A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers

Abstract

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.