| Course | MT300 Management of Information Systems |
|---|---|
| Unit | Unit 8 |
| Paper type | Analytics report memo |
| Length | About 750 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 MT300 Unit 8
Late Changes Caused Nearly Half the Return Trips: An MT300 Unit 8 Analytics Report for an Event Rental Company
[Student Name]
Purdue University Global
MT300: Management of Information Systems
Unit 8 Analytics Report
[Instructor Name]
[Date]
Larkfield Event Rentals and its data are composite; the methods and sources cited are real.
Report
To: Operations manager From: Operations analyst Subject: Return trips, last season
Late order changes caused nearly half of last season's return trips.
Return Trips by Cause
Bar chart, sorted largest first, each bar labeled with count and cost at $165 a trip:
Late order changes: 88 trips, $14,520
Loading misses: 41 trips, $6,765
Wrong site or access information: 27 trips, $4,455
Customer additions on site: 22 trips, $3,630
Equipment failure: 12 trips, $1,980
Total: 190 trips, $31,350.
When Late Changes Arrive
A second chart shows the 88 late changes by hours before delivery. Of these, 71 arrived within twenty-four hours, after the pick ticket had printed and often after the truck was loaded. Only 17 arrived earlier.
The problem is not that customers change their orders; it is that the changes arrive after the truck is already loaded.
Three Actions
1. Change cutoff with a reprinted ticket. Changes within twenty-four hours require a call to dispatch, who reprints the ticket and tells the warehouse. Owner: dispatch lead. Start: March 1. Expected reduction: about 55 of the 88 late-change trips.
2. Second-person check on large loads. Any order over thirty lines is checked by a second person before the truck leaves. Owner: warehouse lead. Start: April 1. Expected reduction: about 30 of the 41 loading misses.
3. Site confirmation text two days out. Customers confirm gate codes, access and setup location by text. Owner: sales lead. Start: April 1. Expected reduction: about 20 of the 27 site-information trips.
Together: about 105 fewer trips, or $17,325 a year.
Method
Data. The source was 1,380 delivery records with a free-text note field, exported to 38 pages. Dispatchers wrote return-trip reasons in their own words.
Coding. I read every note recording a return trip and built five categories from them. A second person, the office manager, coded a random sample of forty notes without seeing my codes; we agreed on 36 of 40. Disagreements were mostly between late changes and customer additions on site, which I resolved by asking whether the customer had contacted the office before the truck arrived.
Three layers of analysis. Provost and Fawcett (2013) distinguish analysis that describes what happened from analysis that supports decisions. The counts are descriptive. The causes and the timing chart are diagnostic. The three actions are prescriptive and rest on estimates, not observations, which is where the evidence thins.
Expected effects. Each estimate is based on the share of trips the action would plausibly prevent: late changes caught by the cutoff, misses caught by a second check, site errors caught by confirmation. The total of about 105 trips is close to the 55 percent reduction the Unit 3 business case assumed, which this season's data roughly support.
Why One Page
The export ran to 38 pages because it listed every delivery. The manager does not need every delivery; she needs to know what to change before April. Laudon and Laudon (2022) describe management information as data organized for a decision, and this report keeps only what serves the decision about return trips. The method and appendix remain available for anyone who wants to check the counts.
What This Means for the Business Case
The Unit 3 business case expected the new platform to cut return trips by 55 percent. This report shows that most of the reduction can begin before the new platform arrives, through the change cutoff and the site confirmation text, which cost almost nothing. The platform makes the cutoff easier to enforce, because changes are visible to dispatch the moment they are entered, but the process change matters more than the software. That finding strengthens the case rather than weakening it: the benefits are real, and the company can start collecting them this spring.
What the Data Cannot Show
Trips never logged, such as a driver fixing a problem on the way back, are missing. Notes vary in detail, so some causes may be misclassified. Cost per trip is an average and varies with distance. Few (2012) cautions that a chart can imply more certainty than its data hold, so the bars show counts, not percentages, and the estimates are labeled as estimates.
References
Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten (2nd ed.). Analytics Press.
Laudon, K. C., & Laudon, J. P. (2022). Management information systems: Managing the digital firm (17th ed.). Pearson.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59. https://doi.org/10.1089/big.2013.1508
MT300 Unit 8 instructions, in plain terms
In Unit 8 students convert raw records into a short report that drives a management decision. The report should open with its main finding stated plainly, often as a headline sentence. One or two well-designed charts usually carry the evidence. Instructors expect recommended actions with owners, dates and expected effects. A method section should explain where the data came from, how it was prepared and what it cannot show. Many prompts ask students to distinguish descriptive, diagnostic and prescriptive analysis. Where data are free text, the coding process and any check on it should be described. The report should be short at the front and detailed at the back, so that a busy reader can act on page one alone.
How the MT 300 Unit 8 example is put together
The sample opens with the headline that late order changes caused nearly half of last season's return trips. A bar chart lists five causes with counts and costs, totaling $31,350. A timing chart shows the cliff in changes arriving within twenty-four hours, under the highlighted line that the problem is changes arriving after the truck is loaded. Three actions follow, each with an owner, start date and expected reduction. The method section explains coding of free-text notes and the second-person check, and labels each layer of analysis using Provost and Fawcett. Sections explain why the report fits on one page and what it means for the business case. Limits draw on Few's caution about charts.
Reading the MT300 Unit 8 grading rubric
Analytics reports in MT300 are mostly judged on clarity of the finding, quality of visuals, actionability of recommendations and transparency of method. Graders reward reports that lead with a conclusion. Charts should be sorted, labeled and simple. Actions should be specific, assigned and dated, with expected effects stated as estimates. The method should explain data preparation, especially coding of free text, and any reliability check. Distinguishing descriptive, diagnostic and prescriptive layers shows analytical maturity. Limits of the data should be named. Connecting results to earlier course work, such as the business case, strengthens the report. Data and analytics sources should be cited in APA 7. Reports that bury the finding under method usually lose marks.
MT300 Unit 8 help from the desk
Analytics reports often reproduce the raw export with summary statistics and no conclusion. Another common gap is recommending actions without owners or dates. Some students present percentages without counts or costs, which managers act on less readily. Free-text data is frequently ignored because it is hard to analyze. Limits are often missing. Charts may be cluttered or unsorted. Before drafting, decide what decision the manager faces and what single sentence would change it. Then build the chart that proves that sentence. Bring your data export, the decision your manager faces and the rubric, and the report can be written backward from that decision.
Get an MT300 Unit 8 example written to your instructions
Raw data, a narrative case or a ready-made dashboard: whatever your Unit 8 prompt supplies becomes a finding, a chart and owned actions, built in the tool your course expects and checked against your rubric. Allow 24-48h for it, with units and costs on every figure. First custom sample: free, and written to your own instructions. 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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MT300 Unit 8 questions, answered
What makes an analytics report actionable?
A finding stated plainly, evidence a manager can check, and actions with owners, dates and an expected effect. The test is whether someone could act on Monday without asking the author a question. Reports that describe data thoroughly but stop short of a decision are the usual reason this unit scores lower than expected.
Is statistical software needed for MT300 analytics?
Rarely. Most prompts at this level expect counts, percentages, trends and simple comparisons, which a spreadsheet handles well. The grade rests on choosing the right measure and explaining it for a manager. Where a section supplies a specific tool or dataset, use it, and describe the method in a sentence or two rather than a page.
How is free-text data turned into categories?
Read a sample, draft a short set of categories that cover it without overlapping, then code every record and have someone else code a subset to check agreement. Report the categories, the agreement rate and any records that fit nowhere. That brief paragraph is what makes counts built from handwritten or typed notes believable.
Where can I find a free MT300 Unit 8 sample paper?
The complete MT300 Unit 8 analytics report is on this page, tracing 190 return trips at a rental company to five causes.
What three actions does the MT300 Unit 8 sample recommend?
A change cutoff with a reprinted ticket, a second-person check on large loads and a site confirmation text two days before delivery.