| Course | PM 360 Models, Methods, and Artifacts (PM/360) |
|---|---|
| Week | 3 |
| Paper type | Data gathering and analysis methods paper |
| Length | about 1,011 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | BS in Business |
| Updated | October 2026 |
Free sample paper for PM 360 Week 3
Why Pantries Got the Wrong Cases: Data Gathering and Analysis Methods After a Food Bank's First Two Weeks in a New Warehouse
[Student Name]
University of Phoenix
PM/360: Models, Methods, and Artifacts
Week 3 Assignment
[Instructor Name]
[Date]
Valley Harvest Food Bank, its data, counts and findings are composites written for a model paper.
Valley Harvest Food Bank, the invented Fresno nonprofit followed in this course, moved its warehouse into a new distribution center with three cold rooms and a scanner-based warehouse management system. Two weeks after go-live, partner pantries began calling about errors: cases of canned beans instead of canned corn, a pallet three cases short, frozen chicken missing from an order. Several pantry directors said the old paper system had been more reliable. The project manager, Inés Carrillo, needed to know whether this was a real decline or a few loud complaints, and if real, what to fix first. This paper describes the data gathering and analysis methods she used.
Framing a Measurable Question
The current standard notes that data gathering and analysis methods help teams understand situations, find causes and make decisions, and that the choice of method depends on the question (Project Management Institute [PMI], 2021). Carrillo turned the complaints into two questions: what share of order lines leave the building wrong, and which types and causes of error account for most of them? In the old warehouse, pantry complaints implied an error rate of about 1 percent of order lines, a baseline the team could compare against.
Gathering the Data
Four methods were used, each chosen for what it could show.
A check sheet at the loading dock: for ten working days, the dock lead checked every fifth order before loading and tallied errors by type: wrong item, short quantity, over quantity, wrong pantry, damaged and missing cold item. Checking a sample kept the burden manageable; tallying by type made later analysis simple.
The scan log: the warehouse system records every scan with the location, item and picker. The inventory manager exported ten days of records and flagged picks where the scanned location did not match the assigned one.
Observation: Carrillo and a warehouse lead watched 40 picks across a morning and an afternoon shift, noting what pickers did at each location, without intervening.
Short interviews: ten-minute conversations with eight pickers, four volunteers and three pantry directors, asking what had changed and where mistakes happened.
What the Check Sheet Showed
Over ten days the dock checked 1,640 order lines and found 312 errors, a rate of about 1.9 percent of lines in the sample, nearly double the old baseline. By type: wrong item 141, short quantity 79, missing cold item 41, over quantity 28, wrong pantry 14 and damaged 9.
Pareto Analysis
Ranked by count, wrong item accounted for 45 percent of errors and short quantity for 25 percent, together about 70 percent. Missing cold items added 13 percent. A Pareto chart, with bars for each type in descending order and a cumulative line, showed the classic pattern of a few categories driving most of the problem. The team decided to focus on wrong items and short quantities first, while treating missing cold items separately because of food safety.
Two error types out of six caused seven errors in ten, so fixing everything at once would have wasted effort on the other four.
Searching for Causes
The team drew a cause-and-effect diagram with six branches. Under methods: pick paths changed and pickers were unsure of the sequence. Under materials: similar cans from the same donor stored side by side. Under equipment: scanners occasionally failing to read in the freezer. Under people: many volunteers new to scanning. Under environment: dim lighting in two aisles. Under measurement: location labels placed below the beam rather than above, so pickers scanned the label for the slot below. Doggett (2005) compared root cause analysis tools and pointed out that a fishbone sketch helps a group list and sort suspects, yet it cannot say which suspect actually did it; other tools and data are needed for that.
Testing the Leading Explanation
The scan log offered a test. If label placement caused wrong items, mismatched scans would cluster at locations where two different items sat one level apart. They did: 63 percent of mismatched scans in the dry aisles came from adjacent vertical slots, compared with 18 percent from horizontally adjacent ones. Observation agreed: pickers often scanned the nearest label at eye level, which belonged to the slot below. For short quantities, interviews pointed to the scanner screen, which showed quantity in small type below the item name; several pickers said they assumed one case when the order called for several.
De Koster et al. (2007), reviewing research on warehouse order picking, described picking as one of the most labor-intensive and costly warehouse activities and noted that layout, storage assignment and routing strongly affect both speed and errors. Their review supports treating the layout of labels and similar items as a design problem rather than as a training problem alone.
Recommendations
Three fixes followed, in order of expected effect. First, move location labels so each sits directly above its slot, with colored bands by level. Second, ask the vendor to enlarge the quantity field on the scanner screen and require a quantity confirmation for lines over one case. Third, separate visually similar items from the same donor by at least one slot. The cold-item problem received its own check: a cold-room pick list printed at the start of each shift until the freezer scanner issue is fixed.
Checking the Result
The same check sheet will run for ten more days after the fixes. The target is an error rate at or below the old 1 percent baseline. If wrong items fall but short quantities do not, the team will revisit the screen design with the vendor.
Conclusion
Turning complaints into a measurable question, gathering data with four complementary methods and analyzing it with Pareto and cause-and-effect tools showed that the error rate had nearly doubled and that two error types accounted for most of it. A test using the scan log confirmed label placement as the leading cause. The result is a short list of targeted fixes and a way to know whether they worked.
References
de Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501. https://doi.org/10.1016/j.ejor.2006.07.009
Doggett, A. M. (2005). Root cause analysis: A framework for tool selection. Quality Management Journal, 12(4), 34-45. https://doi.org/10.1080/10686967.2005.11919269
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK guide) (7th ed.). Project Management Institute.
What the PM 360 Week 3 instructions ask
Week 3 in PM 360 typically asks students to explain data gathering and data analysis methods used in projects and to apply them to a problem or decision. Data gathering methods may include interviews, focus groups, surveys, observation, check sheets, benchmarking and document review; analysis methods may include root cause analysis, Pareto analysis, cause-and-effect diagrams, process flows, variance and trend analysis, decision trees or simulation. Prompts often ask students to select suitable methods for a scenario, describe how they would be used and interpret the results. Present any data clearly, explain why each method suits its purpose and lean on the standard plus at least two research articles, cited in APA.
How this PM 360 Week 3 example is built
The example opens two weeks after go-live, when pantries began reporting wrong items and short cases. It turns that complaint into a measurable question: what share of order lines are wrong, and which causes account for most of them? Four gathering methods follow: a check sheet kept by the loading dock for ten days, the warehouse system's scan log, observation of 40 picks on two shifts and ten-minute interviews with pickers and pantry staff. A Pareto analysis of 312 errors shows that two error types make up about 70 percent. A cause-and-effect diagram groups possible causes, and a simple test comparing two picking zones confirms the leading cause, a label layout problem. Three fixes and a follow-up check close the paper.
PM 360 Week 3 grading rubric: where the points go
Graders award most credit for a clear question and methods that fit it. Strong papers define the problem in measurable terms, choose gathering methods that produce the data needed, combine numbers with observations or interviews and analyze the data with appropriate tools. Interpretation matters: the analysis should lead to a conclusion and a recommendation, and claims should be tested where possible rather than assumed. Graders also look for awareness of data quality, sample limits and bias. Research on root cause analysis or the operational setting supports the work. Well-presented data, logical headings and correct APA citations complete an excellent paper.
PM 360 Week 3 help: mistakes to avoid
A common shortfall is describing many methods without collecting or analyzing anything. Even with invented data for a scenario, show what the check sheet or survey would capture and what the analysis would show. Another frequent problem is jumping from a cause-and-effect diagram to a fix without checking which cause matters; the diagram lists possibilities, it does not prove them. Students also forget to define the problem measurably, so they cannot tell whether a fix worked. State a baseline. Some papers use surveys when observation would be faster and more accurate for a physical process. Match the method to the question. If you are unsure how to present your Pareto data, a tutor can help you lay it out.
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- PM 360 Week 4: Planning and Tracking Artifacts
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PM 360 Week 3 questions, answered
What does PM 360 Week 3 usually cover?
It usually covers data gathering methods such as interviews, surveys, observation and check sheets, and analysis methods such as Pareto charts, cause-and-effect diagrams, root cause and variance analysis, applied to a project problem.
Where can I find a free PM 360 Week 3 sample paper?
The Week 3 paper above investigates order errors after a food bank's warehouse move using a check sheet, scan logs, a Pareto chart and a cause-and-effect diagram, free to read.
What is a Pareto chart used for?
It ranks categories of problems or causes by frequency or impact so a team can see the few categories that account for most of the effect and focus on them first.
What is a cause-and-effect diagram?
A diagram, also called a fishbone or Ishikawa diagram, that organizes possible causes of a problem into categories such as people, methods, materials, equipment and environment.
What is a check sheet?
A simple form for recording how often specific events or defects occur, usually by tallying them in categories as they happen, which produces data for later analysis.
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