Littlefield Simulation Write Up

The difficulties I had in obtaining my best results when comparing it to the solution we calculated in class had to do with my strategy. My beginning strategy was really no strategy, I just played around with the idling and purchasing. That strategy allowed me to achieve throughput, but did not allow for me to meet the fixed cost. After continually watching the habits of idling and parts that assisted me in finding a better strategy. I noticed that the left A1 machine can only contribute parts to C7. That means I should only buy parts for it when C7 is running. I start by purchasing 50 units in G1. Then double digit numbers in E1 and C1. E1 should always have units running through it because it contributes to both the 100 and 50 products demanded. I always

In structural terms, we can analyze the organizational supply chain by performing an MMc Queue calculation, based on the figures from the last 120 working days, a 7,5h working day and weighted averages of operating times. This data yields the service rate which is combined with an arrival rate of 39 to calculate the throughput time. One has to consider that the arrival rate in the PW step is lower than in the other processes because only 15% of RAPs are converted into RUNs and reach this step. The cumulated throughput times yield our TAT, which in this case, falls into a range of 0.794 – 1.394 days. Hence, the underwriting process itself seems to be efficient and the difference has to have its main roots in qualitative and organizational defects. However, structural improvement is possible. The three geographically split UTs can be merged into a single unit, making up for different workloads which result in backlogs. Throughput times of the UTs can be decreased by up to 82,5%.

The manager of Shamrock Manufacturing plans replacing of large piece of manufacturing equipment that the company uses in a production process with a more efficient model. The replacement of 3-years old equipment promises reducing the direct manufacturing and electricity costs. The manager of the plant hesitates replacing because the next year he expects promotion in larger Houston plant and the fright that he will he will reduce the operating income.

In order to mitigate this risk, we decided to measure how long it takes for our current inventory system to be used up. We calculated this number to be 12 days. Furthermore, we calculated how much inventory we will need to not have to purchase any during the 50-day uncontrolled period. We calculated this number to be 42653 kits. Based on this information, we decided to change our EOQ to 42653 (+5000 safety stock) to have near 0 inventory the day the simulator stops, minimizing the amount of cash tied up with inventory. This can be seen in exhibit 4, the days before 218 are shown to be ordering approximately 9460 units per ~12 days and once we change our EOQ point to reflect the new strategy, the simulator orders enough to cover the remaining 50 days with minimal inventory left unsold. We also changed the re-order point to 0, so the simulator does not order anymore, and lets the inventory run out. This can be evidenced in exhibit 4 by noticing that the simulator does not re-order at any point throughout the 50-day uncontrolled period. Overall, this strategy was fairly successful. Over the 50-day uncontrolled period, we only had approximately 5000 kits worth of cash tied up in

We became aware of this problem when we saw a decrease in the sales orders and smaller quantities on the orders that were replenished. After brainstorming, considering the facts and generating ideas, we narrowed down our suggestions to two possible solutions (Adair, 2013). We feel either would be simple to

At the outset, I have to acknowledge that I had a problem with this simulation. Initially, in my first two orders, I confused the backlog and inventory sections, so that I believed I had a significant surplus in inventory. Therefore, rather than minimizing the bullwhip of supply and demand, I exacerbated the problem by limiting my supply well below the number of orders coming in, much less below what I could fill with the backlog and incoming orders. This error on my part certainly led to increasing instability, not only in my own incoming orders, but also in the entire supply chain. The simulation also kept auto-filling my initial order, which was only 10,000 cases, despite me ordering a significantly larger number of cases. I could not get the simulation to operate properly and allow me to place a larger order. It kept auto-ordering the 10,000 cases. I could not get it to place an order other than 10,000 cases. Because of this glitch, I continued to increase my backlog. I simply could not order more than 10,000 cases. I address this issue because the glitch certainly impacted my opinion of the computer simulation as a means of learning. In this scenario, the initial computer simulation was absolutely worthless as a learning tool. I made an error in my first order, but I do not think that error was meaningful. Instead, it appears that whatever number of cases I placed in my initial order would be the number of cases ordered over the next several

When dealing with inventory management it’s been an “age-old problem” to balance correctly demand versus stock overages and outages, especially when demand

TSC is planning for large corporate growth over the next couple of years, and there are concerns of whether or not, in its current state, the supply chain strategy is adequate to meet the growing demands.Brad Twiddy, the director of distribution, recently produced a forecast model of future inventory volumes, and examined the affect the inventory had on the supply chain. The model predicts the company will experience capacity issues as soon as next spring. With larger volumes of inventory looming over a static inventory space, it would be prudent to examine alternatives of how to adapt the supply chain to the corporate expansion goals.

Production planning becomes even more complicated because of the influence of the out-of-stocks, which would cause the sales graphs to be heavily distorted. The graphs would be distorted if items were out of stock for a month and the backorders were filled in a short period of time. Whenever there were overstock issues, retail stores would push the items through discounts. Often when there were out-of-stock issues for the wholesalers the company would take the supplies from their retail stores and divert it to the wholesaler’s order. Lastly, when special orders were placed for wholesale, it was not uncommon for the production plans to be put on hold to focus on special orders. As a result from all the production errors, the sales graph would have unnatural spikes, yet these spikes would be included for planning production for the following year.

We also wanted to figure out the optimal reorder point and order quantity using the forecasted average demand of 18 jobs per day. We set the number of reorder point to 95 kits, accounting for the four day lead period and the level of safety stock we wanted to have.

Northcutt Bikes is a bike manufacturer and a service provider of bike parts to the many retail businesses that sell Northcutt bikes. Northcutt Bikes has a warehouse that is 95% utilized however they have an order fulfillment rate of less than 80%. This has caused a loss of revenue due to orders being cancelled when needed parts are not in stock. To resolve the problem, we looked at 3 alternatives including establishing pooling groups within the same geographic region, establishing a continuous review inventory management system or staying the course. Our recommendation is to establish a continuous review inventory system that will reduce unnecessary inventory, increase needed inventory and improve service levels.

The Distribution centers in the 5 regions order every 6 days using a periodic review while maintaining their safety stock levels to ensure a customer service level of 95% (Chopra, 323).

Currently many clinics and wards within the hospital have no form of a supply accounting system. The last point of accounting for CL VIII (medical supply) or CL II (office supply) is when it leaves the Supply Chain Management Branch (SCMB) warehouse. Once distributed to the clinics and wards, it is the responsibility of each section to manually account for their stock. Unfortunately, once in their possession many sections have no method of accounting for their stock. This common practice can be compared to an open vending machine. Employees identify the need for an item, go to the supply room take what they need, and leave without any annotations. This causes many negative second and third order affects. The individual managing the supply is completely in the dark with no clue as to what is left on the shelf at the end of the day. It is not until there is zero balance on hand or the item is critically low that the realization is made "we need more". Normally in the States emergency orders can be filled in a matter of hours by the Prime Vendor (e.g. Cardinal) for items not stocked in the warehouse. Here in Germany we do not have this luxury. Often times it could take days to weeks maybe even months to receive such items. Additionally, this has adverse effects on SCMB as well. When these emergent situations present themselves SCMB has to stop all normal business and jump

The correct amount of inventory is a constant balancing act between maintaining a safety stock your company has determined while not over ordering. Every order has its costs, so making frequent orders is not the easy way out. The tool used to approach this task is known as the EOQ, or the economic order quantity. The goal of the EOQ measurement is to find the inventory lot size at which the company should order. The only two costs associated with this model are inventory holding costs and fixed costs allocated to an order. While this model can help with the decision-making, the assumptions made under it can make it unreliable at times. Lead times and demand rates remaining constant are two of the assumptions made that can vary on unexpected levels

HP’s inventory crisis is caused by the high variability in demand and long lead times for which HP has no effective system in place. An effective demand forecasting system is needed, which not only takes into considerations qualitative factors (such as forecasted consumer behaviour, upcoming special events), but also quantitative factors (such as historical demand patterns, number of backlogs from last period). Along with the forecasting system, a Q inventory model should also be integrated. Using the calculated optimal order quantities and the ROPs from Exhibit 2, the European DC will know exactly when to place a new order. This will smooth out demand variability as the safety stock will ensure that there are minimal product shortages. This implementation will not be successful, however, until all of the departments accept and use the system. For this, HP needs to educate its employees about how the forecasting and Q-inventory system work and value it adds.