A joint replenishment supply model for multi-products grouped by several variables with random and time dependence demand

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<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>This paper aims to propose a supply model of periodic review with joint replenishment for multi-products grouped by several variables with random and time dependence demand.</jats:p>
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<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>The products are grouped by multivariate cluster analysis. The stochastic inventory model describes the random demand of each product, considering the temporal dependency through a generalized autoregressive moving average model. Stochastic programming for the total cost of inventory is obtained considering the expected value of the demand per unit of time.</jats:p>
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<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>The total costs for the products grouped with the proposed model are 6% lower than for the individual inventory policy. The expected shortage units decrease significantly in the proposed grouped model with temporary dependence. In addition, the proposal with temporary dependency has lower costs than when the independent and identically distributed demand is considered.</jats:p>
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<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The proposed policy is exemplified with real-world data from a Chilean hospital, where the products (drugs) are segmented by grouping variables, forming clusters of drugs with homogeneous behavior within the groups and heterogeneous behavior between groups.</jats:p>
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