Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before your products are ready to be shipped to your customers. At every stage of the production flow—from incoming raw materials to the final stage before delivery—we implement testing processes. But how do we actually use this data to optimize quality and yield, minimizing defective products and customer returns?

To avoid unexpected downtime, waste, and defective products, we need to collect and interpret all relevant data and turn it into actionable insights. Meaningful information requires establishing the following capabilities:

  • Continuous collection and interpretation of test and process data for each unit, process, and plant.
  • Automated detection of quality and yield issues.
  • Accurate and efficient root cause analysis.
  • Automatic alerts for abnormal conditions.
  • Predictive analytics to anticipate potential failures.
  • Ongoing measurement of key performance indicators (KPIs).

Why aren’t “PASS/FAIL” criteria sufficient for controlling first pass yield and manufacturing quality?

Many manufacturers rely on a simple “PASS” or “FAIL” test criterion for each parameter. If a unit passes, it moves to the next stage of production; if it fails, it is sent to a technician for further analysis. However, when evaluating product quality, this approach is insufficient. It provides little to no information about edge cases, where one or more parameters barely meet the allowed tolerance. Such edge cases can lead to failures during operation, especially in extreme conditions like high or low temperatures, humidity, electrical overload, or physical impact.

Why do we rely solely on “PASS” or “FAIL” criteria instead of analyzing the detailed results for each parameter?

There are two primary reasons:

  1. Technical challenges in integrating and collecting test data from automated equipment across the manufacturing line.
  2. Information overload. In mass production, it’s nearly impossible to process all the detailed data collected from testing stations. Detailed analysis typically happens only when a critical quality issue arises, requiring root cause investigation.

Due to the difficulty of managing vast amounts of testing data, manufacturers often default to minimal “PASS/FAIL” criteria, discarding valuable detailed information. This oversimplification limits the ability to control processes and improve quality and yield.

Leveraging new technologies for fast and scalable data integration allows manufacturers to collect and analyze data in real time. This enables:

  • Early detection of quality issues.
  • Identification of complex process disruptions.
  • Prevention of delays in delivery.
  • Assurance of the highest product quality for customers.

By focusing on accurate and actionable data, manufacturers can effectively control the quality of their processes and improve overall yield.