SISTEM PENGENDALI PERSEDIAAN JUMLAH VAKSIN DENGAN MODEL PREDICTIVE CONTROL PADA RUMAH SAKIT

Pengelolaan persediaan merupakan bagian dari pengelolaan rantai pasok yang didalamnya berisi proses perencanaan, pengorganisasian, dan pengontrolan inventaris yang berkelanjutan. Pengelolaan persediaan vaksin yang kurang baik dapat menyebabkan terganggunya aktifitas dalam pelayanan pada rumah sakit. Penelitian ini bertujuan untuk menerapkan metode Model Predictive Control ke dalam sistem pengendali persediaan jumlah vaksin yang digunakan untuk menentukan jumlah persediaan vaksin yang optimum berdasarkan level stok vaksin, harga vaksin dan pesanan vaksin. Data yang digunakan yaitu data histori dari data permintaan vaksin Rotavirus dan vaksin Hexaxim selama selama 4 bulan, yaitu bulan tgl 1 Desember 20174 Maret 2018, data ini digunakan sebagai data latih, hasil yang didapat berupa permintaan rata-rata mingguan yaitu 3 dan 3,071 dengan service level 1,6% dan nilai standar deviasi yang dihasilkan adalah 2,075 dan 3,173. Menggunakan data permintaan vaksin yang sama dengan ditambahkan data tanggal 5 Maret 2018-5 April 2018 sebagai data uji, hasil yang didapat menunjukkan bahwa jumlah pemesanan vaksin selama 4 bulan adalah sebesar 61 box dan 61 box, berkurang 9 box dan 6 box bila dibandingkan dengan sistem rumah sakit sebesar 70 box dan 67 box. Sedangkan untuk nilai intensifitas pemesanan menjadi 13 dan 10 kali pemesanan, dan pada rumah sakit hanya ada 6 dan 7 kali pemesanan. Jadi untuk keseluruhan biaya yang dikeluarkan selama 4 bulan dengan menggunakan MPC sebesar Rp 14.040.000,- dan Rp 29.290.000,- sedangkan pada sistem rumah sakit sebesar Rp 15.918.000,- dan Rp 32.182.000,-. Total biaya tersebut terdiri dari biaya simpan, harga vaksin dan biaya pesan, sehingga antara sistem yang ada pada rumah sakit dan sistem yang menggunakan MPC mempunyai selisih Rp 1.878.000,- dan 2.892.000,-. Kata kunci : Model Predictive Control, vaksin, sistem pengendali persediaan, rantai pasok, biaya Stock management is the part of supply chain management systems which consists the processes of planing, organizing, and continuity invetory controlling. Dissatisfactory of vaccine’s inventory control can impact the activity distraction on the hospital service. This research is aim to implement the method of the Model Predictive Control (MPC) to the vaccine’s inventory control system for decides optimum amounts of vaccine’s stock based on the level of vaccine’s stock, vaccine’s price, and total vaccine’s order. The data used in this research is the historical data demand for 4 months (December 2017- March 2018). Its Data utilized as the data training, which is resulting 3 and 3.071 of the average weekly requests then the value of service level is 1.6% and the resulting standard deviation is 2.075 and 3.173. Comparing those result with the data testing (March 2018 – April 2018), it is reducing by 10 boxes and 6 boxes, from original boxes are 70 boxes and 67 boxes, hence become 60 boxes and 61 boxes. Furthermore, for the value of the request intesity is a rising, which 13 times for MPC and 6 times for hospital systems. So, the total amount that spent for 4 months using MPC is Rp 14.040.000,- and Rp 29.290.000,-. Then for the hospital systems is spending Rp 15.918.000,-and Rp 32.182.000,-. Those total costs are based on saving cost, order cost and vaccine’s cost. The result of the comparative cost between MPC and hospital’s systems are Rp.1.878.000,- and 2.892.000,-. Keywords: Model Predictive Control, vaccine, inventory control systems, safety stock, supply chain, cost

Economic order quantity and storage assignment policies

The basic Harris’s lot size model dates back to 1913 (Harris, 1913), hence one century from its publication has been recently celebrated. Starting from the seminal work of Harris, a wide plethora of contributors has faced with the lot-sizing problem for fitting the basic model of the economic order quantity to several environments. In fact, the three key parameters constituting the basic model, i.e. the demand rate, the ordering costs, and the inventory holding costs, have been widely explored in order to relax the assumptions of the original model. However, to the best of the authors’ knowledge, the liaison between holding costs and warehouse management has not been completely addressed. The holding costs have been early considered for simplicity as primarily given by the cost of capital, and thus dependent solely on the average inventory on stock. Conversely, by including a more detailed supply chain costs contribution, the economic order quantity calculus appears depending on a recursive calculus process and on the storage assignment policy. In fact, different approaches of warehouse management, e.g. shared and dedicated storage, lead to highly variable distances to be covered for performing the missions. This leads to a total cost function, and consequently to optimum lot sizes, that are affected by the warehouse management. In this paper, this relationship has been made explicit in order to evaluate an optimal order quantity taking into account storage assignment policies

Two warehouse inventory policy with price dependent demand and deterioration under partial backlogging

In today's era of higher competition in the business, there are many conditions such as offered concession in bulk purchasing, seasonality, higher ordering cost, etc., which force a retailer to purchase more quantities than needed or exceed the storage capacity. So in this situation the retailer has to purchase an extra warehouse named as rented warehouse to stock the extra quantity. In this paper an inventory model for deteriorating products with selling price dependent rate is developed. The occurring shortages are assumed to be partially backlogged and cycle time is also variable. The purpose of the development of this model is to compute the amount and time of order which can optimize the total average cost of the system. A solution procedure and numerical example are presented to illustrate the implementation of the proposed study. Sensitivity analysis concerning with distinct system parameters is also presented to demonstrate the model