Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors.

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing.

Information systems are compelled to adapt by developing agility abilities in order to respond effectively to rapid and unforeseen changes in the business environment, so the organization can grow or even survive in this changing environment. As a result, organizations must be given guidance on how to design, measure, and enhance the agility of their information systems. An iterative, integrated, flexible, and balanced framework has been established in this area. The ITAAM (Information Technology Agility Assessment Model) framework is based on a simple model developed from the Balanced ScoreCard approach, which is extensively used as a performance measuring system. Our approach is made up of a conceptual framework, an evaluation methodology, an agility grid, and a scoring system to compute the Global Agility Index. Furthermore, throughout each evaluation cycle, the agility level is detected, and a set of recommendations, as well as the appropriate organizational adjustment guidelines, are offered. As a result, we can ensure long-term viability and ongoing agility improvement, which is the primary purpose of our new structure. We believe that the framework can be adapted and generalized by sector of activities or by group of sectors that have common concerns and almost the same issues (level of competition, competitiveness, changes, etc.).