Open AccessMultivariate Markov Chain Model for Sales Demand Estimation in a Company
Author(s) -
Annisa Martina
Publication year - 2022
Publication title -
international journal on information and communication technology
Language(s) - English
Resource type - Journals
ISSN - 2356-5462
DOI - 10.21108/ijoict.v7i2.604
Subject(s) - markov chain , stochastic matrix , product (mathematics) , markov model , computer science , econometrics , markov process , profit (economics) , multivariate statistics , estimation , stationary distribution , state (computer science) , mathematics , statistics , economics , algorithm , microeconomics , management , geometry
Estimation of the number of demands for a product must be done correctly, so that the company can get maximum profit. Therefore, this study discusses how to estimate the amount of sales demand in a company correctly. The model that will be used to estimate sales demand is the Multivariate Markov Chain Model. This model can estimate the future state by observing the present state. The model requires parameter estimation values first, namely the transition probability matrix and the weighted Markov chain, where in previous studies an estimation of the transition probability matrix has been carried out, so that in this study we will continue to estimate the weighted Markov chain parameters. This model is compatible with 5 data sequences (product types) defined as product 1, product 2, product 3, product 4, and product 5, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the state probability for product 1, product 2 and product 3 in company 1 are stationary at state 6 (very fast moving), product 4 and product 5 are stationary at state 2 (very slow moving).