Manufacturing businesses need to find new ways to streamline their operations in order to succeed. It used to take months to examine every process, test and retest ideas, and implement changes. Manufacturers are, however, sinking before they have a chance to become better because of this outdated mindset.

What can you do to make manufacturing operations more efficient and faster? A diagnostic analytics or manufacturing analytics combined with root cause analysis tools can make your operations more efficient by providing you with more focused and actionable insights that can be used to continuously enhance your production process.


An analysis of diagnostic data can identify factors causing defects or quality deviations in your production, which is the first step in reducing the rate of faulty units.  The most effective way to achieve this is to implement an automated Root cause analysis.


What are the steps to perform an effective analysis using root cause analysis tools powered by AI analytics?

Different businesses and problems require different approaches to root cause analysis. The identification of root causes has been a very challenging task, especially when a large number of processes and activities are performed.


  1. Getting the Data. The team attempts to identify as many causal elements as possible. Many OEM equipment manufacturers now embed data collection into their machines, making it easier to locate and utilize data for root cause analysis.
  2. Differentiating Causes from Symptoms. To isolate the symptoms from the causes, there are root cause analysis tools and methods that can be employed.
  3. Taking Corrective Action –  In addition to combining the information provided by the technicians who repair faulty units, we make sure we know just what’s needed to revolutionize your manufacturing process.
    Identifying the problem with  root cause analysis tools will lead to the creation of an effective solution.

When the root cause of a problem has been determined, we can use real-time data and insights to devise a practical, real-world solution.

Analyzing these data will surely enable us to investigate quality issues further and even make changes to the design of the product to improve manufacturability.


What is the role of diagnostic analytics in manufacturing?

As we do not dedicate enough time to get all those feedbacks immediately for every faulty unit that was repaired we strongly recommend the need to use a diagnostics analytics system that analyzes the repairs activities while visualizing them in real-time for you.

The goal is to reduce the time you spend figuring out the problem and to increase the time you spend fixing and preventing any future issues.


Combining automated test equipment with manual data for diagnostic analytics

Together, data analytics and repair analytics provide a very reliable, accurate, and quick process using root cause analysis tools.

By integrating high levels of adaptability and rapid design changes, the analytics process should automatically determine the link between the error codes reported by the automated test equipment and the repair analytics of the same unit which was performed by the technician.


Diagnostic analytics as a quality control tool in manufacturing

The technology used in smart manufacturing enhances the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain quality.

In the first year of using our services, our clients report a 30% improvement in quality and yield. Downtime incidents can be reduced and companies can get a return on investment of an average of 4 months.