1287/opre.35.6.895
Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383-387. https://doi.org/10.1287/opre.9.3.383
Taylor, S. (1994). Waiting for service: The relationship between delays and evaluations of service. Journal of Marketing, 58(2), 56-69. https://doi.org/10.1177/002224299405800205
Reading the MGT 240 Module 4 assignment instructions
Variability and waiting lines are central to Aspen's MGT 240, and this paper usually asks students to analyze a service process where customers wait and recommend improvements. The Module 4 instructions in your classroom are what you should follow; one counter's evening peak is analyzed in the example. Describe arrival patterns and service times, including variability. Calculate utilization and explain what happens when it approaches or exceeds 100%. Use Little's Law and a waiting-time formula to estimate waits. Compare options, such as adding servers or reducing service time. Address customers' experience of waiting, not only its length. Recommend an option with its expected wait and cost.
How this MGT 240 Module 4 example is built
The paper begins with Sunport Rent-A-Car's counter, where 90 customers an hour arrive between 6 and 8 p.m. and five agents take an average of four minutes each. Utilization is 90 divided by 75, or 120%, so the line grows by 15 customers an hour and reaches about 30 by 8 p.m. Little's Law turns that line into a wait of about 24 minutes. Using a common approximation for multi-server lines, the paper estimates waits for seven agents at about two minutes and for five agents with express pickup at under four. Taylor's Journal of Marketing article found delays lowered evaluations of service through anger and uncertainty. Larson's Operations Research article argues that fairness and information shape how customers experience queues. A table compares options by agents, utilization, expected wait and cost.
Reading the MGT 240 Module 4 grading rubric
Waiting line papers are judged on correct calculations of utilization and waiting time, an explanation of why variability causes waiting even below full utilization and recommendations that weigh wait against cost. This example shows that utilization above 100% makes the line grow without limit and uses Little's Law to translate line length into wait. The approximation formula is applied consistently to each option. Taylor's research links waiting to customer evaluations, and Larson's work adds fairness and information. The table compares options in the same terms, and the recommendation explains its cost, its remaining risks and the fallback if customers do not use express pickup.
MGT 240 Module 4 help: mistakes that cost marks
Waiting line papers often assume that if capacity exceeds demand, no one waits. Variability in arrivals and service creates waits even below full use, and waits rise sharply as utilization approaches 100%. Explain this with numbers. Use Little's Law to connect line length and wait time. Apply a recognized formula and state its assumptions. Compare options fairly, including reducing service time, not only adding servers. Address how waiting feels, since customers judge service by their experience, not by averages. Keep time units consistent. Finally, consider peaks separately from daily averages, because the worst waits happen during short busy periods. Plan how results will be tracked after the change.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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MGT 240 Module 4 questions, answered
What does MGT 240 Module 4 usually ask for?
Aspen's MGT 240 covers variability and waiting lines in this module, so analyzing a service process where customers wait is typical. Check your classroom prompt.
Why do lines form when capacity exceeds demand?
Because arrivals and service times vary; short-term bursts of arrivals or long services create waits even when average capacity is enough.
What happens when utilization exceeds 100%?
The line grows continuously for as long as demand exceeds capacity, and waits keep lengthening until demand falls.
Where can I find a free MGT 240 Module 4 sample paper?
The complete analysis above examines an airport rental car counter's evening line, calculates utilization and waits and compares staffing with express pickup.
Does waiting time affect customer satisfaction?
Yes. Taylor found that delays lowered evaluations of service, mainly through anger and uncertainty about the wait.