TY - JOUR
T1 - Administración de inventarios en servicios de alimentación con demanda estadísticamente dependiente
AU - Rojas, Fernando
AU - Leiva, Victor
N1 - Funding Information:
The authors would like to thank the editors and referees for their constructive comments on a previous version of this manuscript, which led to this improved version. This version was awarded the ?Best Paper? prize of the ?Operations Management and Value Chains? track in the 50th Annual Meeting of the Latin American Council of Management Schools (Cladea) 2015. This study was partially financed by ?Gants-Conicyt? and the Fondecyt 1160868 project, both pertaining to the Comisi?n Nacional de Investigaci?n Cient?fica y Tecnol?gica (Conicyt) of Chile.
Publisher Copyright:
© 2016, © Emerald Group Publishing Limited.
PY - 2016
Y1 - 2016
N2 - Purpose: The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”. Design/methodology/approach: The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm. Findings: When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system). Research limitations/implications: The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution. Practical implications: Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally. Originality/value: The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.
AB - Purpose: The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”. Design/methodology/approach: The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm. Findings: When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system). Research limitations/implications: The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution. Practical implications: Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally. Originality/value: The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.
KW - Contribution margins
KW - Multivariate distribution
KW - Optimization methods
KW - Probabilistic inventory models
KW - Statistical dependence
UR - http://www.scopus.com/inward/record.url?scp=84999635211&partnerID=8YFLogxK
U2 - 10.1108/ARLA-12-2015-0336
DO - 10.1108/ARLA-12-2015-0336
M3 - Article
AN - SCOPUS:84999635211
VL - 29
SP - 450
EP - 485
JO - Academia Revista Latinoamericana de Administracion
JF - Academia Revista Latinoamericana de Administracion
SN - 1012-8255
IS - 4
ER -