Sikorsky Aircraft and Joy Mining Machinery – both early users of Right Hemisphere 5 Deep Server enterprise media collaboration software – say it’s improving the quality and speed of decision-making among designers, managers, suppliers and customers.
The new software release features a robust media asset management component that can merge and manage product information from disparate enterprise systems, including PDM (product data management), ERP and CRM (customer relationship management) systems to support a model based definition (MBD) engineering communications approach.
Says Dan Armour, technical services manager at Joy Mining Machinery: “We needed to produce our technical publications faster while simultaneously bumping up the quality of these publications. Deep Server promises to accelerate our content creation process by automatically reusing existing CAD data.
“By making it faster and easier to increase the amount of 3D and interactive content in our maintenance training classes, we can also reasonably expect these documents to be much more effective at transferring knowledge to our service personnel and customers.”
“We’re also very interested in Deep Server’s ability to publish product graphics and other data, such as product manufacturing information, into a secure 3D Adobe PDF file. Being a global company with offices and customers around the world, this is a particularly attractive option for communicating with our customers and suppliers without compromising intellectual property by sharing actual CAD data.”
It’s a similar picture at Sikorsky Aircraft, where Dave Cocuzzo, manager of the Engineering Process Transformation Group, believes Deep Server will increase design velocity.
“We’re expecting the software to generate lightweight models and 2D drawing views on-demand that can be scrutinised and interacted with down to the part level by our cross-functional teams,” he says. “Ultimately, we’re looking to further enable the MBD process by facilitating the enterprise-wide consumption of digital engineering datasets.”