Analyzing The Limited Data Of The Standard Deviation Of Demand

1064 Words Mar 3rd, 2016 null Page
From the start of this simulation, we understood that we needed to analyze the limited data that was given to us. More specifically, we looked at the mean and standard deviation of demand for the first 50 days of the simulation. We did this by exporting the data from Littlefield into an Excel document and utilizing the AVG and STDEV functions within it. These functions gave us an average demand of 14.52 jobs per day, and a standard deviation of 4.17 jobs per day for the first 50 days. By comparing these numbers we realized that the standard deviation was nearly 30% of the average daily demand. This told us that although demand would be relatively predictable and overall would show no trend, we could expect to see some dramatic spikes, both low and high, in the future. If these numbers were any indication of future forecast, which we believed them to be, then we would need to calculate our economic order quantity (EOQ) and reorder point (ROP) carefully so that we would could account for the demand swings and be able to achieve a high success rate of job completion within our contract guidelines.
While machine utilization was a major factor in the first simulation, we did not focus quite as heavily on it in this round. Originally, all our machines had relatively high utilization. By looking at the history data of the first 50 days, there were at least 23 days of each station that its machines’ utilization reached the maximum level (see Appendix I). We immediately realized that…

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