Supply Sequence Modelling Using Hidden Markov Models

Anna Borucka · Edward Kozłowski · Rafał Parczewski · Katarzyna Antosz · Leszek Gil · Daniel Pieniak

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Logistics and Transportation Systems Management and Optimization Techniques Forecasting Techniques and Applications

Abstract

Logistics processes, their effective planning as well as proper management and effective implementation are of key importance in an enterprise. This article analyzes the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing company requires the knowledge about the size of potential deliveries because the delivered waste must be properly managed and stored due to its toxicity to the natural environment. In the article, hidden Markov models were used to assess the level of supply. They are a statistical modeling tool used to analyze and predict the phenomena of a sequence of events. It is not always possible to provide sufficiently reliable information with the existing classical methods in this regard. Therefore, the article proposes modeling techniques with the help of stochastic processes. In hidden Markov models, the system is represented as a Markov process with states that are invisible to the observer but with a visible output (observation) that is a random state function. In the article, the distribution of outputs from the hidden states is defined by a polynomial distribution.

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References

2020Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities 2020Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail 2020Daily retail demand forecasting using machine learning with emphasis on calendric special days 2019Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology 2018Big data analytics and demand forecasting in supply chains: a conceptual analysis 2007Hidden Markov Models in Finance 2020Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting 2020Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods

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