| Course | MT435 Operations Management |
|---|---|
| Unit | Unit 6 |
| Paper type | Seminar reflection |
| Length | About 820 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT435 Unit 6
Why 98 Percent Busy Means Nine Hours of Waiting: An MT435 Unit 6 Seminar Reflection on Queues at the Booth
[Student Name]
Purdue University Global
MT435: Operations Management
Unit 6 Seminar Reflection
[Instructor Name]
[Date]
Pacolet Bunk & Loft, the owner's plan and the classmates are composite; the waiting figures come from a standard single-server queue model, and the sources cited are real.
The Plan I Defended
The owner's reasoning seemed obvious to me. The booth is the constraint, as Unit 3 showed, and every hour it sits idle is an hour of beds the shop cannot sell. Goldratt and Cox (2004) had taught me that idle time at the limiting step can never be recovered. So once a second shift is added, the booth should be scheduled to spray 98 percent of its paid hours, with orders lined up so it never waits.
What the Session Showed
The instructor did not dispute that the booth should never starve. She drew a curve instead. For a station that takes about 11 minutes per bed, with beds arriving and taking time at random, average waiting time in the queue rises slowly at first and then sharply. At 80 percent utilization the average bed waits about 0.73 hours for the booth. At 95 percent it waits about 3.48 hours. At 98 percent it waits about 8.98 hours, more than a full shift. The numbers come from the simplest queue model, but Kingman (1961) showed that the same steep climb appears for any single server under heavy load: as the busy share nears one, the term that divides by what is left of the idle share drives the wait upward.
Then Aaliyah, a classmate who runs specimen analyzers in a hospital laboratory, described the same curve from her own floor. Turnaround times, she said, stay steady until volume pushes the analyzers past about nine-tenths busy, and then they collapse; a morning rush that loads the machines to 95 percent produces results hours late. The pattern is not a quirk of spray booths.
Another classmate, Tom, argued the opposite. Idle booth time, he said, is paid time wasted, and a shop that plans its constraint at 85 percent is paying a sprayer to stand around 15 percent of the time.
Running the booth at 98 percent does not make more beds; it makes a queue nine hours long in front of the same number of beds.
How My View Changed
Tom's point stayed with me, but the curve answered it. The booth's output is capped by its spraying time, so loading it to 98 percent instead of 85 percent adds at most a few beds a day of output, while the average wait in front of it rises from about one hour to nine. Little (1961) proved that the number of items in a queue on average equals their arrival rate times their average time in it, so a nine-hour wait means about nine hours of beds, roughly 48 of them, sitting on carts in the finishing building. Those beds tie up money, fill the floor and arrive late to customers.
The resolution is that the booth should never starve, but it should not be planned to be busy every minute. I now think the shop should plan booth loading near 85 percent of paid hours, keep a small buffer of sanded beds in front of the booth so it never runs out of work, and treat the remaining time as capacity for the color changes and re-sprays that arrive unpredictably. At 85 percent the model puts the average wait at about 1.04 hours. Hopp and Spearman (2011) describe exactly this trade between utilization, variability and waiting as one of the basic laws of factory behavior.
What I Would Tell the Owner
If I could make one case to the owner, it would be this: the second shift should be planned for about 85 percent of its hours, not 98, and the difference is not idle time but protection. The booth will still spray nearly all day, because color changes and re-sprays take up the margin, and the beds waiting in front of it will drop from most of a day to about an hour. That shorter queue means fewer carts in the finishing building, fewer dinged parts and dates the shop can keep.
The Question I Could Not Settle
What I still cannot answer is how to plan for re-sprays. Second passes run at roughly 7 in every 100 beds, and they come back unpredictably: a run of dusty days can send three or four back in an afternoon. The simple model assumes arrivals are random but steady on average, and rework may be lumpier than that. If it is, the right planned loading may be lower than 85 percent. I would need a few weeks of the booth's actual arrival times, including re-sprays, to know.
References
Goldratt, E. M., & Cox, J. (2004). The goal: A process of ongoing improvement (3rd rev. ed.). North River Press.
Hopp, W. J., & Spearman, M. L. (2011). Factory physics (3rd ed.). Waveland Press.
Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902-904. https://doi.org/10.1017/S0305004100036094
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
Reading the MT 435 Unit 6 instructions
In MT435 Unit 6 the seminar turns to queues, and the reflection afterward has to show the writer understands why waiting rises so sharply as a resource approaches full use. The plan or belief held before the session should be stated clearly so a change can be seen. What the instructor showed and what classmates argued belong in the reflection as ideas, including the best case against the view the writer ends with. The reasoning should explain the mechanism, not just repeat the curve, and should reconcile the lesson with the earlier one about never wasting time at a constraint. A revised position with a number, and an open question with the data that would answer it, complete the reflection. First person is expected throughout.
Inside the MT435 Unit 6 example
The reflection begins with the owner's 98 percent plan and why it seemed right, citing Goldratt and Cox. The next part records the instructor's curve with its three figures and Kingman's result on heavy traffic, a laboratory classmate's matching experience and another classmate's objection about wasted pay, under a highlighted line on what 98 percent loading actually produces. The writer then explains how the curve answers the objection, using Little's law to turn the wait into beds on carts, and reconciles the constraint lesson with a buffer, citing Hopp and Spearman. A short section states what the writer would tell the owner. The last part asks how lumpy re-sprays affect the right loading and what data would answer it.
Reading the MT435 Unit 6 grading rubric
What a reflection on queues has to show is an understanding of why waiting explodes near full utilization, not merely that it does. Restating the instructor's chart with no link to the operation earns little. Rubrics reward explaining the mechanism, randomness in arrivals and service with little spare capacity to absorb it. Credit goes to reconciling the queueing lesson with the bottleneck lesson rather than treating them as contradictory. Giving the opposing argument its due shows fairness. A revised planned loading with a reason scores better than a general preference for slack. An open question tied to specific data shows the reflection leads somewhere. First-person writing and the queueing sources in APA 7 complete the expectations.
MT435 Unit 6 help: mistakes that cost points
Weak reflections on this topic usually report that the instructor showed waiting rises with utilization and conclude that machines should not be run too hard. Another common gap is the mechanism, left out entirely. Students often present the bottleneck principle and the queueing curve as opposites without reconciling them. The opposing classmate's argument is frequently dismissed in a sentence. Many reflections give no revised number. Before submitting, write in one sentence why waiting at 98 percent loading is so much longer than at 85 percent, without using the word variability. If you can, your reflection explains the curve; if not, it is still describing it.
Get an MT435 Unit 6 example written to your instructions
Recount your Unit 6 session briefly, or paste the alternative assignment offered to those who could not attend, plus your rubric. The composite reflection we return anchors the change of view in one operation and a few correct figures, and gives the opposing argument its due. The first custom sample, free, reaches you in 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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MT435 Unit 6 questions, answered
Do I need queueing formulas in a seminar reflection?
Rarely in full. Since the point of a reflection is the change in your view, one or two figures from the session, quoted accurately, usually do more than a derivation. If you include a number such as average wait at a given utilization, say where it came from. Your rubric may ask you to connect to course readings, a natural place for the formula's name.
Why does waiting grow so fast near full utilization?
Because a busy station has little spare time to recover from random bursts of arrivals or slow jobs. Each burst leaves a backlog that clears only during idle moments, and near full loading those moments almost disappear. The effect is strongly nonlinear: moving from 90 to 95 percent busy can roughly double the average wait.
Can the reflection draw on my own workplace?
It often can, and plenty of students find that a real queue at work sharpens the reflection, whether a help desk, a pharmacy counter or a loading dock. Leave the employer unnamed if that is wiser. Keep the connection to what the seminar discussed, since the reflection is meant to show the session's effect on your thinking.
Where can I find a free MT435 Unit 6 sample paper?
The full MT435 Unit 6 reflection is on this page, testing a plan to run a spray booth at 98 percent against queueing figures.
What loading does the MT435 Unit 6 sample recommend for the booth?
About 85 percent of paid hours, with a small buffer of sanded beds so the booth never starves, which cuts the average wait to about one hour.