While it is not necessary or even desirable to convert an entire factory at once to IoT-enabled processes and machines, it hasn’t always been clear to end-users what to do with the ever-increasing silos of data readily available. Discrete IoT, done properly, is a very scalable undertaking as needs, capacity, and resources expand.
Download our eBook, Voice of the Machine: Manufacturing's Digital Transformation for a roadmap to achieving the Internet of Things ROI, and a foundation for your company's growth into Industry 4.0 manufacturing.
1. Enlist manufacturing and maintenance managers.
Your manufacturing and maintenance teams can pinpoint the persistent problems and instigators of the most headaches from asset management, safety, or maintenance perspective. The issues may range from excessive use of wear parts and other consumables to components that are expensive or inconvenient to repair, to outright workplace safety concerns.
2. Identify the precise data points that can provide insights to address these issues.
Is any of it already embedded in the asset but not being reported to the programmable logic controller (PLC)? Can the part or component manufacturer rectify that? Should some parts have sensors placed where they can unlock additional data? Collaborating with the equipment supplier makes it possible to get the answers here; they will understand the ratings, the operating conditions for which a product was designed, and engineering attributes of the product and how metrics can be provided that can lead to actionable programs.
Note that it is not necessary to pull out every parameter to start, with the quality of data being the end goal. The usual starting points for fixed assets are:
3. Determine the cloud platform and user interface needed to establish and centralize data storage and transmission.
Investigate using common application programming interfaces (APIs) that aren’t platform-dependent. This should be an interoperable system to accommodate what likely is a potpourri of an existing network, Ethernet-based, and communication protocols. Data from the component or subsystem delivered to the cloud platform or on-premise edge device can be presented in context with other machine data for a holistic view, regardless of whether that data was generated by a Parker part or a component from another manufacturer.
4. Pinpoint the optimal frequency of monitoring and data collection for any given component or part.
With the benefits sought from IoT adoption now clear, taking a uniform approach to data collection and notification of anomalies can wreak havoc on operational costs and, with digitalization, is entirely unnecessary. As they would not be similar, it is important to distinguish the different schedules required to monitor for safety as opposed to the time elements impacting normal wear and tear of a part.
In summary, taking a discrete approach to IoT can provide clarity to envision a starting point, and outlay the journey. And, committing to this level of examination before deciding to collect and interpret discrete data will pay dividends many times over as your digital transformation proceeds.
Download our eBook, Voice of the Machine: Manufacturing's Digital Transformation for an in-depth look into how a discrete approach to the Internet of Things can lay the foundation for your company's growth.
After more than a century of experience serving our customers, Parker is often called to the table for the collaborations that help to solve the most complex engineering challenges. We help them bring their ideas to light. We are a trusted partner, working alongside our customers to enable technology breakthroughs that change the world for the better.
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