LEVERAGING MACHINE LEARNING TO OPTIMIZE PRODUCTION PROCESSES AND CAPACITY IN THE BREWERY INDUSTRY.

The decrease in the production capacity that had crippled business activities that mostly depend on soft drinks to run their daily activities was overcame by introducing leveraging machine learning to optimize production processes and capacity in the brewery industry, to perfectly achieve this, it was done in this approach, characterizing and establishing the causes of poor production capacity in a brewery industry, designing a conventional SIMULINK model for production processes in the brewery industry, designing leverage machine learning rule base that will reduce the causes of poor production capacity in a brewery industry and simultaneously increase the production capacity, training ANN in the designed machine learning rule base for effective reduction of the causes of poor production capacity in a brewery industry and simultaneously increase the production capacity, designing a SIMULINK model for leverage machine learning, developing an algorithm that will implement the process, designing a SIMULINK model for leveraging machine learning to optimize production processes and capacity in the brewery industry and validating and justifying the percentage improvement in the production capacity of a brewery industry with and without leveraging machine learning. The results obtained were, that the conventional inefficient Process Control and Automation cause of poor production capacity in the brewery industry was 20%. On the other hand, when leveraging machine learning was introduced in the system, it drastically reduced inefficient Process Control and Automation caused poor production capacity in the brewery industry to 17.34% thereby enhancing the production capacity, the conventional Lengthy Fermentation and Maturation Times cause of poor production capacity in brewery industry was10%. Meanwhile, when leveraging machine learning was incorporated into the system, it decisively reduced Lengthy Fermentation and Maturation Times caused by poor production capacity in the brewery industry to 8.7% and the conventional production capacity in the brewery industry was 50000 bottles of drink. On the other hand, when leveraging machine learning was inculcated in the system, it simultaneously enhanced the production capacity in the brewery industry to 6500 bottles of drink. Finally, with these results obtained, it showed that production capacity in the brewery industry was optimized by 33%.