Effective Data Management in a Digital Manufacturing World

Making Sense of Data in Digital Manufacturing

Digital manufacturing is driving efficiency and productivity more than ever before. Much of this is due to the emergence of Industrial Ethernet and its ability to eliminate network silos, drive a convergence of industrial and enterprise networks, and bridge the gaps between OT and IT. Unconnected devices and data silos no longer are operational bottlenecks.

But adapting to a world of digital manufacturing and “always on” data comes with its risks: security risks, database management challenges, and network management issues. The objective is to collect and leverage all relevant data, without introducing new risks to your company.

The Benefits: Improved Plant Visibility and Advanced Analytics

Constant advances in industrial networking and Industrial Ethernet as the standard for plant networks has allowed for interoperability across the plant floor more than ever before. Did you know that manufacturing produces more data than any other sector in the US economy? Tapping into that data allows for full visibility into the performance and working conditions of machinery and assets, which leads to improvement of overall equipment effectiveness, a reduction in downtime, quicker new product introduction, and improvement in inventory turns. With an increased coordination of maintenance schedules, help avoid unplanned downtime and optimize plant cells through predictive maintenance. You’ll also get insights into energy usage, and can allow for optimization of workflows and operations, which will help to reduce costs.

Once your devices are connected, actionable analytics allow data to be integrated into business processes. Create new opportunities such as increased profitability, identified efficiencies, and improved business operations.

Increased Profitability – Analyze data from machinery to help identify production and policy improvements to:

  • Avoid downtime
  • Reduce human intervention
  • Improve maintenance scheduling
  • Diagnose issues with considerable accuracy

Identified Efficiencies – Improve quality, yield, and OEE with real-time data analysis to:

  • Drive better utilization
  • Eliminate poor visibility
  • Identify potential events before major disruptions
  • Ensure quality control production runs

Improved Business Operations – View, understand, and track the flow of materials as they travel around the plant floor through analytics to:

  • Drive better efficiency
  • Avoid production interruptions
  • Allow supply chain process planning with scheduling and inventory management

Be Mindful of the Risks

Despite its many benefits, data management in a digital manufacturing environment has risks. Two of the most common are data overload and security threats.

Connected devices create tons of data. Your facility needs to have a system in place that decides where that data should go, how often it is sent, and how it should be used. There are six key factors to consider to avoid data overload:

  1. Frequency – Avoid unnecessary data pulls. Understand how often specific data adds value to the business, and only pull that often. Pulling data too frequently can cause data overload, network latency, and can sometimes even take the network down.
  2. Prioritization – Determine which services get priority within a network to ensure noncritical traffic doesn’t affect network reliability. This is important as more devices connect online. Some devices may be more sensitive to delay, noise, and data loss.
  3. Processing – Consider a hybrid solution of edge computing and centralized data computing within the data center. Manufacturing data typically requires real-time analysis. Traditional computing models that send data to the core data center for analysis are often impractical in manufacturing settings.
  4. Virtualization – By reducing your dependency on physical hardware, you can achieve increased system availability and ttain predictable application performance. While virtualization helps support business flexibility, it can also impact data center design, data consumption storage, security, and network performance.
  5. Orchestration – This is two-fold. Sharing too much data can be counterproductive and create paralysis within teams. Employees only benefit from data that relates to their daily tasks and outputs. Make sure data is properly mapped out for who should receive it and how they are to consume it.Without proper orchestration, sensitive information can accidentally become available to the wrong people. Automation is key in managing and reducing the complexity of orchestration processes.
  6. Visibility – Collect data from the network as it operates with dashboard reporting tools, allowing OT and IT teams to better understand the operation of the network.

Keeping your Business Secure

Security breaches on the plant floor create safety risks and can cause major downtime. Imagine how detrimental it would be to a company to lose trade recipes or program code. Security breaches on the enterprise side create privacy risks that threaten a company’s reputation and customer trust. Best practices for digital manufacturing can avoid introducing new security risks while helping you collect and leverage all relevant data. Horizon Solutions and Rockwell Automation can help you learn more about specific best practices for digital manufacturing.

Do you want to turn seemingly disparate data into useful information? Why not accelerate your insight into manufacturing through data visualization?

*Adapted from Cisco (2017): Data management in digital manufacturing: Achieving end-to-end visibility in the era of IoT