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18-02-2020
3 min. read
Sjors de Kleijn

How real-time data can improve machines

More efficiency, less downtime, better-performing machines

Industry 4.0's main economic potential is its ability to accelerate corporate decision-making and adaptation processes. This applies to processes for driving efficiency in engineering, manufacturing, services and sales.

Therefore the need for real-time – high data volume – multilateral communication and interconnectedness between cyber-physical systems and people is essential. It helps to compete with your competitors and enables innovation when driving up the IoT maturity model.

What is real-time machine data?

Real-time data (RTD) is information that’s delivered immediately after collection from the machine (or it’s industrial equipment like sensors). There is no delay (or limited to milliseconds) in the timeliness of the information provided. Machine data is usually captured and processed using real-time edge computing devices although it can also be stored for later analysis or directly visualised in dashboards.

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How real-time data improves machines

The availability of huge quantities of real-time data and information enables a better understanding of how things relate to each other and provide the basis for faster decision-making processes. Without real-time machine data, you have more and longer downtime and no insights to optimize machine processes.

Dashboard software visualises the machine data for operators to keep an eye on what’s going on. Examples of real-time data streaming and plotting in dashboards are used for monitoring machine state, batch processing in warehousing, energy data consumption or in sorting lines. 

Data visualisation, from big data, makes real-time data analytics easier and more comprehensible. Any malfunction or deviation is a signal for improving the machine, replacing defective parts or changing configuration settings to speed it up or slow it down. 

Uses of real-time machine performance data

  1. Enhance operator skills
  2. Implement predictive maintenance to reduce downtime
  3. Improve machine productivity and performance
  4. Increase added value for your customers (machine metrics) to improve relationships
  5. Create new service models to boost revenue
  6. Live machine monitoring for quality control and inspection
  7. Monitor machine performance to improve new machine range

Real-time data from machine to BI-tools

Data is usually captured and stored in multiple places. Machine data originates from PLC/HMI and sensors, whereas other business information is sourced from ERP, CRM or finance systems. The interconnectedness makes it possible to achieve the agility that is the key capability required by companies in Industry 4.0. Of course, companies will only be able to leverage the full potential if they implement a strategy where data is visible in dashboards or reports across the enterprise. 

The shop floor needs dashboards that allow machine operators to view processes on the line and identify malfunctions, so they can quickly devote resources to fix them. Management usually wants insight in metrics such as delivery performance, downtime, production output, quality, reject/scrap and safety. Machine builders use dashboards to get insight into the frequency and type of malfunctions that have occurred.

From strategy to real-time data dashboards

Thinking out a data strategy and determining the right manufacturing metrics is usually the hardest part. When you’ve figured this out, the next steps to transfer data from machine to BI-tool are easily implementable:

  1. The first step is data acquisition from machines using an edge device and logging the data in a central repository, which is usually a secure cloud solution.
  2. The next step is bringing all data sources together in a Business Intelligence tool such as Power BI or Tableau. These tools are built to connect and merge data from multiple sources. And it provides calculations to make KPI's presentable to any kind of user. This data is usually imported or sent directly through an API.

To get started with a data collecting method at the machine level, you need to figure out what’s the right solution for both the machine owner and the manufacturer.

From strategy to real-time machine data dashboards From strategy to real-time machine data dashboards

Choosing the right data solution for machine owner and manufacturer

When it comes to improving machines (or equipment effectiveness), the machine owner and machine manufacturer need to work together to collect, analyse and share machine data. When malfunctions occur or software needs to be uploaded to the machine’s PLC, it should be accessed securely from remote to save on costs and valuable time. During the entire life cycle of the machine, the owner and manufacturer need to co-operate to share their insights and knowledge to improve the machine.

Best practise is to combine a solution where hardware, remote service, machine data and real-time dashboards come together, such as IXON Cloud. A smart real-time data processing system – hardware device located at the edge of the machine – collects and transmits machine data securely to the data warehouse for analysis.

Using a role-based access control system you give different people access to the information to manage their daily tasks more efficiently. So the end-user can access the real-time dashboard or control the machine’s HMI on this smartphone, and the service engineer can release new PLC software from his office or access log files to find error codes or parts frequency to determine when maintenance is needed. All using a secure remote VPN connection.

To help you find the right solution, we collected more than 10 criteria for choosing the best IoT solution.

[[Download the complete checklist]]


Or get a quick look into IXON’s real-time data services and dashboards by signing up for the Free Product Tour.

 

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