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The Five Step Guide to Implementing Data Capturing Technology

Final user, OPC UA pub sub over TSN, Systems Integrator, 5G Smart Factory

The Five Step Guide to Implementing Data Capturing Technology

26 Jul, 2020

The ability for a data-driven process to enhance operational efficiency by as much as 60% is the reason why Greenfield facilities are integrating data capturing technology across the manufacturing industry. The advantages a data-driven manufacturing process provides include: visibility into machine performance, optimized production cycles, enhanced collaboration, business insight, and revenue growth. To take advantage of these benefits, data capturing and the analytical process must be developed and successfully integrated.

This article will discuss:

  • The digital technologies needed for implanting a data capture process in greenfield facilities
  • The 5 steps to implementing data capturing technology in greenfield facilities
  • How to take advantage of and put the captured data to use
  • The benefits of a data-driven manufacturing process

 

Data Capturing Technologies and their Applications

 Capturing data on Greenfield shop floors start with putting the right technologies in place. It is important to note that within these facilities, more modern equipment and assets are used to execute operations on the shop floor. Thus, the machines such as computer numerical control (CNC) machines come equipped with digital I/O components and sensors which makes it easier to capture data from them.

The ease of capturing data means plug and play solutions such as human-machine interface devices, smart devices or tablets, and sensors can be attached to the machines. In this case, these devices serve as the interface between humans and shop floor equipment making it possible to directly read machine data and input information into the equipment.

The average shop floor equipment produces large sets of data for every hour it operates. This means a centralized storage system is needed to store these large data sets daily. For many manufacturers, cloud computing offers the more affordable and secures option for centralizing data while for others, on-premise storage solutions are the better option due to the control they provide. If a cloud solution is selected, then the equipment will be attached to routers which serve as the medium for sending or receiving data from the cloud. For on-premise storage solutions, routers can also be used or the machines can be directly plugged into the physical storage centers.

With this equipment in place, data can be read, transferred, and stored. This leaves out data analytics which requires specified applications to aggregate captured data. In most cases, industrial cloud computing platforms provide a few applications for organizing data while industry-specific IIoT platforms provide a diverse suite of tools for developing apps from scratch.  These toolsets can be used to develop apps dedicated to calculating OEE, managing alerts, or analyzing other machine data.

IIoT platforms also serve as supporting solutions for facilities where edge computing is integrated into the operational process. These platforms can communicate with data capturing technologies such as edge devices or sensors by sending actionable information to them. Finally, for large plants where data needs to be broadcast to the shop floor with operators spread around the facilities, communication devices attached to large screen TVs can be used to share up to date production schedules and other important machine information.

The need for real-time communication across interconnected cyber-physical systems means a communication standard must be established. This is where OPC UA over TSN comes into play. With OPC UA, manufacturers can standardize the communication process between edge devices, legacy equipment, and centralized data platforms. 5G networks also provide support for edge computing networks within Greenfield facilities.

 

The Five Steps to Implementing Data Capturing Technology in Greenfield Facilities

The implementation of the stated data capturing technology starts with designing a road map that guides the entire process. Here are the 5 steps to implementation:

  1. Outlining the data to be captured – Diverse equipment and processes produce data within manufacturing shop floors. To implement data-driven manufacturing processes decision-makers must first decide which machines or processes need to be optimized, as well as, the goals of the data capturing project. If the goal is to improve machine utilization, then a road map for machine optimization is what is needed. If the goal is to automate material handling, then a road map for the material handling system should be developed.
  2. Choosing your data capturing technology – The choice of technology determines how effective the process will be. Here, it is expected that the data capturing technology chosen must be compatible with the equipment on the shop floor. In the case of most Greenfield facilities, choosing technologies that support modern equipment is not a difficult proposition. The communication protocol and technology for these cyber-physical technologies must also be considered. In greenfield facilities, 5G networks provide support for near real-time communications while OPC UA over TSN provides a foundation for unifying connectivity across the data capturing technologies used. 
  3. Choosing an industrial cloud computing solution – If you intend to go with the more affordable option of subscribing to an industrial cloud computing solution, then choosing the right option is important. The considerations when making a choice should revolve around the scalability of the cloud solution, its compatibility with existing enterprise management software, and the tools it offers to manage data.
  4. Assign tasks to capable hands – Captured data must be properly processed and analyzed to get the insight into manufacturing processes they provide. To do this applications and individual(s) with the ability to makes sense from the collected data and put it to work are needed. Although most cloud platforms and apps come with intuitive user-interfaces, an experienced data analyst is required to extract meaning out of the large data sets the facility produces.
  5. Create KPIs that enable you to determine if the roadmap works – Once the implementation of data capturing technology is done, the final step is continuously checking if the roadmap is helping with achieving set goals. To do this, key performance indexes (KPIs) are needed. This could be OEE measurements, the quality and quantity of throughput, or the hours your machines function productively. These performance indexes can then be used to develop predictive maintenance strategies or to optimize specific production cycles.

 

The Benefits of a Data-Driven Manufacturing Process

The benefits of integrating data capturing technologies within Greenfield facilities cut across both the organizational and shop floor operations. At the organizational level, the ability to track every process across the facility provides stakeholders with accurate historical data for making decisions.

An example is developing strategies to handle increased customer demand during specific seasons. The data collected from machines can help management determine if purchasing or renting new machines to deal with demand surges is the option to take. At the shop floor level, captured data can be used to optimize production schedules to ensure cycles run efficiently and no machine is underutilized.

 

Conclusion

Developing a data capturing technology road map makes successful implementation possible. A road map ensures you act quickly and efficiently within your specified budget. In many cases, manufacturers who purchase every shiny new object in the market end up spending over their budgets and with non-functional digital data capturing tools. This is why approximately 70% of data capturing initiatives fail.

To reduce your chances of failure, you can follow the steps outlined here to develop and implement a data capturing technology road map and to gain the benefits of a data-driving manufacturing process.

 

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