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The SACMI Data-Centric Smart Factory
Since the breakthrough with image-recognising deep neural networks, Artificial Intelligence (AI) has
proven to be highly successful in a wide range of applications. It has extended what could be achieved via
traditional machine learning techniques by leveraging the computational power of new chips and cloud
infrastructures, as well as new protocols and technologies for data exchange and storage. Big tech firms
have been the true drivers of this AI revolution through their delivery of a new set of data-driven services.
Needless to say, the industrial setting differs from the one in which these companies operate. However,
operation technology and information technology, traditionally two non-intersecting realms, have been
getting closer and closer, even overlapping, as they draw on this new data-oriented perspective. Indeed, AI
applications for manufacturing have the potential to unlock automatic process control and allow predictive
diagnostics of machine components, taking the concept of automation to a new level, especially in
terms of plant efficiency and cost reduction. Automated prescriptive actions powered by algorithms could,
in fact, soon be adopted in critical decisions for plant management and execution of operations. However,
full deployment of such technologies in a plant poses serious challenges because of the strict constraints
that apply in a production environment. For algorithms to reliably support the decision-making process of
both white and blue collar workers, the central role played by data needs to be acknowledged. It is vital to
standardize the information flow generated across the entire factory, from raw material characterisation
to quality control, from order scheduling to warehouse management.
Looking to the future, this short article illustrates the approach that SACMI has taken to make this possible;
it does so by discussing the technologies of choice and illustrating some AI applications that have
been developed.
