With the advance of modern digital tools, organizations are looking at how to transform their activities. For most companies, it starts from reviewing their traditional document-oriented processes and the introduction of new digital tools. The trend is broad and it is obvious for most organizations these days that digital transformation is a matter of survival in a modern digital world. However, a path toward digital transformation is not simple and requires complex changes in the way companies do their business.

My attention was caught by Frederic Zeller’s article BOM or Product Structure? How the concepts have converged. The article speaks about how data management is evolving from representing BOM as a document with a list of items to a better structured and dynamic data set.

The Bill of Materials (BOM) has long been an essential tool in manufacturing, detailing the raw materials, parts, and quantities required to produce a finished product. Meanwhile, the Product Structure outlined the hierarchical organization of a product’s components. However, as modern products have grown in complexity and with the advent of digital tools, there has been a convergence in the two concepts. Today’s BOMs have transitioned from static lists to dynamic, multi-dimensional entities that encompass what was once exclusively termed ‘Product Structure’. Enhanced by Product Lifecycle Management (PLM) software, BOMs now offer a comprehensive view of a product’s hierarchical structure, variations, and configurations. This shift reflects the industry’s demand for a more unified, in-depth, and holistic perspective of products, ensuring better collaboration across various departments. In essence, the distinctions between BOM and Product Structure have diminished, echoing the need for a cohesive and evolving understanding of product design and manufacturing in today’s complex landscape.

I found this transformation important. It is happening with many companies. However, it brings a few important questions as companies are approaching digital transformation.

1. How information about the product structure (aka Bill of Materials) is connected to other business information about customers, business results, contractors, and suppliers?
2. What are the communication interfaces between the modern product structure described in Frederic’s document and other business systems and tools – ERP, CRM, and others
3. How organizations are communicating with other organizations such as contractors and suppliers and using product information (e.g. product structure – BOM)?

Data and Business Transformation

While every company was always run by processes, the way companies are managing processes was continuously transformed over the last few decades. The foundation of all business activities was (and still is) document management. It is a natural result and companies are running their processes using documents. For engineering and manufacturing, it all starts from design, engineering that is released in the way of drawings and passed to manufacturing systems for planning and production. Orders (purchase and work orders) are also largely represented by documents that are saved and sent to people and organizations. While the majority of records are in electronic form, they are electronic copies of paper documents companies were using before.

Digital transformation will change the process I described fundamentally. The problem with documents is that information from these documents is not available easily. Even more, it cannot be analyzed, connected, and interpreted in the way it can help manufacturing companies improve their processes. However, modern businesses are digital, and the only way to make them successful is to build them around data as a first-class citizen (not an afterthought). To make it happen, companies need to think about data first and place data in the center of their business, business systems, and business processes. It will be a step from a current document-oriented paradigm that is used for old fashion processes.

Digital BOM – a new version of a single BOM idea?

The first business systems that changed manufacturing business were MRP systems. MRP 1 and MRP 2 systems literally revolutionized manufacturing companies by providing a single source of truth for planning and ordering materials and components. It was also a place where a BOM that can be used for production planning is managed. The input into MRP was a part list from released Drawings.

Product and process complexity has grown substantially for the last 20+ years. More systems were introduced into the production development process. Multiple CAD systems – MCAD, PCB/ECAD, Software Development tools, Supply Chain Management, MES systems, and others. All these systems introduced some elements of product information. More complex products require a higher complexity the information about these products needs to be available downstream, upstream, and flow between contractors and suppliers. The entire data set represents a gigantic amount of product-related information. Managing this information requires much more robust tools with the foundation of an information model capable to step up beyond a single BOM model in MRP1 or MRP2 systems.

What was before a single BOM managed in an MRP1/2 system is now a much more complex data set and requires data a higher level of sophistication of data management – flexible, granular, and robust to bring all this information in order.

What information models are required to support such a complex set of requirements? I will leave the name of this model to marketing people and today just want to focus on three important capabilities of this model

Granular Data Managament

In order to manage product information and all relationships, systems must step beyond the current document paradigm where data is located in the “envelops” and cannot be available easy analyzed, re-used, and mashed with other data. A part list exported from the CAD system as a document in the past will be represented by a set of granular data objects. Digital tools will allow access to this information based on the process needs and information will be transformed, re-used, and consumed by other tools for the purpose of process management.

An entire product development process will be able to use this information regardless of where it is defined from a product lifecycle standpoint. It includes computer-aided design (CAD) data, product data management capabilities, manufacturing processes, supply chain management, and other traditional data silos. PLM system will become a powerful data aggregator and distributor of data.

Openness and Connectivity

To support processes that connect multiple departments and companies’ digital tools all systems involved in this process must provide robust API mechanisms to connect with the data and enable other tools to get real access to information without complex data export/import tools.

For the last 10-15 years, SaaS, web, and cloud developed a substantial amount of technologies to support API connectivity and mechanisms to get access to data. Therefore, the new SaaS PLM tools can provide much better support to data and product lifecycle allowing data to be independent of software tools.

Graph Model

The question is what information model can be used to manage such complex data structures described above. A single BOM managed by the MRPII system was already complex, but sufficient to manage an entire manufacturing process focused on purchasing and production process. A higher level of complexity of product data, connectivity, and analytics in the supply chain, regulation, and sustainability demands much more complex data representation.

In recent years, the graph information model is fast becoming very popular to manage data complexity in modern data management tools. It is flexible and robust to manage data requirements. At the same graph databases and graph math allows to support sophisticated algorithms such as graph data science to manage complex processes.

What is my conclusion?

The traditional document-centric approach to data management, where information is stored in isolated “envelopes,” is proving inadequate for product information needs in the 2020s. Modern systems are transitioning towards granular data objects to represent specific product details, enabling easy analysis, reuse, and integration with other datasets. For instance, a part list previously represented as a single document exported from a CAD system can now be accessed and manipulated as distinct data objects available in many other systems.

This shift ensures that product development processes can utilize information from any stage in the product lifecycle, seamlessly combining data from CAD, product data management, manufacturing, and supply chain processes. As such, Product Lifecycle Management (PLM) systems are evolving into robust data aggregators and distributors.

However, the success of this new paradigm hinges on robust API mechanisms, ensuring open and seamless connectivity among various digital tools. This is where advancements in SaaS, web, and cloud technologies over the past 10-15 years come into play, offering powerful API connectivity. To manage the increasing complexity of data structures, graph information models are gaining traction, given their flexibility and ability to handle sophisticated algorithms, addressing challenges in areas like supply chain management and sustainability.

What we knew as a BOM or product structure is becoming a graph-based data set managed by robust APIs and becoming available in multiple systems seamlessly. I don’t know how to call this data set – digital BOM or by another name, but the capabilities of this data sets goes much beyond what was included in a simple part list and multiple BOM structures managed in legacy PLM systems developed 20+ years ago.

Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital thread platform including PDM/PLM and ERP capabilities that manages product data and connects manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.

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