Naive, moving-average and seasonal methods tested on six held-back months, plus a bottom-up launch estimate for a new chain: MT236's Unit 4 demand forecast exercise, completed. Searches like "mt 236 unit 4 assignment example", "mt236 unit 4 sample" and "mt236 unit 4 example" land here.
What a finished MT236 Unit 4 demand forecast exercise looks like
Four pages with one chart and three tables. The chart plots 24 months of five-ounce case shipments, 23,400 in year one and 24,870 in year two, rising each September through December. Table one tests three methods on July to December of year two, each forecasting a month ahead: last month's actual scores a mean absolute deviation of 120 cases, a three-month moving average 217, and last year's same month scaled by first-half growth of 6.1 percent just 45. Table two builds the new account from the bottom: 85 stores, four flavors and 1.2 bottles per flavor per store each week, about 147 cases a month, plus a one-time 238-case opening order. Table three combines both into a first-quarter forecast of roughly 6,290 cases.
How a MT236 Unit 4 example is structured
Existing business and new business are forecast separately, because only one of them has a history. The existing side comes first: the chart names the pattern, a seasonal rise into football season and the holidays plus modest growth, since the pattern should decide which methods are worth testing. Three methods simple enough for an introductory course are defined, each with its formula, and scored on months they never saw, because accuracy on fitted data flatters every method. The moving average's weakness is explained rather than just reported: it lags a rising season by design. The new account is then built bottom-up from store count, flavors, a borrowed sales rate and a stated discount for an unknown brand. Pipeline fill is kept apart from ongoing demand. An assumptions box lists six inputs and names the one that would move the total most if wrong.
The pattern chooses the methods
Two years of monthly shipments show growth near six percent and a climb from September through December. A method blind to seasonality is included only to show what ignoring it costs.
Six months the methods never saw
Each method forecasts July through December of year two one month ahead. Mean absolute deviation is computed on those six months only, so no method is graded on months it was fitted to.
Why the moving average trails
Averaging the previous three months drags each forecast toward the lower months behind it. In a rising season that produces an average miss of 217 cases and a forecast that comes in low every single month.
A chain with no history
The Southeast account is built from 85 stores, four flavors and 1.2 bottles per flavor per store each week, which is the Texas chain's rate of 1.6 cut by a quarter for a brand nobody there knows yet.
Filling shelves is not selling
Six bottles per flavor per store plus two weeks of stock at the chain's warehouse make a 238-case opening order. The forecast books it once, in January, and never repeats it.
Six assumptions in one box
Growth rate, seasonal shape, store count, flavor count, borrowed sales rate and the brand discount are listed together. The sales rate is flagged as the input most likely to be wrong and the one with the largest effect.
Where marks go in MT236 Unit 4
A number with no method or assumptions beside it is the weakest forecast a Unit 4 paper can submit, because this assignment generally rewards the reasoning as much as the result. Also common is scoring methods on the very months that built them, since in-sample accuracy flatters whichever method is chosen. Error left unmeasured draws deductions; a mean absolute deviation or a similar figure is expected even at an introductory level. Choosing a moving average for seasonal demand without noting the lag is a common conceptual loss. New products or accounts forecast from history they do not have produce numbers with no basis. Treating an opening order as recurring demand overstates the business and later inflates inventory. Slips in simple averages are frequent and costly, because instructors commonly recompute them.
Get a MT236 Unit 4 example written to your instructions
Share the history or case data your Unit 4 prompt supplies, and say whether the forecast covers existing products, a new one or both. Include the rubric. Methods tested on held-back months and every assumption listed: that first custom sample costs nothing and comes back within 24-48h. Inputs the case lacks appear in the assumptions box, never hidden inside a formula.
MT236 Unit 4 questions, answered
Which forecasting methods does an introductory course expect?
Usually the simple ones: a naive forecast, moving averages, weighted moving averages and basic exponential smoothing, sometimes with a seasonal adjustment. The example uses three because comparing them teaches more than perfecting one. More advanced methods rarely earn extra credit at this level unless the prompt asks, and they make the assumptions harder for a reader to check.
What is mean absolute deviation?
The average size of the forecast errors, ignoring whether each error was high or low. Subtract each forecast from the actual figure, drop the minus signs, add the results and divide by the number of periods. It comes out in the same units as the forecast, cases here, which makes it easy to explain to whoever plans production.
What if the product or customer has no history?
Then the forecast is built from parts that can be estimated: the number of stores or customers, how many items each carries, and a sales rate borrowed from something similar, adjusted for known differences. Every part is stated as an assumption. The example borrows the Texas chain's rate and cuts it by a quarter because the brand is unknown in the Southeast.