| Course | DAT 565 Data Analysis and Business Analytics (DAT/565) |
|---|---|
| Week | 5 |
| Paper type | Graduate forecasting analysis |
| Length | about 1,152 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | MBA |
| Updated | October 2026 |
Free sample paper for DAT 565 Week 5
How Many Pickers on Cyber Monday? Forecasting Order Volume at an Atlanta Fulfillment Warehouse
[Student Name]
University of Phoenix
DAT/565: Data Analysis and Business Analytics
Week 5 Assignment
[Instructor Name]
[Date]
Peachtree Fulfillment Partners, its clients, data and forecasts are composites written for a model paper.
The warehouse examined in earlier weeks, Peachtree's site in Fayetteville, Georgia, ships about 18,000 orders on a typical day and more than 40,000 on its busiest days in late November and December. To handle the peak, it hires temporary associates through staffing agencies, who need about a week of training. Last year it hired 260, too few for the first week of December, when on-time shipping fell to 88 percent and two clients incurred contract penalties. This paper forecasts order volume to plan this year's hiring.
The Data and Its Patterns
The prepared data from Week 1 extend back three years: daily orders by client, about 1,100 days. Plotting the series shows three components. Trend: volume grew about 14 percent a year as clients grew and new clients joined. Weekly seasonality: Mondays are 30 percent above the weekly average, as weekend online orders arrive, and Saturdays are lowest. Annual seasonality: volume climbs from early November, peaks on the Monday after Thanksgiving at more than twice the normal level and stays high until about December 18. Irregular spikes appear on clients' promotion days.
Hyndman and Athanasopoulos (2021) describe decomposing a time series into trend, seasonal and remainder components as a first step in understanding it and choosing methods. Peachtree's series has strong, regular seasonality and a steady trend, which suits methods that model both.
Methods Compared
Three methods were fitted to the first 30 months and tested on the last six, which included last year's peak.
Seasonal naive. Each day's forecast equals the same weekday a year earlier, multiplied by recent growth. This simple benchmark is easy to explain and hard to beat in seasonal data.
Holt-Winters exponential smoothing. This method updates estimates of level, trend and seasonal pattern with each new observation, giving more weight to recent data.
Regression with promotion calendar. A regression on trend, day of week, week of year and a variable for each client's announced promotion days, which clients share with Peachtree a few weeks in advance.
Forecasting by Client or in Total
A practical choice is whether to forecast total volume directly or forecast each client and add them up. Client-level forecasts capture each client's promotions and growth, but small clients' daily volumes are noisy. The team forecast the ten largest clients individually, about 75 percent of volume, and the remaining 24 clients as a group. Adding these forecasts gave slightly better accuracy than forecasting the total directly, and it lets account managers review the forecasts for their own clients.
Accuracy
Accuracy was measured with mean absolute percentage error on the six-month test period. The seasonal naive benchmark erred by 11.8 percent on average; Holt-Winters by 8.9 percent; the regression with promotions by 6.4 percent. During the four peak weeks alone, errors were 14.2, 10.6 and 7.9 percent. The promotion calendar helps most during the peak, when clients run their largest sales.
Makridakis et al. (2020) reported on a forecasting competition with 100,000 time series and found that combinations of methods generally performed better than single methods and that many sophisticated methods failed to beat simple benchmarks. Their findings supported both including a naive benchmark and testing a combination. A simple average of Holt-Winters and the regression erred by 6.6 percent overall, close to the regression alone, so the regression is used, with Holt-Winters as a check.
The simplest forecast was wrong by twelve percent; knowing when clients planned their sales cut that error almost in half.
The Peak Forecast
The regression forecasts average daily volume of 39,500 orders for the two weeks after Thanksgiving, with a peak day of 47,000 on Cyber Monday. Uncertainty matters as much as the central estimate. Based on the model's past errors, there is an 80 percent chance that the peak two-week average falls between 36,000 and 43,500 orders.
Judgment and Adjustments
Fildes et al. (2009) studied forecasts in supply chain companies and found that managers frequently adjusted statistical forecasts, that large adjustments often improved accuracy while small ones generally did not, and that positive adjustments were often too optimistic. Peachtree's account managers routinely raise peak forecasts based on clients' hopes. This year, adjustments will be allowed only when a client provides specific new information, such as an added promotion, and each will be recorded and evaluated after the peak.
From Forecast to Hiring Plan
During the peak, each associate in picking, packing and replenishment handles about 75 orders per eight-hour shift, based on last year's labor records. Covering 39,500 orders a day therefore takes about 527 associate-shifts. Peachtree's 260 permanent associates in those roles cover 260, leaving about 267 to fill with temporary staff at the central forecast. At the upper end of the range, 43,500 orders, about 320 would be needed. Allowing for absences of about 4 percent, the plan sets temporary hiring at 330, staged: 150 start training on November 3, 110 on November 10 and 70 on November 17 as a reserve. Contracts with staffing agencies allow releasing up to 20 percent with a week's notice if volume runs low, which limits the cost of planning for the upper end.
The cost of the plan is easy to compare with its alternative. Each temporary associate costs about $4,800 over the peak season including agency fees and training. Hiring 70 more than the central forecast requires costs about $336,000 if all are kept; last year's shortfall cost about $410,000 in contract penalties, overtime and lost client goodwill. Planning for the upper part of the range is the cheaper mistake.
Updating the Forecast
The forecast will be rerun weekly from October 1 with the latest data and clients' updated promotion calendars. If actual volume in the first two weeks of November runs more than 10 percent above or below forecast, hiring will be adjusted.
Daily and Shift Detail
The two-week average hides day-to-day swings. Cyber Monday's forecast of 47,000 orders is about 19 percent above the period average, and Saturdays fall well below it. The staffing plan therefore uses the daily forecast to schedule overtime and flexible shifts on the heaviest days rather than holding the full temporary workforce every day. Shift managers will receive a daily forecast each Thursday for the following week, with the range shown for each day.
Limits
Forecasts cannot anticipate a new client signing in October, a viral product or a carrier disruption. The range is based on past errors and may understate uncertainty in an unusual year, so managers should watch early November volumes closely and be ready to act.
Conclusion
Peachtree's order volume has clear trend and seasonal patterns that forecasting methods can capture. Tested on held-out data, a regression with client promotion calendars beat a simple benchmark and exponential smoothing. Presented as a range and translated into staged hiring, the forecast gives the warehouse a better chance of avoiding last year's shortfall without overspending on temporary labor.
References
Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3-23. https://doi.org/10.1016/j.ijforecast.2008.11.010
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54-74. https://doi.org/10.1016/j.ijforecast.2019.04.014
What the DAT 565 Week 5 instructions ask
DAT 565's fifth paper asks graduate learners to project trends and patterns in business data forward. Prompts may ask students to plot time series, identify trend, seasonality and irregular components, apply methods such as moving averages, exponential smoothing or regression, measure forecast accuracy and use forecasts to support decisions, often in Excel or another tool. Some versions ask students to compare methods or discuss the limits of forecasting. Use a real or realistic time series tied to a decision, test methods on held-out data and cite forecasting research in APA. Present forecasts with ranges rather than single numbers, and explain how the forecast will be updated as new data arrive.
How this DAT 565 Week 5 example is built
Our sample paper begins with three years of daily orders, showing growth of about 14 percent a year, strong weekly patterns with Mondays highest and a holiday peak that more than doubles volume from mid-November to mid-December. Three methods are fitted to the first two and a half years and tested on the last six months: a seasonal naive forecast, Holt-Winters exponential smoothing and a regression with client promotion calendars. Research from a large forecasting competition supports combining methods and comparing against simple benchmarks, and studies of judgmental adjustments caution against overriding forecasts without reason. The regression with promotions performs best. The peak forecast, with an 80 percent range, sets temporary hiring at 330 associates, staged by week.
DAT 565 Week 5 grading rubric: where the points go
Graduate graders reward forecasts that are tested against new data and tied to a decision. Strong papers describe the time series' components, choose methods that fit them, compare methods on held-out data with accuracy measures and present forecasts with uncertainty ranges. Credit goes to including a simple benchmark, to using research on forecasting accuracy and judgment, to translating forecasts into a decision such as staffing and to a plan for updating forecasts. Graders also look for honest discussion of what forecasts cannot anticipate. Clear charts or tables, accurate error measures and APA citations of forecasting research complete a strong paper.
DAT 565 Week 5 help: mistakes to avoid
Forecasting papers often fit one method and report its forecast as fact. Compare at least two methods against a simple benchmark, and test them on data the model has not seen. Another frequent gap is reporting a single number; decisions need ranges. Show them. Students also ignore known events, such as promotions or holidays, that drive spikes. Include them where possible. Some papers override forecasts with judgment without saying why; research suggests adjustments help only with specific information. Document any change. Finally, connect the forecast to a decision, such as hiring or inventory, and plan how often it will be updated. A tutor can help you build a holdout test in a spreadsheet and read the error measures.
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DAT 565 Week 5 questions, answered
What does DAT 565 Week 5 usually cover?
It usually covers forecasting trends and patterns: identifying trend and seasonality, applying methods such as exponential smoothing and regression, testing accuracy and using forecasts in decisions.
Where can I find a free DAT 565 Week 5 sample paper?
The Week 5 paper above forecasts order volume for a fulfillment warehouse's holiday peak, and the full paper can be opened here.
What is exponential smoothing?
A forecasting method that weights recent observations more heavily than older ones; the Holt-Winters version adds components for trend and seasonality.
How do you measure forecast accuracy?
By comparing forecasts with actual values on data not used to build the model, using measures such as mean absolute percentage error.
Should managers adjust statistical forecasts?
Research suggests adjustments can help when based on specific information, such as a planned promotion, but often hurt when made routinely or optimistically.
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