| Course | BFO-7010 Business Forecasting |
|---|---|
| Module | Module 3 |
| Paper type | Discussion forum: initial post and two replies |
| Length | About 489 words |
| Format | Discussion post with APA 7 citations |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for BFO 7010 Module 3
Module 3 Discussion: A Delphi Panel for a New Delivery Route
No History to Model: A Delphi Panel for a Propane Route Into Greenhouse Country
Our company is adding a delivery route to greenhouse growers north of Vineland, a market we have never served. We have no history, so the statistical methods from Module 2 have nothing to work with. Hyndman and Athanasopoulos (2018) say judgment is the only option in exactly this situation, but they warn that it is subject to biases, including anchoring on an initial number, optimism about one's own plans and inconsistency from one forecast to the next.
I would use three structured approaches. First, a Delphi panel of five people: two senior dispatchers, our agricultural sales rep, a grower we already serve and our operations manager. Each would forecast first-year gallons privately with reasons; a facilitator would share the anonymous range and reasons and ask for revised forecasts over two or three rounds. Anonymity matters, because without it everyone would defer to the operations manager, and the written reasons help the panel learn from each other rather than simply averaging.
Second, forecasting by analogy. Our existing farm route serves 40 poultry and produce operations that use about 1.1 million gallons a year. If the greenhouse route signs 25 growers whose heating loads are about 60 percent higher per site, the analogy points to roughly 1.1 million gallons, but the panel should judge how similar the two markets really are.
Third, scenarios. A cold winter with 30 signed growers, a mild winter with 15 and a middle case give planners a range for trucks and storage instead of a single figure.
Lawrence et al. (2006) found that judgment works best when it is structured, when forecasters get feedback on their accuracy and when the reasons behind adjustments are recorded. We should keep a log of the panel's forecast and compare it with actual gallons each month.
Reply to Priya
Priya, you described your manager adjusting the statistical sales forecast upward every quarter because "the model doesn't know about our promotions." Fildes and Goodwin (2007) found that adjustments help when they are based on information the model lacks, like a planned promotion, but hurt when they are small, frequent and based on hunches. One fix would be to require a written reason for each adjustment and track whether adjusted forecasts beat the original model. If your manager's promotion adjustments improve accuracy and the others do not, the record will show it.
Reply to Tom
Tom, your point that sales staff overforecast because they want enough inventory to avoid stockouts is a good example of a forecast turning into a goal. At our company, the opposite happens when quotas are tied to forecasts: reps lowball so they can beat their numbers. Either way, the remedy in the text is the same, separating the forecast from the incentive. Would your company consider having an analyst prepare the forecast from the reps' input rather than letting the reps set it?
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.
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
BFO 7010 Module 3 instructions, in plain terms
The Module 3 forum covers judgmental forecasting and counts toward the 25 percent of the grade carried by the eight discussions. Expect to explain when judgment is necessary, describe biases such as anchoring, optimism and inconsistency, and apply structured methods: the Delphi method, forecasting by analogy, scenario forecasting and the judgmental adjustment of statistical forecasts. The first post should run about 400 to 500 words and replies about 200 to 300. A real new product, market or event from your own work gives the post the concrete case the textbook's methods need. If you have no new product or market of your own, a published example, such as forecasting demand for a new hospital service, works as long as you explain why data are missing.
How the BFO 7010 Module 3 example is put together
The first paragraph explains why statistical methods cannot be used for the new route and names the biases the text warns about. The next three paragraphs apply three structured methods, Delphi, analogy and scenarios, with details that show how each would work and which bias each reduces. The closing paragraph cites research on when judgment works and recommends keeping a forecast log. The replies apply evidence on judgmental adjustments to a classmate's workplace and connect sales force bias to the separation of forecasts from incentives, ending with a question. Numbers in the analogy paragraph, forty farms and a heating load sixty percent higher, show the reader how the comparison would actually be made.
Where the marks sit in the BFO 7010 Module 3 rubric
Each forum is one of eight sharing 25 percent of the course grade. For judgmental forecasting, mentors look for an accurate account of when judgment is appropriate, correct descriptions of the Delphi method and other structured approaches, attention to bias and a specific application. Replies should engage with what classmates wrote and add evidence or a new angle. Posts that meet the length guidance and cite the text and a second source score best, and a forecast log or similar way to learn from errors shows practical understanding. Mentors also notice when a post recognizes the limits of its own method, such as how similar an analogy really is.
BFO 7010 Module 3 help from the desk
Judgmental forecasting posts tend to describe the Delphi method without saying how anonymity and feedback work, or to recommend judgment where data exist. Explain why data are unavailable, then show each method step by step for your case. Name the bias each step guards against. In replies, bring in research on adjustments or incentives rather than simple agreement. A free first sample of a Module 3 forum post for BFO-7010 is available on request. Give the panel's makeup and the number of rounds if you propose Delphi, and say what the facilitator does between rounds. Keep scenarios few and labeled so planners can use them.
Write yours, or have the desk draft it
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BFO 7010 Module 3 questions, answered
Where can I find a free BFO 7010 Module 3 sample paper?
The discussion post above plans a Delphi panel, a forecast by analogy and scenarios for a new propane route, with replies on adjustments and sales force bias.
When should judgmental forecasting be used?
When there are no data, as for a new product or market, or when information the model lacks, such as a promotion, will affect the outcome.
How does the Delphi method work?
Experts forecast anonymously with reasons, see the group's range and reasons and revise over several rounds until views converge.
What biases affect judgmental forecasts?
Anchoring on an initial number, optimism about one's own plans, inconsistency and the influence of senior or vocal participants.
When do judgmental adjustments improve statistical forecasts?
When they rest on specific information the model lacks; small, frequent adjustments based on hunches usually reduce accuracy.