Paper-based engineering systems are inefficient and costly. Continuing to rely on them in today’s market means putting yourself behind the competition. There’s more than one way to digitize your systems. Whether you rely on cloud databases, or use machine learning to improve your processes, you can find something that works best for your business.

By Robert Hopkin, Utilities Industry Specialist, Accenture

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Save costs and time: Benefit from digitizing and integrating engineering data. | Image: Sven Mieke / Unsplash

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Most companies have some form of a digital engineering system in place. But, unless you’ve scanned and digitized all of your information, that means you’re still relying on pen and paper in some way to sum up and interact with engineering data. 80% of organizations estimate that at least half (in some cases up to 90%) of their information is unstructured and therefore unusable.

There are still companies that rely on a paper-based engineering system. Even though, doing so is damaging your company’s chance of having a consistent workflow. Whether your company uses complete or partial paper-based engineering systems, you can profit from digitizing your information.

 

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80%

of organizations estimate that 50 – 90 percent of their data is unstructured and largely inaccessible, and therefore, unused.

92%

of organizations believe the ability to make data-driven decisions will have a major impact on achieving business goals.

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Understanding the problems with using paper-based systems can motivate you to change them. The next step is then recognizing and utilizing the systems available, and finding which one works best for your company.

Want some ideas on how to prepare your team to switch from paper to digital? Check out our resource on ways you can do exactly that.

Problems with paper-based engineering systems

Paper-based systems in any department are no longer efficient, especially with the changes and monitoring available through AI technology.

Here are the major ways in which staying with paper will hurt your organization:

No real-time updates

When you’re relying on paper to track engineering data, the only people who know about the system changes are the ones making them. In a global business, this doesn’t work. Your managers and operations department needs to know what’s going on across your organization.

Human error

Because you’re not relying on AI to give you data analysis or integration, that means you’re going to have a higher level of errors in your systems. Paper based models, even partial ones, rely on human skill and judgment to function. Relying on traditional methods often leads to data inconsistencies, and requires people to work through a huge backlog of data.

 

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85%

of organizations are developing strategies around data aggregation, data lakes, or data curation, as well as mechanisms to turn data into insights and then actions.

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Siloed systems

Many enterprise and engineering systems work on a siloed model with no data integration. That means missing data can turn into bad maintenance and a deconstruction of your current systems. When you’re still relying on paper to store information, there’s no way to create a holistic solution.

Incomplete data integration

Data integration can’t happen when you’re still relying on paper systems. There’s no way you can store all of your information on a digital database when it’s still in physical form. And lack of data integration means you don’t know everything that’s happening in your business, and can’t make informed decisions that impact the future of your organization.

Lack of exact data insights

When you don’t have data integration, you can’t have data insights. There’s no way to analyze your data when you don’t know everything you have on hand. Without data insights, you won’t know what areas of your business will need more attention, and which ones can be left to their own devices.

There’s no way to analyze your data when you don’t know everything you have on hand.

 

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Breaking down the silos: Integrated systems and data empowers companies to maximize value. Click on the Image to find out more.

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What digitization can look like

Digitization can look like several different things. In practice, it means making changes to your company workflow. One example is a cloud-based platform that everyone has access to change and upload. Many global companies do something similar to this by using Google Docs, or a custom-made system to store their documents and company data. Many employees use VPN technology to remote company servers, should they need to work away from the office.

 

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Digitized global research and development

Digitized R&D improves engineering capabilities through several methods. Especially when it comes to prototyping, it can radical reduce cycle development times. To make digitized R&D work, your organization needs a fluid database that can change as your development progresses. Much of the R&D work that needed to be done on site can be done in virtual anywhere. While there are still many engineering challenges that must be tackled on site, more intangible issues can be tackled in and outside of the office.

Automating repeatable engineering processes

Optimizing repeatable processes can improve productivity costs by 60%. Areas in which you can perfect include planning, predictive measures, maintenance, and asset management. What parts of your systems can be digitized? What parts can be completed through AI, machine learning, algorithms, or other digital technologies?

The first places to look include repeatable processes. If you’re doing something many times a day, chances are there’s a way to automate it. When you optimize these processes, you’re saving your team a huge amount of time, and deploy their skills for something that can a machine not do.

 

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60%

of productivity costs can be reduced by optimizing repeatable processes.

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The switch from paper to digital

There’s no one way to go digital. If you feel like you’re beholden to certain options, know that you have choices. Pick the one that’s best for how your company does things.

There are three different methods available to make the switch.

  • Upload Systems and Processes
    What processes are you already using on a regular basis? What methods are you using to distribute work tasks, product prototypes, and client experience?
    Create scripts in your database that reflect your methods. Document the system you’re currently using, and the changes you want to make.
  • Smart Scanning Documents
    Digitizing paper plans and items can sound like a horrible, time-consuming exercise. But it doesn’t have to be if done the right way.
    Machine learning algorithms are great at recognizing and interpreting asset symbols. These aren’t globally standardized symbols. Each project can vary in what symbols mean. But using these machine learning algorithms can interpret these symbols automated and save your company a huge amount of time.
    Accenture offers Engineering Data Digitization (EDD) to help in digitizing engineering documents such as P&IDs, Isometrics, and PFD’s. You can also create a context-based knowledge graph through data integration to help you visualize your information.
    EDD is an AI process that takes siloed information and creates data consistency and fast document retrieval. Doing this also helps minimize inefficiencies in operations.
  • Manual Input
    When all else fails, you can rely on manual input to change or upload data into your systems. This method of creating or updating information is not recommended, as it is subject to error. However, you’re likely going to need to rely on some method of manual input. The best way to use this method is to use something else first, and then manually update or change anything that may not have been done correctly.
    Paper-based engineering is inefficient and siloed. Continuing to rely on this type of management process is only going to increase the errors in your organization’s workflow. Instead, consider utilizing EDD in your processes to streamline your data.

About the author

 

Robert Hopkin

Rob is a utilities industry specialist focused on digital transformation across the value chain at Accenture. Get in touch with him via LinkedIn.

 

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