I recently published an article for CESMII about the surprising first step manufacturers should take if they want to predict and prevent quality issues..  It got me thinking a little deeper about how manufacturers can apply artificial intelligence (AI) and machine learning (ML) to as they evolve into predictive, data-driven operations.

Manufacturing has always been about achieving three key goals: efficiency, quality, and reliability. In the past, manufacturers achieved these goals through trial and error, by adjusting processes based on experience and gut feeling. Additionally, they have invested in automation, sensors, data collection, and other digitization technologies like MES to improve on these goals.  However, with advancements in technology, manufacturers can now leverage advanced analytics, including machine learning (ML) and artificial intelligence (AI), to take their operations to the next level.

One of the major benefits of advanced analytics in manufacturing is the ability to predict quality and reliability outcomes. This is achieved by collecting large amounts of data on various aspects of the manufacturing process, such as input materials, process parameters, machine performance, and final product quality. The data is then analyzed using ML algorithms to identify correlations and patterns and make predictions about how changes to the process will impact final product quality. By doing this, manufacturers can proactively address quality and reliability issues before they become a problem, rather than just reacting to issues after they arise.

Advanced analytics also provide manufacturers with a deeper understanding of their processes, allowing them to identify areas for improvement. By analyzing the data generated by their processes, manufacturers can set quality targets and optimize their process setup to achieve those goals. This leads to increased efficiency, as manufacturers can make changes to their processes that result in higher throughput and yields, and lower waste and energy usage.

One application of advanced analytics in manufacturing is predictive maintenance. This involves using ML algorithms to analyze machine and equipment data to identify patterns and anomalies that indicate when maintenance is needed. Using these technologies, manufacturers can effectively schedule maintenance at a convenient time, reducing downtime and increasing efficiency.

Advanced analytics can also make a big impact in manufacturing is energy management. By collecting data on energy usage, manufacturers can identify areas for improvement and make changes to their processes or invest in new technologies that will reduce energy usage and lower costs.

Reducing waste and improving yields is yet another area where advanced analytics can accelerate results.   By analyzing process parameters, product quality, and waste generation data, manufacturers can identify areas for improvement and make changes to their processes that generate more efficient operations and a better bottom line.

Despite the many benefits, there are still challenges that manufacturers face when implementing advanced analytics in their operations. One major challenge is the sheer volume of data that is generated and the complexity of analyzing this data. Another challenge is the need for specialized skills and knowledge, such as data science and ML, to effectively use advanced analytics, which can be a barrier for smaller manufacturers without the resources to invest in these areas.  They should start with a platform that goes beyond a PC environment and has a no/low code capabilities to execute analytic cycles.

Another challenge is the lack of standardization in the data that is collected. Different systems may use different data formats, and there may be discrepancies in the data that is collected, making it difficult to effectively analyze the data and leading to incorrect predictions or decisions.  Data management is another area that needs to be considered to integrates these disparate sources and make them ready for analytics.

Despite these challenges, the benefits of advanced analytics in manufacturing are clear. By predicting quality and reliability outcomes, manufacturers can proactively address these issues and ensure their operations are running smoothly. By understanding their processes and optimizing their process set up, manufacturers can improve efficiency, increase throughput and yields, and reduce waste and energy usage.  The first step of many manufacturers is to exercise the data they have which will uncover problems with their data, missing data, etc.  This will allow them to start working on their data collection and improve the quality of data which is the lifeblood of advanced analytics.

As an example, I assisted a food manufacturer in their efforts to implement advanced analytics. To start, they conducted a proof of concept by manually collecting and cleansing their data, and then analyzing it to address a specific production issue. They were pleasantly surprised to discover that their data held significant value, enabling them to enhance quality, productivity, and reduce expenses related to raw materials and energy. However, they also became aware of “dirty data” issues and insufficient data collection in other areas. As a result, in addition to reaping the benefits of their existing data, they were able to design improvements to their manufacturing data landscape and acquire additional process knowledge to make further enhancements.

In conclusion, advanced analytics, including ML and AI, are transforming manufacturing operations. By collecting and analyzing vast amounts of data, manufacturers can predict quality and reliability outcomes, understand their processes, and optimize their process set up to achieve their goals. These advancements are leading to increased efficiency, higher throughput and yields, and reduced waste and energy usage. While there are challenges to overcome, the benefits of advanced analytics in manufacturing are too great to ignore.