OPM 3010 Module 4 Written Assignment: Forecasting Bread Demand and Ordering Flour Example

Reviewed by Edgar Pennington, MBA Thomas Edison State University Updated October 2026

This OPM 3010 Module 4 example answers the written assignment on forecasting and managing capacity, inventory and resources in Operations Management, from TESU's Bachelor of Science in Business Administration in Operations Management. OPM-3010 turns in this module to forecasting demand, judging forecast accuracy and managing stock with tools like the economic order quantity and reorder points. The paper forecasts weekly bread demand at one distribution center served by the composite Vineland bakery, using exponential smoothing and a moving average, and finds through error and bias measures that both lag a rising trend. It then uses the EOQ model and safety stock to set how much flour to order and when, explaining what each figure means for the plant.

CourseOPM-3010 Operations Management
ModuleModule 4
Paper typeModule 4 written assignment: forecasting and managing capacity, inventory and resources
LengthAbout 591 words, 5 pages
FormatAPA 7 student paper
SchoolThomas Edison State University
ProgramB.S.B.A. in Operations Management
UpdatedOctober 2026

Free sample paper for OPM 3010 Module 4

1

Forecasts That Lag a Rising Trend, and the Flour Order That Follows

Student Name

B.S.B.A. in Operations Management, Thomas Edison State University

OPM-3010: Operations Management

Mentor Name

Month Day, Year

What this page is doingThe title names the forecast's main weakness, lagging a trend, which the error and bias measures reveal.
2

Forecasts That Lag a Rising Trend, and the Flour Order That Follows

Forecasting Demand

The Cherry Hill distribution center's weekly orders, in cases of sliced bread, rose over eight weeks: 1,240, 1,310, 1,275, 1,390, 1,420, 1,365, 1,480 and 1,510. The plant needs a forecast for week 9 to schedule production.

Collier and Evans (2017) describe simple exponential smoothing as a weighted average in which the new forecast equals the old forecast plus a fraction, alpha, of the last error. I used alpha = 0.3 and a starting forecast of 1,250.

The mean absolute deviation (MAD) is 75.0 cases, and the average error, the bias, is 72.5. Seven of eight errors are positive: the forecast is almost always too low. Gardner (1985) reviewed exponential smoothing and noted that simple smoothing lags behind data with a trend, which is why trend-adjusted versions were developed. That is exactly what happened here.

For comparison, a three-week moving average forecast weeks 4 to 8 at 1,275.0, 1,325.0, 1,361.7, 1,391.7 and 1,421.7, with a MAD of 78.0 over those weeks, against 103.5 for exponential smoothing in the same weeks. Both methods lag; the moving average lags slightly less because it drops old data entirely.

The plant should therefore use a trend-adjusted method, such as double exponential smoothing, and in the meantime add a judgment adjustment: forecast about 1,540 cases for week 9, continuing the recent rise of roughly 30 cases a week, rather than the 1,424 that simple smoothing gives.

WeekDemandForecastError
11,2401,250.0negative 10.0
21,3101,247.063.0
31,2751,265.99.1
41,3901,268.6121.4
51,4201,305.0115.0
61,3651,339.525.5
71,4801,347.2132.8
81,5101,387.0123.0
9| 1,423.9
What this page is doingReading the bias as well as the MAD is what reveals the trend, since a forecast can have a tolerable average error while being consistently low.
3

Inventory: How Much Flour to Order

The plant uses about 24,000 fifty-pound bags of bread flour a year, or 76.9 bags a day over 312 production days. Each order costs $85 to place and receive, and holding a bag for a year costs 25 percent of its $22 price, or $5.50. Harris (1990), a reprint of the 1913 article that first derived the economic order quantity, shows how it balances ordering and holding costs.

ElementCalculationResult
Economic order quantitysquare root of (2 x 24,000 x 85 / 5.50)861 bags
Orders per year24,000 / 861about 28
Annual ordering plus holding cost(24,000 / 861) x 85 + (861 / 2) x 5.50about $4,737

When to Order: Reorder Point and Safety Stock

Lead time from the mill is four production days, and daily usage varies with a standard deviation of 18 bags. For a 95 percent service level, z = 1.65.

When flour in the silos falls to about 367 bags, the plant should order 861 bags. The rising bread demand matters here too: if demand keeps climbing, daily flour usage will rise, and the reorder point should be recalculated monthly.

ElementCalculationResult
Lead time demand76.9 x 4307.7 bags
Safety stock1.65 x 18 x square root of 459.4 bags
Reorder point307.7 + 59.4about 367 bags

Capacity

The rising trend raises a capacity question. At 1,540 cases a week from Cherry Hill alone, the Vineland plant, already at 92 percent of capacity, will run out of room within months, which supports the second plant planned in Module 3.

Conclusion

Simple forecasts lag a rising trend, and the bias measure shows it. A trend-adjusted forecast, an EOQ of 861 bags and a reorder point of 367 bags will keep the plant supplied while the second plant is built.

References

Collier, D. A., & Evans, J. R. (2017). OM (6th ed.). Cengage Learning.

Gardner, E. S., Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1-28. https://doi.org/10.1002/for.3980040103

Harris, F. W. (1990). How many parts to make at once. Operations Research, 38(6), 947-950. https://doi.org/10.1287/opre.38.6.947

What the OPM 3010 Module 4 instructions ask for

The fourth written assignment covers forecasting and managing capacity, inventory and resources. Expect problems that ask you to forecast with moving averages and exponential smoothing, measure error with MAD and bias, compute economic order quantities and set reorder points with safety stock. Show each step in a table and interpret the results. When a method performs poorly, say why, and recommend what to do about it, since a forecast that consistently misses in one direction tells you something about the data. Report MAD and bias for each method over the same weeks so the comparison is fair. For inventory problems, state the holding cost as a dollar amount per unit per year before computing the EOQ. Label each forecast table clearly by week.

How the OPM 3010 Module 4 example is put together

The paper forecasts eight weeks of demand with exponential smoothing in a table, computes MAD and bias and explains the positive bias with research on smoothing and trends. It compares a moving average over the same weeks and recommends a trend-adjusted forecast. Two tables compute the EOQ and the reorder point with safety stock, and short sections link the trend to flour usage and plant capacity. The conclusion summarizes the three decisions. The paper also links the rising demand to flour usage and plant capacity, so the forecast informs both the reorder point and the second plant decision. Every number is shown with its formula.

OPM 3010 Module 4 rubric: what earns full marks

Grades for this assignment reflect correct calculations, suitable methods, accurate interpretation of error measures and clear recommendations. Papers that use bias, not only MAD, to diagnose a trend show statistical insight. Comparing methods over the same weeks demonstrates fair evaluation. Linking forecasting, inventory and capacity, rather than treating them as separate problems, shows the integrated thinking operations management requires. Recommending a better method when the data show a trend, and adding a judgment adjustment in the meantime, shows practical sense. Tables for each calculation make the work easy to check. Interpreting each figure for the plant, not only computing it, earns full credit. Stating the service level and its z value shows care.

OPM 3010 Module 4 help from the desk

Students lose points by computing forecasts without measuring error, by ignoring bias, by using the wrong holding cost in the EOQ or by forgetting safety stock in the reorder point. Others compare methods over different weeks. Measure MAD and bias. Explain what a consistent sign in the errors means. Show the EOQ inputs clearly. Add safety stock for uncertain demand. If errors are mostly one sign, say what that means and recommend a method that handles trend. Recalculate the reorder point when usage changes, and state the service level you chose and why it fits the business. A free first sample on your own OPM-3010 forecasting and inventory assignment is available on request.

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OPM 3010 Module 4 questions, answered

Where can I find a free OPM 3010 Module 4 sample paper?

Bread demand forecasts, a flour EOQ and a reorder point for a bakery are worked in the assignment above.

Why does simple exponential smoothing lag a trend?

Because it averages past demand without a trend term, so a rising series is always forecast too low.

What does forecast bias show?

Whether forecasts are consistently too high or too low; a large positive bias means demand is underforecast.

What is the economic order quantity?

The order quantity at which yearly ordering costs plus holding costs are lowest.

How is a reorder point with safety stock calculated?

Lead time demand plus safety stock, where safety stock equals z times the standard deviation of daily demand times the square root of lead time.