From flexible data models and free upgrades to autonomous PLM data services

From flexible data models and free upgrades to autonomous PLM data services

Flexibility is one of the most important requirements in product lifecycle management. PLM  debates about flexibility, customization and configuration are decades long. It comes and goes in waves – flexible data models, complexity, customization, configurations, out-of-the-box, templates and repeats again. And I think, there is always an “elephant in the room” when you discuss flexibility of data models and customization in PLM – upgrades. As much as we love flexibility, when it comes to upgrades, everything can come in full stop. Over customized environment stuck in the past and left manufacturing companies with dead investments without ability to move forward.

Aras Corp. came with the innovative business solution – to guarantee free upgrades for customers using Aras subscription. Combined with a vision of fully customized environment, Aras is basically saying to you – you can customize whatever you want, we take care to upgrade yuo to the next level of Aras Innovator as far as upgrade is concerned. You can check CIMdata publication from 2014 explaining how Aras redefines customization and update.

According to recent CIMdata commentary – Aras PLM Platform: Redefining Customization & Upgrades (Commentary) , there is something beyond contract obligation to do a job.

We discovered that there was much more to the story than just a contractual guarantee. Fundamentally, the Aras PLM Platform is engineered to be highly configurable—even customizable—without resulting in expensive, complex and time-consuming version-to-version upgrades and re-implementations. Today, with more than 1,100 upgrades completed by Aras, we have updated this commentary to reacquaint and update the PLM community on this feature of the Aras offering.

At its core, the Aras PLM Platform is an object-oriented, web-based solution that relies on a service-oriented architecture (SOA) with a behavioral modeler that provides a graphical drag-and-drop environment that enables real-time system definitions and alterations that do not require complex programming.

The solution’s behavioral modeler stores the XML solution models (e.g., object model, relationships, business logic, and methods) as normalized data structures in the database, separate from the underlying web services framework

I think, Aras combined two decades of PLM innovation and achieved its perfect technological state. I want to applause Aras. At the same time, I want to remind that each generation of technology goes through an S-curve of development – slow improvement of impractical product, explosive growth and fast improvement and then slowing iteration and refinement as you going to the next plateau. The picture below shows the last piston based aircraft, which was perfect at the time jet engines were still imperfect. Analogy with PLM, first attempts to develop flexible data models and PLM abstractions were done with object databases, then moved into fast development of variety of data modeling engines and finally got to Aras object oriented SOA model-based abstraction, which is great.

The question is what is next? As in each S-curve development, next technology is catching up and (while is still imperfect) can outperform the previous one. And to think about next S-curve, we need to think about drivers that will make a difference and also abstraction (paradigm) for the future change.

In the past, mobile technologies were stuck in the paradigm of PC. A way to think about it was as “mobile” is another type of PC. Nothing wrong, but it didn’t created a new development paradigm. The big shift happened when we started to think about “screen size” as a paradigm. It doesn’t matter if you work on PC or tablet or phone. You just think about screen size and adapt. Therefore best web development techniques are actually made around screen size and not device type. This is how responsive UI technologies were born.

For the last several mouths I’ve been following Oracle Autonomous Database development. If you haven’t heard about it, please check this page. In a nutshell, Oracle is introducing automated way to manage databases based on customer requirements. Think about tuning, performance, scale, availability, failures. According to Oracle, all problems will be gone in the Oracle Database Cloud.

Oracle introduces the world’s first autonomous database cloud. Oracle Autonomous Database Cloud eliminates complexity, human error, and manual management, helping to ensure higher reliability, security, and more operational efficiency at the lowest cost.

This autonomous database cloud integrates applied machine learning to deliver self-driving, self-tuning, self-recovering, self-scaling, and self-securing administration—without human intervention—resulting in streamlined operations, more efficient consumption of resources, and higher security and reliability. With built-in automation at all levels to perform maintenance tasks, companies can now use their valuable IT resources to focus on extracting more value from the data they currently manage.

Oracle autonomous database cloud reminded me an idea I shared few years ago – user driven data models. It was clearly a dream three years ago. However, with new advanced cloud data management technologies, machine learning and AI tools, it is not a science fiction to to think about self tuned, self adapted user driven PLM database.

Making such analogy, you can think about current PLM technology paradigm as “company”. So, each PLM (database) is made for company. This is a place where current flexible data model abstraction is perfect. What can make a difference? Self adapted and self tuned PLM databases driven by massive amount of user requirements from multiple companies is the future. Large scale of requirements will allow to apply new techniques of machine learning and optimization. Going beyond single company paradigm will allow to scale technology, optimize cost and simplify data management processes. Going beyond company abstraction will bring a change and will become a new paradigm for PLM.

Think about PLM for a group of companies, industry, vertical, global manufacturing. What are limiting factors – scale and cost. How to make SQL database to scale? Currently, each database (PLM) requires implementation, solution definition, tuning and upgrades. And each one is individually made. This is where Aras technology is perfect. Now, let’s move forward. Think about 1000s of companies running PLM solutions. To think about it as 1000s database won’t make it efficient. High maintenance and optimization cost. Talk to companies running single tenant data management systems in the cloud. They will tell you more. Also, it doesn’t scale. Check my article about microservices and scaling. Instead of scaling database for a company, future PLM data management technology can scale set of granular PLM services providing solution for 1000s of companies or even more. In such case, cost and optimization can achieve completely different level. Autonomous deployment, configuration and self-tuning can bring this PLM solution to the next level of efficiency.

What is my conclusion? PLM data management technology will move towards autonomous data management service clusters available for companies based on their requirements, industry, partners, dependencies and other needs. These cloud services will be automatically tuned, managed, upgraded and optimized for performance and cost. Autonomous PLM will allow to optimize services for teams, companies and individuals involved into product development and manufacturing . It will allow to scale and also significantly optimize cost of the solution. It is still imperfect, but it is in the beginning. Just my thoughts…

Best, Oleg

Want to learn more about PLM? Check out my new PLM Book website.

Disclaimer: I’m co-founder and CEO of OpenBOM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.


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