How to put manufacturing optimizations into practice


Manufacturing optimization is the practice of improving manufacturing processes with machine learning and big data techniques through a closed-loop process. It’s the gold standard for improving a product or process in manufacturing and spans from one-time optimization exercises to incremental continuous improvements made bit-by-bit on factory floors. Adopting the concept of manufacturing optimization into your current processes will reduce costs, keep your builds on schedule, and give you a greater chance of hitting your launch timeline, yield and quality KPIs.

As the leading manufacturing optimization platform, Instrumental enables brands to collect data across their supply chain and pull it into one accessible place. The platform proactively identifies issues throughout the line and notifies engineers, who can immediately act to close the loop. 

Instrumental customers use the platform to collect data across their supply chain into a single, cloud-based source of truth. For one customer, let’s call them Customer X, Instrumental was able to help them collect data from return centers and automatically correlate this data to visual anomalies detected during assembly of those units. This resulted in the team being able to find root cause with astonishing speed – this is the panacea of what optimization is about. Let’s explore how they used Instrumental to put manufacturing optimization into practice to achieve these results. 

Firstly, Customer X used Instrumental to gather information from every single point in the supply chain including:

  • Upstream component and sub-assembly suppliers,
  • Their final assembly, test, and pack facility, and
  • Their downstream returns & refurbishment center. 

The team at Customer X, wanted to find out more about product return issues. If they were able to identify that people were returning their product for a specific issue that traced back to a manufacturing defect, they could re-strategize and explore upstream corrective actions. With features in the Instrumental platform, Customer X was able to pull data from their upstream suppliers in the form of images and functional test data. They also collected data from their final assembly, testing, pack-up facility, and their returns center. Once all of this information was in a single cloud system, Customer X utilized Instrumental to gain additional, actionable insights. 

Instrumental’s Discover AI looks through every image that comes off the line and proactively finds issues, without being told what to look for or where. It identifies potential anomalies and notifies engineers, who can see it in the platform and dig right in. In this example, engineers could see that multiple zif connectors were not fully closed. It was also clear that the units whose zif connectors weren’t completely closed for the camera also failed in post-drop tests.

Contextually presenting data to stakeholders can take hours making slide presentations and discussing the information in meetings, but Instrumental makes that easier. Instrumental can push information down to the factory for SOPs and help engineers prioritize top issues. Executives can print an auto-generated report with one button and see if the defect rate goes down once the engineering team intervenes.

This chart includes data from a different customer and shows how teams can act to successfully close the loop, by making interventions, and validating the results. In this case, the failure rate went to zero. 

These actions add up. In development, Customer X was able to accelerate the maturity of their product. In production, they saved quarters and dollars on every unit.

Learn more

At Instrumental, our mission is to build self-improving, self-optimizing assembly lines. We work with our customers to understand their challenges, how they hope to solve them, and how we can support them in doing so.

To learn more about this example, some of our other customers’ products and processes; and Instrumental, watch this video.

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