Above data clearly show that the introduction or expansion of the operation into the third wharf will lead to a reduction in the time of ship arrival unlike when the company used the first and second wharfs (Robinson, 2004). When the ship arrival time is reduced, it means that the company’s operations will also expand, and this would make the company experience the economic benefits thereof. Also, with the introduction of the third wharf, the loading time for each ship will reduce and the arriving ships will not have to take a long time as they await a berth. With the increase of the third wharf, 135 (115 +20) ships will be expected to arrive in the following year due to the increase in company operation and a reduction in the time taken to load and berth a ship. Increase of the third wharf will also lead to a reduction in waiting times and demurrage costs incurred, hence being economical for the company. If the company builds the third wharf, it will end up saving as follows:From the above statistics, it is quite evident that the mean for ship inter-arrival times is greater than the median which is responsible for the positive skew, while the median for ship loading times is higher than its mean which has contributed to the negative skew. These statistics clearly indicate a high contrast to a normal distribution as the ship inter-arrival times indicate a positively skewed distribution while ship loading times indicate a negatively skewed distribution (Lee et al. A normal distribution usually has skewness and its excess kurtosis is equal to zero. However, in this case, the data show both positive and negatives distribution.From the descriptive statistical data, it is evident that the data from Superior Grain Elevator Inc are not normally distributed and thus cannot be used to make the assumption that the introduction of a third wharf would be economical or would increase the company’s economies of scale (Kros, 2008). In case Superior Grain Elevator Inc wants to use descriptive statistics, it should adopt alternative methods of quality assessment such as the use of the simulation model as in Table 1, as this would help the company to come up with more accurate data (Grant & Leavenworth,
Grant, E. L., & Leavenworth, R. S. (1996). Statistical quality control (7th ed.). Boston: WCB/McGraw-Hill.
Kros, J. F. (2008). Spreadsheet modeling for business decisions. New York: McGraw-Hill/Irwin.
Lee, A., Lee, J., & Lee, C. (2000). Statistics for Business and Financial Economics (vol. 1). World Scientific.
Robinson, S. (2004). Simulation: The practice of model development and use. John Wiley & Sons.
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