FORECASTING SOLID MEDICAL WASTE DEMAND USING EXTRAPOLATIVE DAILY DATA SETS: A CASE STUDY OF A MEDICAL SOLID WASTE PROCESSING SERVICE PROVIDER IN INDONESIA

Russian Journal of Agricultural and Socio-Economic Sciences

Maulina E.¹  Gumilar R.¹  Arifianti R.¹ 

[1] Postgraduate Program of Business Administration, Faculty of Social and Politic Science, University of Padjadjaran;

关键词: solid medical waste;    demand forecasting;    forecasting;    moving average;    weighted moving average;    exponential smoothing;   

DOI  :  10.18551/rjoas.2019-11.20

来源: DOAJ

【 摘 要 】

Due to a service provider for collecting and incinerating of solid medical waste failed to fulfill the requirements of the Indonesian Ministry of Environment regarding the difficulties to predict the amount of medical solid waste in the primary and secondary source. This research was to study the most optimal time series model of demand forecast for service provider for solid medical waste incineration. In this study, the use of extensive records from past data required due to the need intensive supervision to handle infection risk and over demand that may happen every day. The forecasting methods analyzed included: moving average, weighted moving average, exponential smoothing, and exponential smoothing with trend adjustment. The optimal forecasting model was measured using Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percent Error (MAPE). The result showed that exponential smoothing which assumed stable fluctuation of the actual value (α = 0.10) is the most optimal forecast model based on the values of MAD and MAPE. Meanwhile, based on MSE showed that MA (n = 7) was the most optimal forecasting model. The difference in optimal forecasting measurement models guide discretion to choose the right forecasting model based on optimal forecasting model interpretation.

【 授权许可】

Unknown