# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 2 Related Work  
Decision support systems have evolved together with the development of technology [1], and as mentioned in the Introduction, there are several types of DSSs defined in literature [2], namely: data-driven, model-driven, document-driven, communication-driven, and knowledge-driven DSSs. A decision support system can be of one type; however, it can also contain subsystems representing different DSS types. The characteristic features of data-driven DSSs are the access and processing of time-series, which can be internal company data or external ones [3]. Data that are processed within the system can be stored in various locations starting from a file system, where a query and retrieval functionality is provided to a data warehouse [4; 5], where advanced data manipulation methods are available. An appropriate data repository is particularly important for further analysis, inference, and decision support. Thanks to this, it is possible to integrate data from various sources and to clean and prepare them. Such data-driven systems are used for data originating, e.g., from sensors [6; 7]. The data stored in a system repository can be processed by means of advanced data analysis methods in order to derive a model assisting in decision-making. Systems where a model plays a central role are called model-driven DSSs [8]. They enable a non-technical user to access a model through a dedicated interface. Additionally, the created model is intended to be applied repeatedly in the same or a similar decision situation. The models utilized in the system can be of various types, e.g., differential equation models, analytical hierarchy process-based models, or forecasting ones. From the end user's perspective, what matters is the functionality of the system. Forecasting became crucial for the activity of many companies as prediction-based decision-making can significantly support the company's operation. As a result, another type of system was distinguished: the forecasting support system (FSS) [9]. A system of this type is characterized by the following two components: a database containing historical time-series and a set of forecasting methods. Another important feature of FSSs is the user's ability to evaluate forecast quality. Decision support systems where the prediction module played a significant role were designed for a variety of applications, e.g., medicine [10], industry [11], or stock market data [12]. Another very important methodology that can significantly improve company operation is key performance indicators (KPI) [13; 14; 15]. KPIs are defined by the domain expert [13] and are used to evaluate the efficiency of business processes, monitor their changes, and make decisions about improvements in a company [14; 16; 17]. KPIs are widely used in supply chain management, as they offer the overall visibility of the supply chain and help to assess the accuracy of a supply/demand plan, as well as execution performance [18]. Considering the domain of the system's application, a comprehensive review of decision support methods being applied in the upstream oil and gas industry was presented in [19]. This study included a review and classification of over one-hundred works published in the last forty years. In addition, several studies were reported in the field of artificial intelligence application to predict the properties of gas and other petroleum products [20; 21]. It can be noticed that the area of gas demand forecast drew significant attention among researchers, yet most of the published research is connected with natural gas demand prediction [22]. Recent works in this field cover prediction at various granularity levels starting from hour [23] to year [24] and from a single building [25] to the whole country [24]. The work by [24] was motivated by the need to improve present and future energy supplies. Additionally, it was highlighted in [23] that predictions are essential for the conclusion of contracts and the development of pipeline infrastructure.