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QUESTION
Background
Waste One is one of a small handful of providers of waste collection services in the metropolitan area of Marianapolis, a large city in the U.S. Pacific Northwest. There are over 200 independent municipalities within the metropolitan area of Marianapolis, each of whom choose their own waste collection provider. Waste One prides itself on being the more comprehensive waste collection company. Their service includes curbside collection of waste and recycling and they offer a version of unit-based pricing, where residents choose from one of two container sizes for their trash. Residents can choose to pay a monthly fee for a 50-gallon container, or a larger monthly fee for a 96-gallon container. Thus, in addition to competing to obtain contracts with specific municipalities, Waste One essentially has two products its customers can choose from (recycling collection is always included at no extra cost). Meanwhile, their main competitor, Dumpke, only collects trash curbside and does not collect recycling. Dumpke’s service requires customers to use their own trash cans, but has no limit on the amount of trash collected. Waste One tends to attract municipalities that are wealthier and more concerned about the environment.Management Directives
The Board of Waste One wants to evaluate the pricing strategy for the monthly rate on the 96-gallon container. Waste One has much higher margins on the 96-gallon container, as the additional cost for this container versus the 50-gallon container is minimal (small increase in the fees paid to the dump). However, the Board is concerned that it must also keep the price of the 50-gallon container somewhat close to the price of the 96-gallon container to keep customers from choosing the cheaper (lower margin) option. You are given the attached data and variable definitions, along with the knowledge that the firm has $550,000 in monthly fixed costs. You are then asked to determine the monthly rates that will maximize profit for Waste One. This will require estimating demand and cost functions and making some assumptions. The following issues/questions should be addressed within your executive report and presentation.
1. What is your recommended monthly price for the 96-gallon container and what do you expect Waste One’s monthly profit to be in this scenario?
a. If this monthly price is consistent with historical prices, explain why the board should accept your recommendation of only minor change.
b. If this monthly price is not consistent with historical prices, explain why the board should accept your recommendation of radical change.
2. Explain the process you went through to determine your demand function.
a. Provide rationale for each of the variables included in the demand function.
b. Explain whether the coefficient estimates are statistically significant and whether the sign on the coefficient estimates are what you expected.
3. Provide rationale for all of the assumptions that were made to determine the appropriate monthly rates.
4. Explain what changes could occur over the next year that would alter your recommended prices.
5. Do you agree with the board’s decision focus on the monthly price for the 96-gallon container and to keep the price of the 50-gallon container somewhat close to the price of the 96-gallon container? Explain your rationale and provide suggestions if you disagree.
| Subject | Business | Pages | 5 | Style | APA |
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Answer
Demand Function Analysis
Introduction
To come up with a viable pricing strategy for 96-gallon container at the Waste One Company is becoming an elusive aspect in the waste management market at Marianapolis. The pricing strategy at Waste One firm is on monthly basis. The board is also interested in setting the price for 50-gallon container close to the pricing levels for 96-container. In this case, there is need to devise a model that can be applied by the board to determine monthly rates that can maximize profit levels by the firm.
Discussion
In building the demand function can equally be applied to determine price per 96-gallon container, the analysis applies multivariate regression model. The response variable is set as Average price for monthly Waste One collection with 96-gallon service. On the other hand, the independent variables in the model are grouped into costs involved in service delivery, subsidies and income levels. Cost involved variables are monthly spending on billboards, amount spent on local advertising in each month while subsidies incorporates municipalities that subsidize waste collection for their residents. It is assumed that the residents within Marianapolis prefer Waste One firm services to other competing firms in the market. Empirical aspect for the model is set as follows:
Based on the empirical model above, the sign for the estimated coefficients of totalAd, AdBIdb and number of monthly residential clients of waste for 96-gallon is expected to take positive sign in the estimation process. This is due to the fact that increase in cost involved in service delivery increases price in the long run. Increase in number of monthly residential clients of waste for 96-gallon waste coefficient is expected to take positive sign. This is due to the fact that increase in customer base indicates rise in demand for the service or commodity. As such, the service provider reacts by increasing the price levels. Therefore, the expected sign for the coefficient is positive sign. The expected sign for city subsidy is negative one. Subsidy implies that the government caters for a portion of price. As such, the expected sign is negative one. The expected sign for estimated coefficient of income level is expected to take positive sign.
The F statistics presented on table 1 in the appendix is applied to test the following hypothesis:
H0: β1=β2=…=βi=0 for i=1, 2, 3…
Against
H1: β1≠β2≠…≠βi≠0 for i=1, 2, 3…
It is established that F (5, 46) = 37.18, p-value<0.010. The stated null hypothesis is rejected in favor of the alternative one. The predictor variables jointly influence on monthly rates for 96-gallon. They account for 80.2% variation in the monthly rates (R-Square=0.802). A multivariate regression model to estimate viable price level is set as follows:
Illustration on figure 1 in the appendix, depicts that the error term () does not violate normality assumptions. The standing charge for 96-gallon container of waste when collected is 52.2897. It is established that the variable “Q_W1 (No. of customers)” has a negative influence of 0.00003860 units that is statistically significant at 0.05 alpha levels (p-value<0.010). On the other hand, the variable “TotalAd (amount spent on local advertising each month)” has positive influence of 0.00022428 units that is statistically insignificant at 0.05 alpha levels (p-value=0.2741). Similarly, the variable “AdBIdb (amount spent on billboard ads)” has a positive influence of 0.0016 units that is statistically insignificant at 0.05 alpha levels (p-value=0.1327). On the other hand, the variable “income” has negative influence of 0.0051 units to the monthly rates that is statistically significant at 0.05 alpha levels (p-value=0.0136). It is established that the variable “city subsidy” has positive influence of 0.0656 units to the monthly rates that is statistically significant at 0.05 alpha levels (p-value=0.0137). Based on the analysis, I do agree that the board should focus more on monthly price for 96-gallon container and maintain 50-gallon container closer to 96-gallon price. This is due to the fact based on the fitted model, one can determine 96-gallon container price given such factors in the model.
Recommended monthly price for 96-gallon container is 52.2897$ in the event that monthly price is much consistent with historical prices provided in the dataset. However, the value is quite high as per the provided dataset. On the other hand, recommended monthly price under radical change is 30$ and below putting into consideration all the factors influencing on price change.
Based on the fitted model, it is recommended to the waste one firm to cut short some of the expenses involved in service delivery such as advertising costs and the amount spending on billboards. This substantially increases cost involved in service delivery thus lower profit margins. Additionally, it is recommended to the firm to reduce clientele base since increase in customers has negative effect to the monthly rates. This implies that the cost of service delivery is significantly high in the long-run. In the long-run event, the firm will significantly make high profits.