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Why is data critical in smart manufacturing

Why is data critical in smart manufacturing
More and more, manufacturers rely on IIoT to achieve further operational efficiency and cost savings. In this regard, data, and more importantly having the right data, becomes crucial.
That was the point raised in a recent blog post by Sight Machine titled "Why Data Comes First in Manufacturing Analytics."
According to the post, when it comes to data the manufacturer should look at two things, namely data fitness and data readiness. “If the manufacturer doesn’t have the right data for a specific use case, the project won’t succeed,” it said. “If in fact you do have data aligned with your purpose, the next step is to see if it’s actually ready for use. That’s data readiness. If your data fails this test, you can’t access and consume it to drive your use cases.”
In terms of data fitness, the manufacturer needs to assess whether they have the right data to avoid a misalignment between the manufacturer’s objectives and data requirements, the post said.
“For example, a common manufacturing analytics objective is figuring out the cause of rejects. This requires data showing what happened to parts that were rejected. Sounds simple, right? The thing is: the data needs are anything but,” it said. “Reject analysis entails associating specific produced parts or batches with both production process data (what happened when the part or batch went through a given process) and quality data (was the part or batch rejected and if so, why). The difficulty here is that many manufacturers just don’t generate or collect production process or quality data for particular parts or batches.”
The post further cited predictive maintenance as an example. “Currently, predictive maintenance is top of mind for many manufacturers. The cost savings can be significant and aren’t difficult to quantify. But predictive maintenance requires both process data and maintenance records for machines and their components,” it said. “It also involves downtime data with reason codes that can trace back to component failures. All these metrics must be captured in a consistent and automated manner at the machine and component level. Once again, most manufacturers just don’t have reliable data at this level of granularity.

Data requirements

The post cited some examples of data requirements for typical use cases. They are summarized as follows.

Downtime analysis

  • Machine-specific process data
  • Machine-specific downtime signal (event) data
  • Downtime reason association
  • Downtime event-to-process data association (indication of when downtime occurred during the production process) 

Predictive maintenance

  • Component-specific process data
  • Component-specific maintenance records (historical information to build predictive algorithms)
  • Component-to-machine mappings
  • Machine-specific downtime (event) signals
  • Downtime reason association (at component level)
  • Downtime event-to-process data association (indication of when downtime occurred during the production process)

Reject analysis

  • Machine-specific process data
  • Serialized quality data (pass/fail at part/batch level)
  • Quality-state reason mapping
  • Process-to-quality data association (serial — part/batch level)

Material/input use optimization

  • Machine-specific process data
  • Inputs/materials (volume) for each process and what impacts them (this can be complex; for some inputs, time and temperature can impact input use)
  • Process-to-quality data association (serial — part/batch level) to understand how inputs impact yield
The post concluded by saying a thorough understanding of data fitness is key for manufacturers. “There’s a fine line between what’s possible and what can be worked around. That said, an accurate understanding of data requirements is essential to realistically assess the feasibility of a project and the adjustments to scope and budget that may be necessary,” it said.

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