Application of a Predictive Model to Reduce Unplanned Downtime in Automotive Industry Production Processes: A Sustainability Perspective

Abstract

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.

1. Introduction

The automotive industry is constantly seeking advanced technologies to enhance its competitiveness, reduce costs, and minimize waste, all aligned with Industry 4.0 advancements. In this context, machine learning emerges as a powerful tool capable of transforming various aspects of industrial operations, including predictive maintenance [1,2].

Predictive maintenance, utilizing machine learning models, offers the possibility to identify potential failures at an early stage, enabling the implementation of preventive actions before unplanned downtime occurs. This results not only in greater equipment reliability and efficiency but also in significant operational cost savings and reduced environmental impact, promoting sustainability in industrial operations [3,4,5].

Unplanned downtime represents a significant challenge in the automotive industry, with direct impacts on operational efficiency and profitability. According to Deloitte (2017) [6], poor maintenance strategies can reduce a plant’s overall productive capacity by 5% to 20%, and unplanned downtime is costing industrial manufacturers approximately USD 50 billion annually. These figures underscore the importance of effective predictive maintenance strategies to avoid costly production interruptions. Traditional approaches, such as run-to-failure or time-based preventive maintenance, often result in either excessive downtime or premature replacement of still-functional components—both of which negatively affect operational continuity and resource efficiency.

Despite recent advances in predictive maintenance research, many existing studies in the automotive context focus primarily on either theoretical modeling or the application of generic machine learning methods, often without integrating operational decision-making tools. Few works offer a comprehensive methodological approach that combines problem identification, critical asset prioritization (e.g., via AHP), and failure mode selection (e.g., via PFMEA) within a single structured framework. This study seeks to fill this gap by presenting an integrated predictive maintenance methodology that is both technically sound and practically applicable.

This study aims to apply a predictive model based on machine learning in the context of the automotive industry, validating its ability to reduce the impact of unplanned downtime. The model implementation process involves identifying the core problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP) and selecting the critical failure mode based on Risk Priority Number (RPN) derived from Failure Modes and Effects Analysis (FMEA). Subsequently, predictive algorithms are implemented to select the best-performing model based on error metrics.

The collected data underwent transformations and cleaning for model preparation and feeding. Five machine learning models were trained, with Random Forest standing out in terms of accuracy. This model was validated with real data, achieving an average accuracy of 80% in predicting failure cycles.

The results indicate that the application of the predictive model can effectively contribute to reducing the financial impact of unplanned downtime by allowing proactive actions based on model predictions. This study highlights the importance of a multidisciplinary approach in engineering, integrating machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the viability and effectiveness of predictive models in promoting sustainability [7,8].

In this context, this article aims to explore the following question: “Is it possible to use supervised machine learning models that enable planning and scheduling through predictive maintenance predictions with reduced errors in assets involved in productive processes of an automotive company, in order to avoid unplanned interruptions in production?”

2. Theoretical Reference

2.1. Content Analysis

To deepen the understanding of the application of predictive models based on machine learning in predictive maintenance and their relationship with sustainability practices in the automotive industry, a structured content analysis was conducted using the Scopus database. The review focused on journal articles published between 2018 and 2023, in the fields of Engineering, Computer Science, Environmental Science, and Business.

After applying the appropriate filters, 15 articles were selected for analysis (Appendix A). To organize the discussion and extract meaningful insights, the articles were grouped into three main analytical axes: (i) the machine learning methods applied; (ii) the industrial sectors in which they were applied; and (iii) the operational and sustainability-related outcomes reported.

2.1.1. Methodological Approaches

The majority of the selected articles employed supervised learning models—such as decision trees, random forests, support vector machines, and neural networks—for failure prediction, anomaly detection, or condition monitoring [8,9,10,11]. A smaller group explored unsupervised learning techniques, including clustering and anomaly detection in sensor data [12,13]. Although some studies incorporated advanced models like deep learning [14], few addressed the issue of model explainability or adopted hybrid frameworks that integrate data analytics with decision-making tools such as AHP or PFMEA.

2.1.2. Sectoral Applications

While a significant portion of the studies focused on the automotive industry, other sectors like oil and gas, aerospace, and general manufacturing were also represented. Regardless of the industry, the key motivations for implementing machine learning were similar: minimizing downtime, extending equipment life, and reducing maintenance costs through early failure detection [9,15,16].

2.1.3. Sustainability and Operational Outcomes

Many studies emphasized benefits beyond operational performance. These included reductions in energy consumption, emissions, and material waste—demonstrating the potential contribution of predictive maintenance to broader sustainability goals [11,17,18]. However, practical implementation remains a challenge, as most of the reviewed works were either theoretical or conducted in controlled/laboratory environments, with limited application to actual production data.

It is worth highlighting the contribution by Jain et al. [19], who conducted a systematic literature review on predictive maintenance and vehicle health diagnostics using machine learning. While their review provides a broad overview of algorithmic trends and challenges in the field, it lacks a detailed analysis of the integration between predictive models and operational decision-making methodologies—a critical element for successful industrial adoption.

In summary, although the literature demonstrates growing interest in the use of machine learning for predictive maintenance, there remains a notable gap in studies that combine robust predictive algorithms with structured prioritization and failure analysis frameworks. This study seeks to address that gap by proposing and validating an integrated methodology that encompasses problem identification, equipment prioritization (AHP), critical failure mode selection (PFMEA), and predictive modeling, using real-world production data from the automotive industry.

2.2. Applications of Machine Learning in Industry

In any industrial setting, developing monitoring techniques and decision-making processes on the production line is crucial for understanding production and avoiding bottlenecks [20]. Machine learning (ML) is a technology that incorporates concepts from artificial intelligence, statistics, and computer science. Its primary goal is to construct and refine algorithms that learn from databases, aiming to create generalized models capable of making accurate predictions and/or identifying patterns in unfamiliar data [21,22].

While artificial intelligence (AI) encompasses the science enabling machines to perform human tasks, machine learning specializes in training machines to learn from data. This technique can be applied in various ways; in this article, the Python system was used to process data and implement ML [23].

There are different approaches to machine learning: (i) Supervised learning: where data are labeled and algorithms associate inputs with known outputs; (ii) Unsupervised learning: where data are unlabeled and underlying structures are explored without known responses; (iii) Semi-supervised learning: a combination of labeled and unlabeled data for model training; (iv) Reinforcement learning: where the system learns by interacting with the environment [23,24]. These approaches have different objectives and applications. For instance, ML algorithms can predict equipment failures by anticipating their failure modes. Machine learning has excelled in predictive maintenance, bringing significant advancements to industrial maintenance management. According to James et al. [25], ML models are designed to identify complex patterns in data and use these patterns to make predictions or decisions. This enables the implementation of predictive strategies that anticipate failures and prevent unplanned downtime, optimizing production processes.

Hastie et al. [26] highlight that ML algorithms are widely used in pattern recognition, natural language processing, computer vision, and data analysis. In the industrial context, these algorithms are applied for fault detection, demand forecasting, logistics optimization, and predictive maintenance. Models such as Linear Regression, Decision Trees, Neural Networks, and Random Forests are common for predicting failures and optimizing equipment maintenance.

Moreover, the use of ML in predictive maintenance offers advantages such as the ability to handle large volumes of real-time data and the flexibility to adapt to different types of equipment and operational conditions. These models can be continuously improved as more data are collected, resulting in increasingly accurate predictions. The application of deep learning techniques allows for the identification of even more complex patterns, continuously enhancing predictive maintenance processes [27].

To bridge these two areas, it is important to recognize how machine learning, with its capability to analyze large volumes of data and identify complex patterns, seamlessly integrates with predictive maintenance. The synergy between these technologies allows not only for predicting failures but also for better understanding the root causes of these failures, offering a more robust and informed approach to industrial maintenance management.

In the specific context of the automotive industry, machine learning offers strategic advantages due to the sector’s unique operational characteristics. Automotive production systems are typically high-volume, highly automated, and require strict adherence to quality standards and delivery deadlines. Even brief periods of unplanned downtime can cause cascading delays across the supply chain and significantly affect production targets. In this setting, predictive maintenance enabled by ML helps mitigate risks, ensures equipment availability, and sustains production continuity.

Moreover, the integration of ML with manufacturing systems allows for early fault detection, anomaly tracking, and optimized scheduling of maintenance activities. These capabilities are especially valuable in environments where tool wear, mechanical fatigue, or variations in environmental conditions can directly affect part quality and production efficiency. By enabling more informed decision-making, ML contributes to reducing unplanned stops, waste, and rework—factors that are essential not only for operational efficiency but also for aligning production practices with sustainability goals.

2.3. Predictive Maintenance

In addition to predictive maintenance, the concept of prescriptive maintenance has been gaining increasing attention in recent years. This approach not only anticipates potential failures but also recommends specific corrective or preventive actions, based on the outputs of predictive models combined with optimization techniques. As such, it represents a step beyond prediction, enabling automated and data-driven decision-making in maintenance management and contributing to more efficient and proactive industrial operations.

Predictive maintenance (PdM) aims to provide significant benefits to industrial operations, playing a crucial role. According to Shingo et al. [28], one of the main requirements for effective implementation of PdM is the availability of a sufficient amount of data from all parts of the manufacturing process. The outcomes include cost reduction, increased operational efficiency, extended equipment lifespan, and efficient resource utilization [29].

According to de Farias et al. [30], there are four categories of maintenance: corrective, preventive, predictive, and prescriptive. In corrective maintenance, intervention occurs when a failure is detected or there are signs of it. Preventive maintenance uses schedules at specific times. On the other hand, PdM uses time-based information and knowledge to anticipate a possible failure, thus avoiding downtime. An example of applying predictive maintenance using explainable machine learning models, along with data collected solely from the operational state of industrial equipment to predict future maintenance, can be seen in [31].

The studies conducted demonstrate the effectiveness of various machine learning implementations in conducting predictive maintenance. However, most research utilizes methods known as “black boxes”, which focus on predictive performance without providing insights into root cause analysis and explainability. Despite the increased predictive power compared to simpler and more interpretable approaches, the logic behind the predictions of these methods is difficult, if not impossible, to fully explain. In this context, the ability to develop a highly accurate predictive model is achieved at the cost of being unable to fully elucidate the primary causes of imminent failures [32].

Despite the growing adoption of predictive maintenance strategies using machine learning, several limitations continue to hinder widespread industrial implementation—particularly in the automotive sector. One of the main challenges lies in data availability and quality. Effective model training requires a substantial volume of consistent and accurate operational data, which is not always available, especially when data collection depends on manual processes or legacy equipment without sensors.

Another important limitation involves the integration of predictive models into existing maintenance and production systems. Many manufacturing facilities operate with heterogeneous systems and fragmented databases, which complicates the implementation of end-to-end predictive frameworks. Ensuring compatibility between ML solutions and enterprise resource planning (ERP), manufacturing execution systems (MESs), or SCADA platforms requires significant technical and organizational effort.

Additionally, while complex machine learning models—such as deep neural networks—offer high predictive performance, they are often considered “black boxes”, lacking transparency regarding how predictions are made. This lack of interpretability can limit trust in the models among operational staff and decision-makers, reducing their practical usefulness. Interpretable models or explainable AI (XAI) techniques are therefore essential to bridge the gap between technical precision and operational adoption.

Finally, implementation costs—including infrastructure, training, and model maintenance—can be significant, particularly for small- or medium-sized enterprises. These barriers highlight the need for modular, scalable, and interpretable predictive maintenance solutions that can be effectively adapted to diverse industrial contexts.

3. Methodological Procedures

For the implementation of an effective methodology enabling the application of a predictive model in the automotive industry, it is necessary to follow a systematic and well-structured approach. This study followed a sequence of steps that included problem identification, data collection and transformation, machine learning model training, and validation of the results. The main methodological steps adopted are described below.

3.1. Problem Identification

For the implementation of an effective methodology, it is crucial to identify and clearly understand the existing problem within the studied process. This step allows for a thorough analysis of the underlying causes of the problem, aiming to map them accurately. Subsequently, it becomes crucial to propose appropriate and effective solutions for the identified causes, with the aim of eliminating or mitigating them.

To identify the problem, an approach will be adopted that starts with a detailed mapping of the process under study, through the development of a process flowchart. This visual representation will enable a clearer and more comprehensive understanding of the involved steps, interactions among different components, and potential failure points. Once the process is mapped, a series of brainstorming sessions will be conducted to analytically develop an Ishikawa diagram and propose solutions based on the results obtained through a cause-and-effect matrix comparison.

With the problem, its causes, and solutions defined, the next step will be to define the steps to be followed for the application of predictive models, if validated as an efficient solution for the identified problems and causes.

Therefore, to structure the methodology for applying predictive models, this topic will be divided into six groups: (1) selection of the target equipment through a multi-criteria decision-making method, (2) selection of failure mode based on the Risk Priority Number (RPN) from the Process Failure Mode and Effects Analysis (PFMEA), (3) ETL (Extract—Transform—Load) of data, (4) training of machine learning (ML) models, (5) evaluation and selection of the model, and (6) application.

Although the problem identification was primarily informed by the expertise of process specialists, an exploratory analysis of historical downtime records was also conducted to reinforce the observations. Patterns of failure recurrence, downtime frequency, and related production losses were reviewed using descriptive analytics to validate the concerns raised during brainstorming sessions. This cross-verification provided additional confidence that the selected failure mode represented a critical and recurring issue in the production environment.

3.2. Equipment Selection

To start, it is necessary to choose the equipment to be analyzed before proceeding with the subsequent steps. Therefore, there is a need to use methods that prioritize one among the numerous available assets. This approach is crucial because the quantity of assets, the number of decision variables, and the volume of data involved make this process complex. Moreover, using a prioritization method eliminates the subjectivity of choice.

For this context, a multi-criteria decision-making method will be used, with common methods including PROMETHEE (Preference Ranking Organization Method for Enrichment), ELECTRE (Elimination Et Choix Traduisant Realité), and the AHP (Analytic Hierarchy Process). In this study, the AHP method was chosen primarily due to its flexibility in addressing a wide range of decision-making problems, regardless of the types of criteria used or the number of alternatives. The AHP method will be implemented using Excel software, chosen for its ability to handle large matrices of data and its user-friendly interface with clear syntax.

After defining the decision method, it is necessary to define the criteria to be used in the AHP implementation. Given the context of where the data were collected, the type of operation the studied equipment performs, and the objectives of this study, criteria related to equipment reliability and maintenance were selected. These criteria include MTTR (Mean Time To Repair), MTBF (Mean Time Between Failures), availability, and number of downtimes. With the criteria defined, the next step is their prioritization through pairwise comparisons.

To conduct quantitative and objective comparisons and prioritization of criteria, a scale is necessary. Saaty’s scale [33] was chosen due to its widespread use. It proposes a numerical evaluation ranging from 1 to 9, aiming to determine the relative importance of one criterion compared to another.

Once the importance of each criterion is defined, it becomes possible to proceed with the implementation of the remaining steps of the AHP method using Excel to obtain the prioritization order of the equipment on which the study will be conducted.

To assess the robustness of the AHP-based prioritization, a sensitivity analysis was conducted by varying the weights of the decision criteria within a ±20% range. The results indicated that the top-ranked equipment remained stable across multiple scenarios, suggesting that the prioritization was not overly sensitive to minor changes in weight assignments. This validation reinforces the reliability of the AHP decision-making process adopted in this study.

3.3. Failure Mode Selection

After selecting the equipment for study, the next step is to define the failure mode for which predictive analysis will be conducted. To accomplish this, the Process Failure Mode and Effects Analysis (PFMEA) will be used to prioritize a failure mode from the available options. This prioritization will be based on the Risk Priority Number (RPN) analysis, where the potential failure with the highest RPN will be prioritized due to its higher severity, occurrence, and detection relationship compared to others. Therefore, critical failures can be addressed analytically, optimizing resource efficiency and making data-driven decisions.

While the PFMEA used the standard criteria—severity, occurrence, and detection—to calculate the Risk Priority Number (RPN), qualitative factors such as cost of failure, impact on product quality, and maintenance effort were also considered during expert discussions. Although not formally included in the RPN calculation, these additional dimensions influenced the final decision regarding the most critical failure mode and will be considered in future iterations of the method for enhanced prioritization.

3.4. ETL (Extract–Transform–Load)

The ETL (Extraction–Transformation–Loading) process applied to machine learning plays a crucial role in preparing and preprocessing data before using them in machine learning models. The primary goal of the ETL process in this context is to provide high-quality, consistent, and suitable data for training and evaluating models, ensuring the effectiveness and reliability of the results obtained.

The Extraction step involves obtaining raw data from the chosen equipment through a database from a Manufacturing Execution System (MES) or an Excel file (.xlsx). Next, the Transformation step cleans, reformats, and enriches the data. This may include removing missing or inconsistent data. Finally, the Loading step prepares the transformed data into a suitable format for use in machine learning algorithms. This can include dividing the data into training, validation, and testing sets, balancing classes, normalizing values, and final feature encoding.

These steps will be executed using Power Query version 2.119.701.0 software in conjunction with Python and the Pandas library, as they meet all requirements for executing this process effectively.

Regarding the manual collection of balancer height and nitrogen pressure data, standard operating procedures (SOPs) were defined to reduce inconsistencies. Operators followed a predefined checklist during inspections, and dual-recording was adopted for critical values. To improve accuracy and traceability, future implementations will prioritize sensor-based automation of data collection and integration with the existing MES system, reducing human error and increasing data reliability.

3.5. Training of Machine Learning Models

The training and implementation of the Machine Learning models will be carried out using Python 3.11.5, with the libraries Seaborn, Pandas, Numpy, Matplotlib, and Scikit-learn. Initially, Pandas will be used to import the data to feed the models, perform descriptive statistical analysis, and clean and process the data. Next, the dependent and independent variables of the model will be defined. Subsequently, the data will be divided into training and testing sets. This division is essential to assess the generalization ability of the models used and will be performed using the ‘train_test_split’ function from the Scikit-learn library. With the data separated, the next step will be training the selected models. For this study, five regression models were selected to compare their results: Gradient Boosting Regressor, K Neighbors Regressor, Support Vector Regressor, Decision Tree Regressor, and Linear Regression.

Although this study focused on five machine learning algorithms—Gradient Boosting, KNN, SVM, Random Forest, and Linear Regression—the choice was based on their proven applicability in industrial regression tasks and interpretability. Nonetheless, advanced ensemble techniques (e.g., XGBoost AND Stacking) and deep learning models (e.g., fully connected neural networks) were identified as promising candidates for future work. These models may further improve predictive accuracy, especially in scenarios involving complex or high-dimensional datasets.

3.6. Model Evaluation and Selection

The evaluation and selection of the model are crucial steps in the modeling process. In this context, various evaluation metrics can be applied to measure the performance of the models and identify the one that best meets the established objectives. Starting with the Mean Absolute Error (MAE), this metric will be used to calculate the average of the absolute differences between the predicted and actual values. By applying the MAE to the models in question, we can obtain a direct measure of the average size of the prediction errors, regardless of the direction of the errors. This allows us to evaluate the models’ ability to make accurate predictions.

Next, the Mean Absolute Percentage Error (MAPE) will be used as an additional metric to evaluate the models’ performance. The MAPE calculates the average percentage difference between the predicted and actual values. By applying the MAPE, we can obtain a measure of the average magnitude of the percentage errors in the predictions. This metric is particularly useful in forecasting scenarios, as it provides an understanding of the relative error in relation to the actual values.

Another metric is the Mean Squared Error (MSE). The MSE calculates the average of the squares of the differences between the predicted and actual values. By using the MSE, we consider both the magnitude and the direction of the errors, giving more weight to larger errors. This metric helps evaluate the models’ performance by taking into account the dispersion of the errors.

Finally, the Root Mean Squared Error (RMSE) will be used, which is the square root of the MSE. The RMSE provides a measure of the average magnitude of the prediction errors, in the same unit as the target variable. This metric facilitates the interpretation of the results and the comparison between different models or benchmarks.

3.7. Application of the Chosen Model

In the application stage, the developed methodology is practically implemented using the previously trained machine learning models. At this stage, predictions or decisions are made based on the models, and the results obtained are carefully evaluated and discussed, considering their relevance to the established objectives.

During the application phase, it is common to encounter challenges and obstacles that require critical analysis. These challenges may involve the availability of quality data, the proper adjustment of models in new contexts, or even integration with other systems or processes. It is essential to proactively address these challenges, seeking solutions and suggesting possible improvements for future applications.

As the methodology is applied, it is important to closely monitor the results and conduct an in-depth analysis to identify any discrepancies between the predictions and the actual outcomes. This allows for a more comprehensive understanding of the models’ performance in different scenarios and helps to identify opportunities for optimization.

Furthermore, interaction with stakeholders is crucial at this stage. It is essential to maintain constant dialogue with the stakeholders, sharing the results and seeking feedback to continuously improve the application of the models. Collaboration between data specialists, end users, and other stakeholders is fundamental to ensuring that machine learning solutions meet the needs and expectations of all parties involved.

4. Results

This section will present the results of the implementation of the proposed models, allowing for an empirical analysis of the key concepts discussed. The evaluation of the results will validate the implemented models and verify their effectiveness in achieving the established objectives. Additionally, this section will delve into the methods used.

The fundamental step preceding all others in this study is the process mapping of the analysis to clearly understand the problem and its possible causes. Following this, it is necessary to conduct a brainstorming session with stakeholders to identify potential solutions for the identified causes. Once this is complete, the solution that best addresses the identified causes, either completely or partially, is chosen.

4.1. Process Mapping

For process mapping, a process flowchart tool was used to structure the flow of events, decisions, and cycles from start to finish. The studied process begins with the decision of whether or not the tooling is included in the production schedule. If it is, the tooling goes through a review process. Once the review is completed, the tooling remains on standby until the production date arrive. On the production date, the tooling is transported to the external setup of the machine and then enters production.

The identified problem occurs during this event, as it was reported by process specialists during a brainstorming session that, during mass production, the tooling experiences various failures, leading to numerous unplanned stops. These unplanned stops cause significant financial and operational impacts. The identified problem was a loss of productivity due to these unplanned stops.

4.2. Root Cause Analysis

Mapping the process flow and identifying the problem, defined as the loss of productivity due to unplanned stops, necessitated a cause analysis. For this purpose, the Ishikawa diagram [34] was used. This was developed in collaboration with process specialists, and the results are presented below.

The mapped causes after applying the tool were as follows:

• Labor: Inadequate skills and insufficient training.
• Method: Lack of standardization and incorrect maintenance.
• Machine: Premature failures and excessive stamping cycles.
• Environment: Contamination.
• Material: Out-of-specification material and lack of material quality.
• Measurement: Lack of monitoring of critical parameters.

4.3. Definition and Selection of Solutions

To define and select solutions related to the identified causes, a comparison matrix was constructed between the causes obtained from the Ishikawa diagram and the solutions proposed by the specialists.

After analyzing the results of the matrix, it was observed that predictive maintenance is the solution that addresses the highest number of causes. Additionally, it aligns with the company’s current state, which already has a well-structured preventive maintenance system and aims to implement predictive models based on machine learning as the next step in the studied process. With this in mind, the development of the work continued, following the proposed steps to achieve the established objectives.

4.4. Tool Selection

For selecting the tooling, the Analytic Hierarchy Process (AHP) method was chosen as the appropriate approach due to its ability to handle multiple criteria and their interrelationships in a structured manner. As highlighted by Saaty [35], AHP provides an analytical framework that allows decision-makers to compare and weigh different criteria according to their relative importance.

One of the main advantages of using the AHP method is its ability to incorporate the subjective preferences of the specialists involved in the decision-making process [30]. By allowing experts to express their opinions and weigh the importance of criteria, AHP helps reduce the inherent subjectivity in decision-making. Another advantage of AHP is its ability to handle both qualitative and quantitative criteria in an integrated manner [35]. This means that the method allows the combination of subjective information, such as preferences and opinions, with objective data, such as metrics and indicators, to evaluate and compare alternatives.

Therefore, using the AHP method for selecting the tooling in this work provides a systematic and transparent approach to decision-making, considering multiple criteria and incorporating the preferences of the specialists involved. This methodology helps ensure a well-founded and consistent choice, contributing to the quality and reliability of the results obtained.

4.4.1. Application of the AHP Method

Saaty [35] proposed a structured process for applying the Analytic Hierarchy Process (AHP), which will be detailed in the following sections.

Definition of the Objective

The objective of applying the method is to prioritize and choose only one asset from the 34 available for study and analysis.

Construction of the Hierarchy

According to Saaty [35], the construction of the hierarchy in the Analytic Hierarchy Process (AHP) involves organizing criteria and alternatives into different levels, representing their hierarchical structure. Criteria are grouped at higher levels, while alternatives are placed at the lower level. This hierarchical structure enables a more systematic analysis and facilitates understanding of the dependencies and influences among the elements in the hierarchy.

The criteria were chosen based on the objectives established by this study, the industrial context in which the assets are situated, and the availability of data types. The defined criteria are as follows: (i) Quantity of downtimes; (ii) MTBF (Mean Time Between Failures); (iii) MTTR (Mean Time to Repair); and (iv) Availability.

The criterion “Quantity of downtimes” relates to the volume of existing data on the asset’s failure history. The greater the number of failure occurrences stored in the database, the more data available for feeding the decision model and, consequently, the machine learning models.

MTBF, MTTR, and Availability were chosen as they are key performance indicators within the maintenance sector of an industry, responsible for measuring the reliability and efficiency of assets. According to Smith [36], these indicators are widely used to assess the reliability of systems and components, directly influencing asset availability and performance. Measurement and monitoring of these indicators play a crucial role in maintenance management and strategic decision-making regarding the reliability and efficiency of industrial assets.

Establishment of Pairwise Comparisons

The establishment of paired comparisons is a fundamental step in the AHP method proposed by Saaty [35]. In this step, systematic comparisons are made between criteria and alternatives regarding their relative importance. These comparisons are conducted using a preference scale ranging from 1 to 9, as shown in

Table 1. Pairwise comparison matrix between criteria.

Criterias	MTBF	MTTR	Availability	Number of Stops
MTBF	1	3	3	1/7
MTTR	1/3	1	3	1/9
Availability	1/3	1/3	1	1/9
Number of Stops	7	9	9	1
Totals	8.6667	13.3333	16.0000	1.3651

Source: Author, 2023.

.

Saaty [35] emphasizes the importance of paired comparisons as a means to gather valuable insights into decision-makers’ preferences and priorities. Through paired comparisons, a consistent hierarchical structure can be established, where criteria are compared against each other and alternatives are compared against each criterion. It is through these comparisons that the necessary data for constructing the preference matrix are obtained, which will be used in subsequent steps of the AHP method.

The comparison of criteria was carried out by a technical expert familiar with the process, and the values were recorded in an Excel spreadsheet. The result of the criteria evaluation is shown in Table 1.

Based on the analysis of the above table, it is observed that the MTBF criterion has a higher relative preference compared to MTTR and Availability, with a value of 3. This indicates that MTBF is considered moderately more preferable than these criteria. Similarly, the MTTR criterion is slightly more important than Availability, with a value of 3.

Regarding the Quantity of Stops criterion, the assigned values are 9 and 7, reflecting its relationship with the other criteria. Quantity of Stops is considered significantly more preferable than MTTR and Availability, with a value of 9, indicating a strong preference. Likewise, Quantity of Stops is considered significantly more important than MTBF, with a value of 7, indicating a strong preference. These comparisons provide a basis for the analysis of the relative importance of the criteria and will be used in calculating the priorities of the criteria in the AHP method.

Calculation of Pairwise Comparison Matrix Consistency

After defining the paired comparisons between the criteria, it is necessary to calculate the consistency indices and the consistency ratio. According to Saaty [35], these indicators are used to verify the consistency of the comparisons made and ensure the reliability of the results.

The consistency index (CI) is calculated using the formula $CL={\displaystyle \frac{\left(\lambda max-n\right)}{(n-1)}}$, where λmax is the largest eigenvalue of the pairwise comparison matrix and n is the order of the matrix. Next, the random index (RI) is calculated, which depends on the size of the matrix and is provided in specific tables made available by Saaty. Finally, the consistency ratio (CR) is calculated using the formula $={\displaystyle \frac{CI}{RI}}$.

The consistency ratio allows for the evaluation of whether the comparisons made are sufficiently consistent. If the value of CR is less than or equal to 0.1, the comparisons are considered consistent. Otherwise, it is necessary to review the comparisons and adjust the values to achieve greater consistency. These calculations are essential to ensure the reliability of the results obtained in the decision-making process using the AHP method.

Alonso et al. [37] propose using

Table 2. Table of λmax and random index for dimensions greater than 15.

n	16	17	18	19	20	21	22	23
RI	1.5978	1.6086	1.6181	1.6265	1.6341	1.6409	1.6470	1.6526
	24	25	26	27	28	29	30	31
	1.6577	1.6624	1.6667	1.6706	1.6743	1.6777	1.6809	1.6839
	32	33	34	35	36	37	38	39
	1.6867	1.6893	1.6917	1.6940	1.6992	1.6982	1.7002	1.7020

Source: [37].

for RI values of matrices larger than 15, which is the maximum size provided in the table proposed by Saaty [33],

Table 3. Random consistency index table (RI).

n	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49	1.52	1.54	1.56	1.58	1.59

Source: Saaty, 2005 [33].

.

Given that, for the studied data, the pairwise comparison matrix had an n equal to 4, and the value of RI (Random Index) proposed by Saaty is 0.9 [33], the next step is to calculate λmax. To achieve this, the pairwise comparison matrix was normalized (

Table 4. Normalized pairwise comparison matrix between criteria.

Criteria	MTBF	MTTR	Availability	Number of Stops
MTBF	0.1154	0.2250	0.1875	0.1047
MTTR	0.0385	0.0750	0.1875	0.0814
Availability	0.0385	0.0250	0.0625	0.0814
Number of Stops	0.8077	0.6750	0.5625	0.7326
Totals	1.0000	1.0000	1.0000	1.0000

Source: Author, 2023.

) by dividing each value in the matrix by the sum of its respective column. With the normalized matrix, the arithmetic mean of each row was calculated, resulting in the relative priority vector (

Table 5. Relative priority vector.

Criterias	Relative Priority Vector
MTBF	0.1581
MTTR	0.0956
Availability	0.0518
Number of Stops	0.6944
Totals	1.0000

Source: Author, 2023.

) that ranks the criteria in terms of their importance levels. This process follows the methodology proposed by Saaty [35].

Subsequently, the relative priority vector is used in a matrix multiplication with each row of the unnormalized pairwise comparison matrix. This multiplication results in a new matrix (

Table 6. Vector λ.

Criteria	Λ
MTBF	4.4243
MTTR	3.9856
Availability	4.1199
Number of Stops	4.5047
$\lambda max$	4.2586

Source: Author, 2023.

), denoted as the λ matrix. Finally, the arithmetic mean of the λ matrix is calculated, obtaining the value of λmax (Table 6), which is essential for analyzing the consistency of the pairwise comparison matrix [35].

With the value of $\lambda max$ calculated, it is possible to calculate the consistency index (CI) and consistency ratio (CR), as presented in

Table 7. CI and CR results.

CI	CR
0.0862	0.0958

Source: Author, 2023.

.

Given that CR < 0.1, indicating acceptable consistency in the comparisons made, it can therefore be concluded that the pairwise comparison matrix is consistent enough to proceed with the subsequent stages of decision-making analysis.

Calculation of Pairwise Comparison Matrices Aggregating Each Criterion to the Decision Alternatives

After ensuring the consistency of the pairwise comparison matrix, the next step is to develop pairwise comparison matrices for each criterion with respect to the alternatives. A matrix will be developed for each criterion, where each of the 34 alternatives will be compared against each other, thereby defining their level of preference relative to the evaluated criterion. Similar to the pairwise comparison matrix for criteria, the assessment of preference levels was conducted by the same technical specialist as in the initial evaluation.

As previously mentioned, there are 34 alternatives (assets) under study, resulting in pairwise comparison matrices with n = 34. The random index (RI) value proposed by Alonso et al. was used [37]. Following this step, it was necessary to calculate $\lambda max$ for each of these matrices, after which the matrices were normalized. With the normalized matrices, the arithmetic mean of each row was calculated, resulting in relative priority vectors (

Table 8. Table of relative priorities of alternatives in relation to each criterion.

Tools	MTBF	MTTR	Availability	Number of Stops
FET35927330	0.0037	0.0197	0.0081	0.0067
FET85266918	0.0120	0.0110	0.0081	0.0105
FET65636346	0.0067	0.0110	0.0081	0.0105
FET85620376	0.0525	0.0110	0.0193	0.0067
…	…	…	…	…
FET23304797	0.0525	0.0412	0.0476	0.0105
FET91903962	0.0525	0.0412	0.1667	0.0193
FET78749352	0.0525	0.0110	0.0193	0.0193
FET79585955	0.0525	0.0197	0.0476	0.0193

Source: Author, 2023.

) that rank the alternatives in terms of importance relative to each criterion.

Next, the relative priority vectors are used in a matrix product with each row of the unnormalized pairwise comparison matrix of alternatives relative to the criteria. This product yields a new matrix, denoted as the $\lambda$ matrix. Finally, the arithmetic mean of the $\lambda$ matrix is calculated to obtain the $\lambda max$ value.

Once $\lambda max$ is calculated, the next step is to compute the consistency indices (CI) and consistency ratios (CR) again, as presented in

Table 9. CI and CR results for the parity preference matrices of global alternatives.

Criteria	λmax	CI	CR
MTBF—Alternatives	39.3865	0.1632	0.0965
MTTR—Alternatives	35.3610	0.0412	0.0244
Availability—Alternatives	37.0011	0.0909	0.0538
Number of Stops—Alternatives	37.3241	0.0595	0.0595

Source: Author, 2023.

.

Given that CR < 0.1, indicating acceptable consistency in the comparisons made, it can therefore be concluded that the comparisons in the pairwise comparison matrices of alternatives are consistent enough to proceed with selecting the preferred alternative.

Obtaining Priority for the Alternatives

The attainment of composite priorities for the alternatives is a crucial step in the multicriteria analysis process, enabling overall evaluation and ranking of alternatives based on established criteria. This stage involves combining the weights or priorities of the criteria with the evaluations of alternatives relative to these criteria.

According to Saaty [35], the composite priority of an alternative is calculated by the matrix product sum of the relative priority vector of the criteria and the relative priority vector of the alternatives with respect to each criterion. This product yields a new matrix where each element represents the composite priority of an alternative relative to the criteria.

Thus, obtaining the composite priorities for the alternatives (

Table 10. Composite priority of alternatives.

Tools	Composite Priority
FET35927330	0.0076
FET85266918	0.0107
FET65636346	0.0098
FET85620376	0.0150
…	…
FET23304797	0.0220
FET91903962	0.0343
FET78749352	0.0237
FET79585955	0.0260

Source: Author, 2023.

) allows for classification and comparison of alternatives based on established criteria, providing a solid foundation for multicriteria decision-making.

Classification of Alternatives

With the composite priority matrix of criteria calculated, the next step is to rank these values from highest to lowest. This ranking represents the prioritization order of each alternative relative to the objective established at the beginning of the AHP method application. The classification result is presented in

Table 11. Classification of alternatives.

Ranking	Tool	Composite Priority (%)
1°	FET68553464	9.3%
2°	FET18064297	8.0%
3°	FET82469803	7.5%
4°	FET74886320	7.5%
5°	FET32107266	7.4%
…	…	…
29°	FET77832561	0.8%
34°	FET62619706	0.7%

Source: Author, 2023.

.

Finally, the result of applying the AHP method identified the alternative FET68553464 as the priority option, with a 9.3% priority compared to the other alternatives.

4.5. Selection of Failure Mode

As highlighted by Smith [36], PFMEA analysis is a systematic approach that allows for the identification and classification of failure modes based on severity, occurrence, and detection criteria. Prioritization of failures is achieved through the calculation of the Risk Priority Number (RPN), which is a composite measure obtained by multiplying the aforementioned criteria scores for each known failure mode. The selection of the prioritized failure mode is based on the highest RPN, indicating that the failure has a high potential to have a significant impact on the process or system.

With that said, PFMEA analysis was conducted for the prioritized alternative (

Table 12. Failure mode with the highest RPN.

Failure Modes Potential	Potential Failure Effects	Severity	Classification	Causes and Potential Failure Mechanisms	Current Preventive Process Controls	Occurrence	Current Process Detection Controls	Detection	NRP
Crack in the part	Inability to assemble the part	7	CIC	Poor sharpening/punch and die wear	Punch control during preventive maintenance	6	Standard Method: Visual Inspection	5	210
Crack in the part	Reduction in component lifespan	7	CIC	Lack of clearance between the punch and die	Pressure frame adjustment on the machine during calibration operation	6	Standard Method: Visual Inspection	5	210
Crack in the part	Premature wear	7	CIC	Burrs on the cutting line	Adjustment of the punch and die during cutting operation	6	Standard Method: Visual Inspection	5	210

Source: Author, 2023.

) in the AHP application, aiming to identify the failure mode with the highest RPN. In total, 36 different failure modes were analyzed. The failure mode with the highest Risk Priority Number has an RPN of 210, described as “Crack in the part”.

This approach is supported by [36], who emphasizes the importance of focusing on failure modes with higher RPNs to direct prevention and risk mitigation efforts more efficiently.

4.6. ETL

The data extraction covered the period from June 2022 to June 2023, spanning one year, concerning the history of downtime for the specific tooling. Subsequently, these data underwent transformation, involving the elimination of redundant columns, duplicate records, null values, and errors using Power Query. Through this process, two distinct tables were obtained: one encompassing all production sets associated with the chosen tooling, and the other containing information about the strikes performed during these productions. This initiated the production analysis with the aim of identifying those subject to the selected failure mode, “Crack”. Consequently, a filtered table was constructed showing the number of strikes delivered by the tooling during occurrences of the mentioned failure. Upon completing this stage, relevant data for the first predictor were obtained.

As for the second and third predictors, these were defined as the height of the balancers and the pressure of the nitrogen system, due to their direct influence on crack occurrences in the forming process. It is noteworthy that data collection for these predictors does not occur via automated sensors; instead, measurements are manually taken during tooling inspection, potentially impacting data reliability. Thus, the same data transformation process was applied to ensure their integrity and consistency.

Figure 1 illustrates that the target prediction variable (Number of Strikes) exhibits a multimodal distribution. This indicates the presence of multiple distinct clusters of values, each with its characteristic average. Additionally, Figure 2 displays the data distribution through quartiles, minimum, maximum values, and the absence of outliers in the dataset, as depicted by the boxplot graph type. This analysis is crucial as outliers can reduce the accuracy of prediction models, directly impacting the presented results.

For loading the data into the machine learning models, the Excel spreadsheet format (.xlsx) was chosen. In this format, the tables were unified and processed to create

Table 13. Tools—Dataset.

Blows	OP 20 SUP	OP 30 SUP	…	Height B1 (mm)	Height B2 (mm)	…	Height B6 (mm)
2983	120	120	…	19.00	19.00	…	19.00
2927	120	120	…	19.00	19.15	…	19.00
6588	150	110	…	19.00	19.15	…	19.00
6441	150	120	…	19.00	19.00	…	19.00
6138	120	120	…	19.00	19.00	…	19.00
…	…	…	…			…
2713	120	0.8%	…	19.00	19.00	…	19.00
3712	120	0.8%	…	19.00	19.00	…	19.00
6400	120	0.8%	…	11.90	19.15	…	19.00
2571	120	0.8%	…	19.00	19.00	…	19.00
5783	120	0.8%	…	11.90	19.15	…	19.00
5283	120	0.7%	…	19.00	19.00	…	19.00

Source: Author, 2023.

. The dataset used in this study consists of 2 predictors: Predictor 1 is the nitrogen pressure (bar) in the forming die of a forming matrix, and Predictor 2 is the height of the balancers (mm) of this matrix. For Predictor 1, data are collected from seven different points, and for Predictor 2, they are collected from six different points. The data collected over this one-year period total 150 sampling rows. Regarding the response variable, this study utilizes a numerical and continuous variable represented by the number of strikes or cycles of the tooling under study during the data collection period.

With the data properly processed, it becomes feasible to begin using them for training machine learning models.

4.7. Training

During the training stage of the machine learning models, the process began with splitting the data into training and testing sets using the structure ‘X_train’, ‘X_test’, ‘y_train’, and ‘y_test’, whereby the dataset was divided with 30% for testing and 70% for training (cross-validation). In this structure, the variable ‘X’ represents the attributes or features of the dataset, i.e., the predictors, while the variable ‘y’ represents the target variable that we aim to predict. To obtain the ‘X’ variables from the dataset, the ‘drop’ operation was used to remove the ‘Strikes’ column, leaving only the predictors in the dataset. The variable ‘y’ stores the ‘Strikes’ column, which is the variable we intend to analyze or predict. This approach to splitting the data is crucial to ensure that the machine learning model is properly trained with relevant attributes and subsequently evaluated for its ability to generalize and perform well.

4.8. Evaluation and Selection of the Model

For model evaluation and selection, accuracy and performance are crucial criteria. To quantify how well the model fit the data, four error metrics were used: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The evaluated models were Gradient Boosting, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, and Linear Regression.

The specific hyperparameters used for each model were as follows: For the K-Nearest Neighbors (KNN) Regression model, “weights = ‘uniform’, algorithm = ‘auto’, p = 2” was employed. The ‘n_neighbors’ parameter was tuned using GridSearchCV, which conducts a grid search on the training data, evaluating different KNN models with varying ‘n_neighbors’ values through cross-validation, to find the best value within the specified range (1 to 99 in this study), as a wider range did not yield significant gains. For the Gradient Boosting Regression model, “n_estimators = 100, learning_rate = 0.1, max_depth = 3, min_samples_split = 2, min_samples_leaf = 1, max_features = ‘auto’, loss = ‘ls’“ was used. The Support Vector Machine (SVM) used default parameters including “C = 1.0, kernel = ‘rbf’, degree = 3, gamma = ‘scale’“. In the case of Random Forest, the parameters employed were “n_estimators = 100, max_depth = None, min_samples_split = 2, min_samples_leaf = 1, max_features = ‘auto’, random_state = 0”. Finally, Linear Regression does not require specific hyperparameters as it is a simple linear model. These values are defaults used when no specific values are provided, except for ‘n_neighbors’ as mentioned.

The result of applying these metrics based on the performance of the models presented is shown in

Table 14. Error metrics for the evaluated models.

	MAE	MAPE	MSE	RMSE
Gradient Boosting	568.26	0.17	577,792.10	760.13
KNN	667.42	0.20	699,428.12	836.32
SVM	621.02	0.18	651,951.23	807.43
Random Forest	568.19	0.17	577,750.50	760.10
Linear Regression	569.83	0.18	577,750.50	760.10

Source: Author, 2023.

.

Therefore, it can be observed that the model with the best performance and accuracy is the Random Forest, as it showed the best results in all evaluated metrics (MAE, MAPE, MSE, and RMSE), and for this reason, it was chosen as the model. For ease of explanation and understanding with stakeholders, MAPE was used because it reflects percentage values for error analysis. The prediction result on the test data can be seen in Figure 3.

4.9. Application of the Model

During the model application stage, data that were not part of the test set were used. For this purpose, data from July 2023 to August 2023 were extracted and processed following the systematic approach described earlier. Subsequently, predictions were made using the Random Forest model with the updated data, as shown in Figure 4.

Figure 4 presents a scatter plot comparing the predicted values generated by the Random Forest model with the actual observed values for the failure cycles. Each point represents a prediction-observation pair, allowing for a direct visual assessment of the model’s accuracy. The proximity of the points to the diagonal line (y = x) indicates a high degree of alignment between predicted and real values. This graphical representation effectively illustrates the model’s predictive capability and supports the numerical results reported earlier, offering intuitive insight into its performance and error behavior.

In

Table 15. Error metrics for the chosen model on new data.

	MAE	MAPE	MSE	RMSE
Random Forest	896.58	0.20	1,280,554.39	1131.62

Source: Author, 2023.

, it is possible to observe the error metrics for the chosen model applied to the new data, which were used instead of the test set from the trained model.

5. Discussion

In this section, we will discuss the results obtained from the implementation of a machine learning predictive model in the automotive industry, emphasizing its practical implications, challenges faced, and impact on sustainability and operational efficiency.

The results of this study demonstrate that the Random Forest predictive model achieved an average accuracy of 80% in predicting failures. This high accuracy confirms the model’s effectiveness in identifying complex patterns in data and anticipating potential failures. The ability to predict failures enables the implementation of preventive actions, significantly reducing the number of unplanned downtime events. Comparing the tested models, Random Forest showed the best performance across all error metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). This suggests that Random Forest can better capture variations and patterns in data compared to other models such as Linear Regression, Decision Trees, SVM, and Gradient Boosting.

The application of the predictive model has brought several practical benefits to maintenance management in the automotive industry. The reduction in unplanned downtime resulted in increased operational efficiency, making production more continuous and less prone to interruptions, thereby enhancing productivity. Additionally, there was a significant cost reduction as the decrease in unplanned downtime lowered costs associated with corrective maintenance and production losses. Another benefit was the improvement in the quality of the final product, leading to reduced rework and less waste [38].

While this study focused on the automotive industry, the proposed predictive maintenance approach is broadly applicable across various industrial sectors. Industries such as aerospace, energy, mining, and food processing—all of which rely heavily on complex and continuous production systems—could benefit from similar strategies to anticipate failures, reduce downtime, and optimize resource usage. The combination of machine learning algorithms and structured decision-making tools like AHP and PFMEA offers a scalable and customizable solution adaptable to different operational contexts and equipment types.

Despite the positive results, the implementation of the predictive model encountered some challenges. Data quality was a critical factor, as the model’s accuracy depends directly on the quality of the collected data. Incomplete or inaccurate data could compromise the model’s effectiveness. Integration with existing systems also posed challenges, as it required adjusting the technical compatibility between the predictive model and maintenance and production management systems. Furthermore, operational staff needed training to understand and effectively use the predictive model results, which required time and resources.

Predictive maintenance based on machine learning significantly contributed to operational sustainability. Failure prediction allowed for the implementation of preventive actions, avoiding the waste of materials and resources. More efficient operation resulted in lower energy consumption, aligning with environmental sustainability goals. Moreover, predictive maintenance helped extend equipment lifespan, reducing the need for frequent replacements and consequently decreasing the environmental impact associated with the production and disposal of new equipment [39,40,41,42,43,44].

This study makes a significant contribution to the existing literature in several regards. Firstly, it demonstrates the practical application of machine learning algorithms like Random Forest in predictive maintenance within the automotive industry, filling an important gap in practical and applied research. Furthermore, the integration of predictive models with sustainability practices highlights an innovative approach that combines operational efficiency with environmental responsibility. Insights gained into the challenges and benefits of practical implementation provide a solid foundation for future research, especially in improving data quality and system integration. Lastly, this study expands understanding of the impact of predictive maintenance on extending equipment lifespan and reducing waste, contributing to industrial sustainability.

Recent contributions in the field further underscore the relevance of machine learning (ML) in driving sustainable outcomes across diverse industrial applications. Studies have demonstrated that ML models can be strategically integrated with reinforcement learning and graph neural networks to optimize dynamic systems, such as traffic flow and industrial logistics, thereby reducing emissions and enhancing energy efficiency [45]. In the domain of sustainable construction, several works have employed advanced ML techniques to improve the performance of recycled and alternative materials, such as waste-based concretes and biochar, aiming to lower carbon footprints and improve structural durability [46,47,48,49]. Physics-informed machine learning has also emerged as a powerful tool to enhance the mechanical modeling of recycled materials, contributing to the circular economy [50]. Furthermore, ML-enabled exploratory analysis of industrial activity and urban dynamics has proven effective in mapping and planning land use with sustainability in mind [51,52]. In manufacturing, ensemble and symbolic regression techniques have been applied to optimize the use of supplementary materials in concrete formulations, while predictive algorithms have been successfully used to assess the compressive strength of eco-friendly alternatives like biomedical waste ash [53,54]. Collectively, these studies reinforce the potential of ML not only to enhance predictive maintenance strategies, as discussed in this article, but also to address broader sustainability challenges across multiple sectors.

6. Conclusions

This study demonstrates the effectiveness of applying machine learning predictive models in predictive maintenance within the automotive industry. The Random Forest model, in particular, proved highly accurate in predicting failures, achieving an average of 80% accuracy. Implementing the model enabled proactive actions, resulting in a significant reduction in unplanned downtimes and consequently enhancing operational efficiency.

The application of predictive modeling brought substantial practical benefits, including reduced operational costs, increased productivity, and improved final product quality. Moreover, machine learning-based predictive maintenance contributed to operational sustainability by reducing material and resource waste, lowering energy consumption, and extending equipment lifespan.

However, the implementation faced challenges such as the need for high-quality data and integration with existing systems. These challenges underscore the importance of a robust ETL (Extraction–Transformation–Loading) process and training operational staff to maximize the predictive model’s benefits.

Although the proposed methodology and model showed promising results, some limitations should be acknowledged. One of the main limitations lies in the data quality and availability, especially for variables that are manually collected and not continuously monitored by sensors, which may impact prediction accuracy. Furthermore, the dependence on historical data from a single tooling limits the generalizability of the findings to other tools or production environments. Additionally, integration challenges with existing maintenance and production systems can affect the scalability and real-time applicability of the solution.

The implications of this study are broad, suggesting that adopting machine learning technologies can transform maintenance management in the automotive industry. Technological innovation, coupled with a well-structured approach and high-quality data, can promote more efficient, sustainable, and cost-effective operations.

Financially, the reduction in unplanned downtime directly translates into lower maintenance costs, fewer emergency interventions, and less production loss—key factors in increasing overall profitability and operational stability.

Another limitation concerns the theoretical foundation of the study, which was based on a literature review conducted exclusively in the Scopus database. Although Scopus is a widely recognized and comprehensive scientific source, consulting additional databases such as Web of Science, IEEE Xplore, or ScienceDirect could enrich the theoretical analysis and ensure broader coverage of relevant studies.

To further expand on the findings of this study, the following recommendations are proposed:

(a) Explore New Algorithms: Investigate the effectiveness of other machine learning algorithms, including advanced deep learning techniques, to further enhance the accuracy of failure predictions.

(b) Expand Scope: Apply the model to different types of equipment and production processes within and beyond the automotive industry to verify the generalizability of the results.

(c) Improve Data Collection: Invest in sensor technologies and monitoring systems that ensure continuous and high-quality collection of operational data, which is critical for the effectiveness of predictive models.

(d) Integration with Management Systems: Develop solutions that facilitate the integration of predictive models with maintenance and production management systems, making implementation more efficient and less susceptible to technical incompatibilities.

This study underscores the importance of multidisciplinary approaches in engineering, integrating machine learning techniques for more effective maintenance and production management. Predictive maintenance not only enhances operational efficiency and product quality but also promotes sustainability, an increasingly crucial factor in the modern industry.