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Old habits die hard. Although technology has made leaps and bounds in recent years, many manufacturing companies are still using the same processes that were implemented decades ago. Mainstream product lifecycle management systems were designed 20-30 years ago. and they are not capable to support modern business models, organizing business processes, help to supporting data sharing and data exchange. This can be a major cause of inefficiency and can lead to missed opportunities. However, changing these old habits can be difficult, as they often become ingrained in company culture. It can be tough to break old habits, especially when they’ve been a part of your routine for years. But sometimes it’s necessary to make changes if you want to continue growing and improving as a manufacturer.

Working for many years with manufacturing companies, implementing PLM systems, and advising both software vendors and industrial companies, I found some data management habits very typical and dangerous at the same time. In this post, we’ll discuss some of the most common bad data management habits I had a chance to see in manufacturing and how to overcome them. I also want to talk about important principles of future PLM and how PLM software vendors can support business transformation and modern manufacturing business models.

Engineers and “Old Habits”

I honestly believe that engineers are the most innovative people in the world. However, I found that when it comes to data and process management, engineers have a strong inclination not to change some of their behaviors and attitudes that were formed over years (or even decades). I found such data management ‘habits” very interesting and went back to some old ways of computer work and data management tools. I found it as a mix of engineering reluctance to change as well as protection of their individual working environment and processes. Some of these habits go back to the time when engineering was more isolated from the rest of company activities. In such a way most product development processes implemented are known “through over the manufacturing wall” methods. The old data management habits were reinforced through repetition. And these habits are really self-reinforcing.

While manufacturing companies are discussing their digital journey and how to remain competitive in the modern manufacturing world, it obviously raises many questions about data storage, data quality, coordination of development teams, modern customer experience, how to bring products to the market faster, and how to engineering and manufacturing innovation.

5 Typical Data Management Old Habits

Working with many companies I found these three data management habits (or best practices) very prolific and widely used by many engineers and industrial companies. Coming to companies you can usually see signs of typical old data management best practices – legacy databases and old solutions, tons of Excels, shared folders. A typical PLM marketing slaps Excel, spreadsheets, and legacy PDM/PLM solutions as a core of the problems. Although these are problems, I think it is important to understand why these habits a bad from the analysis of data management principles. Here is my take on 5 bad habits:

1- Organization of data silos

An absence of a good data management strategy brings silos. Engineers are placing their files in a variety of storage solutions. By itself, each of them can be an efficient way to store files. It can even be a PDM solution coming together with a CAD system. The last can be actually not that bad. But then, multiple Excels are transferred between other databases used for manufacturing, procurement, etc. No one is thinking about information flow and only trying to ensure that the data they need is located with them and protected.

2- Wrong data

The outcome of #1 is the fact people in silos are using the wrong data, which is not updated on time or mistakenly transferred with some wrong data inside. Although for engineers and other people it sounds like a natural thing to “send a report” or “to export data, zip it and send it” and it solves the problem, but long term, the number of decisions taken is based on the wrong data is growing. Last-minute orders, contract manufacturer mistakes, production orders made based on the wrong information are accumulated. But no one is actually counting it and connects to wrong data management habits.

3- No focus on data quality

Every $1 invested in data quality can easily cost $10 or in some extreme cases up to $100 to fix the problem. Duplicated BOMs, missed information about parts needed for ordering, data inconsistency between multiple databases and multiple Excels. How many times, you’ve been trying to figure out why two Excels contain conflicting data.

4- No easy way to access data

Everyone needs to get access to data, but no one wants to do data management. This is a reality of many engineering and manufacturing organizations I worked with. Data locked in disk drives, Excels, private databases, and archives – these are just top examples, but there are many others. In addition to that, a high level of complexity of mainstream PLM systems creates another barrier to everyone in the company, partners, and suppliers to get access to updated information.

5- Confusion between data governance and data management

For many companies, the border between data governance and data management is blurred and companies are not making the right decisions. If the only tool you have is a hammer, you tend to see every problem as a nail. Translating it to the world of PLM you can see that if the only tool you have is Excel, then don’t expect good data governance. I will talk about it in my conclusion.

Digital PLM Future

So, how to move from old and bad engineering data management habits in the digital PLM future where seamless, everyone in the company, as well as contractors and suppliers, have seamless access to the right information when they need it.

To achieve a digital PLM future, manufacturing companies need to focus on solving two separate problems – data management and PLM governance.

The first is data management. It is about how to bring the next generation of data management systems capable to cope with the complexity of modern manufacturing and data. It includes a focus on data capturing, preparation, cataloging information, automation of data loading and indexing, the architecture of a seamless data flow, focus on data security.

Second is PLM governance, which solves problems of who owns the data, how everyone in the value chain can access the information. The question of how to bring relevant and accurate data for decision making and who can access the data from both company and business standpoint. Also, it includes the questions of how to protect the IP and follow privacy regulations.

What is my conclusion?

When the only tools you have is files and Excel, then every data management problem looks like sending emails with file attachments and managing Excels loaded in the cloud file drive. This is why we need modern PLM tools. This is how not how the digital PLM future should look like. Digital transformation in manufacturing demands shifting towards modern data management solutions capable to solve the network of manufacturing companies, helping them to orchestrate processes, and optimize the information flow. Just my thoughts…

Best, Oleg


Disclaimer: I am the author at PLM ECOSYSTEM, focusing on developing digital-thread platforms with capabilities across CAD, CAM, CAE, PLM, ERP, and IT systems to manage the product data lifecycle and connect various industry networks. My opinions may be biased. Articles and thoughts on PLMES represent solely the author's views and not necessarily those of the company. Reviews and mentions do not imply endorsement or recommendations for purchase.

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