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Typically, semiconductor fabs have an array of IT applications covering WIP, track & trace, scheduling, maintenance, reporting and analytics. While this approach might have worked in the past, the high demand from the market subjected the industry to a great deal of pressure. Legacy MES needs to evolve from a traceability solution to a full-fledged platform supporting advanced scenarios in Semiconductor.
The future of digital transformation in the semiconductor industry is to pursue industry 4.0 to go beyond chips. It begins with a re-engineered operation and a re-imagined business, which at its core needs a MES capable of supporting this large scale and all-pervasive strategy.
Predictive Maintenance becomes a strategic initiative in a highly complex, automated semiconductor fab. Predictive Maintenance equips process owners and maintenance personnel to proactively detect equipment-related issues before there is a breakdown, allowing owners to eke out the last bit of performance. Production and schedule adherence are protected, and unplanned stoppages avoided.
The 2021 global chip shortage bears testimony that the industry’s perceived capabilities have fallen short on reaping the full potential of digital transformation and automation.
A truly Industry 4.0-enabled semiconductor fab needs more than a MES. It needs an IoT data platform, which encompasses the core MES functionality, but has the capability to unleash the potential of IoT through enhanced data storage and manipulation capabilities.
The Semiconductor Industry has been at the forefront of digitalization. Chips manufactured by the industry have enabled increased data storage, real- time data processing at the edge and the very manifestation of IIoT and AI in value chains worldwide. However, the automotive and medical device industries are outpacing semiconductor in terms of digitalization, with the semiconductor industry lagging behind.
Speed, efficiency and effectiveness. These are three very different things but are often associated with each other. According to folklore, the fastest doesn’t always finish first – so is efficiency and effectiveness what you should be striving for? Design of Experiments (DOE) is like this. You can conduct experiments fast, but what if you miss an important interaction because you can only experiment with one factor at a time?