Forecasting Lumpy Demand for Planning Inventory: The Case of Community Hospitals in Thailand The pattern of lumpy demand affects the healthcare industry such as small community hospitals which encounter sporadic demands of slowmoving medicines. The objective of this work aimed to study performance of forecasting methods for inventory planning. This work compared two forecasting methods: Croston(CR) and the Teunter, Syntetos, and Babaiu0027s (TSB) methods. Furthermore, this work proposed a combination of Exponential and Poisson distribution and the use of average interdemand interval combining with average demand to forecast lumpy demand. It assumed that amount of onhand inventory would be equal to forecasting values. Mean square error (MSE) and number of shortage period were used as performance indicators of these methods. The simulation used 2 types of vital medicines, obtained from the community hospital in Mae Hong Son province from January 2015 to December 2017. The results from CR and TSB methods provided lower MSEs when the smoothing constants were unchanged until 12 weeks. Meanwhile, adjusting the smoothing constants every 4 weeks provided lower shortages. Meanwhile, the other two proposed methods led to lower shortages comparing with those of CR and TSB methods.

Inventory Replenishment Policy for Medicines with NonStationary Stochastic Demand: The Case of a Newly Opened Hospital in Thailand Inventory and replenishment planning of medicines play a vital role in hospital management. A large replenishment quantity can lower the percentage of medicine shortage, which might be critical for a patientu0027s life, but somehow needed to be traded off with high inventory cost from excessive stock. Especially for a vital medicine with nonstationary stochastic demand e.g., antivenom serum or adrenaline, demand for these medicines found to be intermittent since itu0027s an unpredictable event, but a shortage of these medicines when needed will result in a serious loss. Adequate recorded data allow the pharmacist a better decide on its replenishment policy. However, this case study is not yet existed since a hospital has just opened for four months. Hence, the objective of this paper is to propose a replenishment policy (Rn,Sn) for this type of medicine including optimal orderupto level and period with lowest expected total cost using Tarim and Kingsmanu0027s MILP. A newly opened hospital in Thailand was selected as a case study. The results were compared with current policy and now being cited as its reference orderupto level.