GB527's Unit 9 demand forecast, shown complete, tests three methods, a moving average, exponential smoothing and seasonal indices, on a held-out year, scoring each on MAD, MAPE and tracking signal. Searches like "gb 527 unit 9 assignment example", "gb527 unit 9 sample" and "gb527 unit 9 example" land here.
What a finished GB527 Unit 9 demand forecast looks like
Five pages with two charts and an error table. Twenty-four months of aftermarket cylinder sales to dealers are plotted first: 5,460 units in year one and 5,900 in year two, with peaks in April and October that follow planting and harvest. Year one builds the models; year two tests them, one month ahead at a time. The error table reports a three-month moving average at a MAD of 154 and MAPE of 32.0 percent, exponential smoothing with alpha of 0.3 at 128 and 26.4, and a seasonal-index method at 26.9 and 5.6. A second chart tracks the seasonal method's running tracking signal, which ends the year at 4.2. The final page re-bases the forecast on year two, adds a stated six percent growth assumption and gives a monthly plan totaling about 6,250.
How a GB527 Unit 9 example is structured
Honest testing is the spine of the paper: fitting data and testing data are separated before anything is computed. The history is plotted and described first, naming the seasonal pattern and the growth, because the pattern should choose the candidate methods. Each method is defined with its parameters, the moving-average window and the smoothing constant, so results can be reproduced. Errors are measured three ways: MAD for size in units, MAPE for size relative to demand, and a tracking signal for direction, since a method can be accurate on average and still biased. The winner is chosen on the test year, then examined for its weakness, persistent under-forecasting from a level that lags growth. The remedy and its assumption are stated plainly. A monthly forecast closes the paper, with a note on how often it will be re-estimated.
A year to fit, a year to test
Year one sets the seasonal indices and starting levels; year two is forecast one month ahead at a time and never used for fitting, so every error reported is out of sample.
Three methods, parameters stated
A three-month moving average, exponential smoothing with alpha 0.3, and a seasonal-index method smoothing a deseasonalized level, each described closely enough to rebuild in a spreadsheet.
Error in units, percent and direction
MAD, MAPE and tracking signal side by side. The seasonal method cuts MAPE from 26.4 percent to 5.6, yet its signal reaches 4 in the fourth month and finishes at 4.2.
Why the best method runs low
Demand grew about eight percent while the smoothed level trailed behind, so the method under-forecast by a cumulative 113 units. The paper names the bias rather than hiding it inside a good MAPE.
A re-based year three
Levels are reset on year two, a six percent growth assumption is added and labeled, and the monthly forecast peaks near 733 in April, with re-estimation planned each quarter.
Where marks go in GB527 Unit 9
Judging a method on the data used to build it is the most common flaw, and it makes every method look better than it will perform. Error has to be measured, and a forecast carrying no MAD, MAPE or similar figure is commonly marked down however smooth the fitted curve looks. Choosing a smoothing method for seasonal demand without testing a seasonal alternative is a frequent conceptual loss. Papers reporting accuracy but not bias miss the tracking signal, which is what warns a planner that the forecast is drifting. Arithmetic slips in index calculation are common and costly. Growth assumptions buried in formulas, never stated, weaken the result. A forecast table with no sentence on what the numbers mean for production or purchasing leaves the unit's final question unanswered.
Get a GB527 Unit 9 example written to your instructions
Provide the demand history, ideally two years or more by month or week, what the item is, and the Unit 9 prompt with its rubric. Methods are tested on data they did not see, with MAD, MAPE and bias reported; the first custom sample is free, turned around in 24-48h. Sensitive series can be scaled first.
GB527 Unit 9 questions, answered
Which error measure should I report?
Report at least two, one for size and one for direction. MAD gives error in units, MAPE in percent, which helps when comparing items of different volume, and a tracking signal or cumulative bias shows whether the forecast leans high or low. If your course names a specific measure, lead with that one and add the others briefly.
How much history do I need for a seasonal forecast?
Ideally two or more full seasonal cycles: one to estimate the indices and at least one to test them. With a single year, indices can still be estimated, but the test must use a shorter holdout, and the paper ought to admit that each month's index rests on a single observation.
What does a tracking signal above 4 mean?
That errors are piling up in one direction faster than random variation would explain. Many texts use limits around plus or minus 4. A positive signal means demand keeps exceeding the forecast. The response is to find the cause, such as growth the method ignores, and adjust the model rather than simply widening the limits.