# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 8 Conclusions  
A sensory data-driven decision support system dedicated to LPG distributors was presented in this study. The solutions for data acquisition were described, and two cases of using the system for the task of forecasting consumption (short-term prognosis) and demand (medium-term prognosis) for LPG were presented.  
The presented workflow can be implemented in various technologies. Due to the fact that they change very quickly, reference to specific tools was avoided in the work. Depending on the available infrastructure, the system can work as a distributed solution; in particular, it can be a cloud solution where servers collecting and processing data are located in the service provider's data center. Such a situation occurs in the case of system implementation at AIUT Ltd. AIUT offers telemetry (remote monitoring of LPG levels) and analytical services (analysis of gas consumption, KPI, and planning of delivery routes (this task was not described in the article)) to LPG suppliers all over the world (e.g., in Poland, Great Britain, India).  
By definition, the operation of the system requires close cooperation between users well established in a given field of application (data analyst) and data analysis (data scientist). As the cases of use presented in the article show, proper preparation and analysis of the collected data can lead to a quality of forecasts with accuracy useful for the end user.  
It is good practice to share the results of research described in an article; in line with this postulate, as well as taking into account the fact that the presented solution will be of a commercial nature, files with data for short-term forecast task were uploaded at  
[[https://github.com/adaa-polls/dss4](https://github.com/adaa-polls/dss4) lpg]([https://github.com/adaa-polls/dss4](https://github.com/adaa-polls/dss4) lpg) (accessed on 8 April 2021). The website also includes software that allows performing the forecast task using a gradient boosting method (the input data format has to match the format of the shared files) and to generate a summary report of the calculations performed.  
As future work, it is planned to extend the existing overall analytical functionality of the introduced DSS using predefined ready-to-use analytical templates. One of the interesting and valuable approaches that can be included in such a package is adaptive learning, which in the case of a drop in the quality of predictions, automatically adapts the predictive models to the changing customer characteristics. Additionally, the predictive models can be further analyzed by extending the feature set for more specific features like temperature represented as day-degree, number of inhabitants, or customer house area. However, the use of new characteristics depends on their availability to the LPG supplier. At the current stage of research, access to such data was not possible. Another possible extension is customer profiling and support of the LPG suppliers in making marketing decisions.  
Conceptualization, L.W. and M.S.; methodology, L.W. and M.S.; software, J.H.; validation, J.H. and L.W.; formal analysis, M.S.; investigation, M.S.; resources, M.S.; data curation, M.K., J.H. and Z.L.; writing-original draft preparation, M.K., M.S., and Z.L.; writing-review and editing, M.K., J.H., L.W., and M.S.; visualization, M.K., J.H., and L.W.; supervision, M.S.; project administration, M.S. and Z.L.; funding acquisition, M.S. and Z.L. All authors read and agreed to the published version of the manuscript.  
This research was realized in co-operation with Silesian University of Technology and AIUT Ltd. and co-funded by the Polish agency National Center for Research and Development within Grant POIR.01.01.01-00-0104/17 (System for decision support and management of operational and process knowledge for the LPG gas distributors market).  
Not applicable.