Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We...

Including employees and stakeholders in the budgeting process enhances decision-making, encourages trust, and increases the probability of goal attainment. The present study investigates the simultaneous impact of motivational and cognitive variables of budget participation on managerial...

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the...

We present an approximation method for the hybrid flow shop scheduling problem based on relaxation and machine learning techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised learning method for neural networks, which...

This study proposes a feedforward deep neural network to predict the parameters of the lithium-ion battery in electric vehicles. Correlation analysis is used to select the candidate parameters for the proposed model with no categorical variable. A direct feedforward deep artificial neural...

Managing information flow has always been a challenging and critical driver of performance increase in manufacturing companies. Each bit of information related to the manufacturing process has an information flow value that can impact the process. Recent studies have focused on the traditional...

This study investigates the potential of predictive analytics in improving Key Performance Indicators (KPIs) forecasting by leveraging Lean implementation data in supply chain enterprises. A novel methodology is proposed, incorporating two key enhancements: using Lean maturity assessments as a...

Artificial intelligence (AI) systems can assist in analyzing medical images and aiding in the early detection of diseases. AI can also ensure the quality of services by avoiding misdiagnosis caused by human errors. This study proposes a deep neural network (DNN) model with fine-tuned training...

The outbreak of COVID-19 has caused an unprecedented increase in the usage of e-Learning platforms. The closure of educational institutions globally has significantly impacted the traditional education system. Without physical interaction, engaging students in an e-Learning environment has...

This study proposes integrated image processing and deep learning approaches for wheat rust disease detection and grading. Wheat stripe rust is one of the diseases affecting the crop in nearly all wheat-growing countries, where humidity and high temperatures characterize the growing season. The...