| Course | GF585 Strategic Financial Planning and Forecasting Analytics |
|---|---|
| Unit | Unit 9 |
| Paper type | Variance analysis |
| Length | About 850 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MS in Finance |
| Updated | October 2026 |
Free sample paper for GF585 Unit 9
Owning a $771,888 Miss: A Unit 9 Variance Analysis of a Linen Service's Forecast
[Student Name]
Purdue University Global
GF585: Strategic Financial Planning and Forecasting Analytics
Unit 9 Variance Analysis
[Instructor Name]
[Date]
The linen service, its forecast, monthly results and events are composite and illustrative; the articles cited are real.
The Bridge
The bridge walks from forecast revenue of $16,980,288 to actual revenue of $16,208,400 in four steps, each valued at forecast rates for everything not yet varied.
1. Hotel accounts. The forecast assumed 144 hotel accounts; the year averaged 140. Four accounts at the forecast 2,650 pounds a week and 62.5 cents cost $344,500.
2. Hotel usage. Each of the 140 accounts sent 2,600 pounds a week instead of 2,650. Fifty pounds a week across 140 accounts at 62.5 cents cost $227,500.
3. Hotel price. Renewals settled at 62 cents rather than 62.5. Half a cent on 2,600 pounds a week across 140 accounts cost $94,640.
4. Clinic timing. Two clinics expected to start in January began service only after year end, costing $105,248 of healthcare revenue.
The four pieces sum to $771,888, the full gap. Running the steps in a different order, price before accounts, for example, would move a few thousand dollars between the hotel lines but would not change the total or the ranking.
What the Miss Did to EBITDA
EBITDA fell short of forecast by $252,431, about a third of the revenue miss. The pounds that were never washed, about 1.03 million for the year, carried no labor, gas or utilities, which saved about $317,000. Linen replacement, tied to revenue, fell by about $85,000. And the twenty-third route, planned for the two clinics, was never added, which saved $118,000.
Causes From the Operating Record
Each piece of the bridge is matched to an event, with the source of the evidence.
1. Accounts. A competitor won two hotels in January with a lower price, and two hotel signings in the sales plan never closed. Source: the customer file and the sales team's lost-deal notes.
2. Usage. One large hotel closed a wing for renovation from May through August, and summer occupancy across the region ran about 2 percent below the tourism bureau's spring outlook. Source: route sheets and the bureau's monthly reports.
3. Price. Three large hotel groups accepted a three-year renewal only at 62 cents. Management traded half a cent for contract length. Source: renewal letters.
4. Clinic timing. Both clinics delayed their openings for construction. Source: the clinics' own revised opening dates.
Only the price concession was a decision; the other three pieces were events the forecast had treated as certain, which means the forecast was built on the sales plan rather than on the evidence for each part of it.
Was the Forecast Unlucky or Biased?
A separate question is whether the misses were random. Monthly revenue forecasts were compared with actuals for all twelve months. The forecast ran high in ten months and low in only two, February and November, each by less than half a percent. Mean absolute percentage error was 4.73 percent, and mean percentage error, which keeps the sign of each miss, was 4.61 percent. When the two measures are almost equal, nearly every miss went the same way (Hyndman & Koehler, 2006).
The misses also clustered. May through September carried $532,693 of the total, about 69 percent of the year's miss in five months. Monthly misses ran from 6.6 to 8.1 percent of actual revenue across those months, against less than 4 percent in most of the rest of the year. A random forecast error would not concentrate in the season when hotel volume matters most.
The pattern fits a forecast that took the sales plan and the tourism outlook at face value. Research on forecasting in companies finds that adjustments made by managers tend to be optimistic, particularly when they reflect plans rather than evidence (Fildes et al., 2009). Reviews of judgmental forecasting reach a similar conclusion: forecasters anchor on targets and give too little weight to signals that run against them (Lawrence et al., 2006).
Method Changes for the Next Forecast
Three changes follow from the analysis.
1. Discount the pipeline. Hotel and clinic signings enter the forecast at a probability based on the sales team's past close rate, about half, rather than at full value.
2. Use the latest occupancy data. The summer occupancy assumption is updated each month from the tourism bureau's actual room-night reports, not fixed at the spring outlook.
3. Track the bias. Mean percentage error is reported monthly beside the forecast so a lean in one direction is visible within a quarter rather than at year end.
Limits of the Analysis
The bridge values each step at forecast rates, which is a choice. The usage effect for the renovated hotel and the regional occupancy shortfall cannot be separated precisely from route sheets alone, so they are reported together. The bias test uses one year of monthly data; a second year would show whether the lean is a habit or a single bad season.
References
Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3-23. https://doi.org/10.1016/j.ijforecast.2008.11.010
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Lawrence, M., Goodwin, P., O'Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493-518. https://doi.org/10.1016/j.ijforecast.2006.03.007
GF585 Unit 9 instructions, in plain terms
Unit 9 of GF585 asks you to explain why last year's forecast missed and what will change. Build a bridge from forecast to actual in a stated order, valuing each step at forecast rates for everything not yet varied, and confirm that the pieces sum to the gap. Carry the revenue miss through to profit, explaining why the two differ. Match each piece of the bridge to an event from the operating record and name the source of the evidence. Then ask a separate question: was the error random or biased? Answer it with monthly errors, using a measure that ignores sign beside one that keeps it. Note whether misses cluster in a season. Close with the method changes the next forecast will carry and the limits of the analysis. Cite forecast accuracy research in APA 7.
How the GF 585 Unit 9 example is put together
Four numbered bridge steps open the analysis, each with its arithmetic, reconciling exactly to $771,888, with a note that a different order would shift only a few thousand dollars. A section explains the $252,431 EBITDA gap through about 1.03 million unwashed pounds and a route never added. Each piece is then tied to an event and a record, from a competitor's January wins to renovations and clinic construction delays. The highlighted sentence separates the one deliberate concession from three events treated as certain. The bias section reports ten high months and the two error measures, citing Hyndman and Koehler, then the May to September cluster, citing Fildes and colleagues and Lawrence and colleagues. Three method changes and the analysis's limits close the paper.
Where the points sit in the GF585 Unit 9 rubric
The GF585 rubric for variance work weights a reconciling bridge heavily, so pieces that sum exactly to the reported gap, in a stated order, earn the first block of points. Profit treatment matters next: graders check that revenue and EBITDA variances are reconciled through cost behavior. Assigning each piece to an evidenced event, rather than a guess, earns credit for accountability. The bias test is where analyses separate, because comparing an unsigned error measure with a signed one shows whether the forecast leaned. Instructors in the MS in Finance program value distinguishing controllable decisions from external events and turning the findings into concrete method changes. Hyndman and Koehler, Fildes and colleagues and Lawrence and colleagues, listed in APA 7, supply the research this unit expects.
GF585 Unit 9 help: mistakes that cost points
Many GF585 Unit 9 analyses list variances whose sum falls short of the reported gap, which leaves the reader wondering what is missing. Others report the revenue miss and stop, never explaining the effect on profit. Causes are described as market conditions instead of specific events with records behind them. Only one error measure is reported, so a consistent lean looks like ordinary noise. Seasonal clustering goes unnoticed. The paper ends with a promise to forecast more carefully rather than a named change in method. Whatever gap your case presents, keep this order: a reconciling bridge in a stated sequence, the profit effect explained, an event and source for each piece, a bias test with signed and unsigned errors, the seasonal pattern and specific changes for next year.
Get a GF585 Unit 9 example written to your instructions
Every dollar of the gap gets a cause. Upload the forecast and the actual results your GF585 Unit 9 case pairs, any operating notes that explain the year, and the rubric; the analysis reconciles exactly, tests the monthly errors for bias and names the change the next forecast will carry. Free first sample; 24-48h typical. 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 GF585 papers and MS in Finance samples
- GF585 Unit 1: Water Use Forecast Miss Post
- GF585 Unit 2: Healthcare Share Strategy Memo
- GF585 Unit 3: Linen Service Driver Model Build
- GF585 Unit 4: Quarterly Linen Revenue Forecast
- GF585 Unit 5: Linen Service Bundled Scenario Set
- GF585 Unit 6: Bakery Forecast Model Review Reflection
- GF585 Unit 7: Seven Input EBITDA Sensitivity Table
- GF585 Unit 8: Healthcare Pounds Trend Exercise
- GF585 Unit 10: Planning Report
- GF584 Unit 9: Private Placement Debt Issuance Brief
- GF583 Unit 9: Bank Fees and Relationship Review
- GF582 Unit 9: Credit Scoring Model Comparison
- GF581 Unit 9: Thai Expansion Financing Memo
GF585 Unit 9 questions, answered
Does the order of decomposition really matter?
It moves small amounts between lines, never the total. Valuing the account effect at forecast usage and price, then usage at actual accounts, and so on, gives a bridge that reconciles exactly. Reverse the order and a few thousand dollars shift between effects. The sample states its order once and keeps it throughout, which is the thing graders check.
What if my case gives only annual totals?
Then the bridge and the cause assignment still work, but the bias test needs a series. With only annual data, compare this year's miss with prior years' misses if the case supplies them; several misses in one direction suggest the same lean the sample finds month by month. Say plainly which test your data allowed and which it did not.
Why report mean percentage error as well as MAPE?
Because they answer different questions. Mean absolute percentage error measures how big the misses were regardless of direction; mean percentage error keeps the sign, so misses that cancel show as near zero and misses that lean one way do not. In the sample the two are almost equal, 4.76 and 4.64, which says nearly every miss went the same way.
Where can I find a free GF585 Unit 9 sample paper?
This page holds a full GF585 Unit 9 variance analysis of a linen service's $771,888 revenue miss, with a reconciling bridge, causes from records, a bias test and APA 7 references.
Why did EBITDA fall by only about a third of the revenue miss?
Unwashed pounds saved labor, gas, water and chemicals, linen replacement fell with revenue, and a planned route was never added, so about $519,000 of cost disappeared along with the lost sales.