Disruptive technologies like ride-sharing, 3D printing and self-driving vehicles are driving rapid transformation across many industries. The Industrial Internet of Things (IIoT) is no exception, with manufacturing ripe for disruption due to the prevalence of manual processes.
Given that human error is responsible for 85% of quality problems, it’s no surprise that roughly 1 in 4 companies who plan to implement IIoT are doing it to improve quality.
So how can companies expect to improve quality through the interconnectedness of devices? This posts looks at several key drivers of the transformation.
Feeding the Big Data Machine
IIoT has the capability to deliver vast amounts of data that companies can mine for important trends, triggers and leading quality indicators. The size of datasets possible with IIoT far outpaces what companies can collect manually, with machine sensors providing a level of detail that can only be analyzed with advanced computing capabilities.
It’s a stark comparison to existing manual and paper-based data collection methods still in use at many companies. Manual data can take weeks or even months to analyze, by which point it may not even be actionable anymore. IIoT and Big Data have the potential to change that, providing real-time insights into production processes to improve quality performance.
Of course, companies need a way to effectively manage all the data that IIoT can provide. Advanced reporting and analytics can help, but filling the talent gap for IoT project implementation and data management remains a challenge for companies.
Reducing Operator Error
In today’s factories, most machinery remains under the control of individual operators. As we mentioned above, human error is a huge contributor to quality problems.
Companies can use IIoT to reduce the likelihood of human error by turning work instructions from something a person has to memorize into automated processes. For example, a company could send a material recipe directly to machines on the line via a programmable logic controller (PLC). This process reduces the number of steps the operator needs to take, in turn reducing the opportunities for mistakes.
Enabling Predictive Maintenance
In a similar vein, IIoT enables organizations to automate data collection on equipment performance. Rather than relying on a person to detect calibration and maintenance issues, machine learning capabilities can provide advance notification of when equipment is about to fail. By delivering this information directly to the Quality Management System (QMS), companies can repair or replace equipment before it malfunctions.
This knowledge can result in less unplanned downtime, errors and waste. Even an hour of downtime can create huge costs in excess of $1 million for a plant, so this is one area where we expect IIoT to make big strides.
Optimizing Machine Production
Equipment sensors will enable machines to monitor output in real-time. When sent to downstream equipment, this data will allow equipment to make corrections and adjustments as needed to keep the final product within specification.
- Reduced scrap and rework.
- Lower costs.
- Increased customer satisfaction.
- Improved overall equipment effectiveness (OEE).
One area where we’re likely to see IIoT have a huge impact is in new product development. By embedding smart sensors in products before they leave the plant, companies will be able to:
- Identify different types of users according to how they use the product.
- Add features to existing products and offer new services based on usage data.
- Evaluate product performance to improve overall reliability.
These types of product enhancements support the driving mission of quality, which is improving customer satisfaction. When combined with an automated QMS, IIoT has the potential to revolutionize manufacturing as we know it.