How manufacturing analytics improves root cause analysis

In previous blogs, we’ve described how manufacturing analytics can help companies achieve operational excellence through data and AI technologies. A closely related discipline in the manufacturing context is root cause analysis.   

Root cause analysis (RCA) is the quality management process by which an organisation searches for the root cause of a problem, issue or incident after it has occurred. Manufacturing analytics and RCA techniques are closely related, with manufacturing analytics often serving as a key enabler for effective RCA in manufacturing environments.  

Manufacturing analytics provides large amounts of data from sensors, machines, production lines and other systems that can be used to identify patterns and correlations that may not be apparent using traditional RCA methods. By analysing trends, anomalies and deviations in the data, manufacturing analytics helps to pinpoint the root causes of production problems more efficiently and accurately.  

Let us explore the various ways in which manufacturing analytics can enhance RCA. 

In today’s manufacturing landscape, automated problem detection powered by manufacturing analytics is transforming the way problems are identified and addressed. Traditionally, detecting machine failures or process inefficiencies has been a reactive task, often resulting in costly downtime. However, with real-time data from sensors and IoT devices, analytics tools can now monitor equipment and processes 24/7, identifying anomalies as they occur. 

Key benefits of automated problem detection include:

Early identification
Analytics tools detect subtle deviations from normal operations, flagging problems before they escalate. 

Reduced downtime
Proactive detection means issues are caught earlier, preventing extended breakdowns and delays. 

Improved efficiency
Continuous monitoring ensures that production runs smoothly, and deviations are corrected in real time. 

For example, when equipment shows signs of wear, analytics can trigger maintenance alerts before a failure occurs. This moves manufacturing from a reactive maintenance model to a predictive one, improving overall operational efficiency. Automated detection combined with RCA can significantly improve troubleshooting by identifying the earliest signals of a problem. 

Manufacturing analytics provides unprecedented visibility into production processes, making root cause analysis (RCA) more effective. With real-time data flowing from multiple sources, stakeholders can easily access detailed insights into machine performance, process variables and product quality – all at the touch of a button.  

This increased visibility benefits RCA in several ways: 

• Historical data: Analytics tools store and organize historical performance data, allowing engineers to track when and where a problem first appeared. 
• Process insights: Detailed process data helps identify patterns and trends, highlighting potential causes of issues across production stages. 
• Comprehensive dashboards: Interactive dashboards display key metrics and real-time production information, simplifying decision-making. 

For example, together with Settels Savenije, a high-tech company supplying to the semiconductor and medical industry, we developed a comprehensive dashboard to help the engineers visualize real-time production data and help identify process anomalies. 

The company was faced with unpredictable and shorter-than-expected lifespans of complex machinery, resulting in frequent, costly, unexpected downtime and expensive component replacement. There was often no clear reason for the failure and engineers had limited insight into why the machines were failing.   

The dashboards enable engineers to monitor machine performance on a daily basis, compare different suppliers, investigate failure modes and perform root cause analysis of failures. 

Manufacturing analytics accelerates root cause analysis (RCA) by eliminating guesswork and enabling data-driven investigations. Instead of manually sifting through production logs, analytics systems automatically process vast amounts of data to identify correlations and anomalies that point to the root cause of problems.  

Key benefits include: 

• Speed: Analytics drastically reduces the time it takes to detect and diagnose problems, cutting RCA timelines from days to hours. 
• Precision: Using statistical process control (SPC) techniques, analytics can detect when a process goes out of control, and this can be further explored using RCA to identify the underlying reason. 

Implemented SPC techniques include quantile-quantile and cumulative sum plots1. The dashboard helped our client Increase production quality and regain control over production process via worldwide statistical process control implementation.