Harmon Foods Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

Harmon Foods Case Study Solution

Introduction:

Treat is a ready to eat breakfast cereal which is manufactured by breakfast division of Harmon Food Industries. The Treat was an important manufacturing product of the company which plays an important part in company’s sales. As the company was facing many problems, the sales forecasting of the respective product was fluctuating and the variation in projected and actual sales was increasing.

Some of these problems include manufacturing problem. The manufacturing of products was done according to the schedules and no change has been done as changes in structure had been very expensive. Moreover, the lead time on ordered material was very higher that is up to several weeks. Therefore, giving small orders creates higher ordering cost to the company. However, if creating surplus of raw materials, higher storage space is required, which would increase the holding cost of products. Lastly, tight production schedules were preferred in order to save its operations from overtime and unnecessary cost.

In order to decrease operational cost, company tried to reduce its operating cost. The main focus was on advertising cost as it was increasing overtime. Therefore, in order to make efficient costing on advertising, company planned to repeat its advertising in a popular morning show. However, it has been very expensive but every dollar was worth of it as this way was providing high recognition of the product due to repetition of advertisings in morning.

The company was also facing budgeting problems. These problems were occurred due to forecasting error as the company had weak forecasting. The fluctuations in forecasting were ranging from 50% to 200%. However, it has been noticed that first three quarters have little resemblance with forecasting but the sales of quarter 4 wasdeclining from corporate expenditures.

Company was promoting its Treat product through two promotional schemes such as Consumer packs and dealer allowances. The dealer allowance used by the company was the Push Marketing Strategy, whereas, the consumer packs were a sales promotion tactic used by the company for enhancing its sales of case shipments. However, these promotions were reducing the return of the product with a great percentage at around 20% but it was enhancing the sales which needs to be analyzed through statistical analysis by using the below mentioned methodology.

Methodology of Data Analysis:

The analysis file contains the monthly data for a period of 5 years from Jan 1983 to Dec 1987. It includes three major variables which are: Case shipments sold and promotions by providing consumer packs and giving away dealer allowances to boost its sales through push marketing strategy. The data also contains monthly seasonality index which is used for determining the seasonality through graphical representation.

The data contain one dependent variable which is case shipments sold and two independent variables: consumer packs and dealer allowances. The methodology used for the analysis of the data sets in cases include Linear Regression Model and Multiple Regression Model along with the Residual Plotting of the data as well as calculating the time series seasonality on the basis of un-standardized residuals and Moving average of the Case shipments sold.

Hypothesis:

H_o1: Case shipments sold variable is not affected by promoting Treat through consumer packs.

H_o2: Case shipments sold variable does not have any impact on promotion through dealer allowance.

H_o3: Consumer packs and dealer allowances do not impact the case shipments sales.

H_o4: Sales of case shipment does not vary with the seasonal impact of time.

Harmon Foods Harvard Case Solution & Analysis

Linear Regression Model for consumer packs:

The model was run to check the impact of individual independent variable over dependent variable. The first table contains the impact of consumer packs on case shipments. The analysis contains three major tables which need to be considered for taking the decision. First of all, it is necessary to interpret the ANOVA table which shows that the model is significant as the p-value is less than 5%. Null hypothesis in this case will be rejected and there lies a significant impact of consumer packs over the case shipments sold.

Moreover, the F-value is greater than 10 which shows the fitness of the linear regression model. R shows the correlation of consumer packs and case shipments and it is 0.488 which shows moderate positive correlation between them. However, the Adjusted R Square shows that case shipments are varied at a rate of around 22.2% and there is a scope of other variables to impact the case shipments by 77.8% which is not covered in this model.

Furthermore, the coefficient table shows the linear regression model and sensitivity of variation in the dependent variable due to independent variable.....................

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