# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 6 Key Performance Indicators  
Key performance indicators (KPI) are one of the simpler tools that support the management of almost every area of an organization's functioning. The indicators can be used to assess business processes in companies of different trades. The biggest challenge related to business processes' management through monitoring KPI values is to define proper measures that really illustrate the most important parameters of the process. People involved in the performance or supervision of a given process are usually able to define from several to even several dozen different indicators, all of which describe the monitored process in a certain context. The issue is how to select crucial indicators for each group of users. While defining the indicators [13], the pyramid rule applies. The majority of indicators refer to the so-called shop floor where technical and maintenance personnel analyze indicators related to the current quality, failure frequency, and efficiency of performed processes. Successive indicators are defined for lower and higher management. The final group of indicators is defined for managers. The higher the position in the management hierarchy, the higher the frequency at which technical indicators are replaced with economic and organizational ones. In addition, according to the applied terminology, the indicators for higher management and managers are referred to as key indicators (usually, there are only several of them), while those for technical and lower managers are called performance indicators (PIs). It is possible to define several dozen performance indicators as particular groups of personnel or department managers are only responsible for the values of indicators associated directly with their duties. Part of the KPIs related to measuring the efficiency of technical resource use are defined as overall equipment effectiveness measures [16]. Machine learning methods can be used to investigate the impact of particular process indicating parameters on the PI and KPI values. In particular, it is possible to define a KPI without quoting its analytical formula, treating the parameter only as a certain value that can be calculated (e.g., average cost of supplying 100 L of liquefied petroleum gas to an individual user), while machine learning methods can be used to check which of the monitored process parameters (apart from the obvious ones, such as the number of kilometers covered) impact the value of the indicator. The data analysis method applied in such investigations should generate a data model that can be interpreted by humans (e.g., a tree or set of rules). Such attempts have been described in the literature, e.g., in [38; 39]. While developing a decision support system for LPG distributors, over 100 performance indicators were defined. The indicators allowed monitoring the areas of secondary bulk distribution or customer tanks. Within the secondary bulk distribution, the following indicators were defined:delivery indicators that analyze the volume of gas supplied by a set number of tankers to a set number of clients within a set period of time,
* capacity indicators that analyze the efficiency of tanker use from the point of view of the volume of supplied gas in relation to the tankers' capacity,
* trips indicators that analyze the efficiency of tanker use from the point of view of the number of kilometers covered and the number of performed deliveries,
* costs indicators that analyze the costs of the deliveries and the tankers' maintenance in terms of the supplied volume of LPG. The following indicators were defined within the customer tank area:
* drop size indicators that illustrate the volume of gas supplied to the tank,
* LPG level indicators that point to the level of gas in the tank, in particular the states below and above the warning values and the lack of gas in the tank,
* consumption profile and consumption forecast indicators that describe the (real and predicted) volume of gas used.