Managing Information in PLM: The size and complexity of products, coupled with the scope of PLM (engineering, manufacturing, requirements, MBSE, quality management, etc.), mean that a PLM system must handle enormous quantities of information. This includes both business objects and the relationships between them, varying based on the granularity of the information.
Graph Theory Application: Graph theory shoul be considered for this management but must be carefully studied and adapted. The PLM business objects are complex, polymorphic and subject to revisions, while the relationships are strongly typed, carry applicability information (like dates or batch numbers), and require interpretation for optimal navigation among objects.
Digital Thread Management and Impact Analysis: Managing digital continuity and analyzing the impact of modifications necessitates a reasoned navigation through the graph of relationships. For graphs with tens of thousands of objects and hundreds of thousands of relationships, this task is nearly impossible for humans without powerful filters.
If filters are static, we revert to « classical » views (requirements, MBSE, engineering, manufacturing, quality), which diminishes the benefits of graph theory.
Dynamic filters, defined according to the context or type of change, enhance search efficiency and navigation. In such cases, AI should be strongly considered, as it can provide a holistic and comprehensive view of digital continuity and the impacts of modifications.
Conclusion:This is where artificial intelligence (AI) plays a crucial role. AI can provide a holistic and exhaustive perspective on digital continuity and the implications of modifications, something indispensable in complex PLM environments. The integration of AI in PLM, particularly in conjunction with graph theory, transforms what would otherwise be an overwhelming influx of data into a structured, navigable, and insightful resource. Graph theory alone, albeit useful, achieves its full potential only when complemented with advanced AI tools, making it an indispensable asset in modern PLM strategies and impact analyses.
Implementing a Product Lifecycle Management (PLM) solution involves a multi-disciplinary approach that requires various key roles, a strategic methodology, and meticulous execution to ensure that the solution effectively supports the organization’s objectives.
Here is an outline of what I recommand:
1. Key Roles: Solution Architect, Business Consultants, Functional Consultants, and Technical Engineers
The overall success of a PLM implementation largely relies on the synergy of various key roles, which include:
Solution Architects: They are responsible for guaranteeing the global coherency of the implemented solution. They provide a holistic view, making sure that all parts of the implementation align with the organization’s overall strategy and objectives. The Solution Architect translates business requirements into technology requirements (data model, configuration rules, and processes) and defines the overall PLM architecture.
Business Consultants: They bridge the gap between the organization’s operational needs and the technical solution. They understand the business operations in depth and help translate these requirements into actionable implementation strategies.
Functional Consultants: They are responsible for configuring the PLM software to meet the organization’s needs as outlined by the business consultants. They understand the software’s capabilities and limitations, and work towards creating an optimal setup.
Technical Engineers: They execute the implementation plan, handling software installation, integration, and support.
2. Hybrid Methodology: V Cycle and Agile
My belief is a hybrid of two proven approaches – the V Cycle and Agile.
The initial phase of the project utilizes the V Cycle method to establish the core model and the common foundations of the solution. This model includes defining and understanding the business requirements, designing the system architecture, and developing test strategies. This methodology helps ensure that we develop a system that is not only technically sound but also addresses all the operational needs of the business. The main objective of this core model is to ensure the overall consistency of the solution, to guarantee overall digital continuity (i.e. digital thread). During this phase, the use of a System Engineering approach can be particularly effective.
Once the core model is established, we switch to an Agile approach for implementing user features. This iterative method allows for continuous delivery of small, incremental changes based on user feedback and testing. Agile promotes flexibility, encourages collaboration, and helps manage changing priorities effectively, leading to an overall better fit solution for the organization. During this phase, the Solution Architect continues to oversee the process, and update the core model, ensuring the overall coherence of the solution.
A fundamental principle of today’s PLM implementation methodologies is to limit, and better avoid, custom developments and instead maximize the use of the parameterization capabilities of the implemented software. This approach not only reduces the time and cost of implementation but also makes the system easier to maintain and upgrade.
Custom developments can introduce complexities into the system, making it difficult to upgrade or adapt to changing business needs. On the other hand, parameterization allows for flexibility and scalability, enabling the system to evolve with the organization. Hence, we strive to utilize the software’s existing capabilities to the fullest extent and only accept custom developments when absolutely necessary.
By following this structured approach, we can ensure a smooth, effective PLM implementation that is tailored to customer organization’s needs and easy to maintain in the long run.
4. Working in a Closed Loop
Each business or functional requirement should be benchmarked with the capabilities of the software. This process allows us to quickly identify and close any gaps, preferably without the need for custom development. Whenever possible, gaps should be closed through parameterization or reformulation of the requirements. It’s important to note that requirements are often expressed as solutions and can often be reformulated in a more effective manner. This nevertheless requires a perfect understanding of the customer’s processes in order to be a credible force of proposal.
By adhering to this approach, such a PLM implementation methodology is designed to deliver an effective, sustainable solution that can evolve with customer’s organization and deliver maximum value from his investment.
