Structure and Models of Product Data for Connected Processes in PLM

In my last article about End-to-End business processes, I share some ideas about the importance of establishing connected digital processes as a foundational step in digital transformation. Siloed systems and processes is a bad idea when you look how to improve the organizational performance. It is even worst idea when you think about how to coordinate multiple organizational efforts related to the product lifecycle from the earlier ideation/design to production assembly and future maintenance.

The goal of end-to-end processes is to enable seamless collaboration and information sharing among various stakeholders involved in product development, manufacturing, supply chain management and support. The foundational piece of effective process system is the manufacturing organization is how to structure and organize product data.

I want to mention a very interesting post and discussion with Prof. Dr.-Ing. Martin Eigner about New Software Technologies for PLM systems. Check this out. It has great perspective. Here is a passage:

My opinion is that software technologies of the 90s are no longer relevant for future PLM systems (see my comment to Jörg Fischer). I would like to illustrate this using the example of the Digital Thread and Multi-BOM. My extended View on Digital Thread is the connection of the configuration items, i.e. the information elements to be considered in the event of a change and their affiliation to engineering processes, for example engineering change or quality management. The main objectives are to support the reconfiguration (according to EN DIN 9000/9001) in the event of damage and to support change management to determine the affected items and processes (Fig. 1). The Digital Thread is a highly networked graph.

The discussion with Prof. Dr.-Ing. Martin Eigner, the earlier discussion with Prof. Dr. Jörg W. Fischer and the work I do at OpenBOM was the inspiration to share more thoughts about structured product data management.

In this article, I will share my thoughts about how to manage product data, what options and best practices exists today and what can be future perspective on organization of strctured product data in manufacturing organization. I will also brieflky touch options company have today to manage product data using exisitng PLM products and how it can support decision making and process orchestration.

Product Development Ecosystem and PLM Holy Grail

Before we dig into the structure of product data, it’s important to understand the broader PLM ecosystem and how it organized in every company. A typical PLM ecosystem comprises various stages, including ideation, design, engineering, manufacturing, quality assurance, and maintenance. Each of these stages involves different teams, tools, and data types.

For many years, PLM holy grail was to build a single system that can absorb and manage information across product lifecycle stages. While it is very appealing goal, the realization of this goal is not practical. Multiple systems, departments and organizations are using different systems and an opportunity to bring everyone “on the same system” or even worst (to use one database) is loosing even its strongest opponents in 2023.

What seems to be really important is to step beyond systems and focus on the data that describes products and all their relationships. Shifting focus from applications to data is important and getting traction in many organizations. Combined with cloud web services and modern data architecture, it can be a foundation of future product data structures to support companies and end-to-end processes.

Structured Product Data

Structured product data is the backbone of a product development and manufacturing processes. In the modern ecosystem, the data is scattered between multiple data management systems such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationships management (CRM). They might use different data management technologies, but combining all information related to a product, including its design specifications, materials, manufacturing processes, quality standards, and maintenance procedures.

To effectively manage this data, it must be structured in a way that is consistent, accessible, and easy to understand. Here are some key aspects of structured product data in product development and manufacturing:

1. Bill of Materials (BOM): A Bill of Materials is a hierarchical list of all components, sub-assemblies, and materials required to manufacture a product. It serves as the foundation for product data in PLM, helping define the product’s structure and relationships between various parts.
2. CAD Models: Computer-Aided Design (CAD) models are essential for visualizing and representing the product’s physical and geometric characteristics. These 3D models provide a visual representation of the product, aiding in design, analysis, and manufacturing.
3. Specifications and Requirements: Detailed specifications and requirements documents outline the performance criteria, materials, and quality standards for the product. These documents guide the design and manufacturing processes and ensure that the product meets customer expectations.
4. Change Management Data: PLM systems must track changes made to product data throughout its lifecycle. This includes change requests, approvals, and records of all modifications, ensuring transparency and compliance with regulatory standards.
5. Manufacturing Process Data: Information related to manufacturing processes, such as work instructions, routing, and quality control plans, is crucial for ensuring efficient production and high product quality.
6. Supplier and Vendor Data: PLM systems may include data about suppliers and vendors, such as sourcing information, pricing, and lead times. This data is critical for making informed decisions about sourcing components and materials.
7. Maintenance and Service Information: For products that require ongoing maintenance and service, structured data regarding maintenance procedures, spare parts, and service manuals must be readily available to support post-production activities.

So, the question is what to do with the data stored in multiple systems and what is needed to develop end-to-end connected processes?

Product Data Management Practices

In the past 15-20 years, the ideas of integrated product data management technologies were slowly shifting from a “single system” paradigm to “integrated data management platforms”. Although the idea of a single database is transforming into the idea of single vertical integrated management platform, many companies are struggling with implementation of a single vendor data management stack for product development. You cannot realistically rely on Siemens, Dassault or PTC PLM data management stack for everything. And this is not a good idea to build a future PLM strategy using technologies and platforms developed back in 1990s. All companies trying to do so today, must be on a high alert of failure because these technologies won’t scale for complex data sets and connected integrated process management.

Data integration practice was one of the main tools to connect systems and processes in the last two decades. There are different flavors of data and system integrations. The lowest level of these practices is to organize data synchronization and pipe data from one system to another. It is not efficient practices, but it is currently used by majority of implementations. It is simple and easy to implement, but it ends up as a mess that needs to be managed. Still one of the most popular.

Another practice is organization of centralized integrated event driven “hubs” that can perform more granular data integrations between systems. The advantages of these hubs is their event driven process organization combined with more granular data management practices. Most of advanced integrations are using these techniques.

A growing trend in data integrations is switching to REST API and web service data integrations. These technologies are much more reliable and easy to implement. But these technologies must be accompany by data modeling best practices and data modeling technologies that can make them reliable and semantically rich to provide a foundation for integrated end-to-end processes.

Data Architecture for 2030s

Over the last 20 to 30 years, the PDM and PLM industry went a long way towards improving data management and developing scalable platforms. The data management architecture of these solutions goes back to the time when PDM/PLM developers didn’t trust and couldn’t rely on commercial database products. Therefore, early PDM and PLM used proprietary solutions, developing a variety of data stores using file formats, embedded databases and management tools. However, the end game of these experiments with proprietary data management tools was to switch to industry standards adopted by large manufacturing companies. The decision was not only technical but also political. IT oversaw technology adoptions in a company, and PDM/PLM needed to pass muster. This is easier to do if you run on top of the industry standards such as IBM, Oracle and Microsoft.

Here is what I can see a future of data modeling for PLM strategies in 2030. The databases are becoming a tools. Similar to polyglot programming that ended the debates about “best programming language”, modern polyglot persistence architecture will end the debates about “best database for PLM”.

The important question remains is what is the informational model for connected product data that can be a foundation of scalable data organization. I think, graph models and data organization has a strong potential to become one. I wrote about it in my previous articles. Check this out

The importance of knowledge graphs for future PLM platforms

Why Graph Knowledge Model is a Future of PLM and Manufacturing Processes

Why do you need Graphs to Embrace PLM Complexity

The ability of graph models to merge information without reformatting data is very strong and can be used for building modern integration stacks between different data representations. How you can combine two Bill of Materials together (EBOM and MBOM) from two different systems and run queries on both data structures? Graphs gives you the answers to make it happen.

Graph models are a source of intelligence that is changing the way PLM, ERP, CRM, and other applications can provide a unique value to manufacturing companies by introducing intelligence and insight about data and processes. The technology used for graph and network modeling is transforming PLM architecture and becoming the foundation of new intelligent applications.

The next very promising step here is to use graph knowledge model for creation of LLM to support AI driven applications and process. Read my article about PLM AI advancements, LLM and co-pilots.

The future step for PLM can be developing Graph Centered Applications that can use modern graph data model to support connected business processes. You can find examples of these applications developed by vendors and service providers. Check my earlier articles – PLM analytics and graph data science.

Structured product data benefits

Organization of structured product data using modern data modeling concepts and technologies can provide many benefits to manufacturing organizations and product development processes. Here is a short list:

1. Collaboration: It facilitates collaboration among cross-functional teams by providing a single source of truth for product information. Teams can access accurate, up-to-date data, reducing errors and miscommunication.
2. Traceability: Structured data enables traceability throughout the product’s lifecycle, making it easier to track changes, monitor quality, and ensure compliance with regulations and standards.
3. Decision-Making: Access to well-structured product data empowers decision-makers with the information they need to make informed choices, whether related to design changes, manufacturing processes, or supplier selection.
4. Efficiency: Streamlined access to product data improves efficiency in design, manufacturing, and maintenance processes, reducing lead times and costs.
5. Regulatory Compliance: PLM systems with structured data help organizations meet regulatory requirements by maintaining comprehensive records and documentation.

Focusing on these benefits, manufacturing companies can build a strategic planning for using modern technologies for data management and organization structure product data to support end-to-end business processes.

What is my conclusion?

Structured product data is the lifeblood of connected processes in PLM. It provides the foundation for effective collaboration, decision-making, and traceability throughout a product’s lifecycle. By organizing and managing product data in a structured and consistent manner, organizations can optimize their product development processes, improve product quality, and enhance their competitiveness in the marketplace. As technology continues to evolve, PLM systems will play an increasingly pivotal role in driving innovation and efficiency in product development.

Using modern graph based data models for organization of structured data can provide many benefits and provide a foundation for engineering and manufacturing process data management. This is data model and data services that can be used by all software tools starting from enterprise resource planning systems and product lifecycle management (PLM) systems to provide real time data management for manufacturing companies and engineering teams. This approach will preserve current investments in enterprise PLM and ERP systems and allow to achieve faster ROI in digital transformation.

Just my thougths…

Best, Oleg

PS. I will continue the discussion about structured product data, usage graph models and new integration architectures for BOM management and future of AI in PLM. Stay tuned…

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital-thread platform with cloud-native PDM & PLM capabilities to manage product data lifecycle and connect manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.