The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance. Therefore, it is necessary to adequately manage the information that facilitates decisionmaking. This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant. This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the mediumterm, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data. In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the mediumterm with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.
In business and engineering, decisionmaking approaches and models are developed in response to the uncertainty of technological and demand conditions. In business, it is possible to identify a strategic [
Another characteristic of the market context is a wider range of products, resulting in the transformation of manufacturing from mass production to flexibility; in the latter case, this versatility leads to greater wear and fatigue on machines because of the high rate of change in the configuration, potentially resulting in a loss of reliability. This finding means that it is necessary to consider more extreme measures in terms of both the prediction and the anticipation of failure. Thus, predictive maintenance engineering has developed and perfected technologies for condition monitoring and predicting failures before breakage occurs [
In maintenance field, when the decisionsmaking is related to strategies or policies, in the long term, the considerations of fuzzy uncertainty are convenient. Thus, the literature review, carried out by Mardani et al. [
The integration of Principal Component Analysis (PCA) and Machine Learning (ML) techniques can facilitate decisionmaking in these environments. PCA is a very efficient method to find attributes that are influential in explaining the greater variation of a data set characterized by many explanatory variables in many registers [
This work consists of a segment of a global and modular framework for Maintenance Decision Support Systems [
This paper addresses that tactical goal and has the objective of providing better knowledge and predictions by projecting reliability behavior in a mediumterm future (yearly horizon taken over monthly intervals), integrating this new functionality into the classic Balance Scorecard (BSC) and making it possible to extend its current function of measuring the current situation to a new aptitude: predicting evolution based on historical data [
In the proposed Custom Balance Scorecard design, Matlab© [
The PCA algorithm has been used in the exploratory phase. In the analysis phase using ML techniques, ANN is used for its versatility as algorithms for supervised and unsupervised learning and for its suitable behavior against other ML techniques that are used for prediction [
The production plant presents in its management system a clear division between maintenance and production, occurring equally for its databases; therefore, there is no single database where we can access all the information jointly in an integral manner. Because of this, we have to access maintenance and production data separately, so we have two distinct tables identified as dataWO, Figure
Imported data from CMMS: (a) table dataWO; (b) dataWOF.
There is a first preparatory, preliminary data step in which the starting data correspond to the work orders, WOs, which have received the papermaking machines, M1 and M2, during a calendar year. These data have been extracted from a CMMS database; Figure
Original attributes of the maintenance work order.
STATE_WO  Numerical categorical 
TYPE_WO  Word categorical 
WO_NUMBER  Alpha numerical 
REQUESTING_DEPT  Word categorical 
REQUESTING_DATE  Date 
REQUESTING_HOUR  Date 
WORKSHOP  Word categorical 
URGENCY  Word categorical 
TERM_DATE  Date 
WORK_DESCRIPT_1  Alpha numerical 
WORK_DESCRIPT_2  Alpha numerical 
HOMOGENEOUS_GROUP  Numerical categorical 
LOCATION  Alpha numerical 
ELECTRIC_CODE  Alpha numerical 
SECTION  Numerical categorical 
INSTALLATION  Numerical categorical 
DEVICE_DESCRIPTION  Alpha numerical 
PRINCIPAL_WO  Alpha numerical 
ENROLLMENT  Alpha numerical 
TYPE_REPAIR  Word categorical 
SEQUENCE_NUMBER  Numerical categorical 
TYPE_INMOBILIZED  Numerical categorical 

Numerical 

Numerical 
ASSET_CONDITION  Word categorical 
TYPE_WORK  Numerical categorical 
SCHEDULED_DATE  Date 
SCHEDULED_HOUR  Date 
WORK_DESCRIPT_1_FINAL_1  Alpha numerical 
WORK_DESCRIPT_1_FINAL_2  Alpha numerical 
IMPLICATION_FAULT  Word categorical 
ELEMENT_FAULT  Alpha numerical 
CAUSE_FAULT  Alpha numerical 
SUBSTITUTION  Alpha numerical 
ID_SUBSTITUTE_ENROLLMENT  Alpha numerical 
ID_SUBSTITUTED_ENROLLMENT  Alpha numerical 
START_DATE  Date 
START_HOUR  Date 
FINAL_DATE  Date 
FINAL_HOUR  Date 

Numerical 

Numerical 

Numerical 

Numerical 

Numerical 
REPAIR_DATE  Date 
In the exploratory data phase, the statistical technique of PCA has been used to reduce the data dimension and find the principal axes that best represent the variation of data. These axes are orthogonal to each other and are calculated using a linear base change application by choosing a new coordinate system for the original set of data in which the largest variance of the dataset is captured on the first axis (called the first component); the secondlargest variance is the second axis, and so on. This methodology reduces to a problem of eigenvalues and eigenvectors on the covariance matrix of the data, obtaining a reduction of the dimensionality of the data on those axes that make a more substantial contribution to its variance in general; therefore, many principal axes are used whose sum represents approximately 80% of the variation of the original data [
The PCA parts of a data set are tabulated such that each line represents an observation, instance, or individual and each column represents an attribute or variable. Consider that a data set consisting of
To reduce the size of the variables, one must find another vector subspace that is aligned with those vector components that involve more variation, and one must form a basis for these components to be represented in an orthogonal, that is, a linearly independent system. This problem is reduced to finding a vector space whose vectors,
However, in this case, the variation is not reduced but is used to find the principal components and axes or their own values and vectors of the data. In accordance with this philosophy, we will attempt to find those components and principal axes that explain the maximum variation of the data. Thus, instead of matrix
Once the principal components are obtained,
This transformation expresses the original data in axes that coincide with the natural variation. One aspect to be considered in this analysis is that this transformation is linear; therefore, it is not suitable for representing nonlinear problems. In cases of nonlinearity, it is advisable to use ML clustering algorithms, as will be observed later.
In this phase, the preparatory data step uses as input data, in addition to the previous data, the production values and their responses as efficiency variables and failure times for an operational year for both papermaking machines (M1 and M2). Data are extracted and grouped from two databases: maintenance and production. The data obtained present 35 attributes and 12 instances corresponding to each month for each machine (identified as M1 and M2). Figure
Original attributes of the manufacturing report (production database).
ID_MACHINE  Alpha numerical 
MONTH  Numerical categorical 
NON_PLASTERED_PRODUCTION  Numerical 
PLASTERED_PRODUCTION  Numerical 
VOLUME_PLASTERED_PRODUCTION  Numerical 
TOTAL_PRODUCTION  Numerical 
WORKS_DAYS  Numerical 

Numerical 
CUTOUT_PRODUCTION  Numerical 
DAILY_CUTOUT_PRODUCTION_TON  Numerical 
DECREASE_MACHINE_%  Numerical 
AVAILABLE_HOURS  Numerical 
IDLE_TIME_EXTERNAL_CAUSES  Numerical 
MAINTENANCE  Numerical 
SCHEDULED  Numerical 
BREAKS  Numerical 
PRODUCTION_REST  Numerical 
TOTAL_IDLE  Numerical 
DAILY_IDLE_TIME  Numerical 
OEE  Numerical 
START_NUMBERS  Numerical 
CUTOUT_TIME_CHANGES  Numerical 

Numerical 
AVERAGE_REAL_WIDTH  Numerical 

Numerical 
AVERAGE BUDGETED WIDTH  Numerical 
WIDTH_DECREASE_CMS  Numerical 
WIDTH_DECREASE_TON  Numerical 
CAPE_PRODUCTION  Numerical 
COST  Numerical 
COST/TON  Numerical 
INTERVENTIONS_NUMBER  Numerical 
MEAN TIME BETWEEN FAILURES  Numerical 
MEAN TIME TO REPAIR  Numerical 
MEAN TIME TO FAILURE  Numerical 
ML is divided into two techniques [
Machine learning techniques.
Supervised learning uses classification and regression techniques to develop predictive models. The difference between these techniques is that the classification predicts responses in discrete or categorical variables, whereas regression predicts responses in a continuous variable [
From the algorithms of ML (Support Vector Machine, Discriminant Analysis, Naive Bayes, Nearest Neighbor, Decision Trees,
According to Rumelhart et al. [
ANN regression trained with backpropagation perceptron multilayer.
In the forward propagation stage, we select an input data set for training
This is a nucleus activation function and is performed iteratively for each output layer until the final output,
The backward propagation stage consists of measuring the error committed as the difference between the calculated value,
This process is repeated for
It is possible to integrate new functionalities into a custom control panel of the industrial plant. In this case, predictive analysis was added for the expected availability response of a productive area, considering the main core of the industrial plant; thus, it is possible to anticipate the information. The future availability in the mediumterm (at monthly intervals) of both machines allows the maintenance department to correct possible deviations that are out of tolerance before they occur, improving their response. In the first phase, which is exploratory, we use PCA to discover the smallest dimensions that explain the variation of the data. Applying the PCA to the set of WOs of productive area 2 (composed of M1 and M2), principal components or axes, PCi, are found; these are sufficient to explain the variation of the original data contained in the WOs of the productive area. As a result of the PCA, 5 components are identified that would explain 100% of the variation; therefore, 2 linearly dependent vectors are detected among the input variables, reducing in two the original dimension; on the other hand, from 5 principal components 3 would account for 78.6% of the variation in data. This work aims at the number of interventions, costs, and maintenance times and will represent the results of PCA on the first 3 principal components. In Figure
(a) Principal components variance; (b) projected data on principal components.
Table
PCA results. Principal components values.
Metrics  PC1  PC2  PC3  Metrics modulus 

TOTAL_COST  0.4696  0.476  0.036  0.6697 
ORDER_COST  0.1598  0.5714  0.6089  0.8502 
PARTS_COST  0.2323  0.3913  −0.5737  0.7323 
WORKFORCE_COST  0.5234  −0.2668  −0.0987  0.5957 
REPAIR_TIME  0.5235  −0.2668  −0.0987  0.5957 
ESTIMATED_REPAIR_TIME  0.3793  −0.2661  0.2076  0.5077 
OPERATORS_NUMBER  0.09  −0.2839  0.4861  0.5701 
In the second phase, ML, the clustering technique is used to discover patterns hidden in the data, such as the natural grouping. For this technique, from the data stored in the dataWO table shown in Figure
For the clustering technique, two algorithms have been used. The first technique, hierarchical clustering, allows the creation of a dendrogram, which is a tree diagram that measures the number of natural groups, or clusters, depending on the distance criterion that is fixed between data. In this case, by setting a distance value on the ordinate axis, the tree is trimmed by a horizontal line that cuts the dendrogram in as many intersections as natural groups appear. In this case, Figure
(a) Dendrogram; (b) data; (c) ANN SOM trained; (d) SOM topology.
The clustering technique is again used, performing a second algorithm of an SOM, ANN, on the subset of total cost data and repair time as the chosen variables reflecting costs and times of the plant’s intervention maintenance. An SOM or Kohonen consists of a competitive layer that can classify a set of vector data with any number of dimensions into as many classes as neurons have a layer [
The network is configured by 2 dimensions, 2 × 4, discovering a pattern of 8 natural groups in the data; these are distributed with a clear linear relationship between them. In addition, there are discrepant data that have no linear relationship and reveal an unconventional repair; this is extraordinary and realized in one of the machines, and it was not cataloged like normal repair. This finding reveals an error in the introduction of the information in the CMMS. This event was also revealed by the green dot (single group) of the hierarchical clustering figure (see Figure
(a) Artificial Neural Network SOM trained; (b) SOM topology sample hits.
Finally, the regression technique enables prediction of the future availability values of both main papermaking machines (M1 and M2) using the OEE indicators of each papermaking machine and its MTTF, such as average runtime before failure. In addition, these values are calculated simultaneously in the trained ANN model. A trained neural network with input data (predictors) is used that combines the three production variables and the two target output variables, which are the overall efficiency of each OEE machine and average time to MTTF failure, measured for 12 months of the year for each machine. For this technique, from the data stored in the dataWOF table shown in Figure
For the papermaking machines M1 and M2, 3 input variables, a 10layer feedforward network with hidden neurons, and 2 layers of linear output neurons can adjust arbitrarily suitable multidimensional mapping problems, given consistent data and sufficient neurons in their hidden layer. The network will be trained with 70% of the data using the backpropagation algorithm of Bayesian Regularization; 15% of the data will be used for validation, and the remaining 15% will be used for the test. An MSE performance function is used, as shown in Figures
(a) ANN regression performance for M1; (b) regression fit.
(a) ANN regression performance for M2; (b) regression fit.
For each machine that forms the productive area, the result of the adjustment is observed by comparing the output variables OEE and MTTF (in blue) measured with the output variables foreseen by the OEEp and MTTFp model (in red). The results of M1 are shown in Figure
Output variables measure (blue) versus predicted (red): (a) M1; (b) M2.
It is noted that the fit is acceptable for training, which is provided by the global adjustment regression coefficient
For machine M1, discrepant values are observed in the validation setting for month 10 and for both OEE and MTTF indicators; there are two reasons for this reason. First, there is overadjustment when the data have not been prepared well, and there are data with erroneous or poorly conditioned input information. Second, there are a low number of observations or minimal historical information. In this paper, it has been verified that the input data for the ANN did not present poor conditioning; therefore, the overadjustment problem is discarded, and the few data (i.e., the few observations) available are considered the main cause of discordance of the inputs for validation. The problem of feeding the network with few data to train and validate the ANN is due to unavailability of more data for reasons of good performance in the industrial plant, a fact that would undoubtedly improve the learning of the network and therefore its efficiency and accuracy. However, this fact highlights another very interesting aspect of the ANN; the network is easily adaptable and configurable given a low number of observations. Here, this adaptability makes it possible to accurately predict 11 hits of 12 possibilities; thus, there is a 91.67% probability of success in this case.
For machine M2, there is a nearly total adjustment for the OEE indicator but not for the MTTF indicator, for which it is evident that, in month 6, there is a discrepancy in the prediction, as in M1; the minimal data (observations) used for the validation obtain the same precision as M1.
Another relevant aspect of the result is the acceptable precision in the prediction of availability indicators, which are based exclusively on time, by simply using as input three productive variables as predictor variables (daily paper mass, paper surface density, and machine speed). In addition to the two output variables, OEE and MTTF indicators as objectives to be predicted are calculated simultaneously, a fact that reflects an additional value of this type of networks and greater computational efficiency, by obtaining in a single simulation the prediction of more than one objective variable.
A PCAML model has been developed such that it can be integrated into scorecards with a traditional focus, BSC, thus including a tactical definition of longerterm strategic approaches such as a scorecard based on BSC. This new extension allows better control over the maintenance function of an industrial plant in the mediumterm, with a monthly interval, in such a manner that allows the measurement of certain indicators of those productive areas that were previously considered strategic. This model of PCA and an ML algorithm using ANN can be integrated very easily into any traditional control panel by converting the developed source code to packages of different programming languages and including them in a library to be used as a function in a spreadsheet or a standalone executable application. In addition, at the control panel, this model is provided with ML to discover structures and behavior patterns that are relatively hidden in WOs. By utilizing a clustering or clustering technique, natural groups are determined in the cost variables and maintenance workforce; in addition, predictions about the availability of the productive area are made through the indicators OEE and MTTF. Thus, the scorecard model on a paper production plant has been validated.
As possible future works, this methodology could be applied to civil engineering and in this case applying a fuzzy uncertainty due to particular characteristics of this sector.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors want to thank the Department of Construction Engineering and Manufacturing and the College of Industrial Engineers of UNED for their support through Projects 2017IFC08 and 2017IFC09.