Impredstock : Multivariate Prediction Model Stock Of Spare Parts for Medical Equipments

Research output: Contribution to journalArticle


The demand for the stock of spare parts is one of the largest sources of uncertainty and selecting the best prediction method for each reference is a complex task. The methods to use in the prognosis are selected according to the amount of data and the different behavior patterns. In the last decade, the development of mathematical models for predicting the demand for spare parts has opened a path for several applications in different areas of society, using techniques for analyzing series of temporal, causal regression methods and Soft-Computing techniques. However, it has been observed a lack of practical applications for making a prognosis of the stock of spare parts for medical equipment, in relation to the relevant theoretical proposals developed in this application area. In addition, existing solutions do not always manage to improve the accuracy of the prognosis, due to the preference for the use of highly complex methods. In this research paper, the MPREDSTOCK model is proposed. It is responsible for the process of predicting the stock of spare parts for medical equipment through the multiple linear regressions as the method of solution. The model includes algorithms for predicting the stock of parts and technical availability of a piece of medical equipment, the calculation of operational reliability and failure frequency of one of its devices and it is part of the " Prediction and stock management Module " belonging to the SIGICEM.
Translated title of the contributionImpredstock : Modelo de Predicción Multivariante Stock de Repuestos para Equipos Médicos
Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalRevista Cubana de Ciencias Informáticas
Issue number10
StatePublished - 31 Dec 2019


  • Accuracy
  • Demand forecasting
  • Model
  • Stock of spare parts

CACES Knowledge Areas

  • 727A Industrial and process design


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