| Course | MGT 240 Operations Management |
|---|---|
| Module | Module 8 |
| Paper type | Supply chain analysis |
| Length | About 1,048 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Business Administration |
| Updated | October 2026 |
Free sample paper for MGT 240 Module 8
Steady on the Shelf, Wild at the Mill: Tracing the Bullwhip Effect in a Private-Label Toilet Paper Supply Chain
Student Name
Business Administration Program, Aspen University
MGT 240: Operations Management
Instructor Name
Month Day, Year
Steady on the Shelf, Wild at the Mill: Tracing the Bullwhip Effect in a Private-Label Toilet Paper Supply Chain
Fox River Tissue, a composite company in Green Bay, Wisconsin, operates a paper mill that converts purchased pulp into toilet paper sold under grocery chains' own brands. It ships to four regional distributors, which supply about 900 grocery stores across the Midwest. People use toilet paper at a steady rate, and store sales reflect that, varying little from week to week except during rare panics. Yet the mill experiences wild swings: some months it runs overtime and buys pulp at premium prices, and others it idles lines and stores excess rolls in rented warehouses. This paper measures the variability at each stage, explains its causes and recommends how the chain can coordinate.
Measuring Variability
Using two years of weekly data, excluding a brief panic-buying period, the coefficient of variation, the standard deviation divided by the mean, was calculated at four stages.
Variability grows eightfold from shelf to pulp. This is the bullwhip effect: small changes at the end of the chain become large swings further back.
| Stage | Coefficient of variation of weekly volume |
|---|---|
| Store sales to consumers | 0.06 |
| Distributor orders to the mill | 0.22 |
| Mill production orders | 0.31 |
| Mill pulp orders to suppliers | 0.48 |
Four Causes
Lee et al. (1997) analyzed the bullwhip effect and identified four causes. Demand signal processing occurs when each stage forecasts from the orders it receives rather than from end demand, and adds safety stock to each update, amplifying changes. Order batching occurs when firms order in large lots, such as full truckloads once a month, so orders arrive in lumps. Price fluctuation occurs when promotions lead buyers to purchase ahead, then stop. Rationing and shortage gaming occur when buyers inflate orders during shortages, expecting to receive only part. All four appear at Fox River. Distributors forecast from store orders, not sales; they order full trucks every two to four weeks; the mill offers quarterly promotional discounts to distributors; and during a pulp shortage in 2024, distributors doubled orders and then canceled many.
Why Managers Amplify
Sterman (1989) studied participants in the beer distribution game, a simulated four-stage supply chain, and found that they consistently placed orders that produced large oscillations. They failed to account for orders already placed but not yet delivered, the supply line, and reacted to current shortages by ordering more. Sterman concluded that the problem lay in how people misperceive feedback in systems with delays. Fox River's buyers show the same pattern, ordering more pulp when inventory falls without counting shipments already on the way.
How General Is the Effect
Cachon et al. (2007) examined industry-level data and found that the bullwhip effect is less universal than often assumed: many retail industries smooth demand rather than amplify it, while manufacturing industries more often showed amplification, and seasonality affected the results. Their findings caution against assuming every chain shows the effect, which is why this paper measured it first. At Fox River, the measurements confirm that amplification is real and grows at each stage.
Causes and Remedies
| Cause at Fox River | Remedy |
|---|---|
| Distributors forecast from store orders | Share store sales data with the mill and distributors weekly |
| Monthly full-truck orders | Weekly orders on trucks mixing products for several distributors |
| Quarterly promotional discounts | Steady pricing with a year-round volume rate |
| Inflated orders during shortages | Allocate scarce supply by past sales, not current orders |
| Buyers ignoring the supply line | Order rules that count inventory on order as well as on hand |
What Shortage Gaming Cost
The pulp shortage of 2024 shows how expensive the bullwhip can become. When distributors heard the mill might allocate supply, their orders roughly doubled for six weeks, although store sales did not change. The mill bought pulp at spot prices about 30% above contract, ran overtime and shipped as much as it could. When the shortage eased, distributors canceled about 40% of open orders and held excess stock for two months, during which mill orders fell to almost nothing and two lines sat idle. The mill estimates the episode cost about $2.3 million in premium pulp, overtime, idle time and storage, more than its entire profit that year.
A Further Option: Vendor-Managed Inventory
Some chains go beyond data sharing by letting the supplier manage the customer's inventory. Under vendor-managed inventory, Fox River would see each distributor's stock and sales and decide shipments itself, within agreed limits. This removes one layer of forecasting from orders and lets the mill plan production from actual consumption. Two of the four distributors have expressed interest; the plan is to start with one, compare its variability with the others for six months and expand if the results are good.
Gaining Partners' Cooperation
Data sharing requires grocery chains and distributors to agree. Fox River will offer distributors a steady price and shorter lead times in exchange for weekly store sales data, and offer grocery chains more reliable supply. Each partner gains lower inventory and fewer stockouts.
Linking to Earlier Topics
Shared sales data improve forecasts, as forecasting research suggests, and smaller, more frequent orders reduce the cycle stock carried at each stage. Steadier production also eases the setup and capacity planning the mill now does under constant pressure.
Rollout
The changes will be introduced in order of ease. Steady pricing and allocation by past sales are the mill's own decisions and can begin next quarter. Order rules that count the supply line require changing the mill's purchasing spreadsheet and training two buyers. Weekly mixed-truck shipping requires reworking delivery routes with the mill's carrier and will start with the two distributors closest to Green Bay. Data sharing and the vendor-managed trial depend on partners and will be negotiated during the first six months.
Measures
Fox River will recalculate the coefficient of variation at each stage quarterly, aiming to reduce pulp order variability below 0.25 within two years, and will track inventory days at each stage, overtime hours and store stockouts.
Conclusion
Fox River's supply chain turns steady consumer demand into volatile mill and pulp orders through forecasting from orders, batching, promotions and shortage gaming, aggravated by the human tendency Sterman documented. Matching remedies to these causes, and offering partners clear benefits for sharing data, can calm the swings that make the mill's operations so costly.
References
Cachon, G. P., Randall, T., & Schmidt, G. M. (2007). In search of the bullwhip effect. Manufacturing & Service Operations Management, 9(4), 457-479. https://doi.org/10.1287/msom.1060.0149
Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546-558. https://doi.org/10.1287/mnsc.43.4.546
Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321-339. https://doi.org/10.1287/mnsc.35.3.321
Reading the MGT 240 Module 8 assignment instructions
Supply chain coordination brings Aspen's MGT 240 to a close, and the final paper often asks students to analyze how decisions at one stage affect others and recommend ways to coordinate. Let your classroom's Module 8 wording settle the details; the example traces one product's chain from stores to raw material. Describe each stage of the supply chain. Measure variability of demand or orders at each stage. Explain the causes of any amplification, using research. Consider evidence about how general the effect is. Propose remedies matched to causes, with the cooperation each requires. Draw on earlier course topics, such as forecasting and inventory, and set measures to show whether coordination is working.
How the MGT 240 Module 8 example is put together
The paper opens with Fox River Tissue, which sells private-label toilet paper through four distributors to about 900 grocery stores. Weekly data show the coefficient of variation rising from 0.06 at stores to 0.22 for distributor orders, 0.31 for mill production and 0.48 for pulp orders. Lee, Padmanabhan and Whang's Management Science article identifies demand signal processing, order batching, price fluctuation and rationing as causes. Sterman's Management Science article on the beer game found that participants underweighted the supply line, producing oscillations. Cachon, Randall and Schmidt's Manufacturing and Service Operations Management article found many retailers smooth demand while manufacturers more often amplify. A table matches each cause at Fox River to a remedy. The plan shares store sales data, moves to weekly mixed-truck orders and replaces promotional deals with steady pricing.
Reading the MGT 240 Module 8 grading rubric
Supply chain papers are judged on measuring variability at each stage, explaining causes with research, matching remedies to causes and recognizing that coordination requires partners' cooperation. This example measures variability with one statistic at four stages, so amplification is visible. Lee, Padmanabhan and Whang's four causes organize the diagnosis, and Sterman's experiments add the human tendency to ignore pipeline orders. Cachon, Randall and Schmidt's evidence shows critical reading, since it qualifies the textbook account. The table pairs each cause with a remedy, and the plan considers what partners gain. Measures repeat the variability calculation over time.
MGT 240 Module 8 help: mistakes that cost marks
Bullwhip papers often describe the effect in general terms without measuring it in the chain being studied. Calculate variability at each stage with the same measure. Another weakness is listing every remedy without linking it to a cause found in the data. Use research to explain causes, and consider evidence that the effect varies across industries. Remember that remedies such as data sharing need partners to agree; explain what each partner gains. Draw on earlier topics, since forecasting and inventory decisions drive ordering. Avoid recommending changes only the company itself would benefit from. Finally, measure variability again after changes to show whether the bullwhip has weakened.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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MGT 240 Module 8 questions, answered
What does MGT 240 Module 8 usually ask for?
Aspen's MGT 240 ends with supply chain coordination, so analyzing the bullwhip effect or another coordination problem and recommending remedies is typical. Check your classroom prompt.
What is the bullwhip effect?
The increase in variability of orders as they move up a supply chain from retailers to manufacturers and suppliers, even when end demand is stable.
What causes the bullwhip effect?
Lee, Padmanabhan and Whang identified forecast updating, order batching, price fluctuations and rationing with shortage gaming.
Where can I find a free MGT 240 Module 8 sample paper?
The complete analysis above traces the bullwhip effect through a toilet paper supply chain and recommends data sharing, smaller orders and steady pricing.
Is the bullwhip effect found everywhere?
Not always. Cachon, Randall and Schmidt found many retailers smooth demand, while amplification was more common among manufacturers.