## 2 Introduction  
The proper functioning of an enterprise is an important element of maintaining its continuity. Therefore, it is necessary to constantly track and analyze the signals coming from both the company itself and its environment in order to quickly respond to changes, modify adopted strategies, and introduce innovative solutions so as to adapt the company's operation to the changing market conditions on an ongoing basis. Logistics processes and their effective planning, proper management and effective implementation are of key importance here. The article examines the supply logistics, i.e., the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing company requires knowledge about the size of potential deliveries because the delivered waste, which in this case is the production raw material, must be properly managed and stored due to its toxicity to the natural environment. In the article, hidden Markov models are 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 [1]. It is not always possible to provide sufficiently reliable information with the existing classical methods in this regard. Therefore, the article proposes modeling techniques that use stochastic processes. In hidden Markov models (HMMs), 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. HMMs can be defined as a machine learning model, particularly as a discrete method, which is a statistical strategy for modeling systems intended to contain Markov processes with hidden states. It is possible to train HMMs on a specific sequence of observations. In addition, the sequence of observations on a specially trained HMM l can also be evaluated to determine the probability of observing such a sequence within the constraints of a particular model. With HMMs, a sequence of observations can be evaluated to see whether it fits a given model well. The higher the score, the greater the match between the observation sequence and the observational data used to train the model. In addition, HMMs allow decoding. This means that we can discover the \"best\" sequence of hidden states and, as a result, maximize the expected number of correct states. It is widely known that HMMs can be used in different applications [2; 3], but still, the use of HMMs in many new application fields is yet to be explored [4; 5]. Moreover, research results presented in literature indicate that models obtained using HMM have been successfully applied in the different areas and achieve better results compared to models obtained using, for example, naive bayes (NB), decision tree (DT), nearest neighbors (KNN), support vector machine (SVM), artificial neural networks (ANN) or radial bias function (RBF) [6; 7; 8]. The main problem in the construction of the hidden Markov models is the proper selection of the number of states and the type of connections between them as well as the estimation of the model parameters that will ensure its high predictive ability. The use of HMMs allows for the prediction of the most probable sequence of the next states based on the observed sequence of events. This issue is discussed in detail in the article on the example of a manufacturing company. On the basis of the weight of the product received at the plant, Markov states were distinguished and specific amounts of raw material delivery were assigned to them. Five observed states were assumed corresponding to five levels of supply from very low, through low, medium, high, to very high. On the basis of the observed sequence of events, a sequence of the most probable hidden states was identified, the occurrence of which depends only on the state in the preceding moment. For the sequence of the hidden states, a matrix of transition probabilities between the states was determined.