OEE can be considered as a set of “best practices” metrics that evaluate the overall production time that can be counted as tangibly productive. A 100% OEE score means perfect production with minimal wastage and no downtime.
It’s important to note that companies do not essentially need to harness real-time data to calculate OEE, since the equation came into the picture in the late 1960’s – long before the advent of cloud computing or IoT sensors. But the value of OEE as a metric becomes even more impactful when real-time data is brought into the equation. It can help companies derive a more clear and detailed observation on OEE. For instance, floor managers in plants can monitor daily, weekly or seasonal fluctuations in production and equipment effectiveness with real-time data collection and analysis to avoid equipment failure.
Increasing the Efficacy of OEE as a Metric – The Need for a Robust Data Infrastructure
Collating equipment data is now easier than ever, thanks to the ubiquity of smart sensors, temperature monitors and remote sensing devices that can feed data in real-time to an OEE monitoring platform or OEE software for plant managers to evaluate. Since sensors are no longer tethered to a physical network and can be small enough to be embedded on equipment or within a product as well, data collation for OEE and monitoring efficiency is no longer confined within the four walls of a manufacturing plant. Using IIoT sensors on the equipment, any organization can effectively track the effectiveness by collecting data and analyzing availability and quality metrics from anywhere, anytime.
However, the quality and effectiveness of the OEE insights generated depend a lot on the kind of data infrastructure a company maintains. Siloed processes across multiple functions often serve as a barrier to data integration and processing, resulting in reduced analytics effectiveness and slow decision making. An effective real-time OEE platform needs to be seamlessly connected to all the functions and stakeholders involved in the production process. It should also standardize data collection and processing methodologies, terminologies and reporting procedures across all sites to facilitate fast and effective decision-making.
Companies need to have an effective data engineering process in place in order to derive rich insights on manufacturing performance out of the collated OEE data. With robust data governance and a continuously monitored data pipeline, companies can drive small yet significant improvements in the OEE scores, which can eventually transpire into substantial enhancements in profitability and efficiency.
Leveraging AI and ML to Catalyze OEE
Real-time data can serve as a powerful catalyst driving sustainable manufacturing growth from the shop floor to the board rooms. By leveraging powerful AI and ML engines, companies can derive meaningful OEE insights that not only enhances decision-making but also help companies meet a diverse set of requirements across the manufacturing value chain, such as: