Sidestep catastrophic product recalls using AI and ML
Predict product failure and revise manufacturing processes to improve possible product flaws with precision for reduced production cost, speed, and accuracy.
Product Recalls befall due to the inflexible nature of manufacturing processes that discourage improvisations in products.
Time is of the essence in the bout of a product launch. Most managers end up neglecting to think about what they would do if a new product goes wrong.
Despite having the best design and engineering, product defects can occur during manufacturing or supply chain.
Artificial Intelligence can help identify the source of production problems, like assembly line faults, factory defects, or supplier errors.
The goal of predictive maintenance is to help predict failures that can ultimately be prevented. For such a scenario, data is collected over a period to monitor the state of equipment.
Manufacturing companies have loads of data such as equipment year, make and model, log entries, sensor data, error messages, engine temperature, and other factors.
In AI, the “training” process enables the AL algorithms to detect irregularities and test correlations while exploring patterns across the various data feeds.
In predictive maintenance, our goal is to detect a failure of events. This is started by collecting historical data about the machines, their performance, and maintenance records to predict future failures.
Equipment’s condition can be determined with the Usage history along with maintenance and service history.
To properly reflect the machines’ deterioration processes, we need to get historical data that reaches far back, enough to cover production machines’ long operational life span.
Other information that needs to be answered by both data scientists and domain specialists is data about mechanical properties, machine features, environmental operating conditions, and standard usage behaviour.
With all this information, it becomes possible to decide which modelling strategy fits best with the available data and the desired output.
Predictive maintenance maximizes the use of its resources, it detects anomalies and failure patterns and provides early warnings, and avoids a possible Product recall.
Most companies currently deal with the problem mentioned above by being pessimistic and through specific maintenance programs to replace questionable components before failures.
This can create extra expenses that can be avoided with proper prediction. Regular maintenance can prevent failures, but when not needed, it is an additional expense. Hence, it is not optimal from a cost outlook.