How To Approach OMGT2229 Assignment 3: Forecasting, EOQ And Decision Trees

Short answer: To score well on OMGT2229 Assignment 3, state your assumptions before every calculation, use the same test window when you compare forecasting methods, check EOQ feasibility inside each discount band, and show a full decision tree with probabilities and a rolled-back expected value. Markers reward clear logic and honest interpretation more than a bare final number.

The original assessment brief is available on our question page: OMGT2229 Strategic Supply Chain Assignment 3: Harvey Norman Forecasting, EOQ and Sourcing Decision Tree.

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What the assignment really asks

RMIT’s OMGT2229 Strategic Supply Chain Management Assignment 3 is an individual quantitative and qualitative case study worth 40 marks, split evenly between two tasks. You submit an Excel workbook with labelled tabs and a Word report of roughly 1,500 to 2,000 words. Each task question starts on a new page, and references follow Harvard style.

Task 1 centres on a retailer’s monthly demand for one consumer electronics product. You forecast with two methods, measure error, choose a method, then apply EOQ with an all-unit quantity discount. Task 2 asks you to compare sourcing options (overseas, local, hybrid) with decision trees under demand and currency uncertainty. Both tasks end with a visual and a reflective journal.

The original brief is not yet on our solved questions hub. You can see the working style in our worked OMGT2229 sample, for reference only.

Step-by-step approach

1. Set up the workbook first. Create one tab per question part (data, forecasts, errors, EOQ, trees). Use cell formulas, not typed numbers, so a marker can trace each result.
2. Write down your assumptions. For the static method, decide the seasonal period length and justify it. With 18 months of data, a six-month cycle is easier to defend than a 12-month one, but whichever you choose, say why.
3. Forecast with both methods. A moving average repeats one level; a trend-and-seasonality method follows the shape of the data.
4. Compare on the same periods. Compute MAD, MAPE and tracking signal over identical months, then report the minimum and maximum signal.
5. Choose and defend a method. Link the choice to bias, not just the smallest error.
6. Run EOQ for every price band. If the EOQ sits outside its band, use the band’s nearest limit and cost it there.
7. Build each decision tree. Label decision and chance nodes, probabilities and payoffs, then roll back with a discount rate.
8. Interpret, then recommend. Say what risk each option carries, not only which NPV is highest.

Our experts can also walk you through report layout and Harvard referencing through our report writing help service.

Choosing between forecasting methods

In our worked sample, the static method had the lower error and a tracking signal inside +/-4, while the moving average drifted well above it. Quote the pattern, not just the totals.

EOQ with a discount: what markers check

Markers look for four things: annual demand converted correctly from monthly figures, holding cost tied to the unit price in each band, a feasibility test, and a total cost that includes purchase, ordering and holding cost. A common slip is to compute EOQ once at the base price and stop. Under an all-unit discount, the cheapest price does not automatically win, because larger orders raise holding cost.

Decision trees: a clear layout

Draw left to right. Use a square for decisions and a circle for chance events, write probabilities on branches, and place payoffs at the ends. Do the same layout for each option so that the comparison table at the end is easy to read. State how you interpret currency moves (for example, a stronger home currency raising or lowering your cost) and keep that reading consistent across both periods.

Formulas to show, and how to present them

Write each formula once, then reference the Excel cell that applies it. Markers can then follow your logic without hunting through the workbook.

Round only at the display stage. If you round the EOQ before costing it, your total cost can drift by a few dollars and will not match a marker’s key.

Writing the qualitative parts well

The infographic should teach a reader one clear idea, such as how forecasting inputs move from point-of-sale data to ordering decisions. Keep text short and cite the sources behind each claim. The reflective journal needs a specific moment: for example, what surprised you when the tracking signal flagged bias, or how a currency scenario changed your preferred supplier. Name the learning, then say how you would act differently.

Mark-maximising checklist

• Every table in the report matches the Excel workbook.
• Each task question begins on a new page, as the brief requires.
• Assumptions are listed before the calculations that use them.
• Error measures are compared over the same months.
• EOQ is tested for feasibility in every price band.
• Trees show probabilities, payoffs and the discount factor.
• The recommendation mentions risk as well as expected value.
• The infographic and reflective journal are your own work and cite real sources.
• Harvard references are complete and match the in-text citations.

Common mistakes

• Comparing the two forecasts over different time windows.
• Reporting a tracking signal without saying what the control limits are.
• Using the lowest price band’s order quantity without checking whether it is the cheapest total.
• Leaving out the discount factor, or counting period 0 when the brief excludes it.
• Writing the journal as a summary of the numbers instead of a reflection on what you learned.

What students worry about

Students often ask how to pick a seasonal period when the data run for only a year and a half, whether a hand calculation must match Excel’s built-in trend functions, and how much detail the infographic needs. The safe answer is to be transparent. Say what you assumed, show a formula, and explain what would change if the assumption were wrong. Many also worry about the 2,000-word limit, so keep calculations in the workbook and use the report for interpretation. Our student reviews show how others have used expert feedback on this kind of work.

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FAQs

How many words should the Word report be?

The brief sets 1,500 to 2,000 words. Keep tables and appendices for the workbook or an appendix, and use the body for interpretation.

Which forecasting method should I prefer?

Prefer the one with lower error and a tracking signal that stays within its limits. If demand trends upward, a plain moving average will usually lag.

What does a tracking signal tell me?

It is the running sum of forecast errors divided by the running MAD. A value that drifts beyond roughly +/-4 suggests bias.

Do I always use the lowest price in an all-unit discount problem?

No. Compute total cost for each band at a feasible order size, then compare. The lowest price can lose once holding cost rises.

How should I treat exchange-rate moves in a decision tree?

Convert the foreign price to your home currency at each rate scenario, state which direction raises cost, and apply the same reading in every period.

Can I use AI tools for the infographic and journal?

Check your unit guide and RMIT’s academic integrity rules. The journal in particular should reflect your own thinking and be written by you.

About the expert

Renshu Phua holds a master’s degree in Business Analytics from the National University of Singapore. The profile lists 8 years with Singapore Assignment Help and 2,322 completed orders, with work on essays, case studies and business management reports.

References

• Chopra, S. and Meindl, P. (2019) Supply chain management: strategy, planning, and operation. 7th edn, Global edn. Harlow: Pearson.
• Harris, F.W. (1990) ‘How many parts to make at once’, Operations Research, 38(6), pp. 947-950.
• Hyndman, R.J. and Athanasopoulos, G. (2021) Forecasting: principles and practice. 3rd edn. Melbourne: OTexts. Available at: https://otexts.com/fpp3/ (Accessed: 19 October 2026).
• Hyndman, R.J. and Koehler, A.B. (2006) ‘Another look at measures of forecast accuracy’, International Journal of Forecasting, 22(4), pp. 679-688.