Artículo Scopus
Managing slow-moving item: A zeroinflated truncated normal approach for modeling demand.
Registro en:
10.7717/PEERJ-CS.298
Autor
Huerta, Gonzalo
Institución
Resumen
This paper proposes a slow-moving management method for a system using of intermit-tent demand per unit time and lead time demand of items in service enterprise inventorymodels. Our method uses zero-inflated truncated normal statistical distribution,which makes it possible to model intermittent demand per unit time using mixedstatistical distribution. We conducted numerical experiments based on an algorithmused to forecast intermittent demand over fixed lead time to show that our proposeddistributions improved the performance of the continuous review inventory model withshortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantityper cycle, and the adequacy of the statistical distribution of the lead time demand) fordecision analysis using the Technique for Order of Preference by Similarity to IdealSolution (TOPSIS). We confirmed that our method improved the performance of theinventory model in comparison to other commonly used approaches such as simpleexponential smoothing and Croston’s method. We found an interesting associationbetween the intermittency of demand per unit of time, the square root of this sameparameter and reorder point decisions, that could be explained using classical multiplelinear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demandwas positively related to the decision of reorder points. Our study examined a decisionanalysis using illustrative example. Our suggested approach is original, valuable, and,in the case of slow-moving item management for service companies, allows for theverification of decision-making using multiple criteria