MGT 3332 CASE STUDY #1

MGT 3332

CASE STUDY #1

Highline Financial Service offers three types of service to its client. Freddie Mack (Managing partner) has a data from three categories of services over the past eight quarters. Seem like other company’s factors have not changes “in terms of advertising or promotion, and competition doesn’t change” (Stevenson, William (2011-02-15).  This data will be utilized to estimate demand for each service for the subsequent four quarters using Naive Forecast and Moving Average. Naive forecast “uses a single previous value of a time series as the basis of a forecast. But naïve forecast has one weakness of the naive method is that the forecast just traces the actual data, with a lag of one period; it does not smooth at all.” Stevenson, William (2011-02-15). Moving Average forecast “uses a number of the most recent actual data values in generating a forecast” Stevenson, William (2011-02-15).

As we can see each graph present for each term Highline Financial service provides. I will now analysis the whole graph using forecast. Service A seem to be increase in each quarter, service B seem to be decrease and service C is the most unstable one.

First I will use Naïve Forecast there are three items need to be figured MAD, MSE and MAPE. First step I will used period as a quarter and year like (1,2,3,4,5,6,7,8)  then input the Service A numbers (60,45,100..).  “With seasonal variations, the forecast for this “season” is equal to the value of the series last “season.””Stevenson, William (2011-02-15), so we have (60,45,..) for Naïve forecast. then calculated the Error by using actual (service A)- Naïve Forecast we have (-15,55,-25,..), next used |E| and sum the |E| we have 207. Then we will used Error2 and calculated it sum we have 8775. Then using |E| to calculated MAD=207divide for 7 is equal to 29.57, then calculate MSE=8775 divide for (7-1) is equal 1462.5. last used MAPE (Absolute Percentage Error)for each period |E| divide for ServiceA then sum all the result and divide for 7 (number of error)

MSE=8775/(7-1)=1462.5

MAD=207/7=29.57

MAPE=2.6089/7=0.37.

Second I will use Moving Average to calculate MAD, MSE and MAPE to final compare and find out which method is the best used for this case, like above I will still used period as a quarter and year like (1,2,3,4,5,6,7,8)  then input the Service A numbers (60,45,100,75..).  For Moving average I used the average between 2 numbers. For example with a giving number (60+45) divide for 2 and equal 52.5 I have the second number for Moving Average continue we have (72.5, 87.5, 73.5, ….). Used all the same methods to Error, |E|, and Error^2 and Absolute Percentage Error.

MAD = 157/7=22.43

MSE = 5796.5/(7-1)= 966.08

MAPE = 1.986/7=0.284

By compare the MAD, MSE, and MAPE of each method Naïve Forecast and Moving Average to find the best method for forecast the next year.

Based on the above data we can clearly see that Moving Average is the best method for next year forecast. With all the MAD, MSE, and MAPE (mean percent of error) is all lower than naïve forecast method.

Conclusion:

Mr. Freddie should prepared more data to construct a viable financial and personnel plan for the next year and using Moving Average he should be able to do it because it easy to understand and used moreover very low percent of errors.

Summary:

Highline financial services ltd offer 3 service to its clients. We have analysis and calculated the errors percent to predict the forecast for next year is all based on quantitative records and the information given. With all the information and using Moving method Highline Financial services can easily forecast the outcome for the upcoming year. But if it’s provided more data and information we can forecast more accuracy.  However, guessing the result thought software and numbers will not be ensure and can’t be guarantee.  If there is some even small change in economic, internal environment with cause the forecast go wrong.