Supply Chains Are Getting Smarter—But Only the Prepared Will Survive

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1. https://ecommercenews.co.nz/story/eight-ways-ai-automation-will-transform-supply-chains-in-2026 - Please view link - unable to able to access data
2. https://www.gartner.com/en/newsroom/press-releases/2025-09-16-gartner-predicts-70-percent-of-large-orgs-will-adopt-ai-based-supply-chain-forecasting-to-predict-future-demand-by-2030 - Gartner forecasts that by 2030, 70% of large organizations will implement AI-based forecasting to predict future demand. This approach aims to eliminate manual inputs, offering scalable automation within demand planning. AI-based forecasting can dynamically detect complex patterns across time series data, enabling more frequent and granular forecasts. It can also learn from various datasets, which is required for making automated predictions on new product introductions and promotional initiatives that have limited or no historical data within a given dataset. Despite its potential, adoption of touchless AI forecasting remains limited today. Broader adoption is often hindered by a lack of clear vision among SCP leaders and ongoing challenges with data completeness, availability, and accessibility. Additionally, process changes required for implementation can face resistance from employees accustomed to traditional forecasting practices. To implement AI-based touchless forecasting, Gartner recommends SCP leaders follow this five-part plan: 1. Define a touchless forecasting vision. Analyze current collaboration processes, individual workflows, time lost to traditional methods, and the forecasting tools and systems in use, then identify specific areas for improvement and articulating the business case. 2. Establish the business change parameters. Identify the processes, workflows, and metrics that must be redefined to support touchless forecasting, making it a business-critical initiative that requires strong change management. 3. Define the touchless data strategy. Move beyond a sole reliance on historical sales data by developing a comprehensive data strategy that includes both internal and external sources. Engaging stakeholders, including trading partners, is essential to ensure data quality, governance, and recurring feeds. 4. Create a technology enablement roadmap. Transitioning to AI-based forecasting requires investment in technology and skills, which can be built internally or outsourced through supply chain planning solutions, analytics platforms, or forecasting-as-a-service models. Organizations should secure IT support and evaluate solutions based on engine performance, vendor expertise, and alignment with their data strategy. 5. Plan for the adoption journey. Successful touchless forecasting depends on organizational trust in AI-generated outputs, which requires ongoing communication about the inherent uncertainty in forecasting. Leaders should ensure results are explainable, benchmark AI forecasts against simple models, and highlight value-added contributions through regular analysis and reporting.
3. https://www.forbes.com/sites/joemckendrick/2025/10/04/supply-chains-2026-less-globalization-more-ai/ - A recent survey indicates that 70% of executives are advancing AI integration into supply chains, with quality control and risk identification as primary applications. AI is projected to be a top capital investment for 75% of companies by 2026, with 77% of leaders reporting a return on investment within 12 months. Additionally, 63% anticipate AI will make decisions across all major supply chain functions within the next five years. The survey highlights that organizations lagging in AI adoption risk competitive obsolescence as AI-driven decision-making becomes the operational standard.
4. https://www.bps-lts.com/resources/news/key-warehouse-automation-trends-in-2026-ushering-in-a-new-era-of-smart-logistics - The warehouse automation landscape in 2026 is marked by several key trends: 1. Deployment of intelligent picking and sorting robots by companies like Amazon, DHL, and GXO, operating 24/7 to reduce human error and improve throughput. 2. Dominance of Autonomous Mobile Robots (AMRs) in intralogistics, utilizing sensors and AI for dynamic route planning and obstacle avoidance, expected to account for over 60% of new automation deployments by the end of 2026. 3. Integration of IoT-connected devices and advanced analytics for data-driven warehouse automation, optimizing inventory placement, predicting equipment maintenance, and improving labor allocation. 4. Adoption of cloud-based Warehouse Management Systems (WMS) integrated with Enterprise Resource Planning (ERP) systems, enabling real-time inventory visibility and automated procurement triggers.
5. https://www.sdcexec.com/warehousing/press-release/21197822/logisticsiq-warehouse-automation-market-post-covid19-opportunities-worth-30b-by-2026 - The warehouse automation market is projected to reach $30 billion by 2026, driven by increased adoption of Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). Companies like Geek+, Grey Orange, and Locus Robotics are leading this trend. The grocery industry, characterized by high-volume shipments and frequent deliveries, is particularly poised for automation, with online grocery and micro-fulfillment centers contributing to a $5 billion opportunity by 2026. Retailers such as Kroger are partnering with technology providers like Ocado to build automated customer fulfillment centers, modernizing and streamlining operations.
6. https://www.gartner.com/en/supply-chain/topics/supply-chain-ai - Gartner's research indicates that by 2026, 15% of supply chain software will implement Software Bills of Materials (SBOMs) to prevent cyberattacks. Supply chain AI is poised to redefine job roles, customer demands, and workplace productivity. Gartner categorizes AI initiatives into three types: 1. Everyday AI, focused on productivity without significant market differentiation. 2. Boundary-pushing AI, aiming for improvements in larger-scale operations but not yet broadly augmenting supply chain management. 3. Game-changing AI, leading to market leadership through innovative solutions. To successfully introduce AI, Gartner recommends a five-step approach: 1. Identify impactful, measurable use cases. 2. Assemble necessary skills and talent. 3. Gather relevant data. 4. Select appropriate AI techniques. 5. After initial proofs of concept, determine the optimal placement of AI expertise within the organization.