For many years, CAD/PLM – ERP integration was a topic that triggered many questions. As you probably note, I put “CAD/PLM” and not a traditional “PLM”, because for many companies the integration is translated into “engineering to manufacturing” process. If I go back a few decades, the traditional engineering process will be presented as a way to “release drawings” and through the over the wall of manufacturing. I can see a manual entry of BOM from drawings to MRP system as a roots of what we call today “PLM-ERP integration”.
The data management mechanisms were evolved on both sides – engineering and manufacturing. New tools were added for design, companies moved from 2D to 3D, data management evolved to support more complex use cases, configurations, design suppliers, contract manufacturing, supply chain. I’m sure you can add more, but even without that, it is clear that data and processes were evolving, which increased the tension alongside PLM-ERP border.
PLM-ERP integration is getting more complex. What we can do to make it more seamless, reliable and, most importantly, supporting business processes of the 2020s.
Why PLM & ERP is getting more complex?
PLM & ERP integration is a task that often feels like solving a jigsaw puzzle. Why connecting engineering to manufacturing is getting more complex then ever? There are three main reasons for that and all of them are related to complexity of engineering and manufacturing.
- Product complexity is growing
- Process complexity is growing
- Organizational complexity is growing
On top of the growing complexity, the main reason for PLM and ERP integration challenges is although both represent the two complementary parts of manufacturing word, they ware different in the way they organize information and processes. While both systems are essential for managing different aspects of a product’s lifecycle, their data management needs and structures are not the same and requires “mapping” for the lack of better word.
The key differences lie in how these systems manage object lifecycles and structures, as well as the logical conflicts that arise from business process rules related to change management, manufacturing, and supply chain planning. Therefore, mapping data between PLM and ERP systems is not just a technical necessity but a critical step for successful integration.
The Challenge of Mapping PLM and ERP systems
Mapping data between PLM and ERP is no easy feat. The first illusion of many engineers and operational managers that they can send data from their design (or engineering) such as CAD files and BOMs to ERP. While it is technically possible, it is just a tip of the iceberg and its best will map attributes of CAD file to some item master property. In the long run will bring you to the dead end. Will talk later about it.
A detailed mapping of both systems is much more complex. It requires extensive preparation and a deep understanding of the details involved in both systems and, most important, processes. Rather than focusing solely on mapping of attributes, it’s more effective to discuss data modeling—a process that allows the creation of a cohesive data set explaining the relationships between two distinct data management systems and processes. In the case of PLM and ERP, we’re dealing with extremely complex data sets, and this article will delve into the critical elements of the data model needed to integrate these systems effectively.
7 Essential Elements of a Data Model for PLM & ERP Integration
Product Structure:
The product structure forms the backbone of data management in both PLM and ERP systems. However, the way it is managed can differ significantly between the two. In PLM, the product structure focuses on the engineering view, capturing every detail necessary for design and development. In contrast, ERP systems manage the product structure from a manufacturing and supply chain perspective. Effective integration requires a data model that can harmonize these views, ensuring consistency across both systems.
Revision Control:
Revision control is fundamental in managing engineering changes within a PLM system. It helps in tracking and finding the correct product structure at any given point in the lifecycle. When integrating with ERP, the data model must ensure that these revisions are accurately reflected in the manufacturing and procurement processes, allowing seamless updates and preventing discrepancies.
Effectivity Control:
Effectivity control, often based on dates, is a crucial rule in ERP systems used to include or exclude components in the product structure. The data model must account for these rules to ensure that the product configurations sent from PLM to ERP are accurate and up-to-date, avoiding potential conflicts during manufacturing.
Configuration Dependencies:
Configuration management is essential in defining what product configuration is transferred between systems. PLM systems manage complex product configurations based on design and engineering requirements, while ERP systems focus on manufacturability and supply chain constraints. The data model must capture these dependencies to ensure that the right configurations are available in both systems.
Product Portfolio Dependencies:
Product portfolios often span multiple products, with rules applied across various models and variants. While these dependencies are primarily managed in PLM, they also impact ERP and sales systems (such as CPQ software). The data model should allow for the application of these portfolio rules across systems, ensuring consistency in product offerings.
Vendor and Supply Chain Structures:
ERP systems typically manage vendor and supply chain structures, but these structures also impact the information used in PLM systems, particularly in the context of sourcing and procurement. The data model must bridge these structures, ensuring that supply chain information flows seamlessly between PLM and ERP.
Production & Maintenance Traceability:
As products move through manufacturing, deviations can occur, leading to differences between the Manufacturing BOM (MBOM) and the Engineering BOM (EBOM). Additionally, maintenance activities may introduce further changes, often involving different vendors and components. The data model must accommodate these traceability requirements, ensuring that both PLM and ERP systems are aligned and up-to-date.
What Data Model Is Needed to Integrate PLM and ERP?
Given the complexity outlined above, the data model required to integrate PLM and ERP systems must be robust and flexible. It should be capable of handling the relationships between objects in a way that allows for different attributes to be used across systems. This flexibility is key to managing structure and ensuring that both systems can work in harmony without compromising data integrity. Another critical element of a successful data model is its ability to support flexible data processing logic. Different processes and rules in PLM and ERP systems require a model that can adapt to various scenarios, ensuring that data flows correctly between systems without losing its context or meaning.
To sum up, those are three elements of data modeling service that can help you to integrate PLM and ERP driven processes:
- Flexible data organization, including mechanisms to define objects with different property sets
- Connections allowing to create relationships between data objects with different semantics
- Flexible business logic allowing to run different transformations of data based on rules
Once you get these data organization you can map PLM and ERP processes. You can get the above technologies in multiple ways by hiring engineers, using a combination of integration software or some toolboxes capable to manage databases and business logic. After two decades of experience, I think, I’ve seen so many great (and not) tools and techniques that helped to map and integrate PLM and ERP. But these there characteristics were the same for most successful ones.
What is my conclusion?
Integrating two complex systems like PLM and ERP is not a task that can be scripted overnight. It requires a systematic approach, starting with a robust data model capable of mixing, merging, and processing data from both systems. In future articles, I’ll explore different data integration approaches and discuss how to choose the right one for your specific needs. Bridge the gap between PLM and ERP in a modern manufacturing world is becoming more challenging than ever. The main reason for that is growing complexity. Therefore, it is becoming super critical for all manufacturing companies to realize the level of complexity and not trying to dumb it down to spreadsheets transfers. Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I’m advocates for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased.
