Robert Jäger, Senior supply chain expert and product manager at PSI metals
After several years implementing PSI metals planning solutions at multiple plants, Robert Jäger spent the following 10 years in consultancy and technical sales support role, helping in the requirements analysis and the design of planning solution architectures tailored to specific metals supply chains. As product manager and building upon his supply chain expertise, Robert is now driving the PSI metals Planning product road map with a vision centred on smartness, adaptiveness, collaboration and sustainability.
Industrial AI, beyond the stars or down to earth?
Since the very beginning of AI, industrial applications were driving and inspiring research. The first expert systems were based on an extensive knowledge model mimicking domain experts’ reasoning and resulted in very successful industrial applications, also in the steel industry.
The internet revolution at the beginning of this millennium, cheap data storage and fast processors along with the sponsorship by the internet technology giants, made data driven AI a reality. This led to a revival of AI, the focus switching to data-centric technology vs model driven applications. Today, neural networks and machine learning (ML) are embedded into our daily lives, and are used in navigation systems, e-commerce applications, smart cars, social media …
With Industry 4.0 initiatives around the world, also the steel industry has been looking intensively at data-centric AI technology. We see many successful use cases and potential of ML in the metals industry: quality prediction, quality prescription, defect detection, maintenance prediction, forecasting of energy demand or replenishment or order delivery dates… just to name a few.
Unfortunately besides the successes, we also see up to 70% of failures of applying this data driven AI technology in an industrial context. These model
accuracy failures are mostly related to lack of data or data consistency, lack of prediction power in the data, and lack of business and process understanding by the (IT and data focused) project team. Key for a successful industrial AI project is that both production experts and data scientists are collaborating closely. Advances in explanatory AI, model interpretability and easy-to-use AutoML will be able to close the gap between production experts and data scientists. So the way forward is that ML becomes a commodity like Excel became a commodity for reporting and calculation technology.