ncreasing demand requires increasing manufacturing productivity. At first glance, this seems simple and in reach of a button. But productivity in manufacturing is affected by multiple variables including availability, performance, and quality.
To increase productivity, manufacturers can improve these variables through manufacturing process optimization or by purchasing a new or additional machinery.
How to measure productivity?
According to OEE website, OEE or Overall Equipment Effectiveness is defined as “the gold standard” for measuring manufacturing productivity. OEE measurement is based on monitoring three main variables: availability, performance, and quality. Availability examines the planned and unplanned stops as lost production time, performance examines the maximum production capability of the machine compared to the actual performance, and lastly, the quality represents the number of lost products (defects) from the total manufactured goods. In other words, OEE provides manufactures with an accurate evaluation of the process in their production line.
OEE then is calculated by multiplying these three factors:
Aside from production optimization, giving attention to OEE contributes to budget management. To increase productivity and quantity, manufacturers tend to purchase new machines. However, understating the precise imperfections in the manufacturing process allows maximizing current machines’ productivity, lessening the need for investing money on new ones. As astonishing OEE sounds, to get hold of an accurate OEE measurement, what manufactures actually need is comprehended and well-formed data.
OEE in the Industry 4.0 era
Although OEE was first introduced in the late 70s, it is the perfect measurement in the era of Industry 4.0. As mentioned, an accurate OEE calculation requires data, and Industry 4.0 is all about machine learning and real-time data. This enables manufacturers to adopt Industry 4.0 in order to precisely measure the utilization of their manufacturing process in real time. However, analysing the process and performance data is impeded by the inability of connecting all the machines to a centralized platform that will facilitate monitoring as well as offering valuable information regarding the machines.
In order to extract relevant data for OEE calculation. Siraj’s Automatic Connectivity Generator communicates with multiple machines through a variety of communication protocols and automatically converts all data streams into a unified format that any application or cloud platform can seamlessly understand and process.
For an end-to-end solution, our clients, for example Tadiran Group, a leading manufacturer and distributor of air conditioning systems, are employing Siraj’s solution for better insights regarding every machine and the ability to improve performance accordingly.
We started their joint POC journey with Tadiran as part of their Quantum SPARK POC Runway Program participation. If you want to learn more about the program and apply to the next cohort (application deadline is January 15), check out our full program page.