This paper presents an integrated framework that combines traditional network optimization models with large language models (LLMs) to deliver interactive, explainable, and role-aware decision support for supply chain planning. The proposed system bridges the gap between complex operations research outputs and business stakeholder understanding by generating natural language summaries, contextual visualizations, and tailored key performance indicators (KPIs). The core optimization model addresses tactical inventory redistribution across a network of distribution centers for multi-period and multi-item, using a mixed-integer formulation. The technical architecture incorporates AI agents, RESTful APIs, and a dynamic user interface to support real-time interaction, configuration updates, and simulation-based insights. A case study demonstrates how the system improves planning outcomes by preventing stockouts, reducing costs, and maintaining service levels. Future extensions include integrating private LLMs, transfer learning, reinforcement learning, and Bayesian neural networks to enhance explainability, adaptability, and real-time decision-making.

Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

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An efficient supply chain planning is the fundamental block for building a successful and well-organized supply chain mechanism. Many businesses are unable to achieve the desired operational excellence due to manual operative approaches, lack of visibility and poor supply chain planning. This restricts brands from creating synchronized, smooth and responsive supply chains. The most crucial activity in supply chain management is planning. Supply chain planning is the process of accurately planning a product flow from raw material sourcing to reaching the final consumer.

Machine learning is one technology that has revolutionized industries by helping optimize their day to day processes. One such segment where the technology has made its mark is supply chain optimization and management. ML in the supply chain has made it possible for businesses to discover patterns and identify variables that impact the networks' success. Supply chain optimization or even management requires a business to continually look into data and discover the changing patterns. What has been a manual or a semi-automated process, now doesn't need manual intervention thanks to ML in the supply chain.

Artificial Intelligence is once again changing the way we shop. After disrupting the world of digital commerce, AI has seeped into the grassroots of consumerism and overhauled its next target -- retail stores. A study from Juniper Research predicts the adoption of AI in the retail industry will amount to a staggering figure of US$ 7.3 billion per year by 2022. In retail operations alone, AI has the potential to drive savings of about US$ 340 billion. Such striking figures paint a compelling picture of the prowess of AI in the retail backdrop.

One area of interest in today's end-to-end supply chain is machine learning. And this is certainly a topic that we have written about quite often. Over the last few months, Steve Banker, Clint Reiser, and I have written about artificial intelligence and machine learning in a number of contexts and how it impacts the supply chain. These topics have included transportation management, warehouse management, and supply chain planning, among others. This technology continues to be a hot topic for companies as is evident by how often the Logistics Viewpoints team and others are writing about it.

Autonomous supply chain planning is neither pie-in-the-sky nor purely aspirational. Rather, it is the only viable response to today's globalized, e-commercialized, omnichannel business environment, where organizations must cope with a constantly shifting deluge of information pouring into the enterprise from all corners of the marketplace. There is no longer any way companies can manage supply chain planning by increasing the size of their planning teams. Secondly, the combination of seasoned, yet simultaneously tech-savvy, talent needed for the job is in increasingly scarce supply. Thirdly, and perhaps most crucially, people, no matter how many or how smart, don't have sufficient brainpower to deal with the scale of inputs and outputs in the modern supply chain --only machine learning can keep up.

The Inventory Planning and Control (IPC) team owns Amazon's global inventory planning systems. We build the systems that decide what, when, where, and how much we should buy to meet Amazon's business goals and to make our customers happy. We do this for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. Our systems are built entirely in-house, and are on the cutting edge in automated large scale supply chain planning and optimization systems. IPC fosters new game-changing ideas, continuously improves, resulting in sophisticated, intelligent and self-learning models.

Companies are cutting supply chain complexity and accelerating responsiveness using the tools of artificial intelligence. Through AI, machine learning, robotics, and advanced analytics, firms are augmenting knowledge-intensive areas such as supply chain planning, customer order management, and inventory tracking. What does that mean for the supply chain workforce? It does not mean human workers will become obsolete. In fact, a new book by Paul Daugherty and H. James Wilson debunks the widespread misconception that AI systems will replace humans in one industry after another.