## Intelligent System to Support the Decision-Making Process in Lean Maintenance Management
### Decision Trees in the Assessment of the Effectiveness of Lenn Maintenance  
Due to qualitative nature of the dependent variables, classification decision trees with the use of the classification and regression trees (CART) algorithm were used. Not all surveyed enterprises used the same solutions, methods, and tools, therefore the main criterion for selecting this method was its insensitivity to the occurrence of atypical observations, which are believed to come from a different population, and the possibility of its effective use in datasets characterized by numerous shortcomings in independent variables. Additionally, the following advantages of CART classification trees determined the choice of the method:  
* Taking into account non-monotonic dependencies through successive divisions with respect to the same variable;
* Simple interpretation of results in comparison with other methods;
* Suitability for tasks, where the a priori knowledge of which variables are related and how they are uncertain and intuitive;
* Non-parametric and non-linear;
* Estimating and ranking the importance of individual predictors (input variables) in the process of shaping the value of the dependent variable;
* Very useful for classification issues. The CART tree for a dependent variable--a mean value of the OEE indicator--was designed for 24 enterprises out of the studied group of enterprises, which had analyzed this indicator and implemented the TPM method. The following explanatory variables (predictors) were assumed: enterprise size, production type, industry, ownership type, capital, company condition, machine type, 5S implementation, 5S activities, SMED implementation, way of supervision, maintenance strategy, actions undertaken to prevent unplanned downtimes (number of prevent actions--NPA), machine classification, spare parts classification, actions within TPM implementation (NTPMA indicator), and mean time to repair. The following were assumed while creating the tree: equal costs of the incorrect classification, Gini coefficient, stop rule, and a minimum size criterion in the divided node \(n\geq 2\), which will allow for a detailed analysis of a tree structure and for 10-fold cross validation as a quality measure. A tree consisting of 12 divided nodes and 13 end nodes was chosen for the analysis. In order to assess the quality of the chosen tree, its validation for a new dataset was conducted. Thirteen decision rules may be defined for the created tree, which has 13 end nodes. The chosen decision rules, for which the highest values of OEE were reached (over 85% and for the range 70 to 85%) with the use of additional lean maintenance methods and tools, were presented below. Decision rules established on the basis of the decision tree are:  
1. If an enterprise represents metal processing, aviation, or paper and wood industry, does not run partial supervision by outsourcing, and possesses an MSI indicator at the level of \(\
eq\)4,5 and realizes machine classification, then it reaches an average value of the OEE indicator of over 85% (node 12). 2. If an enterprise represents an industry other than metal processing, aviation, or paper and wood. possesses an MSI indicator at the level of 3.5-5.6 and NPA number \(>\) 3, and the mean time to repair is below 1 h, then it reaches a mean value of the OEE indicator within the range from 70 to 85%. 3. If an enterprise represents an industry other than metal processing, aviation, or paper and wood and possesses an MSI indicator at the level other than that of 3.5-5.6 and an NTPMA indicator at any level other than high, then it reaches a mean value of the OEE indicator within the range from 70 to 85%. If an enterprise represents an industry other than metal processing, aviation, or paper and wood, possesses an MSI indicator at any level other than that of 3.5-5.6a and an NTPMA indicator at a high level, and realizes supervision on its own with the service through outsourcing, then it reaches a mean value of the OEE indicator within the range from 70 to 85%. 5.