Littlefield Simulation Write Up
Littlefield Simulation Write-up
December 7, 2011
Operations Management 502
Team 9
Littlefield Lab
We began our analysis by searching for bottlenecks that existed in the current system. It was easily identified that major issues existed in the ordering process. Without calculations, you could tell the reorder point was too low since the historical plots showed inventory levels at zero for two or more days at a time. The number of jobs in customer orders showed correlating spikes at the same time of the inventory outages. We reviewed the utilization and queues of the other stations in the system but were hesitant to make in immediate changes since we were not entirely certain the effects of correcting the inventory policy.
To correct the inventory policy, we want to find the optimal ordering quantity based on the calculation EOQ=. Demand was calculated by taking the average number of customer orders per day over the first 50 days. We came up with a figure near 12.24 orders per day which we multiplied by 268 days for the entire simulation. Setup cost was provided for us at $1,000 per order and holding cost was generated by multiplying the cost per unit by the interest rate which gave us a yield of 60. Based on this information, EOQ was 331 units after rounding. From the history, this was the second change we made to the inventory policy, up from 299. This was a result of a discussion in demand, where we had calculated demand based on 218 days for the simulation instead of the 268 days of the actual simulation. Next we wanted to fix the reorder point so we could eliminate inventory shortages and determined we would like to do so while maintaining a 95% service level. Since R=mean DDLT+z*std. dev. of DDLT, we needed to calculate Mean DDLT, z & std. deviation of demand. For mean DDLT, we used the 12.24 * 4 day lead time which gave us a rounded value of 49. Excel was utilized to figure z=1.64 for the service level of 95%. We found the std. dev. of DDLT by multiply
December 7, 2011
Operations Management 502
Team 9
Littlefield Lab
We began our analysis by searching for bottlenecks that existed in the current system. It was easily identified that major issues existed in the ordering process. Without calculations, you could tell the reorder point was too low since the historical plots showed inventory levels at zero for two or more days at a time. The number of jobs in customer orders showed correlating spikes at the same time of the inventory outages. We reviewed the utilization and queues of the other stations in the system but were hesitant to make in immediate changes since we were not entirely certain the effects of correcting the inventory policy.
To correct the inventory policy, we want to find the optimal ordering quantity based on the calculation EOQ=. Demand was calculated by taking the average number of customer orders per day over the first 50 days. We came up with a figure near 12.24 orders per day which we multiplied by 268 days for the entire simulation. Setup cost was provided for us at $1,000 per order and holding cost was generated by multiplying the cost per unit by the interest rate which gave us a yield of 60. Based on this information, EOQ was 331 units after rounding. From the history, this was the second change we made to the inventory policy, up from 299. This was a result of a discussion in demand, where we had calculated demand based on 218 days for the simulation instead of the 268 days of the actual simulation. Next we wanted to fix the reorder point so we could eliminate inventory shortages and determined we would like to do so while maintaining a 95% service level. Since R=mean DDLT+z*std. dev. of DDLT, we needed to calculate Mean DDLT, z & std. deviation of demand. For mean DDLT, we used the 12.24 * 4 day lead time which gave us a rounded value of 49. Excel was utilized to figure z=1.64 for the service level of 95%. We found the std. dev. of DDLT by multiply