How to prevent cloud PLM integration mistakes

Cloud is huge enabler for collaboration between people. It makes your data and processes accessible everywhere from a browser. It can help you to collaborate between engineers and suppliers. It can help you to integrate systems and people across enterprise.

Let me speak about the last one. The integration topic is actually tricky. I’ve been sharing some of my thoughts about cloud integration challenges – Integration is holding back PLM cloud adoption few months ago. Last week, I had a chance to attend two webinars about PLM and integration.

Become a Connected Manufacturing Enterprise with Agile Integration by Jitterbit webinar provided a deep insight and shared customer experience about integrating Autodesk PLM360 with other systems. The webinar recording is here. The following picture gives you a perspective on a problem of “connected manufacturing” and architecture solutions like Autodesk PLM360 and Jitterbit are solving this problem.

Here is the view that shows you the reality of mixed (cloud and on-premise) integrations.

Another webinar by CIMdata – “PLM & ERP: What’s the Difference, and Why Should you Care?” is providing another perspective on integration challenges between engineering an manufacturing.

Companies are moving into mixed cloud and on premise environment. This is a reality and we cannot avoid it. So, for a foreseeable future, we will have to deal with integration of multiple systems – some of them will continue to run on premises and some of them will be elsewhere (public cloud). It made me think about potential mistakes you can run into while integrating systems.

1- Lost data semantics 

Most of integration scenarios are about how to send data back and forth between systems. It is hard to keep semantics of data and not to loose it when exchanging information. So, define what data means and keep an overall integration data schema. Otherwise, the result can be messy.

2- Data transfer limitation 

Although some of integration infrastructure can allow you to implement data exchange quickly, you can underestimate the bandwidth requirements. Sending large packets of data can cause significant latency and create runtime errors and problems. Check what monitoring tools are available to handle such situations.

3- Transaction management 

Most of manufacturing systems are sensitive to transactions. To manage distributed transactions can be tricky and require some fine tuning. Pay attention on how you handle error processing when integrating transaction system managing ordering, lifecycle and bill of materials.

What is my conclusion? The complexity of integration is growing. Cloud systems are bringing many advantages, but will create additional challenges to IT and professional services. Most of integrations are not working out of the box. New tools running from the cloud can help you to integrate it faster, but it will require good coordination with IT and planning upfront to prevent potential mistakes. Data integration is hard and requires experience and familiarity with manufacturing systems. Just my thoughts…

Best, Oleg

photo credit: freefotouk via photopin cc