PLM Platforms, Networks and Value of Data Sharing in Manufacturing

PLM Platforms, Networks and Value of Data Sharing in Manufacturing

In today’s competitive manufacturing environment, data is more important than ever. By collecting and analyzing data, manufacturers can improve quality and efficiency, while reducing costs. Data can also help manufacturers stay ahead of the competition by enabling them to adapt more quickly to changes in the market. In short, data is a valuable asset that should be leveraged to its fullest potential.

World Economic Forum together with the Boston Consulting Group created a paper where they speak about the value of data in manufacturing. Check the article – Share to Gain: Unlocking Data Value in Manufacturing. The potential value of data sharing simply by focusing on manufacturing process optimization has been estimated at over $100 billion.

The article presents several use cases of how production processes, historical process data, data collection, and data analysis can help manufacturing companies improve business intelligence and business processes. Here is a very interesting passage:

Advanced analytics and artificial intelligence (AI) are transforming the world of manufacturing. As data becomes increasingly, important in their factories and supply chains, most manufacturers have applied these emerging technologies within their companies. Yet manufacturers can capture even more value by going beyond their own four walls to leverage data shared across value chain steps and companies. True masters of digitalization not only apply their own data but also improve existing applications by sharing data and applying new ones that would not be possible without data sharing

Sharing data across multiple companies seems to be a big deal. The article made me think about what companies are doing these days to improve practices of data collection, real-time data analytics, data sharing, cloud computing, data strategy, and the ability to access data across multiple companies.

My attention came across Catena-X Automotive Network. This is a pure European initiative outside of software vendor platforms with the goal to connect automotive companies’ value chains. Here is what I captured from their website:

We share the vision of continuous data exchange for all contributors along the automotive value chain. A goal, we will only be able to achieve together. We offer the network and the technology for one of the central challenges of the automotive industry as we believe that innovation emerges from collaboration. With a powerful and holistic system, we ensure the economic viability of all network partners – from small and medium-sized enterprises (SME) to corporate groups. Europe-wide.’

My favorite use case and picture from Catena-X is related to the provisioning of continuous data chains across multiple companies.

While the use case presented by Catena-X is extremely interesting, the implementation can be very challenging. I was not able to find much information on the website about what technologies and specific services will be used to turn the picture above into a real functioning system. The Catena-X network specifically talks about SMB companies and these companies are traditionally have very challenging relationships with PLM technologies in manufacturing companies. PLM technologies in the manufacturing industry mostly were focusing on how to provide a solution for a single manufacturing company and solve the problem of process improvements in a specific manufacturing company. These products were less focused on big data analytics, manufacturing data, and predictive analytics by collecting data from multiple sources and enabling manufacturers to collaborate and share data between multiple companies and the supply chain.

When I was looking for examples, my attention was caught by xML Solutions article written by Marc Young of – Collaboration Best Practices by 3DX. I like Marc’s analysis of data sharing use cases. Here is a great summary of challenges that focus on a single problem – Is it the Right Data?

Typical problems in this multivendor scenario relate to the data itself. Sometimes team members end up designing against the wrong data, overwriting data, or spending time trying to actually find the correct data. And once they find the correct data, how do the team members know it is the right data? Is the correct data being sent or received from the OEM and is it placed in the correct location for the supplier? Answering these questions takes time and resources.

A very interesting observation from the article is related to the limitation of existing PLM technologies in general. The article presents two scenarios when data can be either shared by allowing a lower Tier supplier to access the data of OEM or establishing a complex “gateway” process that will be exchanging data using specific protocols (basically pumping data between companies).

In my view, none of these best practices will provide a sufficient solution to fulfill the needs of cross-tier data communication. Even in the scenario when companies will be able to establish a “gateway process” to pump data between products, these solutions will not provide a data foundation for data collection, analysis and will not create an environment that enables manufacturers to perform predictive analytics of business data to find process flaws and make production processes analysis by focusing on a holistic data journey.

What is the solution in my view? To solve the problem of cross companies data collection and data sharing, manufacturing data analytics, and data-driven manufacturing, companies must bring new platforms that fundamentally architectures for cross-company communication and data sharing. A multi-tenant data management technologies are available, but yet not adopted by the manufacturing industry. There are many reasons for that. Among most challenging are 1/ legacy PLM platforms and solutions; 2/ absence of education and knowledge data sharing technology and 3/ open platforms for data collection, data sharing, and communication.

What is my conclusion?

The value of data in the manufacturing industry is huge and technologies enabling cross-company communication, data collaboration and companies can bring substantial value to the industry. The problem of data sharing is widely recognized, but existing technological gaps are preventing companies from using them in production and expanding them. The problems are related to technologies, product architecture, trust, and adoption. At the same time, there is an understanding among companies that the value can be unlocked by appropriate solutions. The next few years can be critical to bringing technologies capable to meet the demands of the industry and unlocking value. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital network-based platform that manages product data and connects manufacturers, construction companies, and their supply chain networksMy opinion can be unintentionally biased.


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