Monday, November 11, 2019

Bayer Pharmaceuticals, Garbagnate, Italy - Smart Factory



The plant  produces and packages pills,

The Garbagnate pilot, which was launched in September 2017, uses machine learning and analytics to increase operational efficiency, a digital twin (digital replica technology) for employee laboratory scheduling, and augmented reality (AR) devices to reduce changeover times when switching product lines. In each case the front-end applications are connected to 15 data sources and integrated into the plant’s IT infrastructure. Ongoing operations are now more transparent to employees on the factory floor and they get real-time updates on performance, helping them make smarter decisions, react faster and utilize resources more efficiently,


According to  Hans-Walter Hoehl, head of efficiency, innovation and projects within the product supply unit at Bayer’s pharmaceuticals division, in certain areas, such as coating tablets, further optimization of the manufacturing process and elimination of  root causes for potential variations could be done due to smart features implemented.


Data scientists played a key role in digitizing Bayer Pharmaceuticals’ plant by developing the algorithms that turn data into useful insights to enable more efficient production of drugs. Three steps were required to develop the algorithms. First, the data had to be prepared: different data sources need to be “cleaned” and connected to be applicable for advanced analytics. Next, the algorithms had to be set up through a modeling process. Different machine learning techniques were implemented to enable the algorithms to make predictions. Finally, the company needed to figure out how to visualize the insights in ways that employees on the factory floor could interpret easily.


The digital twin:  A machine learning-powered digital replica of a laboratory now takes care of employee scheduling to maximize efficiency. Output increased by 40% and  it also makes  work better and easier.

AR, another visualization tool, is helping significantly reduce the time it usually takes to change from producing one product to another, increasing the utilization of factory machines.  Not only was the production time reduced but employees no longer have to follow lengthy, complicated paper-based instructions.

 Digital transformation also requires lots of employee training. Internal experts on quality control, packaging and engineering need to be an integral part of the process and managers must learn to make decisions based on data rather than on prior experience only.



https://innovator.news/using-data-as-an-asset-9e3d6d3cbf0b

http://www.boussiasconferences.gr/files/_boussias_conferences_content/presentations/smart_factory/2019/oscar_lazaro_smartfactory_19.pdf

https://www.capgemini.com/research/digital-transformation-review-twelfth-edition/industry-leader-perspectives-saskia-steinacker-bayer/

http://www3.weforum.org/docs/WEF_4IR_Beacons_of_Technology_and_Innovation_in_Manufacturing_report_2019.pdf

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