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Embracing a true PLM platform and solution is not an easy endeavor for many companies, even with the reckoning of the potential value and ROI offered by a rightly architected PLM solution.  Success in any Enterprise software implementation like PLM often requires careful planning, dedicated resources , right technical expertise, executive sponsorship, and a receptive culture, among other things.  When done the right way the results of such efforts are transformational, producing significant business benefit which can be measured and validated.

One of the biggest challenges to adopting PLM is organizational change management given the breadth and scale of a true PLM solution . Many companies approaches it in phases and rightly so; but the key is how the phases are architected, tracked and measured.  PLM involves managing and linking Data, Processes  and People together as the product goes through it’s lifecycle from inception to design to manufacturing to support and eventually end of life.   The first step of this is often managing Data; specifically Engineering CAD data.  Most solutions start with a way to vault the CAD data along with some basic part numbering schemes and revision rules . Sometimes engineering documents are also vaulted along with the CAD data.   Yes data  vaulted in a central repository brings  lot of benefits like elimination of duplicates , basic check-in-checkout / access controls and  added search capabilities as opposed to it scattered across multiple locations.  But the measured value of this alone may not substantiate the heavy PLM IT investment companies needs to make for a true scalable PLM platform.   Sometimes there is an expectation misalignment on the full PLM value and just the data vaulting value . This at times sends companies to a long and lull “PLM assessment” period  after data vaulting.  Sometimes cultural resistance or organizational change overturns any momentum.  Maybe a technical glitch or integration shortfall previously overlooked becomes a deal breaker . Over-scoped and under supported initiative can also run out of money or time.

Companies make a considerable amount of IT investment on the PLM platform upfront, so that they have a scalable solution for all phases and not just CAD vaulting.  Most of the time they can add more capabilities and processes on the PLM platform without additional IT investments .  So it’s very important to get past the initial data vaulting phase and move to the next phases to maximize the utilization of existing IT investments.  Now the question is where do we go after CAD vaulting. This is where upfront PLM Roadmap definition is so important in terms of  how the phases are architected, tracked and measured.  For companies who have successfully completed data vaulting but do not have a formal PLM Roadmap defined yet, some of the next focus areas to consider can be Engineering process management, BOM Management,  Change management , Requirements management , Project and Program management , in no specific order.

Siemens PLM has introduced lots of new functionality and improvements in the  latest version of Active Workspace 3.3 , the key themes being

  1. User Productivity Improvements
  2. Reduce Information Overload
  3. Configure, Extend, and Deploy
  4. Process Execution and Other Application and Industry Template Exposure

The user productivity improvements are breakdown into three categories.

  1. Improved user efficiency

First focus area for user productivity is  improved user efficiency and proficiency, which is achieved through the use of accelerators such as drag and drop and multiple select to do bulk actions. Some key capabilities are

  • Universal viewer
  • Tab overflow
  • Command stack for analysis
  • Copy and paste hyperlink improvements
  • Drag and Drop Editing structure editing
  1. Enable “Completing a thought” with a single client

Second focus area for user productivity is to enable users to complete a thought with a single client.  Users are enabled to execute complete use cases with just the Active Workspace UI or with a native authoring application and Active Workspace hosted within it.  In the latest version core features and capabilities are extended for targeted use cases. Some key ones are

  • Manage Security in Single Level Projects Hierarchy – multi-select for project security
  • Achieve secure collaboration by applying project security to configured structure content
  • Effectively manage granular access to data in larger programs through hierarchical project level security
  • Assign existing effectivity criteria to qualify what structured content will be configured
  • Define new effectivity configuration criteria
  • Create a baseline of a structure to capture a view of that structure at a point in time
  • Enable showing only the results from a find in context to easily visualize them
  1. Responsive performance

Third focus area for improved user productivity is to make the client perform and respond as fast as possible to user gestures.  In the latest version server calls are minimized to reduce latency sensitivity.  Things like long running reports are run in the background to free up the client and to allow the user to do other work. Some key improvements are

  • Minimize bandwidth and memory usage through virtual paging and streaming of content
  • Minimize server communications and sensitivity to high latencies
  • Efficient execution through journaling, analysis, and tuning

I will introduce the new user productivity improvement features to you in detail through the subsequent blogs

 

This is a further  followup to my previous articles on digital twins, focusing on the Feedback Loop pillar

Smart Factory loop
The feedback loop starts with the Smart Factory. This is a fully digitalized factory model of a production system connected via sensors, SCADA systems, PLCs or other automation devices to the main product lifecycle management (PLM) data repository. In the Smart Factory, all events on the physical shop floor during production are recorded and directly pushed back to the PLM system or through the cloud. Artificial intelligence (AI) technology is used to study and analyze this information, and the main findings are sent back to either product development
in manufacturing planning or facility planning.
Why is this important? Production facilities and the manufacturing processes tends to change immediately after start of production. New ideas will be implemented, new working methods will be deployed and new suppliers might be selected; all requiring changes to the production system or process. Since these modifications will certainly impact the future, updating them in the system at this stage is becoming a must. Production systems outlive the product lifecycle, and many companies use their production systems to make multiple products. These factors contribute to the increasing need to regularly capture these changes in the PLM system, which can later be used to distribute this information to all parties. The information collected during production can also serve as the basis for improving the maintainability of manufacturing resources. With this information, we can enable much better (sensor) condition-based maintenance, and thus increase uptime and productivity.

Smart product loop
Almost every product made today is a smart product. Many companies are looking for ways to improve the connection with their smart products while they are being used by their customers. Monitoring product use can provide a lot of knowledge for improving products. More than that, connecting to these smart products can generate a new type of business model that may result in more competitive offerings.

PLM Challenge

These feedback loops and the data it generate is a challenge for PLM too. In the short term, the PLM issue for digital twins is how IoT-gathered data can best be put to work—extrapolated, parsed, and redirected? To where? At whose direction? The quick and easy solutions are analytics running on the cloud, machine-to-machine (M2M), and analyses based on Artificial Intelligence (AI). Such questions are expected as digital twins emerge as the next revolution in both data management and lifecycle management.
Ultimately, the use of PLM will allow us to bring digital twins into close correspondence—in sync—with their physical equivalents in the real world. When this comes to pass, we can expect problems to be uncovered more quickly, products to be supported. Products with digital twins will be more reliable with less downtime while operating more efficiently and at lower cost. PLM-powered digital twins will boost user and owner confidence in their physical products. Ultimately, digital twins reflect what users and owners expect to receive when they sign a contract or purchase order.

A classic deployment of a digital twin includes three pillars: product design, manufacturing process planning and feedback loops.

  1. Product design

A digital twin includes all design elements of a product, namely:

  • 3D models using computer-aided design (CAD) systems
  • System models (using systems engineering product development solutions, such as systems-driven product development)
  • Bill-of-materials (BOM)
  • 1D, 2D and 3D analysis models using computer-aided engineering (CAE) systems such as Simcenter™ software
  • Digital software design and testing using application lifecycle management (ALM) systems such as Polarion ALM software
  • Electronics design using systems developed by Mentor Graphics

Using these elements results in a comprehensive computerized model of the product, enabling almost 100 percent of virtual validation and testing of the product under design. All of this eliminates the need for prototypes, reduces the amount of time needed for development, improves the quality of the final manufactured product and enables faster iterations in response to customer feedback.

  1. Manufacturing process planning

The Siemens solutions available today enable the development of three models critical to any manufacturer:

  • Manufacturing process model – the how – resulting in an accurate description of how this product will be produced
  • Production facility model – the where – providing a full digital representation of the production and assembly lines needed to make the product
  • Production facility automation model – Describing how the automation system, including supervisory control and data acquisition (SCADA) systems, programmable logic controller (PLC) hardware and software, human-machine interface (HMI) hardware and software, etc., will support the production system

The value of the digital twin in manufacturing offers a unique opportunity to virtually simulate, validate and optimize the entire production system. It also lets you test how the product, with all its primary parts and subassemblies, will be built using manufacturing processes, production lines and automation.

  1. Feedback loops

When it comes to the feedback loops of the Digital Twins pillars , there are two kinds that have a significant impact on most manufacturers

  • The Smart Factory Loop and
  • The Smart Product Loop.

“Product Design” and “Manufacturing process planning” pillars were in existence for  a while but the “Feedback loops” is a newer one. I will discuss elaborately on it in my next blog .

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