The complexity is on the rise in the modern manufacturing industry and industrial companies. There are multiple dimensions of the complexity – (1) product complexity driven by multidisciplinary design including mechanical, electronics and software components; (2) lifecycle complexity with increasing demand for configurations and supply chain, and (3) system complexity brining the need to connect process and product information between engineering, production and maintenance.
With these three dimensions of complexity, the need for effective traceability and digital thread solutions has become critical element of the PLM solutions. Digital thread, connectivity, and traceability are becoming essential for ensuring seamless collaboration, data transparency, and streamlined processes within product lifecycle management (PLM) systems.
The Challenge of Traceability and Digital Thread
In a previous blog post, I explored some examples and ideas about product traceability. Check this out – Traceability between multiple systems: do we need a product model for digital thread? You can see that manufacturing companies are looking for solutions and technology that is capable to support traceability and establish a digital threads in product development, production, supply chain, sales and maintenance.
In my article today, I’d like to explore technical approaches required to address these challenges, highlighting the future role of knowledge graphs and product models in enhancing traceability, digital thread capabilities and connected processes.
I’ve learned that traditional integration approaches often struggle to maintain a cohesive view of product data and processes across multiple systems, leading to data silos and inefficiencies. The simplest example is long standing complexity of integration between engineering (PLM) and manufacturing (ERP) environments with the demand to link between engineering data (parts and BOM created in PLM) with Material Masters and BOMs created in MRP/ERP systems.
The challenges to integrated and intertwine complex enterprise systems are facing difficulties because of lack in the robustness of technical architecture. The example of Part Revisions is probably the best one because it exists in every company. But there are many others. Thank you to my readers brining examples of digital threads like “idea-requirements-engineering” and “order-asset-maintenance” threads. These threads are essential for efficient product life cycle and manufacturing processes.
This challenge underscores the importance of a robust technical architecture to support these critical aspects of PLM.
Technical Architecture for Digital Threads Solutions
To solve the problem of traceability, PLM vendors need to offer a technical architecture that can efficiently manage and connect disparate pieces of data. Possible architectures include data synchronization, various types of links or establishing graph based product model. Earlier in my blog I was talking about graph based models, which I believe can become an powerful foundation to manage digital threads and traceability. Two key components of this architecture are knowledge graphs and product models.
- Knowledge Graphs: A knowledge graph is a data structure that represents knowledge in a machine-readable format. It connects data points and relationships, enabling a more comprehensive view of information. Knowledge graphs play a crucial role in enhancing traceability by providing a unified framework for storing and accessing data across systems. They facilitate the exploration of relationships between various entities, improving decision-making and data discovery.
- Product Models: Product models are representations of physical or digital products, capturing their attributes, configurations, and lifecycles. A well-defined product model is essential for maintaining a consistent digital thread throughout a product’s lifecycle. It acts as a blueprint, guiding the flow of information and ensuring that all stakeholders have access to accurate and up-to-date data.
The opportunity of digital thread product model using knowledge graph is to establish a mechanism for data from multiple silos and systems to be managed in the way that supports consistent traceability and also enhance solution by introducing connected processes. This capabilities can be realized using graph data models and graph databases that provide a robust a powerful way to manage information compared to traditional SQL RDBs used in existing PLM platforms.
From Traceability to Connected Processes
While knowledge graph and product model is a foundation, processes is what driving organizations. It includes a variety of business processes – manufacturing process, supply chain management, engineering and requirement management.
Moving beyond traceability, organizations have to figure out how to organize connected processes that seamlessly integrate data and services between existing organizations and existing systems.. Achieving this level of integration requires a holistic technical approach, encompassing not only data but also the processes that govern product development and lifecycle management. The later means that a consistent data set collecting and organizing data about product with all dependencies is needed to manage one of these connected processes.
The future opportunity is to use new technology to organize connected processes to support product life cycle and product data management.
Connected processes rely on the combination of data and services. Data should be easily accessible and consistently structured, while services should be designed to interact with this data. Through a combination of data-driven decision-making and automated workflows, organizations can streamline their processes and improve efficiency
Implementation of Connected Process
The implementation of connected processes often involves overlaying existing systems with modern solutions. Software as a Service (SaaS) offerings can play an important role in this implementation. SaaS solutions provide a flexible and scalable platform for integrating data and services, bridging the gap between legacy systems and modern architecture.
Digital Thread Solution SaaS can overlay existing systems and to offer a way to enhance existing systems without the need for extensive redevelopment. These services can connect to knowledge graphs, product models, and other data sources, providing a bridge between legacy applications and the modern architecture required for traceability and digital thread capabilities.
It would be an interesting approach to offer SaaS services capable to overlay the work of multiple applications and multiple companies. Modern PLM architecture and data management is a foundation of these future services. Using multiple databases (polyglot persistence) combined with multi-tenant architecture PLM software vendors can leapfrog from existing 20-30 years old PLM architectures and complement them.
What is my conclusion?
The journey from traceability to connected processes for manufacturing companies requires a robust technical architecture. Knowledge graphs and product models serve as foundational elements, enabling organizations to overcome data silos and achieve a seamless digital thread. Implementation is facilitated by SaaS services that overlay existing systems, offering a pathway to modernization. Ultimately, SaaS services for connected processes, empowered by a rich product model, will open the way for improved collaboration, efficiency, and innovation in product development and lifecycle management. Just my thoughts…
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.