Currently, efficiency in the supply domain and the ability to make quick and accurate decisions and to assess risk properly play a crucial role. The role of a decision support system (DSS) is to support the decision-making process in the enterprise, and for this, it is yet not enough to have up-to-date data; reliable predictions are necessary. Each application area has its own specificity, and so far, no dedicated DSS for liquefied petroleum gas (LPG) supply has been presented. This study presents a decision support system dedicated to support the LPG supply process from the perspective of gas demand analysis. This perspective includes a short- and medium-term gas demand prediction, as well as the definition and monitoring of key performance indicators. The analysis performed within the system is based exclusively on the collected sensory data; no data from any external enterprise resource planning (ERP) systems are used. Examples of forecasts and KPIs presented in the study show what kind of analysis can be implemented in the proposed system and prove its usefulness. This study, showing the overall workflow and the results for the use cases, which outperform the typical trivial approaches, could be a valuable direction for future works in the field of LPG and other fuel supply.

In the research we analyzed the gas usage of different types of buildings. The analysis started with the classical theory of the buildings heating. Hence theoretical relations between gas consumption time series and the outside air temperature was introduced for different types of buildings (housing or industry). Those relations implied different auto-correlations of gas usage time series as well as different cross-correlations between gas consumption and temperature time series for different types of buildings. Hence the auto-correlation and the cross-correlation were used to imply the classification of buildings into three classes: housing, housing with high thermal capacity and industry. To investigate auto-correlation the Hurst exponent was calculated using the global DFA, while to investigate cross-correlation the Kendall's $\tau$ rank coefficient was calculated.