Home>Warehouse IT>WMS>Manhattan Associates launches cloud WMS

Manhattan Associates launches cloud WMS

12 June 2020

Manhattan Active Warehouse Management features a redesigned user experience, using a mobile first approach.

The system’s Unified Control screens allow management team members to quickly visualise, diagnose and take action anywhere in their supply chain. For DC associates, the all new WM Mobile application provides an app-based experience for all transactional work.

Composed of microservices, Manhattan Active WM is cloud-native. It is also designed to be easily extended at the data, services and UI level to meet the needs of each business. To speed time to market and reduce training, Manhattan has also incorporated new configuration wizards to help businesses of all sizes and complexities streamline the implementation process.

With Manhattan Active WM, all functions, including labour management and slotting optimisation, have been streamlined and re-engineered to create a single, unified distribution application. 

Manhattan Active Warehouse Management uses machine learning to orchestrate DC automation, human capital and to optimise the execution of work within the four walls of the DC. 

Automation - embedded WES

The solution’s embedded warehouse execution system (WES) also coordinates the work between any combination of automation, robotics and labour, and the Manhattan Automation Network provides pre-certified integration to the industry’s most innovative DC robotics providers.

In a bid to improve employee engagement, Manhattan Active WM was developed using gamification theory and behavioural sciences.

“Manhattan Active WM is the result of a multi-year collaboration with our customers,” said Brian Kinsella, Manhattan’s senior vice president of Product Management. “We’re delivering a WMS which never needs to be upgraded again, yet is still fully extensible. We’re delivering all new modern mobile experiences for every user who logs in. And we’re delivering an architecture which expands automatically as volumes ramp up, and which embeds machine learning right into the core of the application.”