## 2 Theoretical Background
### Quantitative Methods  
This section examines some of the most significant quantitative methods in the literature concerning performance measurement systems. In a recent paper, [PERSON] et al. [5] presented a model to improve decision support in organizations. The proposed methodology consists of an iterative model that combines companies' objectives and supports their achievement through the execution of their organizational process. This study allows the Board and Executive Management Team (BET) to understand how actions can positively or negatively affect the KPIs that define the company's state. This paper is an approach to enhancing tactical decision-making by combining expert knowledge with data-driven analysis, providing a broad view of the entire company. This paper's main objective is achieved by using so-called Fuzzy Governance Maps (FGM) to help the BET make the best and strategically aligned decisions. The alignment between an organization's processes and objectives to be achieved are described in strategic plans, and its achievement involves the execution of three steps:  
1. Carrying out measurements;
2. Carrying out an analysis of them;
3. Carrying out possible actions that will influence the organization's objectives. In order to obtain measurements from the state of the company, Business Activity Monitoring (BAM) or Process Performance Measurement (PPM) tools are used. These tools enable the evaluation of defined KPIs that help to monitor the state of the company on an ongoing basis. Visualizing and tracking the company's status through observation of indicators can easily be created using so-called dashboards to fulfil the tasks described above. [PERSON] et al. [21] presented a Quantitative Models for Performance Measurement Systems (QMPMM) model that uses AHP to quantify the effects of factors on performance. These are the three main steps of the model:  
1. Identification of factors affecting performance and their relationships;
2. Structuring the factors hierarchically;
3. Quantifying the effect of performance factors. To visualize and understand the factors influencing performance and their relationships, the QMPMM uses so-called cognitive maps. Performance measurement usually involves a wide range of factors and many people from different departments. Finally, the relative effects of hierarchically structured performance factors can be quantified using standard AHP procedures. The quantification process is performed based on the pairwise comparison of the characteristics. For each pair of elements of a particular level, their effect on the aspect of the next higher level (direct impact) or the facet within the same cluster (indirect effect) is compared. Each comparison is assigned a score between 1 (equally important) and 9 (absolutely most important). The result is a pairwise comparison matrix. The authors thus developed an approach to quantify the relationships between the various factors influencing performance. The advantages of the QMPMM approach can be summarized as follows:  
* The factors that determine performance can be identified and then quantified;
* It helps managers quantifying the level of impact of each factor on performance and thus leads to focus on improvement activities;
* Relationships between factors can be identified and expressed in quantitative terms; When combined in the framework, all of the above-described points help to understand the dynamic behaviour of factors affecting performance. Such an approach presents a powerful tool for comparing and monitoring the company indicators fulfilled by the overall evaluation downstream. One shortcoming can be found in the complexity of the AHP, which may arise from the increasing identification of factors to be evaluated. More recently, [PERSON] et al. [22] have proposed a model for assessing the coherence and strategic alignment of an LDC system. The proposed methodology relies on the following steps:  
1.