Abstract

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).

Highlights

  • Markov chain was first created by a Russian professor named Andrei A

  • Based on Table.1, the state probability for product 1, product 2 and product 3 in company 1 are stationary at state 6, product 4 and product 5 are stnary at state 2

  • The transition probability matrix has been calculated in previous studies

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Summary

INTRODUCTION

Markov chain was first created by a Russian professor named Andrei A. Past and present events are independent of past events and dependent only on present events.” [1] The formulation of the problem in this research is how to model the optimization problem of sales demand by using the multivariate Markov chain model. In previous studies [2], the estimation of the transition probability matrix has been carried out, so that in this research it will be continued to estimate the weighted Markov chain parameters. This model will be solved by linear program optimization method, using linprog facility in Matlab software

LITERATURE REVIEW
RESEARCH METHOD
The Current State Probability
The Weighted Markov Chain
The Future State Probability Vector
Analysis
CONCLUSION
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