September 2020 -
Optimal Reorder Policy: Inventory
The goal is to plan the order of the test kits and deciding the number of PPEs six states need to order each based on the daily demand of these states. The states are California, Illinois, Michigan, Ohio, Texas, and Wisconsin.
A critical safety aspect in providing treatment to patients admitted for Covid-19 is the use of disposable personal protection equipment (PPE) used by nurses and doctors. Using data from the CDC on reported COVID cases from April 01st to November 10th – estimate the number of tests performed in each state on each day.
Analyzed data from the CDC regarding the number of infection rates per state and number of tests conducted
Created a mathematical algorithm in R program to calculate Reorder Policy under Normal and Uniform Distribution
Calculated Joint Reorder Policy by combining a joint plan between different States
We began by averaging the number of patients by the days over a 24 hour period.
To find an estimate of the total number of tests performed on each day, we subtracted the total number of cases reported on day x-1 from the total number of cases reported on day x to find the number of new cases reported on day x.
Pairing this number with our insight that about 7% of all tests return positive, we can divide the number of new cases by the positivity rate to find an estimate for the number of tests taken each day.
Using the given costs related to the test kits and assuming the demand is uniformly distributed, we have multiplied the lead time with the mean demand for each state and computed an initial estimate of the order quantity Q0 and the initial reorder point R0.
Then, we have found an estimate of the safety stock for the normal distribution which we used to compute an estimate of the reorder point.
We have computed the reorder point estimates until we observed a substantial decrease in the difference between the previous estimate for both normal distributed and uniformly distributed demand over the lead time.
Join Ordering Plan with $250,000 Ordering Cost and Normal Distribution of Demand
The general trend is that savings go down as ordering cost goes up, meaning that a lower ordering cost is most beneficial. Daily costs increase when ordering costs increase.
In terms of which plan yields the best savings, our findings show a combination of all states allows for the highest daily savings, across all ordering cost values.
In terms of the different possible joint ordering plans, the best plan overall would be with 4 states (Illinois, Michigan, Wisconsin, and Ohio) working in collaboration. Our recommendation is to pool these states as they maximize savings.
Although 4 states combination is the best option, it’d be beneficial to look at logistical or systematic factors involved in these shipments like any restrictive regulations involved and complications that may arise during the distribution process.
Analyzing banks S&P 500 companies recommendations. Surprisingly, there were no trackers following the performance of analyst picks over the long term and I decided to build one.
Redesigning the CODE stroke activation process to reduce the Door-in-door out time for stroke patients
PROJECT WILL BE AVAILABLE AUGUST 2021