# Selection of Production Reliability Indicators for Project Simulation Model
## 4 Results  
### Meta-Analysis  
To develop a mathematical model, a well thought-out literature evaluation should be created. The authors reviewed engineering- and business-related journals that are indexed in academically acknowledged databases. To reveal the most suitable results, the following databases were visited and articles were collected from the following: ScienceDirect, Scopus, Elsevier.  
An additional filter to our search was the date of publication: for the most appropriate findings, only recent (since 2017) articles were processed.  
The authors selected 8 keywords for the search and created a database for the findings. After a careful and thorough review, the relevant articles remained in the database.  
The keyword structure was followed:  
(process OR system OR machine OR production OR manufacturing)  
AND  
(reliability OR uncertainty OR stochastic)  
AND  
(simulation OR analysis)  
Out of the 15 indicators listed in the literature review section, the authors selected 7, which can be directly connected to machine failure and its repair. The scope of the project is the application of indicators that uses time: mean operation time, probability of faultless operation, mean time between failure, mean repair time, mean downtime, mean time to repair, mean lifespan.  
Out of the 15 indicators listed in the literature review section, the authors selected 7 which can be directly connected to machine failure and its repair given in time measurement  
Figure 2: Steps of the research.  
unit: mean operation time, probability of faultless operation, mean time between failure, mean repair time, mean downtime, mean time to repair, mean lifespan.  
Thus, the following indicators were excluded from the scope of the research: failure rate, probability of failure, recovery intensity, probability of recovery, q-percent operation, mean storage life, percentage of storage time. The reason for this decision is simple: the previously listed indicators either provide the likelihood of a problem or it is connected to the product rather than the production process (e.g., percentage of storage time).  
The following table (Table 1) present the authors' finding: Is a certain indicator really crucial for the process analysis to mention it or work with? In this process, relevant articles were processed, with the focus on manufacturing-related production examples or theoretical studies. In the matrix below, \"M\" represents mentions, which means the application of the indicator in a case study, or--in case of theoretical study--introduces the indicator. \"-\" symbol indicates the absence of the indicator from that particular article.  
The examined papers show that the most used indicators are mean time between failure (MTBF), mean time to repair (MTTR), mean downtime, and mean operation time. This is mainly because of the ease of use, calculation, and implementation of these values. The least ones were considered harder in calculating probabilities or indicators that can be calculated from other parameters of reliability. Alternatively, the lifespan of the machinery is just considered to be long-term. The count of mentions can be seen in the figure (Figure 3) below:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
[29] & M & M & M & - & - & - & - \\
[30] & M & M & M & - & - & M & - \\
[31] & M & - & - & M & - & - & - \\
[32] & - & - & - & - & - & - & M \\
[33] & - & - & M & M & - & - & M \\
[34] & - & M & M & M & - & M & - \\
[35] & - & M & M & M & - & - & - \\
[36] & M & M & M & - & - & - & M \\
[37] & - & M * & M & M & - & - & - \\
[38] & - & - & M & - & M & - & - \\
[39] & - & M & M & M & - & - & - \\
[40] & M & M & M & M & - & M & M \\
[41] & M & M & M & M & - & - & M \\
[42] & M & M & M & - & - & M & - \\
[43] & M & M & M & - & - & - & - \\ \hline \hline \end{tabular} M = Mentioned, “-” = Not mentioned; * special name due to the field of study.  
\end{table}
Table 1: Evaluation matrix.