The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths.

In this work, we integrate the concepts of Industry 4.0, smart manufacturing, and sustainable manufacturing in a model that provides a conceptual framework for the study of long-term solutions with a high degree of specialization, according to the specific context of each investigation. This study offers a holistic analysis and evaluation of the main challenges facing the Industry 4.0 concept. We also diagnose the current methodological proposals aimed at solving the challenges of Industry 4.0 and sustainability using a systemic review of the literature from the past 5 years. Firstly, we identify 14 technological trends linked to Industry 4.0. Subsequently, the trends are integrated into the proposed model to identify opportunities, evaluating their relationship with three performance areas. This allows the identification of trends that present the greatest number of opportunities in the context of sustainability. The second stage complements the literature review with a descriptive analysis of the studies and discusses the findings. The study concludes that the identified technological trends positively impact Industry 4.0 challenges, helping to achieve sustainable manufacturing objectives.

Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape.

This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. This review systematically categorizes and evaluates existing models and optimization algorithms, highlighting their strengths, limitations, and applicability in various operational contexts. We explore deterministic, stochastic, and hybrid models, as well as exact, heuristic, and metaheuristic algorithms. By synthesizing the latest advancements and identifying research gaps, this paper offers valuable insights for academics and practitioners aiming to enhance TAS efficiency and effectiveness. Future research directions and potential improvements in model formulation are also discussed.

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.

This study presents an innovative approach to support educational administration, focusing on the optimization of university admission processes using feature selection algorithms. The research addresses the challenges of concept drift, outlier treatment, and the weighting of key factors in admission criteria. The proposed methodology identifies the optimal set of features and assigns weights to the selection criteria that demonstrate the strongest correlation with academic performance, thereby contributing to improved educational management by optimizing decision-making processes. The approach incorporates concept change management and outlier detection in the preprocessing stage while employing multivariate feature selection techniques in the processing stage. Applied to the admission process of engineering students at a public Chilean university, the methodology considers socioeconomic, academic, and demographic variables, with curricular advancement as the objective. The process generated a subset of attributes and an application score with predictive capabilities of 83% and 84%, respectively. The results show a significantly greater association between the application score and academic performance when the methodology’s weights are used, compared to the actual weights. This highlights the increased predictive power by accounting for concept drift, outliers, and shared information between variables.