Faouzi Tayalati

• Doctor of Philosophy
• Industrial Engineering and Artificial intelligence at Abdelmalek Essaâdi University Tangier, Morocco

Injection molding is a widely employed manufacturing process known for its efficiency, precision, and scalability in producing complex plastic components. However, persistent quality defects, particularly shrinkage, remain a significant challenge, directly impacting product reliability and performance. Traditional studies have relied heavily on Ana...

Sales forecasting is an essential task for businesses as it enables suppliers to analyze customer preferences, thereby optimizing profits, reducing costs, and minimizing product returns. Confronting the complexities of sales forecasting, this research introduces a new hybrid model for sales forecasting that combines classic time series analysis wit...

Accurately predicting whether customers will accept offers is essential for improving marketing strategies and increasing engagement in the highly competitive online retail sector. This study applies predictive modeling and key feature analysis to a dataset of 2,198 customers, each characterized by 20 features, including demographics, spending patt...

Understanding legal documentation is a complex task due to its inherent subtleties and constant changes. This article explores the use of artificial intelligence-driven chatbots, enhanced by retrieval-augmented generation (RAG) techniques, to address these challenges. RAG integrates external knowledge into generative models, enabling the delivery o...

Achieving long-term profitability in e-commerce requires effective customer management strategies to reduce customer churn rate. This study presents a novel framework integrating Genetic Algorithms (GA) and the Analytic Hierarchy Process (AHP) to optimize these strategies. GA explores optimal combinations of techniques, while AHP assigns their rela...

Selecting the right injection molding machine for new products remains a challenging task that significantly influences the profitability and flexibility of companies. The conventional approach involves performing theoretical calculations for clamping force, conducting mechanical validations of the mold, and carrying out real trials for new parts....

Selecting the right injection molding machine for new products is a significant challenge for manufacturers. The traditional approach involves detailed calculations of clamping force, mechanical mold evaluations, and hands-on trials. This method is time-consuming, costly, and requires expert skills. This paper explores how machine learning can enha...

Improving operational processes significantly relies on choosing the right suppliers, which emphasizes the importance of using effective evaluation methods. This study explores supplier evaluation through a detailed framework involving five Multi-Criteria Decision-Making (MCDM) approaches. These methods consider seven main criteria and twenty-one s...

The X-bar control chart is a statistical tool widely employed in manufacturing to detect irregularities in product quality and machine deviation over time. This chart helps ascertain whether a process is statistically under control by establishing upper and lower limits derived from the probability distribution of the quality characteristic. Howeve...

Exploring the interpretability of machine learning models is important for establishing trust and facilitating their adoption in practical applications. This study utilizes the Shapley Additive Explanations (SHAP) framework to conduct a comparative analysis of the explainability of Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) models....

Detection of quality defects in injection molding manufacturing remains one of the most challenging tasks due to its heavy reliance on human visual inspection, which has inherent limitations. Computer vision, which addresses image-based problems, offers promising solutions in this area. This article explores the application of machine vision models...

This study explores the implementation of artificial intelligence for process monitoring within smart factories, particularly under the Factory 4.0 paradigm. It proposes an approach centered on a data-centric model for digital twins, enhanced by the application of deep learning methodologies utilizing LSTM models to forecast the melt cushion parame...

In the fashion retail e-commerce sector, personalized product recommendations are crucial for enhancing the shopping experience. This study introduces a method that combines a pre-trained deep learning model named VGG19 with the 10 nearest neighbors algorithm to recommend visually similar products. VGG19 is utilized to extract detailed features fro...

Dimensionality reduction is crucial for managing high-dimensional datasets in machine learning, reducing complexity and overfitting. This study evaluates the efficiency of classification models without and with feature selection using the Boruta algorithm with Random Forest classifiers across three distinct datasets. Feature selection aims to impro...

Detecting anomalies in the injection molding process remains a challenging task, demanding significant resources, data, and expertise due to their impact on cost and time reduction. While traditional methods like statistical process control (SPC) using control charts are widely used for detecting irregularities, they can catch predefined patterns s...

Statistical process control is a key tool in quality management, based on control charts to detect irregularities in product quality and machine deviation. These charts have transformed quality control , offering a systematic approach to monitor process variation and pinpoint anomalies in production. This paper seeks to capitalize on advancements i...

The challenge of overstocking in inventory management is a critical issue that involves balancing the risks of excess inventory against the costs of storage. To address this problem, the article proposes a novel hybrid methodology that assesses and manages this risk by integrating Bayesian networks and fuzzy logic. Firstly, Bayesian networks are em...

In injection molding manufacturing, the selection of the optimal machine from various alternatives is a crucial strategy for enhancing productivity, cost-effectiveness, and maintaining performance standards. This article presents an approach that combines two techniques to make the best choice from three presented options. Firstly, it employs the A...

The injection molding process is considered as one of the most used process in the plastics industry due to its reliability and its profitability; however nowadays, the injection industry marketplace becomes more and more competitive because of the excessive quality demand and the coast reduction requirement. Production workshops strive constantly...

The thermoplastic injection process is an industrial technique that allows getting a high precision plastics part with high production rate. This process is considered one of the most complexes in the plastic industry due to its complexity and variability. The main problems in this technique can occur during two phases: first, during the initial se...