Naperlou: You're saying that it's not a difficult task to share engineering and product data with a variety of systems? If so, that's surprising to me and contrary to what I have heard so often from both PLM vendors and PLM practitioners in both engineering and IT.
A core tenet of Autodesk's strategy with its PLM 360 is that the PLM-related applications or services--project management, costing, product portfolio management, engineering change orders, etc.--are cloud-based tools, but the actual product-related data (CAD models, requirements, drawings, etc.) are stored on-premise, in the Vault PDM system. But even if that scenario, it would be critical to sync up to other enterprise systems where data might be stored.
Beth, the idea of being able to interchange information with a variety of systems is an important one. For engineering and product data this is not a very daunting task. Tools like Jitterbit are great for easing the integration task. PLM does not rely on data with complex semantics, as you might have in a high speed transaction or control system. That makes it a more tractible problem, and brings one closer to the goal of having all the information you need to make product decisions.
Robots that walk have come a long way from simple barebones walking machines or pairs of legs without an upper body and head. Much of the research these days focuses on making more humanoid robots. But they are not all created equal.
The IEEE Computer Society has named the top 10 trends for 2014. You can expect the convergence of cloud computing and mobile devices, advances in health care data and devices, as well as privacy issues in social media to make the headlines. And 3D printing came out of nowhere to make a big splash.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.