Earlier this week, I was talking to one of our prospects – large industrial company that was looking for a way to organize their ordering system and bring a data management platform that will be capable to organize catalogs, product specifications and orders. The key pain point – hard time to find components, duplications, inefficient process. The internal data management platforms and systems were literally not exist, the data is stored everywhere in separate “databases”, the simplest questions like how many components is there was hard to answer. What was fascinating in this discussion is their interest to explore AI solution and to find a way to interact similar to ChatGPT.
The story caught my attention because it represents an interesting trend I can see in the enterprise PLM implementations – companies are more thinking about data and problem solving rather than about “software” and “platforms”. From my perspective, it is a change in the way companies are thinking about data management and data strategy.
In the world of manufacturing, companies are experiencing pressures to innovate, reduce costs, and deliver products faster than ever before. While these objectives drive business growth and competitiveness, the connection with efficient data management and data integration strategy is not always clear. Business systems are focusing on operation. Data management and data models (and especially PLM software), was considered as an “engineering activities”, but it is going to change. Data is becoming a strategic asset for many companies that impacts the decision making. Combining with the switch of manufacturing companies to a service model, it is an important element in delivering services. Think about companies that renting drones or fleet of complex equipment. For them, access data about products the lease, rent, and maintain is essential.
But focus on data management is hard for manufacturing companies. It is different from setting up PLM to manage CAD files and hope for ERP to do everything else. In my article today, I explore the primary reasons why manufacturing companies are often too busy to think about a data management strategy and the implications of this oversight.
Intense Focus on Production and Delivery
For years, data management was considered as a “support” and non-essential activity. Manufacturers operate in an environment where production schedules and delivery timelines are paramount. The primary focus is on keeping the production lines running smoothly and ensuring that products reach customers on time. While ERP with operation planning and supply chain was the focal point, a broader data management and PLM activity focusing on a comprehensive knowledge about the product was considered a second priority. This leaves little room for contemplating and developing a comprehensive data management strategy, which is often viewed as a non-critical activity rather than a foundational pillar of operations.
Complexity of Manufacturing Operations
Let’s be frank. Data management is complex topic. Thinking about data management structures, the way revision control is coordinated with effectivities, supply chain, and many other data-related topics is hard. Engineering combined with manufacturing processes are inherently complex, involving numerous stages, from design and prototyping to production and quality control. Each stage generates vast amounts of data, including design data, additional specifications, bill of materials, process parameters, and quality metrics. Managing this data strategically requires sophisticated systems and integration that need resources and time which many companies find challenging to allocate amid their core operational activities. Thinking about connecting multi-view BOM strategy can be too hard and solving the problem by sending a bunch of Excels seems like a pain relieve. But unfortunately, problems will come later and bring huge pain of cost, mistakes, delays, and other problems.
Legacy Systems and Fragmented Data Sources
It takes long time to implement traditional data management systems. PLM is known for long implementation cycles, customization and complexities. As a result, many manufacturing companies rely on legacy systems that were not designed well for modern data management needs, don’t integrate well and complex to maintain. No one really wants to touch those systems. They systems often operate in silos, leading to fragmented and inconsistent data across the organization. Developing a cohesive data management strategy necessitates significant investment and potential disruption, which companies are often reluctant to undertake due to the immediate impact on their operations.
Resource Constraints
Manufacturing firms often operate with lean teams focused on maximizing efficiency and minimizing costs. Data management tasks, such as data cleansing, integration, and governance, require specialized skills and dedicated resources. However, hiring and training personnel for these roles can be seen as an additional burden, leading companies to prioritize immediate production needs over long-term strategic data management planning. I can see some improvements in this direction and companies are strategically focus on “data strategy”, but it is still very under-funded activity.
Lack of Awareness and Understanding
There is often a lack of awareness and understanding among manufacturing leaders about the importance and benefits of a well-defined data management strategy. The tangible benefits—such as improved decision-making, enhanced product quality, and greater operational efficiency—are not always immediately apparent. Consequently, strategic data management initiatives may not receive the attention and funding they deserve. When I’m hearing a demand for “chat GPT-like” functions in enterprise data management, it makes me feel better. Because I know that it will push companies to recognize the need to learn more about data and data management.
Rapid Technological Advancements
The manufacturing sector is experiencing rapid technological advancements, including the adoption of Industry 4.0, IoT, and advanced automation. While these technologies promise to enhance productivity and efficiency, they also introduce new data management challenges. Keeping up with these advancements requires continuous learning and adaptation, which can be overwhelming for companies already stretched thin with their day-to-day operations.
Short-Term Focus
Manufacturing companies often operate with a short-term focus driven by quarterly performance metrics and immediate business outcomes. This focus can make it difficult to justify investments in a long-term data management strategy, which may offer substantial benefits but require upfront costs and time to implement. Back in old days, PLM implementation was taking long time – from several months to a year+. These things are changing now by introducing new cloud-native solutions.
The Way Forward: Embracing Cloud-Native SaaS PLM and Data Management
To overcome these challenges, manufacturing companies need to rethink their approach to data management. A modern data management technologies and tools combined with cloud-native solutions, provide a pathway to more efficient and effective data management strategies. These solutions enable companies to leverage the power of the cloud for scalable, flexible, and integrated data management without the need for extensive on-premises infrastructure.
What is my conclusion?
The role of data is changing. The questions about bringing AI and improving decision supports will lead companies to think more about data and how data can be used to improve the quality of products, reduce risks of manufacturing and supply chain, and improve maintenance and services. While manufacturing companies face significant barriers to thinking about a data management and product lifecycle management strategy, the benefits of overcoming these challenges are substantial. By investing in modern, cloud-native data management solutions, companies can unlock new levels of efficiency, innovation, and competitiveness. It’s time for manufacturing leaders to recognize the strategic value of data, to bring data management tools and make it an integral part of their operational excellence. 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.
