IN Brief:
- Manhattan Associates has introduced Solution Design Studio as part of its ActivePlatform technology environment.
- The workspace uses AI to convert business-language operating blueprints into approved system configuration.
- The launch reflects growing pressure to reduce the time and specialist effort needed to adapt supply chain software.
Manhattan Associates has launched Solution Design Studio, an AI-powered workspace designed to help business users configure complex supply chain systems using natural-language operational blueprints.
The new workspace forms part of Manhattan ActivePlatform and is being introduced alongside the company’s ProActive tools and Agent Foundry capability. Solution Design Studio allows users to describe operational requirements in business language before reviewing and approving AI-generated configuration changes for live systems.
Warehouse operations leaders, transport managers, supply chain analysts, and implementation teams are the intended users. Instead of relying only on specialist development work or multi-step configuration screens, teams can create or upload blueprints describing how a warehouse, transport network, store operation, or broader supply chain process should run.
Those blueprints then become the system of record for operational intent. After review and approval, Manhattan’s AI agents translate the authorised design into live configuration across Manhattan Active applications. As processes change, users can update the blueprint and redeploy the configuration.
Sanjeev Siotia, executive vice president and chief technology officer at Manhattan Associates, said: “As supply chain and commerce grow more complex, the design and configuration phases are often where speed-to-value suffers. Solution Design Studio simplifies configuration by translating it into natural language for business users, helping customers move faster.”
During testing, Manhattan said Solution Design Studio successfully configured the majority of ActiveWarehouse using externally created designs, compressing work that traditionally took months into minutes. The tool is already being used by Manhattan’s own services teams in targeted implementations, with broader customer rollout expected in the coming quarters.
Supply chain software has become more powerful, but operational change often still moves slowly through system design and configuration. Warehouse management, transport management, labour planning, yard operations, and order orchestration are now tightly connected, so a change in one area can affect labour allocation, carrier selection, inventory availability, and customer service elsewhere in the network.
That level of interdependence has made configuration a bottleneck. Operations teams may know which process needs to change, but the route from process requirement to software update can still involve documentation, technical translation, testing cycles, and specialist support. AI-assisted configuration tools are designed to shorten that loop while keeping approval controls in place before changes are deployed.
Enterprise supply chain software is increasingly moving toward AI-assisted operating models. Blue Yonder’s collaboration with NVIDIA on AI model factories and Mecalux’s work on an AI agent layer for logistics software show how vendors are building systems that can support decision-making, configuration, and execution rather than simply storing operational data.
The adoption test will sit around control. Warehouses and transport networks cannot tolerate unpredictable software behaviour, particularly where fulfilment windows, carrier cut-offs, labour planning, and inventory accuracy are tightly linked. Review and approval steps before AI-generated configuration goes live will therefore be central to how users assess the system.
Procurement expectations are also changing. Businesses are not only looking for systems that can execute predefined processes. They want platforms that can evolve as those processes change, especially in retail, manufacturing, and third-party logistics, where customers may require different service models, new warehouse flows, or revised transport rules at short notice.
Configuration speed can become a practical operational advantage where network conditions are volatile. If a warehouse needs to introduce a new picking strategy, prioritise a different order profile, adjust replenishment logic, or redesign transport execution, delays in software change can become delays on the floor.
Solution Design Studio is designed to reduce the distance between operational decision and system execution. Its success will depend on whether it can make expert configuration faster without weakening governance, testing, or accountability. For large operations, that could reduce the lag between process change and system change, a gap that has long slowed major supply chain software programmes.
