Demand Management Plan for Wild Dog Coffee Company
This paper presents a demand management plan for Wild Dog Coffee Company, covering three core operational areas. First, it applies a simple linear regression model to forecast espresso bean usage based on advertising expenditures, yielding a regression equation and R-squared value to guide purchasing decisions. Second, it compares Just-in-Time (JIT) and Economic Order Quantity (EOQ) inventory management approaches, recommending JIT given the company's storage constraints. Third, it evaluates two staffing scenarios — a fixed schedule and a flexible/seasonal schedule — using shift-by-shift labor cost tables to help management optimize staffing levels relative to daily and weekly demand patterns.
- Impact of Advertising on Product Demand: Regression model linking ad spend to bean usage
- Forecasting Espresso Bean Needs for Month 7: Month 7 forecast using regression equation
- Inventory Management Analysis: JIT vs. EOQ inventory systems compared
- Scheduling Management: Fixed vs. flexible staffing approaches introduced
- Staffing Scenarios and Labor Cost Comparison: Shift tables and weekly labor cost breakdowns
- Best Recommendation: Preferred staffing scenario and key trade-offs
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What makes this paper effective
- Integrates quantitative forecasting (regression analysis) with qualitative management recommendations, giving the plan both analytical rigor and practical applicability.
- Uses structured comparison tables for staffing scenarios, making complex labor cost data immediately accessible and decision-ready for management.
- Grounds recommendations in the specific operational constraints of the business (limited storage space, variable weekly demand), rather than offering generic advice.
Key academic technique demonstrated
The paper demonstrates applied operations management analysis: it moves from a statistical model (simple linear regression) to a concrete operational forecast, then uses that forecast as the foundation for downstream inventory and staffing decisions. This cascading logic — where each section builds on the prior one — is a hallmark of well-structured business planning documents.
Structure breakdown
The paper opens with a regression-based demand forecast, establishing the quantitative baseline. It then addresses inventory strategy (JIT vs. EOQ), followed by a detailed staffing analysis comparing fixed and flexible schedules across two formal scenarios with supporting cost tables. The paper closes with a recommendation section that weighs trade-offs and selects the preferred approach. An appendix provides the underlying linear regression formulas for transparency and reproducibility.
Impact of Advertising on Product Demand
To analyze the impact of advertising on product demand, a simple linear regression model can be used to forecast the pounds of espresso beans used each month based on advertising expenditures. The model used is provided in the Appendix to this paper.
The regression equation is: Y = 542.78 + 0.42X, where Y is the pounds of espresso beans used and X is the advertising dollars spent.
The R-squared value is 0.56, which means that 56% of the variation in espresso bean use is explained by advertising dollars spent.
Forecasting Espresso Bean Needs for Month 7
To forecast the pounds of espresso beans needed for month 7, the regression equation is used with X = 1,350 (the advertising budget for month 7):
Y = 542.78 + 0.42(1,350) = 1,116.78
Therefore, the forecasted pounds of espresso beans needed for month 7 is 1,116.78.
Two additional operational questions follow from this forecast:
Assuming that 30 espresso beverages are made each hour and the coffee shop is open for 14 hours a day (6:00 a.m. to 8:00 p.m.), the company needs to prepare an average of 420 espresso beverages per day (30 × 14).
Since 1.5 ounces of espresso beans are used for each beverage, the company will need an average of 21 pounds of espresso beans per day (420 × 1.5 ÷ 16).
Inventory Management Analysis
There are two primary approaches to inventory management relevant to Wild Dog Coffee Company: Just-in-Time (JIT) inventory management and Economic Order Quantity (EOQ) inventory management.
A JIT system minimizes inventory levels by only ordering and receiving goods when they are needed. The advantages of this system include lower inventory holding costs and less space required for storage. The disadvantages include a higher risk of stockouts and a reliance on suppliers to deliver goods on time (Ufua et al., 2022).
An EOQ system determines the optimal order quantity based on the trade-off between inventory holding costs and ordering costs. The advantages of this system include the ability to take advantage of quantity discounts and to maintain a more predictable inventory level. The disadvantages include the need for more storage space and the possibility of overstocking (Imarah & Jaelani, 2020).
For Wild Dog Coffee Company, the JIT inventory management system may be more suitable because of the limited storage space and the high cost of holding inventory. However, this system also requires a reliable supplier and good communication to ensure that the company does not run out of espresso beans.
References
Forbes. (2020). 13 techniques for implementing a flexible schedule. Retrieved from https://www.forbes.com/sites/forbeshumanresourcescouncil/2020/08/25/13-techniques-for-implementing-a-flexible-work-schedule-policy-efficiently/
Imarah, T. S., & Jaelani, R. (2020). ABC analysis, forecasting and economic order quantity (EOQ) implementation to improve smooth operation process. Dinasti International Journal of Education Management and Social Science, 1(3), 319–325.
Ufua, D. E., Ibidunni, A. S., Papadopoulos, T., Matthew, O. A., Khatoon, R., & Agboola, M. G. (2022). Implementing just-in-time inventory management to address contextual operational issues: A case study of a commercial livestock farm in southern Nigeria. The TQM Journal, 34(6), 1752–1771.
Appendix: Linear Regression Model
The formula for a linear regression model is:
y = mx + b
where y is the predicted number of units sold, x is the advertising spend, m is the slope of the line, and b is the y-intercept.
The slope and y-intercept are calculated using the following formulas:
m = (n∑(xy) − ∑x∑y) / (n∑(x²) − (∑x)²)
b = (∑y − m∑x) / n
where n is the number of data points, ∑xy is the sum of the products of x and y, ∑x and ∑y are the sums of x and y respectively, and ∑(x²) is the sum of the squares of x.
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