| Course | BFO-7010 Business Forecasting |
|---|---|
| Module | Module 8 |
| Paper type | Final project: forecasting plan for an organization |
| Length | About 1,053 words, 6 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for BFO 7010 Module 8
Forecasting the Winter Before It Arrives: A 2025 Delivery Plan for Pinelands Propane
Student Name
MBA in Data Analytics, Thomas Edison State University
BFO-7010: Business Forecasting
Mentor Name
Month Day, Year
Forecasting the Winter Before It Arrives: A 2025 Delivery Plan for Pinelands Propane
1. The Decision the Forecast Serves
Pinelands delivers propane to about 9,000 homes and farms in South Jersey. Two decisions depend on how many gallons it will deliver each month of 2025. Seasonal drivers must be hired and trained by October, and a winter supply contract with the wholesale terminal must be signed in late summer, committing the company to monthly volumes. The forecasting task is therefore defined as monthly delivered gallons for January to December 2025, made in August 2024, with a range wide enough to plan for a cold winter. Hyndman and Athanasopoulos (2018) put problem definition first among the forecasting steps because the horizon and use decide which methods are suitable. Here the horizon is up to sixteen months and the use is capacity, so the plan needs a central forecast, a range and a way to explain both to the operations and finance managers.
2. Data
The plan uses monthly delivered gallons from January 2019 to December 2024, from the dispatch system, and monthly heating degree days for the nearest weather station, together with the 30-year normals for each month. Deliveries average about 380,000 gallons a month, peak in January and bottom out in August. Two data issues were settled before modeling: two months in 2020 included a bulk farm delivery recorded twice, which was corrected from invoices, and gallons from a customer group sold to a competitor in 2021 were removed from all years so the history matches today's customer base.
3. Methods Compared
Each method from the course was fitted to 2019 to 2023 and used to forecast 2024, which none of them saw.
Two findings shape the choice. First, every method that uses weather beats every method that does not, even when the weather fed in is only the 30-year normal; heating demand follows the thermometer, not the calendar. Second, adding autocorrelated errors to the regression improves it slightly and, more importantly, makes its intervals honest. The plain regression's residuals had a lag-1 autocorrelation of 0.36, so its standard errors were understated, a problem the dynamic regression fixes.
| Method | 2024 test RMSE, thousands of gallons | Needs a weather forecast? |
|---|---|---|
| Seasonal naive benchmark | 38.3 | No |
| Additive Holt-Winters | 30.7 | No |
| Multiplicative Holt-Winters | 31.4 | No |
| Decomposition with drift | 26.9 | No |
| Seasonal ARIMA, AR(1) with seasonal MA(1), plus drift | 26.8 | No |
| Regression on trend and heating degree days | 24.2 with normal weather | Yes |
| Dynamic regression, AR(1) errors | 23.7 with normal weather | Yes |
4. The Chosen Model
The dynamic regression explains monthly gallons with a constant, a linear trend and heating degree days, and lets the error follow a first-order autoregressive process (Hyndman & Athanasopoulos, 2018). Refitted on all six years, it estimates that each heating degree day adds about 415 gallons, that underlying demand grows by about 1,050 gallons a month and that about 40 percent of any month's surprise carries into the next. The remaining innovations have a standard deviation of about 19,300 gallons and no meaningful autocorrelation. Pankratz (1991) recommends this structure, a regression that carries the explanation with an ARIMA error that carries the memory, for series driven by an outside variable.
5. The 2025 Forecast
The forecast for 2025 totals about 5.03 million gallons, 4.9 percent above 2024. About two thirds of the rise is the growth trend; the rest comes from a strong finish to 2024 that the error term carries into early 2025 and from 2024 having been a slightly mild year.
The intervals above assume normal weather and so understate the true uncertainty. January heating degree days over the six years varied with a standard deviation of about 50, which adds roughly 21,000 gallons of uncertainty to the January forecast. Combining the two sources, the 95 percent range for January is roughly 600,000 to 710,000 gallons. Petropoulos et al. (2022) note that intervals which ignore uncertainty in the predictors are a common reason forecasts appear more precise than they prove.
| Month | Forecast, thousands of gallons | 80% interval |
|---|---|---|
| January | 654 | 629 to 679 |
| February | 591 | 565 to 618 |
| March | 519 | 492 to 546 |
| April | 399 | 372 to 426 |
| July | 256 | 229 to 283 |
| October | 384 | 357 to 411 |
| November | 493 | 466 to 520 |
| December | 619 | 592 to 646 |
| Year | 5,034 |
6. Scenarios
The scenarios carry the plan. A driver completes about 3,100 gallons per day on winter routes, so the normal January needs about 211 driver-days, close to 10 full-time drivers over 22 working days, while a cold January needs about 230 driver-days, or 10.5 drivers. The plan therefore recommends 10 permanent winter drivers, two trained part-time drivers on call and a supply contract at 5.0 million gallons with a 7 percent upward option, which covers the cold scenario without paying for gallons a warm winter would leave unsold.
| Scenario | 2025 total, thousands of gallons | January |
|---|---|---|
| Normal weather | 5,034 | 654 |
| Cold winter, 15% more heating degree days | 5,326 | 713 |
| Warm winter, 15% fewer heating degree days | 4,742 | 595 |
7. Monitoring and Revision
On the fifth business day of every month, the forecast is set against the gallons actually delivered. The tracking signal, cumulative error divided by mean absolute error, will be reported to the operations manager; a value beyond plus or minus four will trigger a review of the model. Each month the forecast will be updated with the latest actual and with the weather service's 30-day outlook replacing the normal for the coming month. Any manual adjustment, such as for a new poultry farm customer, will be recorded with its reason so its value can be judged at year end, following Fildes and Goodwin's (2007) advice on documenting judgment. The seasonal ARIMA model will run beside the regression as a check that needs no weather input; a gap of more than 10 percent between the two in any month will be investigated before the next driver schedule is set.
8. Limitations
The model assumes the link between weather and gallons is stable. Heat pump installations, which some customers are adding, could weaken it over time, and the trend term would keep forecasting growth if customers began to leave. The plan addresses this by re-estimating the model each spring and by tracking the heating degree day coefficient: a steady fall would be the first sign that the winter peak is flattening.
References
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
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
Pankratz, A. (1991). Forecasting with dynamic regression models. Wiley.
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., ... Ziel, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001
Reading the BFO 7010 Module 8 assignment instructions
The BFO-7010 final project asks you to create and execute a forecasting plan for an organization of your choice, forecasting sales, a budget, future events or any other figure used in a business decision, and to apply what the earlier assignments built. Due at the close of the term for a quarter of the grade, the plan is shown here beside the dynamic regression module. Expect to define the problem, describe and clean the data, compare several methods on a hold-out period, justify the one chosen, forecast with intervals and explain how the forecast will be used and checked. The distributor here is a composite.
How this BFO 7010 Module 8 example is built
The plan opens with the decision the forecast serves, its horizon and its users, then describes the data and the two cleaning problems that were fixed. A single table ranks seven methods on the same test year and notes which need a weather forecast. The chosen model is explained in words, with its coefficients read in gallons. The forecast table gives monthly figures with intervals, followed by an honest adjustment for weather uncertainty. The scenarios section converts gallons into drivers and a supply contract, and the plan closes with a monitoring routine and the limits that would make the model stale. The sections follow the order of a manager's questions.
BFO 7010 Module 8 rubric: what earns full marks
The final project carries a quarter of the BFO-7010 grade, so it is the single largest piece of written work in the course. Mentors look for a clearly defined forecasting problem, data that are described and cleaned, methods compared fairly on unseen data, a justified choice of model and a forecast that is put to use. The fourth course objective, designing a forecasting plan for an existing organization, is the core of the grade, so the step from forecast to decision is where the strongest papers separate themselves. Prediction intervals, a few scenarios, a checking schedule and frank limits show the judgment this course rewards. Mentors also check that the plan names who will use the forecast, how often it will be refreshed and what happens when actual gallons fall outside the interval.
BFO 7010 Module 8 help: mistakes that cost marks
Final projects most often fall short by forecasting without saying what decision the forecast serves, by choosing the most complex model without comparing it to a benchmark and by reporting intervals that ignore uncertainty in the predictors. Start from the decision and its timing. Compare every method you learned on the same hold-out period. Report ranges and scenarios, not only a single number, and translate them into the units managers use, such as staff or contract volume. Close by saying when actual gallons will be compared with the forecast. A free first sample of your own BFO-7010 final project plan is available on request. Keep the plan's length proportionate, with tables doing the heavy lifting.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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BFO 7010 Module 8 questions, answered
Where can I find a free BFO 7010 Module 8 sample paper?
The propane forecasting plan above is a complete final project: decision, data, seven methods compared, a dynamic regression forecast with intervals, weather scenarios and a monitoring routine.
What is a dynamic regression model?
A regression whose error term follows an ARIMA process, so the model explains the series with predictors while using the autocorrelation left in the errors.
What should a BFO-7010 forecasting plan include?
The decision and horizon, the data and how it was cleaned, a comparison of methods on unseen data, the chosen model, a forecast with intervals and a way to monitor it.
What is a tracking signal?
The running sum of forecast errors divided by the mean absolute error; values far from zero warn that a forecast is biased.
Why do weather-based forecasts need scenarios?
Because the future weather is itself uncertain, so a single forecast with normal weather hides the range a planner must be ready for.