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Why product manufacturers should change their document-centric system

 

One way many engineers still manage their product definition information is to store it on a computer in permanent documents with no integration possibilities to other documents. Apps like Excel and Word are the preferred tools of choice.

These applications are then coupled to other systems like a CAD application. And suddenly, a company system that relies heavily on the document-centric approach is born. With several applications, each of them is designed to manipulate its own data files.

 

The system just described is called the document-centric system. One of the major drawbacks of this system is: it is very ineffective in use. Other drawbacks are:

  • Data redundancy: Data redundancy refers to the duplication of data, let’s say we are managing the data of a college where a student is enrolled for two courses, the same student details in such case will be stored twice, which will take more storage than needed. Data redundancy often leads to higher storage costs and poor access time.
  • Data inconsistency: Data redundancy leads to data inconsistency, lets take the same example that we have taken above, a student is enrolled for two courses, and we have student address stored twice, now let’s say student requests to change his address, if the address is changed at one place and not on all the records then this can lead to data inconsistency.
  • Data Isolation: Because data are scattered in various files, and files may be in different formats, writing new application programs to retrieve the appropriate data is difficult.
  • Dependency on application programs: Changing files would lead to change in application programs.
  • Data Security: Data should be secured from unauthorised access, for example a student in a college should not be able to see the payroll details of the teachers, such kind of security constraints are difficult to apply in file processing systems.

 

There is a completely opposite approach to the document-centric approach. It is called the data-centric approach. There are several advantages of the data-centric approach. Few of them are as follows:

  • No redundant data: Redundancy removed by data normalization. No data duplication saves storage and improves access time.
  • Data Consistency and Integrity: As we discussed earlier the root cause of data inconsistency is data redundancy, since data normalization takes care of the data redundancy, data inconsistency also been taken care of as part of it.
  • Data Security: It is easier to apply access constraints in database systems so that only authorized user can access the data. Each user has a different set of access thus data is secured from the issues such as identity theft, data leaks and misuse of data.
  • Privacy: Limited access means the privacy of data.
  • Easy access to data – Database systems manages data in such a way so that the data is easily accessible with fast response times.
  • Easy recovery: Since database systems keeps the backup of data, it is easier to do a full recovery of data in case of a failure.
  • Flexible: Database systems are more flexible than file processing systems.

 

Create a Thread of Information for the Lifecycle

Connected data—enabled through platforms—allow for greater automation to streamline processes, reduce errors, and rework, and eliminate redundancies. The benefits of a common data environment include:

A data management system allows you to create a thread of information for the lifecycle, from design to decommission. Departments can link operational data from building control systems for real-time performance analytics. This connected data helps owners make better decisions for planning and enables predictive maintenance to limit disruptions.

 

Real-time Insights to Optimize Operations

Management wants more than static facts and figures. They want actionable insights to inform better decisions. The technology behind a data management system gives access to real-time, information and subsets of information for greater agility and faster problem-solving to provide a snapshot of business-critical aspects for informed decisions and reduced lifecycle costs.

 

Automation to Plan the Next Project

Data can also support automated environments. Machine learning and AI ingest existing information to simulate possible scenarios. Generative-design software creates thousands of options for monitoring and analyzing current projects or planning the next project.

To give you an example: At one Airbus manufacturing facility, an inefficient layout forced workers to walk long distances to fetch tools and materials. For greater workflow efficiency, Airbus digitally tracked human movement, using generative design to reconfigure the factory. By plugging in existing data and desired outcomes, the company created a better design that used more renewable materials, such as net-zero concrete.

 

In conclusion

Connected data is crucial to develop the next generation of complex products that contribute to a more resilient and sustainable future. Managers are at the forefront of driving this change and are demanding to be part of the platform economy, but they can’t build it alone. They need all industry hands on deck to make it happen—and reap the rewards for everyone.

Now, in a rapidly changing world, more and more product manufacturing companies are ready for a data-driven industry powered by platforms, machine learning, and automation. The next generation of innovation is about gathering and analyzing data to design, build, and operate products for a resilient future.

 

〉〉Learn more in our E-book about How to Break Away From Data Isolation

About the author

Leon Lauritsen

Leon has worked with multiple IT systems from ERP to BI and PLM. His experience ranges all the way from programming to business consulting, project management and business development. Leon started his career in IT development and has further earned a diploma in IT and Economics at Copenhagen Business School and an Executive MBA at Henley Management College.  

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