ABSTRACT
The recent SARS CoV-02 pandemic has put enormous pressure on intensive care staff, making it imperative to relieve them of repetitive tasks with little added value such as drug replenishment. We propose a decision support system based on a hybrid policy to manage the inventory of critical drugs with low and intermittent demand at an Intensive Care Unit (ICU). Demand forecasting is at the heart of any inventory policy. We claim that in the ICU setting drug demand patterns must be therapy based. Heterogeneous data have been collected during an on site study, and information have been extracted to provide a faithful abstract representation of the ward as a system, as well as the potential evolutions of ICU patients clinical conditions. Together with medical guidelines, this provides the foundation of a therapy based demand forecasting tool. This study integrates schedule optimization and demand forecasting, and exploits simulation for evaluation purpose in the long run. At the beginning of every period, drug orders are optimally scheduled with respect to forecast demand. Then, scheduled orders are deployed day by day and confronted with the real demand in a simulated environment. Potential stock outs trigger rush orders to restore safety stocks. The comparison between the proposed policy and a standard policy mimicking current practice in an ICU ward shows that information on therapy patterns can be successfully incorporated into drug replenishment processes to reduce the number of rush orders, a primary goal in designing an efficient system.
