




































    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

21 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

LEVERAGING MACHINE LEARNING TO OPTIMIZE PRODUCTION PROCESSES 

AND CAPACITY IN THE BREWERY INDUSTRY. 

 
1Eze Ukamaka J Ndubuisis, 2Paul-Darlington Ibemezie, 3Daniel Ikechukwu Egu and 
4Okolotu G.I. 

1,2,3Madonna University, Nigeria 
4Department of Agricultural Engineering, Faculty of Engineering, Delta State University of Science and 

Technology, P.M.B. 05, Ozoro, Nigeria.  

DOI: https://doi.org/10.5281/zenodo.15024347 

 

Abstract: 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%. 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

22 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Keywords; learning, leveraging, machine, processes, production 

1.1 Introduction 

The brewery industry is highly competitive, with manufacturers continually seeking ways to optimize production 

processes, minimize waste, and maximize production capacity. Traditional methods of process optimization in 

breweries rely heavily on manual observations, fixed schedules, and historical data that may not adapt swiftly to 

changing conditions (Rathore & Tiwari, 2020). In recent years, machine learning (ML) has emerged as a 

transformative technology in industrial manufacturing, offering advanced data-driven solutions that significantly 

improve process control and forecasting capabilities (Zhou et al., 2021). Machine learning algorithms can analyze 

large volumes of production data in real time, providing insights that enhance decision-making, streamline 

operations, and improve resource utilization (Wuest et al., 2019). The brewery industry, due to its complex 

manufacturing processes involving various stages such as fermentation, bottling, and packaging, is uniquely 

positioned to benefit from machine learning applications. These algorithms can optimize fermentation times, 

monitor quality control in real time, and adjust operations dynamically based on demand and resource availability 

(Oliveira & Cunha, 2019). Furthermore, machine learning supports predictive maintenance, reducing equipment 

downtime and minimizing disruptions in production cycles (Agrawal et al., 2020). Given the need for sustainable 

operations, machine learning also contributes to achieving energy efficiency, which aligns with industry goals to 

minimize environmental impact (Lydon et al., 2021). However, integrating machine learning into brewery 

production faces several challenges, including the need for significant data infrastructure, skilled personnel, and 

an understanding of the nuances in applying AI-based solutions to traditional processes (Singh & Kumar, 2021). 

Despite these challenges, the potential benefits have spurred research into developing tailored ML models that 

address the specific needs of the brewing industry. This study aims to explore how machine learning can be 

effectively leveraged to optimize production processes and increase capacity within breweries, ultimately 

contributing to a more sustainable, efficient, and competitive brewing industry. 

2.0  Methodology 

To characterize and establish the causes of poor production capacity in the brewery industry. 

Here's an example of how poor production capacity in the brewery industry can be characterized by causes, with 

percentages and their corresponding impact in terms of bottles of drinks produced per day: 

Table characterized and established causes of poor production capacity in a brewery industry 

 

 

 

 

 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

23 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 

Cause of Poor Production Capacity Impact (%) Impact (Number of Bottles per Day) 

Inefficient Process Control and Automation 20% 10,000 fewer bottles 

Equipment Downtime and Maintenance Issues 25% 12,500 fewer bottles 

Variability in Raw Material Quality 10% 5,000 fewer bottles 

Suboptimal Scheduling and Resource Allocation 15% 7,500 fewer bottles 

Lengthy Fermentation and Maturation Times 10% 5,000 fewer bottles 

Limited Data Utilization and Real-Time Analytics 8% 4,000 fewer bottles 

Energy Constraints and Environmental Regulations 7% 3,500 fewer bottles 

Labor Shortages and Skill Gaps 5% 2,500 fewer bottles 

Total Production Impact 100% 50,000 fewer bottles 

i) The table indicates the estimated contribution of each cause to the reduction in production capacity, expressed 

as a percentage and in terms of the reduction in bottles of drinks produced per day. 

ii) These values are estimates and may vary based on the specific brewery’s production environment and 

operational parameters. 

To design a conventional SIMULINK model for production processes in the brewery industry 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

24 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 

 

Figure 1.0 Designed conventional SIMULINK model for production processes in the brewery industry 

The results obtained were shown in figures 9.0 to 11.0 

STTATION 1 TRANSFER AND LOADING

STATION 2 FILLING

STATION 3 CAPPING  

Variability in Raw Material Quality

In1 Out1

UNFILLED BOTTLES

In
1

O
u

t
1

Total Production Impact

In1Out1

In1

Out1

Out2

Subsystem2

In1 Out1

In
1

O
u

t
1

In1 Out1

Subsystem

Suboptimal Scheduling and Resource Allocation

In1 Out1

Scope 6

Scope 5

NO OF FILLED BOTLES

5e+004

NO OF CRATES

In
1

O
u

t
1

Limited Data Utilization and Real -Time Analytics

In1 Out1

Lengthy Fermentation and Maturation Times

In1 Out1

Labor Shortages and Skill Gaps

In1 Out1

Inefficient Process Control and Automation

In1 Out1

FILL ING SYSTEM

In
1

O
u

t
1

Equipment Downtime and Maintenance Issues

In1 Out1

Energy Constraints and Environmental Regulations

In1 Out1

EMPTY BOTTLES

In
1

O
u

t
1

Display 9

5e+004

Display 8

5

Display 7

7

Display 6

8

Display 5

10

Display 4

15

Display 3

10

Display 2

25

Display 1

4165

Display

20

CONVENTIONAL

1

CAPPING SYSTEMI

In
1

O
u

t
1

CAPPING  

In1

Out1

Out2

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

25 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

To design machine learning rule base that will reduce the causes of poor production capacity in a brewery industry 

and simultaneously increase the production capacity 

 
Figure 2.0  Machine learning Fuzzy inference system 

Machine learning fuzzy inference system was designed as shown in figure 2.0 to reduce the causes of poor 

production capacity in a brewery industry and simultaneously increase the production capacity 

This has three inputs of causes of poor production capacity, total production capacity and detecting sensor. It also 

has an output of result. 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

26 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 
Figure 3.0 Machine learning rule base 

 Machine learning rule base was also designed to reduce the causes of poor production capacity in a brewery 

industry and simultaneously increase the production capacity 

These rules were extensively elucidated in table 2  

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

27 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Table 2. 0: Machine learning rule base   

The comprehensive rules of designed machine learning rule base, that will reduce the causes of poor production 

capacity in a brewery industry and simultaneously increase the production capacity is shown in table 2.0 

1 IF CAUSES OF 

POOR 

PRODUCTION 

CAPACITY IS 

HIGH REDUCE 

AND TOTAL 

PRODUCTION 

CAPACITY IS LOW 

IMPROVE 

AND DETECTING 

SENSOR IS NOT 

EFFECTIVE 

RECTIFY  

THEN RESULT IS 

POOR 

PRODUCTION 

CAPACITY IN 

BREWERY 

INDUSTRY 

2 IF CAUSES OF 

POOR 

PRODUCTION 

CAPACITY IS 

PARTIALLY 

 HIGH REDUCE 

AND TOTAL 

PRODUCTION 

CAPACITY IS 

PARTIALLY 

LOW 

IMPROVE 

AND DETECTING 

SENSOR IS 

PARTIALLY 

NOT EFFECTIVE 

RECTIFY 

THEN RESULT IS 

POOR 

PRODUCTION 

CAPACITY IN 

BREWERY 

INDUSTRY 

3 IF CAUSES OF 

POOR 

PRODUCTION 

CAPACITY IS 

HIGH REDUCE 

AND TOTAL 

PRODUCTION 

CAPACITY IS 

PARTIALLY 

LOW 

IMPROVE 

AND DETECTING 

SENSOR IS NOT 

EFFECTIVE 

RECTIFY 

THEN RESULT IS 

POOR 

PRODUCTION 

CAPACITY IN 

BREWERY 

INDUSTRY 

4 IF CAUSES OF 

POOR 

PRODUCTION 

CAPACITY IS LOW 

RETAIN 

AND TOTAL 

PRODUCTION 

CAPACITY IS 

HIGH RETAIN 

AND DETECTING 

SENSOR IS 

EFFECTIVE 

RETAIN 

THEN RESULT IS 

HIGH 

PRODUCTION 

CAPACITY IN 

BREWERY 

INDUSTRY 

 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

28 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 

Figure 4.0:  Operational application of the four rules 

To train 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, was shown in the operation 

of the four rules I figure 4.0 

 

Out 1

1

Demux

Demux

Demux

Demux

Zero Firing Strength ?

>

0
Total Firing

Strength

TOTALPRODUCTIONCAPACITY

Input MF

Switch

Rule 4

Rule

Rule 3

Rule

Rule 2

Rule

Rule 1

Rule

RESULT

Output MF

MidRange

-C-

Demux

Defuzzification 1

COA

DETECTINGSENSOR

Input MF

CausesofPoorProductionCapacity

Input MF

AggMethod 1

max

In1

1

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

29 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 
Fig 5 trained 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 

ANN was trained twenty times in four rules 20 x4 = 80 eighty rules that looks identical like human brain. 

. 

Figure 6.0:  Result obtained during the training 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.2

0.3

0.4

0.5

0.6

0.7

0.8

W(i,1)

W
(i
,2

)

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

y{1}x{1}

Input 1 

Neural Network

x{1} y {1}

Display

1.2

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

30 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 
Figure 7.0: Designed SIMULINK model for leverage machine learning 

This will be integrated in figure 1.0  to obtain the results shown in figures 9.0 to11.0 

To develop an algorithm that will implement the process, we have to go through the following process-  

1. Characterize and establish the causes of poor production capacity in a brewery industry. 

2. Identify inefficient Process Control and Automation 

3. Identify Equipment Downtime and Maintenance Issues 

4. Identify Variability in Raw Material Quality 

5. Identify Suboptimal Scheduling and Resource Allocation 

6. Identify Lengthy Fermentation and Maturation Times 

7. Identify Limited Data Utilization and Real-Time Analytics 

8. Identify Energy Constraints and Environmental Regulations 

9. Identify Labor Shortages and Skill Gaps 

10. Identify Total Production Impact 

11.  Design a conventional SIMULINK model for production processes in the brewery industry and integrate 2 

through 10 

12.  Design leverage machine learning rule base that will reduce the causes of poor production capacity in a 

brewery industry and simultaneously increase the production capacity. 

13.  Train 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 

14.  Design a SIMULINK model for leverage machine learning 

y{1}x{1}

Input 1 

powergui

Continuous

Video Viewer

Video

Viewer
ImageImageImage

A B C

A B C

Subsystem1

In1
In2
In3

Out1

Connection PortSubsystem

In1

In2 Out1

Connection Port

Raised Cosine

Receive Filter

Square root

Neural Network

x{1} y {1}
Gaussian Noise

Generator

Gaussian

Fuzzy Logic 

Controller

From Multimedia File

vipfly .avi

V: 240 x320 ,

 15 fps

Image
Display 1

1.2

Display

1.33

0

Clock

AWGN

Channel

AWGN

In1

1

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

31 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

15. Integrate 12 through 14 

16. Integrate 15 in 11 

17. Do the causes of poor production capacity in a brewery industry reduce? 

18. IF NO go to 16 

19. IF YES go to 23 

20. Does the production capacity in a brewery industry improve? 

21. IF NO go to16 

22. IF YES go to 23 

23. Optimized production processes and capacity in the brewery industry. 

24. Stop 

25. End 

To design a SIMULINK model for leveraging machine learning to optimize production processes and capacity in 

the brewery industry. 

 

 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

32 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 

STTATION 1 TRANSFER AND LOADING

STATION 2 FILLING

STATION 3 CAPPING  

y{1}1x{1}1

Input 1 

powergui

Continuous

Video Viewer

Video

Viewer
ImageImageImage

Variability in Raw Material Quality

In1 Out1

UNFILLED BOTTLES

In
1

O
u

t1

Total Production Impact

In1Out1

A B C

A B C

In1

Out1

Out2

Subsystem4

In1
In2
In3

Out1

Connection PortSubsystem3

In1

In2 Out1

Connection Port

Subsystem2

In1 Out1

In
1

O
u

t1

In1 Out1

Subsystem

Suboptimal Scheduling and Resource Allocation

In1 Out1

Scope 6

Scope 5

Raised Cosine

Receive Filter

Square root

Neural Network 1

x{1} y {1}

NO OF FILLED BOTLES

5e+004

NO OF CRATES

In
1

O
u

t1

Limited Data Utilization and Real -Time Analytics

In1 Out1

Lengthy Fermentation and Maturation Times

In1 Out1

Labor Shortages and Skill Gaps

In1 Out1

Inefficient Process Control and Automation

In1 Out1

Gaussian Noise

Generator

Gaussian

Fuzzy Logic 

Controller 1

From Multimedia File

vipfly .avi

V: 240 x320 ,

 15 fps

Image

FILL ING SYSTEM

In
1

O
u

t1

Equipment Downtime and Maintenance Issues

In1 Out1

Energy Constraints and Environmental Regulations

In1 Out1

EMPTY BOTTLES

In
1

O
u

t1

Display 9

6.65e+004

Display 8

4.336

Display 7

6.07

Display 6

6.937

Display 5

8.671

Display 4

13 .01

Display 3

8.671

Display 2

21 .68

Display 12

1.2

Display 11

1.33

Display 1

4165

Display

17 .34

0

Clock

CAPPING SYSTEMI

In
1

O
u

t1

CAPPING  

In1

Out1

Out2

AWGN

Channel

AWGN

In1

1

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

33 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Fig 8 designed SIMULINK model for leveraging machine learning to optimize production processes and capacity 

in the brewery industry. 

The results obtained were as shown in figure 9.0 to 11.0 

To validate and justify the percentage improvement in the production capacity of a brewery industry with and 

without leveraging machine learning 

To find percentage improvement in the reduction of inefficient Process Control and Automation cause of poor 

production capacity in brewery industry with leveraging machine learning 

Conventional inefficient Process Control and Automation =20% 

Leveraging machine learning inefficient Process Control and Automation =17.34% 

%improvement in the reduction of inefficient Process Control and Automation cause of poor production capacity 

in brewery industry with leveraging machine learning= 

Conventional inefficient Process Control and Automation - Leveraging machine learning inefficient Process 

Control and Automation 

%improvement in the reduction of inefficient Process Control and Automation cause of poor production capacity 

in brewery industry with leveraging machine learning= 20% - 17.34% 

%improvement in the reduction of inefficient Process Control and Automation cause of poor production capacity 

in brewery industry with leveraging machine learning=2.66% 

To find percentage improvement in the reduction of Lengthy Fermentation and Maturation Times cause of poor 

production capacity in brewery industry with leveraging machine learning 

Conventional Lengthy Fermentation and Maturation Times =10% 

Leveraging machine learning Lengthy Fermentation and Maturation Times =8.7% 

%improvement in the reduction of Lengthy Fermentation and Maturation Times cause of poor production 

capacity in brewery industry with leveraging machine learning= 

Conventional Lengthy Fermentation and Maturation Times - Leveraging machine learning Lengthy Fermentation 

and Maturation Times 

%improvement in the reduction of Lengthy Fermentation and Maturation Times cause of poor production 

capacity in brewery industry with leveraging machine learning=10% - 8.7% 

%improvement in the reduction of Lengthy Fermentation and Maturation Times cause of poor production 

capacity in brewery industry with leveraging machine learning=1.3% 

To find percentage improvement in the production capacity in brewery industry with leveraging machine learning 

Conventional production capacity =50,000bottles 

Leveraging machine learning production capacity =6, 6500bottles 

%improvement in the production capacity in brewery industry with leveraging machine learning= 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

34 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Leveraging machine learning production capacity - Conventional production capacity x 100% 

                                                  Conventional production capacity                                            1 

%improvement in the production capacity in brewery industry with leveraging machine learning= 

                                                            6, 6500bottles - 50,000bottles x100% 

                                                                           50,000bottles                1 

%improvement in the production capacity in brewery industry with leveraging machine learning=33% 

3.0  

4.0 Results and Discussions 

Table 3 comparison of conventional and leveraging machine learning inefficient Process Control and Automation 

cause of poor production capacity in brewery industry 

Time (days) Conventional inefficient Process 

Control and Automation cause of 

poor production capacity in brewery 

industry (%) 

 

 

Leveraging machine learning 

inefficient Process Control and 

Automation cause of poor 

production capacity in brewery 

industry (%) 

 

 

1 20 17.34 

2 20 17.34 

3 20 17.34 

4 20 17.34 

10 20 17.34 

 

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

35 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

 
Figure 9.0: Comparison of conventional and leveraging machine learning inefficient Process Control and 

Automation cause of poor production capacity in brewery industry 

The conventional inefficient Process Control and Automation cause of poor production capacity in brewery 

industry was20%. On the other hand, when leveraging machine learning was introduced in the system, it 

drastically reduced inefficient Process Control and Automation cause of poor production capacity in brewery 

industry to17.34% thereby enhancing the production capacity. 

1 2 3 4 5 6 7 8 9 10
17

17.5

18

18.5

19

19.5

20

in
e
f
f
ic

ie
n
t
 
P

r
o
c
e
s
s
 
C

o
n
t
r
o
l 
a
n
d
 
A

u
t
o
m

a
t
io

n
 
c
a
u
s
e
 
o
f
 
p
o
o
r
 
p
r
o
d
u
c
t
io

n
 
c
a
p
a
c
it
y
 
in

 
b
r
e
w

e
r
y
 
in

d
u
s
t
r
y
 
(
%

)
 
 

Time (days)

 

 

Conventional inefficient Process Control and Automation cause of poor production capacity in brewery industry (%)

Leveraging machine learning inefficient Process Control and Automation cause of poor production capacity in brewery industry (%)

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

36 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Table 4 comparison of conventional and leveraging machine learning Lengthy Fermentation and Maturation 

Times cause of poor production capacity in brewery industry 

Time (days) Conventional Lengthy 

Fermentation and Maturation Times 

cause of poor production capacity in 

brewery industry (%) 

 

 

Leveraging machine learning 

Lengthy Fermentation and 

Maturation Times cause of poor 

production capacity in brewery 

industry (%) 

 

 

1 10 8.7 

2 10 8.7 

3 10 8.7 

4 10 8.7 

10 10 8.7 

 

Figure 10.0:  comparison of conventional and leveraging machine learning Lengthy Fermentation and 

Maturation Times cause of poor production capacity in brewery industry 

The conventional Lengthy Fermentation and Maturation Times cause of poor production capacity in brewery 

industry was10%. Meanwhile, when leveraging machine learning was incorporated in the system, it decisively 

1 2 3 4 5 6 7 8 9 10
8.5

9

9.5

10

L
e
n
g
th

y
 F

e
rm

e
n
ta

ti
o
n
 a

n
d
 M

a
tu

ra
ti
o
n
 T

im
e
s
  

c
a
u
s
e
 o

f 
p
o
o
r 

p
ro

d
u
c
ti
o
n
 c

a
p
a
c
it
y
 i
n
 b

re
w

e
ry

 i
n
d
u
s
tr

y
 (

%
) 

 

Time (days)

 

 

Conventional Lengthy Fermentation and Maturation Times  cause of poor production capacity in brewery industry (%)

Leveraging machine learning inefficient Lengthy Fermentation and Maturation Times  cause of poor production capacity in brewery industry (%)

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

37 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

reduced Lengthy Fermentation and Maturation Times cause of poor production capacity in brewery industry to 

8.7%. 

Table 5 comparison of conventional and leveraging machine learning production capacity in brewery industry 

Time (days) Conventional production capacity 

in brewery industry (bottles) 

 

 

Leveraging machine learning 

production capacity in brewery 

industry (bottles) 

 

 

1 50000 6 6500 

2 50000 6 6,500 

3 50000 6 6,500 

4 50000 6 6,500 

10 50000 6 6,500 

 

Fig 11 comparison of conventional and leveraging machine learning production capacity in brewery industry 

The conventional production capacity in brewery industry was 50000bottles of drink. On the other hand, when 

leveraging machine learning was inculcated in the system, it simultaneously enhanced the production capacity in 

a brewery industry to6 6500 bottles of drink. Finally, with these results obtained, it showed that production 

capacity in a brewery industry was optimized by 33%. 

1 2 3 4 5 6 7 8 9 10
5

5.2

5.4

5.6

5.8

6

6.2

6.4

6.6

6.8
x 10

4

 p
ro

d
u
c
ti
o
n
 c

a
p
a
c
it
y
 i
n
 b

re
w

e
ry

 i
n
d
u
s
tr

y
 (

b
o
tt

le
s
) 

 

Time (days)

 

 

Conventional production capacity in brewery industry (bottles)

Leveraging machine learning production capacity in brewery industry (bottles)

mailto:topacademicjournals@gmail.com


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

38 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

4.0 Conclusion 

The consistent poor production capacity experienced in a brewery industry is anchored on these factors, inefficient 

Process Control and Automation, Equipment Downtime and Maintenance Issues, Variability in Raw Material 

Quality, Suboptimal Scheduling and Resource Allocation, Lengthy Fermentation and Maturation Times, Limited 

Data Utilization and Real-Time Analytics, Energy Constraints and Environmental Regulations, Labor Shortages 

and Skill Gaps coupled with Total Production Impact. This 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 the 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, the conventional inefficient Process Control and Automation cause of poor production 

capacity in brewery industry was20%. On the other hand, when leveraging machine learning was introduced in 

the system, it drastically reduced inefficient Process Control and Automation cause of poor production capacity 

in brewery industry to17.34% 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 in the system, it decisively reduced Lengthy Fermentation and 

Maturation Times cause of poor production capacity in brewery industry to 8.7% and the conventional production 

capacity in brewery industry was 50000bottles of drink. On the other hand, when leveraging machine learning 

was inculcated in the system, it simultaneously enhanced the production capacity in a brewery industry to6 6500 

bottles of drink. Finally, with these results obtained, it showed that production capacity in a brewery industry was 

optimized by 33%. 

REFERENCES 

Agrawal, A., Gans, J. S., & Goldfarb, A. (2020). Machine learning, process control, and industrial production. 

MIT Press. 

Lydon, M., Kim, J., & Yang, C. (2021). Machine learning in manufacturing: An overview of applications in 

process optimization. Journal of Manufacturing Processes, 54, 112–128. 

https://doi.org/10.1016/j.jmapro.2020.11.029 

mailto:topacademicjournals@gmail.com
https://doi.org/10.1016/j.jmapro.2020.11.029


    

 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 1; January -February 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

https://topjournals.org/index.php/AJSET/index; mail: topacademicjournals@gmail.com 

 
 

 

 

  

39 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 

Oliveira, J., & Cunha, L. M. (2019). Smart breweries: Applications of machine learning in beer production. Food 

Engineering Reviews, 11(2), 75–89. https://doi.org/10.1007/s12393-019-09167-4 

Rathore, A., & Tiwari, M. K. (2020). Advanced manufacturing technologies for the brewery industry. Industrial 

Journal of Brewing, 126(3), 345–360. https://doi.org/10.1111/ijbc.12352 

Singh, S., & Kumar, S. (2021). Challenges in AI and machine learning applications in traditional industries. AI 

in Manufacturing Journal, 4, 23–35. https://doi.org/10.1016/j.aim.2020.12.001 

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2019). Machine learning in manufacturing: Advantages, 

challenges, and applications. Production Engineering, 13(2), 175–187. https://doi.org/10.1007/s11740-

019-00847-3 

Zhou, B., Dai, W., & Yang, H. (2021). Machine learning for smart manufacturing: A literature review. Journal of 

Manufacturing Science and Engineering, 143(4), 1–13. https://doi.org/10.1115/1.4049023 

mailto:topacademicjournals@gmail.com
https://doi.org/10.1007/s12393-019-09167-4
https://doi.org/10.1111/ijbc.12352
https://doi.org/10.1016/j.aim.2020.12.001
https://doi.org/10.1007/s11740-019-00847-3
https://doi.org/10.1007/s11740-019-00847-3
https://doi.org/10.1115/1.4049023

