Warehouse automation shifts to goods-to-person, demand sensing, circular logistics in 2026

Thomas Genestar, managing director of western Europe at Exotec, has identified four strategic pillars that are redefining warehouse and supply chain logistics heading into 2026 and beyond. His analysis reflects a broader recognition that e-commerce growth has made automation and AI essential to operational excellence, shifting them from optional enhancements to foundational baseline capabilities.

The first pillar is goods-to-person (G2P) automation. Traditional warehouse operations require workers to walk to storage locations to retrieve items—a pattern that results in heavy physical strain. G2P systems reverse this logic by automatically delivering bins and items to stationary human operators at fixed workstations. According to Genestar, this approach eliminates the need for workers to walk up to 15km per day, reduces round trips, creates more consistent and predictable task sequences, and minimizes exposure to hazardous or heavy loads. The business case is quantified by the G2P Solutions 2025 market report by STIQ, which found that put-away, storage, and picking operations account for up to 52% of costs in non-automated warehouses. Industrial G2P systems address this cost burden while doubling productivity and improving workers' quality of work life.

The second pillar centers on demand sensing and real-time demand-production synchronization. Traditional forecasting models rely on static historical patterns and produce a single projected forecast. Demand sensing, by contrast, employs AI and machine learning to continuously recalculate demand by ingesting both rapid signals (promotions, seasonality, weather) and weaker signals (emerging trends, economic fluctuations, inflation). Rather than pursuing a single "perfect" prediction, organizations implementing demand sensing aim for regular synchronization between actual demand and production capability. The outcomes are shorter planning cycles, improved peak management, and a structural shift from just-in-time inventory (which prioritizes cost but risks disruption) to more resilient just-in-case models. This transition also supports greater long-term sustainability and supply chain resilience.

The third pillar is circular logistics and remanufacturing. Reverse logistics structures the return flows of used items—take-back, sorting, reconditioning, and restocking—to maximize the lifecycle of industrial assets. Inspired by frictionless consumer return models in e-commerce, industrial operators are adapting remanufacturing with stricter technical standards. Customers return worn parts, receive eligible deposits back, and the parts enter remanufacturing to be reintroduced as "new," creating a closed-loop supply chain.

The fourth pillar is digital twin technology. Long the domain of aerospace and advanced manufacturing, digital twins create a virtual, real-time replica of a warehouse or robotics system. This allows operators to simulate, test, and optimize workflows without interrupting live operations—a decisive advantage in logistics, where every minute of downtime translates directly to lost productivity. Use cases include modelling new workflows before a capacity ramp-up, predictive fault detection, dynamic optimization of robotic trajectories, and simulation of seasonal peaks. Continuously fed by IoT and warehouse management system (WMS) data, the digital twin becomes a forward-looking decision tool that embeds predictive modelling at the core of logistics strategy.

Genestar concluded that logistics is entering a critical chapter. Organizations that succeed will be those that invest in predictive intelligence, think long term, and build supply chains capable of thriving amid disruption—emphasizing that the adoption of these four pillars is essential for maintaining competitive advantage in an industry under growing pressure on lead times, costs, and flexibility.

The warehouse automation landscape is entering a phase where four interconnected capabilities—automation, AI, resilience, and digital intelligence—are becoming baseline operational requirements rather than competitive luxuries. E-commerce expansion has forced organizations to rethink logistics fundamentally, and the trends Genestar outlines reflect a structural shift away from labor-intensive, linear workflows toward systems that are simultaneously more efficient and more resilient.

Goods-to-person automation exemplifies this change. By reversing the traditional logic where workers travel to goods, G2P systems reduce operator fatigue (eliminating up to 15km of daily walking), improve task consistency, and free human workers from the most physically taxing elements of warehouse work. The body cites STIQ data showing that put-away, storage, and picking account for up to 52% of costs in non-automated warehouses—a figure that underscores why G2P adoption is no longer optional for operators facing cost and labor pressures.

Demand sensing and digital twins represent a second wave: moving supply chain logic from reactive prediction and intuition to continuous, real-time data-driven adjustment. Demand sensing replaces rigid forecasting with AI models that adjust for multiple simultaneous signals, shifting companies toward just-in-case resilience rather than just-in-time fragility. Digital twins, borrowed from aerospace, allow logistics operators to run virtual simulations of workflows, fault detection, and seasonal peaks without risking downtime in live operations—a critical advantage in an industry where every minute of downtime is lost revenue. Together, these technologies embed predictive intelligence at the core of supply chain strategy.