The demand is the most important thing to determine in this simulation. Decisions of purchasing more equipment, setting reorder points and quantity were based on the groups demand forecasts made prior to all other decisions. We first determined what the demand was from the customer orders performed in the first 57 days of production. Plotting out the information we believed, based on the first 57 days mean that the average customer order would be approximately 8.5 per day.

Day | 50 | 100 | 150 | 200 | 250 | 300 | 366 | Mean | 8.42 | 9.46 | 8.76 | 9.4 | 8.64 | 8.94 | 8.18 | Std Deviation | 3.11018 | 2.87998 | 3.3719 | 2.96923 | 3.674 | 3.093 | 2.8833 | Range | 2 to 15 | 3 to 15 | 3 to 19 | 4 to 16 | 2 to 17 | 4 to 17 | 2 to 15 |

Day 57: Reorder Quantity (Kits)

As a team we also decided to change the order quantity, which was set at 7200 kits and decreased that to 5400 kits. As a group we reasoned that the decrease was necessary due to the fact that there was not enough cash on hand at Day 57 to continue purchasing 7200 kits due to the various bottlenecks throughout the system that had not yet been changed and updated.

Day 101: Station 2 Machine Count

Within the data set, we focused on the daily job arrivals as noted above and the daily utilization of the 3 stations (Station 1: Board Stuffing, Station 2: Testing and Station 3: Configuring). The simulation began on Day 1 with each station having 1 machine each, in order to determine if there were any potential bottlenecks for each station, we downloaded the data for each machine and determined the utilization of each machine. During the first 101 days of operation, we found that the average job arrival rate for the simulation was 9.46 lots per day and the average utilization rate of Station 2 was 93% but the last 43 days of that period, utilization was at 100%, as such, we noted that the average rate of service for Station 2 was 10.17 lots/day. We also noted that due to the fact that Station 2 had both inputs from Station 1 and Station 3, we concluded that the average job arrival rate would be 2 times the average service rate of station 1, it would be beneficial to purchase an additional machine at Station 2.

Day 147: Lots per order (2):

We also discussed that there effectively where three contract orders to choose from and we noted that the less lead time is used the greater the revenue total. With the assumption, that demand would continue to be a constant throughout the production cycle, we determined that we would fix our contract to number 3. The maximum lead time of contract 3 is only 1 day. We noted as a group, that if an order would not be finished within a day, the factory would lose greater revenue thus decreasing cash and losing opportunity cost of revenue that could have been made. Based on the forecast of the first 57 days, the highest possibly mean of a future order would be 8.42 + 3.11 = 11.53 days, though there was a chance that the order could go up as high as 15 based on the ranges noted in the first 57 days.

In the beginning we calculated the average process time per lot size and determined that the average time for 60 kits or 1 lot per job took on average 1.53 hours which was lower than the 30, 20 and 12 kits. Though after the first 147 days we noted that keeping the lot size at 1 was creating too much variability in wait times throughout the production and creating large spikes in utilization of machinery, for this reason, we determined that decreasing lot size to 30 from the original 60 would create a more continuous cycle of production and decrease variability.

Day 147: Station 2 scheduling rule

Due to the fact that Station 2 was determined to be the bottleneck in the first 100 days of production, and noting that our revenues had not gone up as significantly as we had expected by adding an additional machine to Station 2, the group decided to change the scheduling rule of in Step 2 of the Station 2 queue. The three options that were presented were FIFO (first in-first out), priority to Step 2 or priority to step 4. Seeing that our daily average revenue per job was sitting steady at $750, the group decided that it would be beneficial to change the operations from FIFO and give the priority to Step 4. The group believed that by giving priority to Step 4, would lead to a lower amount of WIP in inventory, as Step 2 production would not impede the final step of sale. Getting the inventory out to customers vs. having the items sit at Station 2 queue waiting for step 2 to be performed if it came in before items from the tuning station would increase revenues and decrease the amount of inventory at hand. After changing priority to step 4 revenues increased to $1,000 per day and further increased to a steady average of $1,250 per day for the rest of the simulation. The FIFO setting is appropriate when the demand is at average or low, but due to demand being above average for most of the remainder period and increased lead time, prioritizing step 4 was appropriate.