A Method for Forecasting the Demand for Pharmaceutical Products in a Distributed Pharmacy Network Based on an Integrated Approach Using Fuzzy Logic and Neural Networks

EasyChair Preprint 3180

Abstract

The pharmaceutical market is an important area of the country's economy, which must be given special attention due to the fact that it is one of the necessary factors for the timely provision of human health. This paper discusses the use of fuzzy logic and a neural network to predict the demand for pharmaceutical products in a distributed network of pharmacies in the face of insufficient information, a large assortment and the influence of risk factors. An integrated approach to solving the forecasting problem using: the theory of fuzzy logic - in forecasting the emerging and unmet demand and the neural network - in the presence of a large amount of retrospective information on the actual sale of drugs is proposed. Using this approach to solve the problem of forecasting demand allows you to take into account both statistical data and the experience and intuition of the managerial staff of the pharmacy network at various stages of demand forecasting. The general algorithm, mathematical formulation and examples of forecasting the demand for pharmaceutical products under conditions of uncertainty of information are given.

Keyphrases: Demand Forecasting, Fuzzy Logic, neural network, pharmaceutical market, uncertainty

@booklet{EasyChair:3180,
  author    = {Ramiz Balashirin Oglu Alekperov and Ilhama Tarlan Kizi Iskandarli},
  title     = {A Method for Forecasting the Demand for Pharmaceutical Products in a Distributed Pharmacy Network Based on an Integrated Approach Using Fuzzy Logic and Neural Networks},
  howpublished = {EasyChair Preprint 3180},
  year      = {EasyChair, 2020}}