How can data analytics and predictive modeling prevent risks in manufacturing operations?

Predictive analytics can analyze historical data and identify patterns and trends. By continuously monitoring real-time data from manufacturing processes, anomalies can be detected early, signaling potential issues or deviations from normal operations.

The judgement much more objective through data compiling. Nowday with the adavanced of IOT, we could easily to gain data. But Raw data sometimes not leading you to the correct goal. Quote that I believe garbage input Will produce garbage output no matter sophisticated process was. So even with AI tech we need also to separate good and bad data before analyze

Some time ago it was discovered that the theory of constraints can be used to optimise production. This has been built upon and now there is a robust system called 6sigma that is built around the theory of constraints. The collection and analysis of the data is the central method of applying 6sigma to a production facility.

When we apply the theory of constraints we are able to clearly identify what we call bottlenecks, these need to be analyzed to see if they are the "timer" which is the process that determines the capacity of the facility or of they are resolvable clearing the way to and from the timer in the process. Some of the most important features that you need to build into your production process are: Maintenance / breakdowns Product failures that need to be remanufactured Urgent jobs that need to be fast tracked. Once you fully understand the capability of the "timer" in your process you will be able to fully optimise the production. Using data to analyse the process is a key part of identifying the "timer"

While it is useful to have data on product failures what is most useful is having data on where the product failure occurs in the process. The 6sigma principal is to identify the cause of the product failure as early as possible and implementing the processes necessary to eliminate the failures. You should be aiming at 99.9998% success in your manufacturing facility. This will optimise the production process while maximising the profit.

The most important outcome of modeling the risk is identifying the source of the risk, and the next important action is to install a process to eliminate the risk from the process. It is important to continue to gather data and modify the process to optimise it after each improvement. This means data collection and analysis is a constant feature of a well run production facility.

The best practice for data collection and analysis is to involve the people on the line who have a first hand view of the line and who know what problems they face. We often forget that the people who work in a position are the best source for the type of data we need to use for effective analysis.

Here, we present critical practices should implement to integrate data analytics throughout the risk assessment process. •Identifying risks •Assessing risks •Monitoring response to risks While it is impossible for an organization to completely avoid risks, incorporating data analytics into the critical elements of an ERM program—risk identification, risk assessment and monitoring implemented risk responses—allows companies to anticipate, manage and respond to adverse events more effectively. In addition, incorporating the analysis of “big data” allows a company to move from the traditional reliance on past events to the ability to make more informed decisions based on forward-looking “what-if” scenarios.