| Course | MT438 Analytics in the Digital Supply Chain |
|---|---|
| Unit | Unit 10 |
| Paper type | Analytics recommendation memo |
| Length | About 860 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT438 Unit 10
End the Blanket Uplift: An MT438 Unit 10 Analytics Recommendation Memo on Unpromoted Products
[Student Name]
Purdue University Global
MT438: Analytics in the Digital Supply Chain
Unit 10 Analytics Recommendation Memo
[Instructor Name]
[Date]
Oakhurst Lawn and Garden, its executives, its products and every figure are composite; the sources cited are real.
Memo Header
To: Vice President of Supply Chain, Oakhurst Lawn and Garden. From: Supply Chain Analyst, Planning. Date: July 13, 2026. Subject: Planning unpromoted products on the statistical baseline from the fall season.
Recommendation
I recommend that Oakhurst stop applying the sales team's blanket uplift to its 140 unpromoted products and plan them on the statistical baseline forecast from the fall 2026 season onward. Overrides should remain allowed, but only when a planner documents a specific reason, a confirmed promotion or a counted increase in stores carrying the product. Everything below supports or qualifies that sentence.
The Evidence
Three figures carry the case, each computed from spring 2026 records and detailed in the appendix.
First, bias. Across the 140 unpromoted products from January through June, the consensus forecast, which adds the sales team's uplift to the baseline, ran 12.9 percent above actual shipments. The statistical baseline alone ran 1.8 percent above. The uplift added error in one direction on nearly every product.
Second, weighted error. Weighted absolute percentage error across the 140 products was 31 percent for the consensus and 27 percent for the baseline. The baseline is not perfect, but it is better by four points on the measure that weights each product by its volume.
Third, cost. The season ended with about 412,000 surplus bags of unpromoted product, roughly $3.87 million at cost. Most of it will be carried five months to the fall season at Oakhurst's annual carrying rate of 22 percent, about $355,000, before any losses from seed that ages past its germination guarantee.
The uplift added about 412,000 bags nobody bought and roughly $355,000 to carry them to fall.
What It Costs
The change is not free. A backtest that replays spring 2026 on the baseline alone, assuming production would have followed the forecast, shows unpromoted products filling 94.8 percent of demand instead of 96.1. Planning on the baseline would therefore have given up 1.3 points of fill rate on these products. In exchange it would have cut the surplus sharply and its carrying cost with it. I state this cost here, before anyone has to ask, because a recommendation that hides its trade-off loses trust the moment someone finds it. Fildes and Goodwin (2007) found that organizations often keep forecast adjustments because they feel safer, not because anyone has measured whether they help; this memo measures both sides.
Five Limits on This Evidence
1. The evidence covers one season. Spring 2026 arrived late, which may have exaggerated the over-forecast.
2. Weather is absent from the analysis. Neither forecast used it, so neither can be credited or blamed for a late spring.
3. The backtest assumes production would have tracked the forecast exactly. In practice the plants smooth production across weeks, which would soften both the surplus and the fill loss.
4. Fines from lower fill are not estimated. Only one account charges fines, and its products are mostly promoted, but some unpromoted lines go to it.
5. There is no evidence about whether the uplift ever created sales. If extra stock on shelves sells extra bags, the uplift may be worth part of its cost.
Davydenko and Fildes (2013) showed that judgmental adjustments to product-level forecasts tend to be optimistic, with upward adjustments less accurate than downward ones, which supports the direction of this finding but cannot replace a test at Oakhurst.
The Pilot
The fifth limit is the largest, and a pilot can answer it. For fall 2026, sixty unpromoted products will move to baseline planning while sixty matched products, paired by volume, season shape and customer mix, keep the uplift. At season end, the two groups will be compared on surplus bags, fill rate and fines. The planning team will run the pilot within the existing system, at no added cost beyond the analysis time.
What Would Change the Answer
If the pilot products lose more than two points of fill rate against their controls, or draw higher fines, the uplift should return and this memo's conclusion is wrong. If they lose two points or less while carrying markedly less surplus, the baseline should extend to all 140 products in spring 2027.
Appendix: How the Figures Were Computed
Bias is total actual shipments minus total forecast, divided by total actual shipments, for the 140 products over 26 weeks. Weighted error is total absolute weekly error divided by total actual shipments. Surplus is ending inventory of the 140 products on June 27 minus the planned safety stock. Carrying cost applies 22 percent a year, from Oakhurst's finance team, to the surplus at cost for five months, following the standard treatment of holding cost as a share of inventory value (Chopra & Meindl, 2019). The backtest replays each week's demand against inventory built to the baseline forecast with the same safety stock rules.
References
Chopra, S., & Meindl, P. (2019). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson.
Davydenko, A., & Fildes, R. (2013). Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29(3), 510-522. https://doi.org/10.1016/j.ijforecast.2012.09.002
Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570-576. https://doi.org/10.1287/inte.1070.0309
The Purdue Global MT438 Unit 10 instructions
The final MT438 assignment is a recommendation memo that turns the course's analytics into a decision for a named executive. Put the recommendation in the first sentence, saying what changes, for which products or processes and from when. Support it with a small number of figures, each traceable to an appendix or attachment. State the trade-off before any reader has to ask; a memo that hides its cost loses trust. Limits on the evidence belong in their own section, specific enough to be useful, such as one season of data or an idealized backtest. A pilot or test that addresses the largest limit is often the strongest close. Say what result would reverse the recommendation. Keep the main text to about three pages, with an appendix showing how each figure was computed. Assume the vice president reads one paragraph and then decides.
How the MT 438 Unit 10 example is put together
Oakhurst's memo opens with a header to the vice president and a one-paragraph recommendation that names the 140 products, the fall start and the rule for overrides. The evidence section gives three figures, bias, weighted error and carrying cost, and a highlighted sentence puts the surplus in bags and dollars. The cost section admits 1.3 points of fill lost in the backtest, citing Fildes and Goodwin on adjustments kept because they feel safe. Five numbered limits follow, from one late spring to no evidence on whether uplifts create sales, with Davydenko and Fildes on optimistic overrides. The pilot pairs sixty products with sixty controls by volume and customer mix. A short section states that losing more than two points of fill would reverse the advice. An appendix explains each calculation, with Chopra and Meindl on holding cost.
MT438 Unit 10 rubric: what earns full points
Decision first is the standard most instructors apply to a closing MT438 memo, and points are lost when the recommendation surfaces on page two. Evidence earns credit when it is selective and sourced; three well-chosen figures outscore a page of tables. Graders reward a trade-off stated openly, because the course has stressed throughout that every analytic choice gives something up. Limits should be specific to the evidence, not generic disclaimers. A pilot design that directly addresses the largest limit shows the student can turn uncertainty into a plan. Naming the result that would reverse the advice shows intellectual honesty. Professional memo format, concise paragraphs and an appendix a reader can verify contribute to the communication marks. Sources should support specific claims about forecasting judgment and inventory cost, cited in APA format as Purdue Global requires.
MT438 Unit 10 help from the desk
The weakest Unit 10 memos read as essays, with background and methods first and the recommendation buried at the end. Many present every figure from the course rather than the few that carry the decision. Students often omit the cost of their recommendation, which a skeptical executive will find quickly. Limits sections tend to be boilerplate, such as noting that more data would help, without naming which data or why. Pilots are proposed without control groups or measures. Few memos say what would change the answer. Before submitting, read only your first paragraph and ask whether an executive could approve or reject the proposal from it alone. Then check that every number in the body appears in your appendix with its calculation. If one does not, either add the working or cut the number.
Get an MT438 Unit 10 example written to your instructions
Include the analysis your Unit 10 memo draws on, the audience it addresses and the rubric. The composite memo written from them opens on the recommendation, keeps evidence to a few sourced figures, states the trade-off unprompted and lists specific limits with a pilot. Expect the opening custom sample, free, 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.
More MT438 papers and BS in Business Administration samples
- MT438 Unit 1: Supply Chain Data Inventory Post
- MT438 Unit 2: Order Data Quality Audit
- MT438 Unit 3: Supply Chain Metrics Exercise
- MT438 Unit 4: Operations Dashboard Critique
- MT438 Unit 5: Grass Seed Forecast Accuracy
- MT438 Unit 6: Production Cut Seminar Reflection
- MT438 Unit 7: Shipment Tracking Technology Brief
- MT438 Unit 8: Fill Rate Root Cause Analysis
- MT438 Unit 9: Late Load Model Evaluation
- MT437 Unit 10: Warehouse Layout Redesign
- MT436 Unit 10: Category Sourcing Strategy
- MT435 Unit 10: Operations Improvement Proposal
- MT434 Unit 10: Distribution Plan
MT438 Unit 10 questions, answered
Where should the limitations go in a recommendation memo?
After the evidence and before the implementation steps is common, so the reader weighs the recommendation with its limits in view. Some writers put one sentence of limits in the opening paragraph too. Burying them in an appendix reads as evasive. Keep each limit specific, such as a single season or a missing variable, and say what data or test would resolve it.
How long should an analytics memo be?
Short enough to read in one sitting, often two or three pages with supporting figures in an appendix. The main text should carry the decision, the key evidence, the trade-off and the limits. Your prompt may set a length, and a memo addressed to an executive generally rewards brevity. Detailed methods belong in attachments the reader can consult if needed.
Does admitting limits weaken the recommendation?
Usually the opposite. A reader who sees the boundaries of the evidence can judge how far to rely on it, and a recommendation that anticipates objections is harder to dismiss. Pairing each important limit with a way to test it, such as a pilot, shows the writer has thought past the first result. Graders in analytics courses often reward exactly that.
Where can I find a free MT438 Unit 10 sample paper?
This page holds the full MT438 Unit 10 memo recommending baseline planning for 140 unpromoted lawn products, with its evidence, trade-off, limits and pilot.
What would reverse the recommendation in the MT438 Unit 10 sample?
If the sixty pilot products lose more than two points of fill rate against their sixty controls, or draw higher fines, the uplift returns.