Ski Jacket
Analysis
The analysis conducted shows the importance of quantity production variability on the profit maximizing verge. A range of values is presented by four Regional Sales Managers; each region includes the minimum, most likely and maximum sales values of a random variable. The sample data is described as simple, limited, and somewhat scarce; therefore, given the grade of uncertainty, the most appropriate and suitable distribution to use is the Triangular distribution. The Monte Carlo Simulation from Microsoft Excel @Risk, will calculate “a model output value many times with different input values. The purpose is to get a complete range of all possible scenerios.”1
For the Region 1 the demand is generated from (3000, 4000, 8000) with a mean of 5000. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 22%. The graph is right skewed, as we see the mean (5000) is right to the median (4875), and the median is right to the mode (4000); its peak represents the most likely value (4000). According to the input the total demand average generated for this region is 5000 jackets.
For the Region 2 the demand is generated from (2000, 4000, 5000) with a mean of 3667. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 17%. The graph is left skewed, as we see the mean (3667) is relatively close, but left to the median (3717), and the median is also close, and left to the mode (4013); its peak represents the most likely value (4000). According to the input the total demand average generated for this region is 3667 jackets.
For the Region 3 the demand is generated from (1500, 2000, 3500) with a mean of 2333. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 18%. The graph is left skewed, as we see the
The analysis conducted shows the importance of quantity production variability on the profit maximizing verge. A range of values is presented by four Regional Sales Managers; each region includes the minimum, most likely and maximum sales values of a random variable. The sample data is described as simple, limited, and somewhat scarce; therefore, given the grade of uncertainty, the most appropriate and suitable distribution to use is the Triangular distribution. The Monte Carlo Simulation from Microsoft Excel @Risk, will calculate “a model output value many times with different input values. The purpose is to get a complete range of all possible scenerios.”1
For the Region 1 the demand is generated from (3000, 4000, 8000) with a mean of 5000. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 22%. The graph is right skewed, as we see the mean (5000) is right to the median (4875), and the median is right to the mode (4000); its peak represents the most likely value (4000). According to the input the total demand average generated for this region is 5000 jackets.
For the Region 2 the demand is generated from (2000, 4000, 5000) with a mean of 3667. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 17%. The graph is left skewed, as we see the mean (3667) is relatively close, but left to the median (3717), and the median is also close, and left to the mode (4013); its peak represents the most likely value (4000). According to the input the total demand average generated for this region is 3667 jackets.
For the Region 3 the demand is generated from (1500, 2000, 3500) with a mean of 2333. One point of interest in the data is the variability of the values. According to the parameters of this data the coefficient of variability is 18%. The graph is left skewed, as we see the