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Key performance indicators are tools for management, decision support and forecasting; they reflect the strategy and vision of the company in terms of objectives and allow to always staying in step with the expectations of the stakeholders. Accurate forecasting of the indicators allows decisions to be reoriented to ensure performance optimization w...
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Citations
... El Haddad et al. [25] investigated the application of ML algorithms for predicting Overall Equipment Effectiveness (OEE) in manufacturing environments. Their findings emphasize the adaptability of ML in improving industrial efficiency through accurate KPI measurement. ...
Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.
... Another research stream, present in the literature, includes the application of various machine learning methods for predicting the most used efficiency measures in industrial settings. An industrial case study from the automotive industry documents the application and comparison of different machine learning algorithms to determine OEE [15]. The application of deep learning algorithms has been documented to predict performance indicators of packaging equipment in [16]. ...
Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.
... • using decision tree, neural networks, support vector machine at intraday forecasting of OEE [13] • simple moving average with Holt's double exponential smoothing method using Python [14] • recurrent neural networks to reveal hidden correlation that affecting to OEE [15] • Person correlation and one-sample T-test with Design Expert software [16] • using grid search algorithm by decision tree, K-nearest neighbor, neural networks and support vector machine [17] • support vector machine, random forest, gradient boost, deep learning [18,19] • OEE prediction by Bayes-based machine learning, decision tree, support vector machine and logistic regression [20,21]. ...
Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.
... OEE is proposed as an operational improvement measure [30]. OEE is a vital tool for eliminating and identifying production losses which help in improving the efficiency of production systems [31,32]. The categories of these losses can be classified into six categories: minor stoppage loss, adjustment loss, breakdown loss, speed loss, and quality defects, start-up loss, and rework loss [33,34]. ...
... Different machine learning methods are applied to predict the OEE of an automotive cable production industry. These methods provide predictor models with improved and better performances [31]. An improved OEE method is developed for a full process cycle in the steel market. ...
Overall equipment effectiveness (OEE) is a key indicator to measure the effectiveness of production systems. This paper aims to evaluate and improve a plastic production line based on OEE evaluation. An integrated framework is proposed to enhance the production system efficiency. This paper presents the data for a Plastic production line in Jordan under real working conditions. The data covers three months. A framework process to improve the OEE of the Plastic production system was proposed. Six major stoppage losses were inspected with the help of Pareto analysis. Furthermore, the actual availability, efficiency, and quality rate measures, together with the whole OEE for each working day, week, and month of the production line were shown. The methodology is based on determining the OEE of a Plastic production line after determining the causes of failures. The fishbone diagram tool is used to determine the root causes of failures. To improve the OEE measure, several losses are identified. The results reveal that the company should improve its policy to improve the production line’s performance and reduce losses. Top management should also pay attention to reducing the speed losses, which consist of 58.1%, and eliminate the planned and unscheduled disruptions covering 12.73% of all losses. This can be achieved by establishing a proper operation management procedure and strategy. This, in turn, optimized the equipment’s effectiveness. The quality procedure should include the changeover program that may be executed every day. Similarly, all preventive maintenance procedures for the six machines should be properly executed in predetermined intervals. There are several limitations in the research. Firstly, the research case study is only the plastic production system. Secondly, the research is related to the downtime or stoppage by analyzing it using fishbone diagram. Further, supported by other techniques such as the Pareto chart, six big losses analyses and CED. This research conducted on a Plastic industry. However, similar studies can be carried out in future in other manufacturing industries like electronic, pharmaceutical, textile industries, etc., and service industry. However, as future research work the contributions of this paper with other lean manufacturing concept like six sigma, quality function deployment, TQM, and just-in-time manu-facturing, can also be conducting to assess the overall production line efficiency. On the other hand, several statistical tests can be implemented based on data collected of TPM performance indicators. The proposed method supports policymakers in their decision-making process on the operations management line. Further-more, it improves the production systems’ productivity quality, and performance, reducing unplanned stop-pages and breakdowns, and reducing maintenance costs.
... For example, Kuo & Lin [7] developed an integration of neural networks and decision trees to predict availability efficiency, a metric strongly correlated with OEE, specifically for a set of washing machines. Meanwhile, Mazgualdi et al. [8] [9] employed SVM, SVM with optimization, RF, XGBoost and ANNs to predict OEE in an automotive cable production industry. Among these models, ANNs and RF demonstrated superior accuracy and performance. ...
Purpose
In the apparel industry, suppliers play a significant role, directly affecting customer service levels and business profits. Integrating sustainable requirements into supplier selection not only aligns with global environmental goals but also enhances business performance, social responsibility and overall industry well-being. This study aims to design a multi-criteria model to evaluate and select the most sustainable suppliers in the fashion industry, trying to balance the conflicts in the set of sustainable development criteria.
Design/methodology/approach
The integration of analytic hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) offers advantages in sustainable supplier selection within the apparel industry. The AHP plays a crucial role in engaging multiple decision-makers with conflicting criteria to reach a consensus during the decision-making process. Conversely, the TOPSIS is used to compute alternative ratings. By simultaneously determining criteria weights and incorporating stakeholder preferences, hybrid models enhance decision-making strength and overcome limitations observed in classical multi-criteria decision-making techniques.
Findings
This research identified and classified 16 critical criteria impacting the selection of apparel industry suppliers, focusing on sustainable development. The criteria were weighted, providing a robust statistical foundation for the selection model. The results indicated that the most influential criteria were staff training, production capability, flexibility and practice of recycling. The proposed sustainable supplier selection model explains to decision-makers how criteria influence supplier ranking results compared to traditional models, supporting managers in making informed and sustainable supply chain decisions through continuous updates and enhancements.
Originality/value
This research provides new insight into the weighted impact of factors related to sustainable supplier selection in the apparel industry. The combination of a precise process and scientific knowledge will improve the quality of sustainable supplier selection.
... In image data classification, the XGB algorithm can be employed to predict the class or label of each pixel or region within an image based on the features extracted from the image. An illustration of the XGB algorithm can be seen in Figure 4. [24] First, XGB initializes the model with a single decision tree that partitions the image data into several groups based on image features such as color, texture, shape, and more. This decision tree is referred to as the base tree or base learner. ...
... A deeper understanding of the sensitivity and robustness of the forecasts may be obtained as well through the use of simulation approaches. System dynamics designing, discrete event simulation, agent-based modeling, and other similar approaches are just some of the common simulation methods that can be used for KPI forecasting [12]. ...
... Methods of expert judgment have the potential to also take into account the numerous viewpoints and preferences held by forecasters and other stakeholders. Methods such as brainstorming, the nominal group technique, and the analytic hierarchy process are examples of popular expert judgment methods that can be used for KPI forecasting [12]. Statistical approaches are based on the use of mathematical models and formulae to analyze historical data and identify patterns, trends, cycles, correlations, and effects among the KPIs and their related factors. ...
Key performance indicators (KPIs) are essential tools for organizations across industries, providing a means to assess and enhance performance, efficiency, and quality. However, predicting KPIs presents challenges due to data nonlinearity, uncertainty, and variability. Traditional approaches, like trend analysis, may struggle to cope with these issues, leading to imperfect and unreliable predictions. In response, this research aims to enhance KPI prediction accuracy using machine learning algorithms. The study compares seven techniques—random forest (RF), XGBoost (XGB), decision tree (DT), linear regression (LR), support vector regression (SVR), neural network (NN), and multi-horizon quantile recurrent forecaster (MHQRF)—using three sales datasets. Evaluation includes four error measures: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared. Results show MHQRF’s superior performance across all datasets, with the lowest MAE (618.6), MSE (1,505,991.394), and RMSE (683.98). However, XGB achieves a higher average R-squared value (0.886) compared to MHQRF, introducing more nuanced considerations for model selection in diverse KPI forecasting scenarios.
... For example, Kuo & Lin [7] developed an integration of neural networks and decision trees to predict availability efficiency, a metric strongly correlated with OEE, specifically for a set of washing machines. Meanwhile, Mazgualdi et al. [8] [9] employed SVM, SVM with optimization, RF, XGBoost and ANNs to predict OEE in an automotive cable production industry. Among these models, ANNs and RF demonstrated superior accuracy and performance. ...
Overall Equipment Effectiveness (OEE) stands as a key performance metric widely adopted in the manufacturing industry, aiding in enhancing productivity. This metric offers a comprehensive overview to higher management, enabling them to identify equipment-related losses. With the advancements in Industry 4.0 technologies, the Manufacturing Execution System facilitates real-time data collection, enhancing production efficiency and reducing manufacturing costs. However, the real-time depiction of OEE often fails to provide decision-makers with timely insights to conduct their tasks effectively. This study formulated machine learning models to tackle this challenge by forecasting the OEE for the upcoming working shift. Firstly, the historical dataset encompasses 31 features collected and processed to estimate the OEE value. Then, prominent machine learning models were utilized as prediction models: Linear Regression, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The results show that the Extreme Gradient Boosting performs well for the OEE prediction with accuracy in training higher than 99% and testing nearly 90%. Our study illustrates an actionable knowledge-discovery process using a real-world data mining approach for the manufacturing industry, potentially applicable to other sectors.
... Machine learning (ML) approaches have been gradually used as a result of the shortcomings of previous methodologies. By using algorithms like decision trees, support vector machines, and simple neural networks, machine learning provided more advanced, data-driven methods for KPI forecasting (Le et al., 2018;El Mazgualdi et al., 2021). More complex and adaptive models were made possible by this shift, which was a major turning point in the development of KPI forecasting. ...
Key performance indicators (KPIs) express the company’s strategy and vision in terms of goals and enable alignment with stakeholder expectations. In business intelligence, forecasting KPIs is pivotal for strategic decision-making. For this reason, in this work, we focus on forecasting KPIs. We built a transformer model architecture that outperforms conventional models like Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) in KPI forecasting over the Rossmann Store, supermarket 1, and 2 datasets. Our results highlight the revolutionary potential of using cutting-edge deep learning models such as the Transformer.
... The utilization of artificial intelligence (AI) and machine learning (ML) in the manufacturing industry has gained momentum in recent years, as evidenced by a number of academic articles. In our prior research, we have investigated various applications of AI and ML in this industry, including product quality inspection [1][2], energy optimization [3] [4], and efficiency enhancement [5][6] [7]. These studies have demonstrated the versatility of AI and ML in the manufacturing industry, highlighting their potential to enhance productivity, reduce waste, and improve product quality. ...
Work measurement is a critical aspect of industrial engineering, where various methods are employed to analyze work and optimize productivity. One of the primary tools used by industrial engineers is cycle time, which determines the time taken to complete tasks within a company. However, traditional human analysis of work can be challenging and time-consuming, requiring significant effort to measure and analyze work through direct observations. This paper proposes a new methodology that uses artificial intelligence algorithms, combining ConvLSTM and Faster R-CNN, to identify and track the actions and items manipulated by operators while computing important metrics like cycle time and total wastes. Our approach enables the analysis of human-object interaction from an egocentric perspective, providing an alternative to traditional methods that involve human analysts. We tested our method on the Meccano Dataset and our own dataset, and the results are promising. The proposed methodology is robust, cost-effective, and useful for measuring standard time and identifying waste in various industrial environments. This research demonstrates the potential of artificial intelligence in work measurement and analysis and can lead to significant improvements in productivity and efficiency.
