| Course | BU 506 Leading Change and Operational Excellence |
|---|---|
| Unit | Unit 3 |
| Paper type | Root cause analysis |
| Length | About 990 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MBA |
| Updated | October 2026 |
Free sample paper for BU 506 Unit 3
Not the Measurers and Mostly Not the Manufacturers: A Root Cause Analysis of Material Problems Found at Staging in a Composite Flooring Retailer's Special Orders
Student Name
Business, Herzing University
BU 506: Leading Change and Operational Excellence
Instructor Name
January 26, 2026
Not the Measurers and Mostly Not the Manufacturers: A Root Cause Analysis of Material Problems Found at Staging in a Composite Flooring Retailer's Special Orders
The Problem as a Measurable Gap
The baseline in Unit 1 found that 70.9 percent of special orders were install-ready on time, 144 of 203. The process walk in Unit 2 suggested that material problems found at staging cost more time than any other single step. Sorting the 59 late orders from the baseline by the reason recorded on my tracking sheet confirms it.
The problem this analysis works is therefore narrow: 27 of 59 late orders, almost half, failed because the material that arrived was not what the order needed. Within those 27, 15 were short on quantity, 9 had cartons from more than one dye lot and 3 were the wrong item.
| Reason the order was late | Orders | Share of late orders |
|---|---|---|
| Material problem found at staging (wrong quantity, mixed dye lot, wrong item) | 27 | 46% |
| Manufacturer shipped after the acknowledged date | 18 | 31% |
| Order placed late after a customer change | 8 | 14% |
| Other (freight damage, missing paperwork) | 6 | 10% |
Generating Candidate Causes
In the company the explanations are already settled by habit. Showrooms blame the manufacturers, purchasing blames the measurers and the warehouse blames everyone upstream. To widen the search before narrowing it, I used a cause-and-effect diagram of the kind Ishikawa (1986) described, sorting possible causes under people, methods, materials, measurement, machines and environment. Doggett (2005) notes that cause-and-effect diagrams are good at generating possibilities but cannot by themselves show which possibility is true, so each candidate below is paired with a test. I avoided relying on a single chain of why questions; Card (2017) shows that such chains tend to follow one path and miss contributing causes.
Five candidates survived the diagram as plausible for the 27 orders: errors in the original room measurement, errors converting square feet to cartons, manufacturers shipping mixed lots despite instructions, the lot requirement not reaching the manufacturer, and picking errors at the warehouse.
Testing Each Candidate
Each test asks what would be true in the records if the cause were operating, then checks.
Measurement errors
If measurers were wrong, the square footage on the measure sheet would be too small for the room. For the 15 short orders I compared each measure sheet with the installer's final square footage. In 3 the measure was short. In the other 12 the measure was adequate and the shortage appeared later. Rejected as the main cause; contributes to 3 orders.
Carton conversion errors
If conversion caused shortages, recomputing cartons from the square footage on the paper form, using the manufacturer's carton size and the company's waste allowance, would give a higher number than was ordered. It did in 10 of the 12 remaining short orders. In 6 the waste allowance had been left out, in 3 the administrator used an outdated carton size after the manufacturer changed its packaging, and in 1 the result was rounded down. Supported.
Manufacturers ignoring lot instructions
If manufacturers ignored instructions, the 9 mixed-lot orders would carry a single-lot note on the purchase order. Only 2 did. Rejected as the main cause; contributes to 2 orders.
Lot requirement never sent
If the requirement was missing, the other 7 mixed-lot orders would have gone out with no lot note, and the original order would show no lot field. That is what the records show: the paper form and the order screen have no place to record a lot requirement, so it reaches the buyer only if someone remembers to mention it. Supported.
Picking errors at the warehouse
If staging picked the wrong cartons, the correct material would have been in the building. For the 3 wrong-item orders and the remaining 2 short ones, it was not; the manufacturer's packing list matched what we received. Rejected.
The Cause the Data Support
The analysis supports one root cause with two forms. Quantities and lot requirements are translated by hand, at a handoff, into a system that has no field or calculation to hold them. The consultant records square feet, the administrator converts to cartons from a price sheet that may be out of date, and the lot requirement exists only in someone's memory. Together these explain 17 of the 27 material problems, 10 conversion errors and 7 missing lot instructions. Measurement errors, manufacturer lot errors and five unexplained cases account for the remaining 10.
What the Tests Change About the Story
The tests overturn the explanations the company had settled into. Purchasing's view that measurers cause shortages accounts for 3 orders. The showrooms' view that manufacturers ship mixed lots accounts for 2. Both explanations are true in a few cases, which is why they have survived: each group can point to an example. Neither explains the pattern. The pattern is explained by a step nobody owns, the translation of a customer's floor into cartons and lot instructions, which currently happens in three heads at two handoffs, with a price sheet that may be out of date and no field to carry the lot requirement forward.
This matters for the next unit. A fix aimed at measurers, such as retraining, or at manufacturers, such as a stern letter, would leave 17 of the 27 problems untouched. The design in Unit 4 therefore has to change the handoff itself, so that the conversion is done once, by a calculation rather than by memory, and the lot requirement travels with the order to the purchase order and to receiving.
How Confident the Conclusion Is
Confidence is moderately high for the conversion finding, because the recomputation is arithmetic from records and the error types are specific. It is high for the lot finding, because the absence of any lot field is a fact about the form and the screen. Two limits apply. The analysis covers six weeks in late autumn, and a spring period with different products might show a different mix. And the tracking sheet records one reason per late order, so an order with two problems was counted once. Neither limit changes the main point: the most common cause of late special orders is a design feature of the order handoff, not the carelessness of anyone working in it.
References
Card, A. J. (2017). The problem with '5 whys'. BMJ Quality & Safety, 26(8), 671-677. https://doi.org/10.1136/bmjqs-2016-005849
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
Ishikawa, K. (1986). Guide to quality control (2nd rev. ed.). Asian Productivity Organization.
The Herzing BU 506 Unit 3 instructions
In Unit 3, BU 506 asks students to take the largest finding from the process walk and find its cause. The instructions usually require stating the problem as a measurable gap, generating competing explanations with a structured method, testing each against evidence, keeping rejected causes visible with their reasons, and stating how confident the conclusion is. Depth matters less than evidence: the chain of causes should stop where the data stop. When evidence supports two causes, report both with their shares rather than forcing one winner. Incident reports and other internal documents from work are the employer's records, so build tests from your own tallies or the case materials provided.
How this BU 506 Unit 3 example is built
A reasons table opens the paper, sorting the 59 late baseline orders and narrowing the problem to material found wrong at staging, with its three subtypes. Cause generation is described next, with Ishikawa for the diagram, Doggett on what diagrams cannot prove and Card on the weakness of a single chain of whys. Five candidates are then tested under run-in headings, each stating what would be true if the cause were operating and what the records showed. A conclusion section names the supported cause in two forms and accounts for every one of the 27 orders. Two closing sections explain how the tests overturn the company's habits and how confident each finding is.
Reading the BU 506 Unit 3 rubric
Points for the root cause analysis are usually spread across problem definition, cause generation, testing, the supported conclusion and confidence, with writing and sources. Problem definition earns credit when the gap is measured and narrowed to one problem. Cause generation is judged on breadth and on use of a structured method rather than accepting the loudest explanation. Testing is typically the heaviest criterion, and full marks go to analyses that state in advance what evidence each cause predicts. The conclusion scores when it accounts for the cases and admits the unexplained ones. Confidence earns points when limits such as period, sample and recording method are tied to how far the conclusion can be trusted.
BU 506 Unit 3 help: mistakes that cost points
Root cause papers lose marks by naming a cause in the first paragraph and then gathering support for it. Five-whys chains that follow one path and stop at a person are another weak point, because they miss contributing causes and turn analysis into blame. Some writers delete the causes they rejected, which leaves the reader unable to see that the winner beat real alternatives. Others never say how many cases the cause explains. Using incident reports from your workplace raises confidentiality questions. If your evidence is thin, our writers can help you design tests that work with the records you can legitimately use and draft the analysis.
Get a BU 506 Unit 3 example written to your instructions
The Unit 3 instructions and the rubric behind them are what we build from, along with the walk findings and any cause finding method your BU 506 section requires. We write a custom example that quantifies the gap, generates candidates before judging them, gives each a falsifiable test and keeps the rejections visible. First custom sample free, back in 24 to 48 hours.
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BU 506 Unit 3 questions, answered
How deep does the causal chain have to go?
Deep enough to reach something inside your control and no deeper. A chain ending at a step nobody owns is unusable, and one ending at leadership priorities gives the improvement nothing to attach to. The practical stop is the first cause that a redesign could actually change, and saying why you stopped there is usually worth credit.
What if the evidence supports two causes?
Report both and say how they interact, since real processes rarely fail for one reason. Note whether the two are independent or whether one makes the other more likely, and rank them by how much of the measured gap each appears to account for. A ranked pair with honest uncertainty reads far better than a single cause asserted with false confidence.
Can I use an incident report from work as evidence?
No. Incident reports, audit findings, corrective action records and quality logs are documents your organization created and controls, and they often carry names as well. Work from your own understanding of how the process fails, build the evidence the analysis tests against inside the composite case, and keep every figure labeled as constructed for the assignment.
Where can I find a free BU 506 Unit 3 sample paper?
This page carries the complete BU 506 Unit 3 root cause analysis free, including the reasons table, five tested causes and margin notes. Analyzing a different problem? Forward the assignment and a custom analysis is prepared without charge in roughly two days.
How do I test a cause in BU 506 Unit 3?
Say what the records would show if that cause were operating, then check. The sample recomputes carton counts from the original forms and checks purchase orders for lot notes, which supports one cause and rejects two others.