Customer responsiveness Essay Example

optimal order quantity for minimizing total inventory holding costs and ordering costs. Initially, LT faced a problem of insufficient inventory for several days due to having a large reorder point of 1440 kits. As a consequence, this led to longer lead times and a decrease in revenue.

The consulting team aims to determine the optimal reorder point to eradicate inventory shortages for the company. They also take into account the potential loss in revenue caused by excessive inventory. To achieve this, the consulting team establishes a service level of 95%, ensuring sufficient inventory to meet demand for 95% of the production period. Additionally, they consider the lead time for reordering materials. The consulting team employs the formula d*l+z*std deviation of the demand*(lead time)^0. 5 to calculate the accurate reorder point.

The recommended reorder point is 3600 kits for the company. In regards to the end of the company's operating lifetime, the consulting team noted that excess inventory will be worthless. Additionally, the consulting team was informed that the factory will be controlled by a computer starting on day 218. The team's responsibilities include forecasting the total demand for the last fifty days, purchasing inventory, and setting the order quantity and reorder point to zero.

The consulting team determined the total demand for the last fifty days by multiplying the average demand by fifty, resulting in 600 batches. Subtracting the old inventory from this total, they determined the inventory that LT needed to order for the remaining fifty days. Additionally, they adjusted the reorder point to match the current inventory level, considering that purchasing inventory takes four days.

In order to ensure

that the last reorder amount would be entered before day 214 and avoid inventory shortages, the consulting team decided to set both the reorder point and order quantity to 0, effectively stopping any further reorders.
During the first 50 days, the average queue for each station was as follows: 756 kits for station one, 342 kits for station two, and 583 kits for station three. This high queue was a result of the high demand exceeding the maximum processing capacity of the stations, causing some jobs to wait in line for processing.

High queues lead to longer lead times. The average queue for each station was initially high, so the consulting team bought one machine for each station to reduce the queue. Three Contract; Lead time ; Revenue: LT provided three contracts with different lead time constraints. Contract 1 had quoted lead times of 7 days, a maximum lead time of 14 days, and a maximum revenue of 750. Contract 1 has the lowest maximum revenue and is currently being used by Littlefield.

However, Littlefield can ensure receiving their full profit within a relatively long lead time by choosing Contract 1, despite it having the highest quoted lead times. Contract 1 allows LT to earn $750 per job if the lead time is seven days or less. However, if the actual lead time exceeds 7 days, LT will incur a loss of $107 per job for each additional day. Furthermore, if the lead time surpasses 14 days, LT will not earn any profit. In contrast, Contract 2 requires a shorter lead time of 1 day, but offers a higher maximum revenue of

$1,000.

Contract two allows LT to earn $1000 per job if the lead time is one day or less. For each day past the quoted lead time, there is a loss of $500 per job. If the lead time exceeds three days (maximum lead time), LT will earn $0.00 on revenue. Additionally, contract two guarantees a minimum revenue of $750 per job if the lead time is 1.5 days. To increase revenue, the consulting team decided to switch from contract one to contract two, as long as the company lead time is less than 1.5 days.

Contract 3: Quoted Lead Times= . 5 days, maximum lead time 1 days, maximum revenue 1,250. Contract three offers the highest maximum revenue and a slightly lower quoted lead time. LT can earn $1250 per job with a lead time of 0. 5 days’ or less. Each additional day after the quoted lead time will result in a loss of $2500 in revenue. If the lead time exceeds one day, LT will not earn any revenue. Contract three also allows LT to earn $1000 per job if the company's lead time is 0.6 days or less.

The consulting team has decided to switch from contract two to contract three if LT's average lead time is 0.6 days or less. According to simulation one, the consulting team observed that LT's process time is 7.2 hours for each job. On Day 6 of the simulation, it was seen that the shortest job lead time was 0.3 of a day or 7.2 hours, which demonstrates that LT is capable of completing a job quicker than usual. Additionally, the consulting

team analyzed the data from simulation one.

The maximum demand for a machine at station one is four, resulting in a queue forming when the demand exceeds this limit. At this point, a new machine was purchased by LT to alleviate the queue (Data shown below). When comparing station two and station three, it can be observed that the utilization rate for both stations is only about 35% when the demand is four jobs. However, the utilization rate reaches 100% at this demand level. Consequently, there is a remaining 65% utilization rate available despite the demand being only four jobs.

Therefore, in order to achieve a 100% utilization rate for station two and three, the demand should be approximately seven jobs (4 * (100% + 65%)). After analyzing these data, the consulting team determined that station one requires four machines to meet its demand of four jobs, while station two and three require seven machines each. The average demand in this simulation is twelve. Based on these findings, the consulting team advised LT to purchase four machines for station one, two machines for station two, and three machines for station three.