Sales Demand Forecasting Using One of Multivariate Markov Chain Model Parameter
The imbalance between demand and supply is frequently occurred in a market. This is due to the availability of goods that cannot match with the demand or the growth rate of customer. This is not preferable since the profit is not on the track. In contrast, the goods are probably over supplied so that company has to expense additional cost for extra storage. Both situations can be anticipated if the demand is precisely estimated. Therefore, in this study we will estimate demand in market situation by implementing multivariate Markov chain model. Multivariate Markov chain model is popular model for forecasting by observing current state in various applications. This model is compatible with 5 data sequences (product types) defined as product A, product B, product C, product D and product E, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the highest transition probability value for the sales demand in a company is found at the transition probability matrix from product C to product C, from very fast moving to very fast-moving condition, which had the highest probability value 0.625 with the highest frequency 105 times.
 W. Ching and Ng. Michael K. “Markov Chains: Models, Algorithms and Applications”, United States of America: Springer+Business Media, Inc., 2006.
 A. Martina, “Penggunaan Model Rantai Markov Multivariat Untuk Estimasi Permintaan Penjualan Pada Suatu Perusahaan”, Thesis, Indonesia: Bandung Institute of Technology, 2015
 L. Megasalindri, “Prediksi Permintaan Penjualan dengan Menggunakan Model Rantai Markov Multivariat” undergraduate final project, Indonesia: Bandung Institute of Technology, 2013
Copyright (c) 2020 Annisa Martina
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