A-CAT CORP MILESTONE Harvard Case Solution & Analysis

A-CAT CORP MILESTONE Case Study Solution

I). Statistical Tools & Methods

A). Family of Appropriate Statistical Tools & Statistical Assumptions

A range of statistical tools would be utilized to analyze the data provided in addendum and in the case exhibits. Through the case study we get to know that the information is related to hypothesis testing and descriptive statistics. The hypothesis tests used include One Way ANOVA and the one sample T tests. Therefore, the statistical tests that we will employ to drive the decisions for the business problems of A-Cat Corp are many such as Excel, SPSS, Stata etc. The assumption behind choosing the above tools is that the data guides us to perform statistical analysis and hypothesis testing along with forecasting for the given case problem.

B). Category of the Case Data

The variable in this study is the demand quantities of the number of the units sold or demand for a particular product and its level of measurement scale. Variables that might be measured at an ordinal or a nominal level are not present in the provided data sets therefore, using SPSS or any other statistical tool would not be appropriate and it would complicate the analysis plan. The data set in this case study is all of scale category and therefore, it is wise to use the most simplistic statistical tool for driving the decision and finding recommendations for the business problems.

C). Most Appropriate Statistical Tools and their Justification

There is no use of any kind of ordinal or nominal data and all the data sets that we have been provided are in the form of the scale measurement therefore, the most appropriate statistical tool for analyzing the business problems of A-Cat Corp would be the excel spreadsheet package and there are two justifications for this. Scale data can be easily analyzed and tests can be performed in the excel spreadsheet and secondly this would make the data analysis process much easier and reliable for us.

D). Quantitative Methods Employed

Different quantitative methods have been employed in the analysis of the case problem and to analyze the case data. First of all, we have employed the forecasting technique of simple moving average to forecast the most reliable data for the years 2009 and 2010 (Freedman, 2005). After this, we have performed the descriptive analysis for the complete data for the years 2006 to 2010. This would remove all sorts of discrepancies from the data and resolve the issues faced by the operational managers of the company. The data has been extracted from company files and no other external sources have been used for data analysis.  Other quantities methods include hypothesis testing methods such as One Way ANOVA, One Sample T tests and forecasting model such as regression analysis.

II). Analysis of the Data

A). Process for Utilizing Statistical Analysis & Its Importance

The main problem which is being faced by A Cat Corporation is that the company is currently facing the issue of overstocking and under stocking for the past several months due to inappropriate forecasting methods which are being used by the company. This is the root cause for all the other problems being faced by the company. Therefore, our process would first focus on determining that whether there has been a significant change in the number of the transformers or not.

In the end, we would be developing a new forecasting model for the estimation of the demand of transformers. The new forecast model for demand estimation of transformers has been based upon certain factors on which the demand is dependent so that the right quantity is determined for the estimated demand in each period and thus the issue of under and over-stocking is resolved.

B). Analysis of Results & their Reliability

The analysis of the data has been performed in the excel spreadsheet. The descriptive statistics between 2007 and 2010 shows that the mean demand for transformers has been increasing with a maximum demand of 1153 units in 2010. The volatility of the demand is also low and now the quality control department of A-Corp needs to use this information for analyzing the performance and setting up control limits for control charts for performance analysis (Chou, 1975).

Next, we have applied the One Sample T test and as the p value is less than the level of significance of 0.05, thus we can conclude that whether the mean numbers of the transformers have increased significantly and it is likely to be equal to or greater than 1000 units. The One-Way ANOVA test has a p value of 0.010 and we conclude that the mean number of transformers has changed significantly between 2006 and 2010. Finally, the forecast model has been formulated by performing linear regression on the basis of historical sales of the refrigerators (Draper, 1998).

The adjusted r square value of 85% also shows that the percentage change in the demand of the voltage regulators on the basis of the sales of the refrigerators is high and the p-value of ANOVA shows that the overall model is significant and fitted (Cohen, 1993). All the above results and the analysis performed is reliable because it is based on the forecasted data for 2009 and 2010 and the historical data provided in the case study. The same trend has been followed by the owners of the company and thus, the data and the analysis performed is said to be reliable.

C). Data Driven Decision

The data driven decision that addresses the forecasting problem is that management of the company should not make use of the forecasting model that we have formulated to estimate the demand of the voltage regulators in future. More than 1000 units of the transformers need to be manufactured to meet the demand of the customers in the market and avoid under- stocking and overstocking (Nelder, 1990). Managers should never rely on their intuitions and they need to follow a rational forecasting approach. Lastly, the operational managers of the company need to identify other variables or factors that impact on the demand of the transformers and then incorporate them in the forecasting models so that the sales can be increased further (Ellis, 2010).............

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