Inventory Management

Daher is expanding its use of LeanDNA APEX to standardize supply chain execution across operations on three continents. The platform is intended to provide common KPIs, workflows and AI-assisted recommendations despite multiple SAP environments and historically manual reporting processes.

The deployment supports tactical buyers and operational teams working on material availability, supplier performance, inventory priorities and execution risks. The objective is to create a shared operational layer above fragmented ERP environments.

The partnership is a concrete example of manufacturers using AI-enabled execution platforms to harmonize decisions without waiting for a full core-system transformation.

REMIRA continues to position AI-powered cloud supply chain software around inventory management, demand response, supply chain integration and operational planning. The product signal is relevant for European retail, wholesale, manufacturing and distribution teams.

The practical relevance is Demand Forecasting, inventory optimization, supply chain integration and proactive demand response. For planning teams, the key question is whether AI-supported signals improve daily stock and order decisions while remaining connected to operational execution systems.

More details are available on the REMIRA supply chain software page.

A new wave of Supply Chain AI experimentation is emerging across the planning community. Inspired by initiatives such as Knut Alicke’s AI-assisted S&OP application, supply chain professionals are increasingly using vibe coding tools to build operational applications without traditional software development teams.

What started as isolated experiments is becoming a broader movement. Examples now span S&OP, demand planning, inventory management, scenario modeling, supplier risk monitoring and planner copilots. Recent community examples include AI-generated planning applications shared by practitioners such as Mahmoud Moursy, alongside other public discussions around IBP engines, manufacturing dashboards and supply-chain planning automation.

The emergence of these tools creates a new layer between Excel and enterprise APS platforms. Rather than replacing established planning solutions, these lightweight applications allow domain experts to rapidly test ideas, automate workflows and address local planning challenges that may never justify a large transformation project.

However, the opportunity comes with significant risks. As more planners become application builders, organizations must address AI governance, data quality, model transparency, business ownership, security, auditability and integration with enterprise systems. Without controls, companies risk creating a new generation of planning silos and shadow applications powered by AI rather than spreadsheets.