Statistical and Machine Learning Approaches to Production Optimization in the Brewery Industry

Abstract

Production collapse in brewery operations is a major industrial challenge marked by sustained declines in output, efficiency, and capacity utilization due to interacting technical, operational, managerial, and external constraints. This systematic review synthesizes existing literature on the root causes of production decline in the brewery and beverage industry, with emphasis on developing economies. Guided by the PRISMA framework and drawing from major scientific databases, the study examines empirical evidence on critical production bottlenecks. The review compares traditional mathematical models with advanced Machine Learning (ML) techniques for root cause identification, highlighting their complementary strengths in interpretability and predictive accuracy. It further evaluates optimization and what-if scenario analysis as decision-support tools for translating predictive insights into practical production improvements. Evidence shows that scenario-based optimization can enhance output, reduce downtime, and improve resource allocation in brewery systems. Despite progress, gaps remain, particularly the absence of integrated root-cause, ML, and optimization frameworks and limited validation rigor. By consolidating fragmented findings and outlining future research directions, this review provides a structured foundation for developing robust, data-driven productivity recovery strategies and strengthening sustainable performance in brewery operations.

Keywords

Brewery production optimization / Root cause analysis / ML models / What-if scenario analysis / Operational efficiency

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

Large Language Model (ChatGPT, OpenAI) was used solely for language editing, grammar improvement, and clarity enhancement. All scientific content, analysis, results, and conclusions were developed and verified by the authors.

Author Contributions

This work was carried out in collaboration between all authors. Author C.M.O. and D.A.E. did the Conceptualization and Methodology. B.E.O. and V.U.O. wrote the protocol, and wrote the first draft of the manuscript. Author O.O.O., C.M.O. and B.E.O. managed the literature searches. All authors read and approved the final manuscript.

Ethics Statement

Not Applicable. No Animal or Human was used in the Study.

Informed Consent Statement

Informed consent was obtained from all individuals included in this study. All authors consent to the publication of this manuscript. The manuscript is original, has not been published previously, and is not under consideration for publication elsewhere.

Data Availability Statement

Not Applicable.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Agraprana AA, Donoriyanto DS. Optimization of production process by applying Theory of Constraint (TOC) at CV. Wijaya Mandiri Label. IJIEM-Indones. J. Ind. Eng. Manag. 2024, 5, 192. DOI: 10.22441/ijiem.v5i1.22515

[2] David CE, Uche R, Nwufo O, Ekpechi DA, Kingsley CC. Integrating machine availability and preventive maintenance to improve productive efficiency in a manufacturing industry. Asian J. Curr. Res. 2024, 9, 91-109. DOI: 10.56557/AJOCR/2024/v9i28610

[3] Nigerian Breweries Plc. Q2 2025: Nigerian Breweries Plc Records N733.19 Billion Revenue. 2025. Available online: https://www.nbplc.com/q2-2025-nigerian-breweries-plc-records-n733-19-billion-revenue (accessed on 19 December 2025).

[4] Premium Times Nigeria. 2024: Nigerian Breweries Plc Crosses the Trillion Naira Mark in Revenue. 2024. Available online: https://www.premiumtimesng.com/promoted/774433-2024-nigerian-breweries-plc-crosses-the-trillion-naira-mark-in-revenue.html (accessed on 19 December 2025).

[5] Food Business Africa. Brewing Majors & Craft Brewers Battle for a Share of the World’s Last Frontier Market for Beer. Available online https://www.theafricareport.com/415775/nigerian-breweries-vows-to-fight-for-market-share-as-tolaram-fuels-guinness-rivalry/ (accessed on 19 December 2025).

[6] Mordor Intelligence. Europe Beer Market-Growth, Trends, and Forecast (2025-2030). Available online: https://www.mordorintelligence.com/industry-reports/europe-beer-market (accessed on 19 December 2025).

[7] Ezeaku NI, Uzorh AC, Obiukwu OO, Godwin SI, Ekpechi DA. Quantitative models for supply chain risk analysis in water tank manufacturing: A case study of a factory in Aba, Nigeria. Asian J. Curr. Res. 2024, 9, 1-12. DOI: 10.56557/AJOCR/2024/v9i18483

[8] El Hammoumi Z, Tebr D, Charkaoui Y, Satauri I, El Beqqali O. Predictive Maintenance Approaches: A Systematic Literature Review. Eng. Proc. 2025, 112, 70. DOI: 10.3390/engproc2025112070

[9] Shamsuddoha M, Khan EA, Chowdhury MMH, Nasir T. Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow. Information 2025, 16, 26. DOI: 10.3390/info16010026

[10] Yang Y, Wang H. Random Forest-Based Machine Failure Prediction: A Performance Comparison. Appl. Sci. 2025, 15, 8841. DOI: 10.3390/app15168841

[11] Bubeník P, Rakyta M, Buzalka M, Biňasová V, Kovaríková Z. Optimization of Business Processes Using Artificial Intelligence. Electronics 2025, 14, 2105. DOI: 10.3390/electronics14112105

[12] Ndukwe DC, Obiukwu OO, Njoku DO, Ekpechi DA. Screw production optimization using artificial neural network (ANN) technology. Asian J. Curr. Res. 2023, 8, 82-95. DOI: 10.56557/AJOCR/2023/v8i48447

[13] Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int. J. Surg. 2021, 88, 105906. DOI: 10.1016/j.ijsu.2021.105906

[14] Ooyama S, Seo Y, Maesako K. Assessing Sustainability Trade-Offs in Craft Beer Production Through Life Cycle and Costing Analysis Scenarios. Sustainability 2025, 17, 11003. DOI: 10.3390/su172411003

[15] Carvalho G, Leite AC, Leal R, Pereira R. The Role of Emergent Processing Technologies in Beer Production. Beverages 2023, 9, 7. DOI: 10.3390/beverages9010007

[16] Duru KB, Uche R, Obiukwu OO, Ekpechi DA, Chukwuemeka KC. Minimization of root causes of declining productivity in a manufacturing company: A case study of Jocalis Company. Int. J. Adv. Multidiscip. Res. Stud. 2024, 4, 1095-1104. DOI: 10.62225/2583049X.2024.4.1.2346

[17] Aniuga C, Ogba I-E. Product innovation and customer satisfaction in Nigeria brewery industry: A study of customers of Star Lager Beer by Nigerian Breweries Plc. in Southeast, Nigeria. Afr. Sci. J. 2021, 3, 43. DOI: 10.5281/zenodo.5648324

[18] Voicu-Dorobanțu R. Crisis-Proofing the Fresh: A Multi-Risk Management Approach for Sustainable Produce Trade Flows. Sustainability 2025, 17, 4466. DOI: 10.3390/su17104466

[19] Ukey K, Chinta L, Majumder H, Patil DS, Mitkari S, Sahu AR, et al. Implementation of the Theory of Constraints (TOC) for a Furniture Manufacturing-Based Organization. Eng. Proc. 2025, 114, 17. DOI: 10.3390/engproc2025114017

[20] Tang J, Dai Z, Jiang W, Wu X, Zhuravkov MA, Xue Z, et al. A Comprehensive Review of Theories, Methods, and Techniques for Bottleneck Identification and Management in Manufacturing Systems. Appl. Sci. 2024, 14, 7712. DOI: 10.3390/app14177712

[21] Ota OU, Obiukwu OO, Okafor BE, Ekpechi DA. Lean optimization of batch production in an aluminium company. Asian J. Curr. Res. 2023, 8, 62-81. DOI: 10.56557/AJOCR/2023/v8i48445

[22] Campilho RDSG, Barbosa FB. Innovations in Manufacturing Processes and Systems for Sustainable Practices. Processes 2025, 13, 2315. DOI: 10.3390/pr13072315

[23] Salazar TMB, San Martín-González MF, Velasquez Domingo JA, Huang J-Y. Life Cycle Assessment of Craft Beer Brewing at Different Scales on a Unit Operation Basis. Sustainability 2023, 15, 11416. DOI: 10.3390/su151411416

[24] Cerna-Arribasplata JA, Rivas-Atauchi AM, Tupia-De-La-Cruz EL. Lean-TPM production model for efficiency and quality enhancement in the brewing industry: Insights from a Peruvian case study. Int. J. Recent Eng. Sci. 2025, 12, 23-32. DOI: 10.14445/23497157/IJRES-V12I1P104

[25] Dumbuya IB, Koroma MJ, Conteh ZB. Enhancing productivity and efficiency in the workplace through the 5S methodology: A strategic approach to operational excellence at Sierra Leone Brewery Limited. Int. J. Res. Commer. Manag. Stud. 2025, 7, 46-61. DOI: 10.38193/IJRCMS.2025.7104

[26] Escárcega Zepeda PA, Hernández Navarro PR, Mondragón Chavero LA, Duque Sevilla RM, Corral Nuñez MdC, López Badilla G, et al. Using DMAIC methodology and Lean Manufacturing tools in the brewing industry in Northwest Mexico to increase delivery times and sales. Partn. Univers. Innov. Res. Publ. 2024, 2, 77-85. DOI: 10.5281/zenodo.13974816

[27] Howard DA, Ma ZG, Engvang JA, Hagenau M, Jorgensen KL, Fausing Olesen J, et al. Energy flexibility potential in the brewery sector: A multi-agent based simulation of 239 Danish breweries. In Proceedings of the 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), Melbourne, Australia, 20-23 November 2022. DOI: 10.1109/APPEEC53445.2022.10072200

[28] Wood G, Martin D. Regulation and restructuring in the craft brewing industry. Growth Change 2025, 56, e70023. DOI: 10.1111/grow.70023

[29] Xie J, Chen Y, Yin R, Yuan X, Guo L, Zhao D, et al. Unveiling Disparities in Beer Consumer Behavior and Key Drivers Across Regions in China. Foods 2025, 14, 3799. DOI: 10.3390/foods14213799

[30] Alo EA, Lugard E. Inventory management practices and performance of brewery manufacturing firms in south-west, Nigeria. Adeleke Univ. J. Bus. Soc. Sci. 2024, 4, 162-175. Available online: https://aujbss.adelekeuniversity.edu.ng/index.php/aujbss/article/view/86 (accessed on 24 December 2025)

[31] Otuagoma S, Obuseh E, Akpovi O, Okpare A, Eyenubo J, Roland T. Assessment of Conveyor Telemetry System in a Manufacturing Firm: A Case Study of a Brewery in Eastern Nigeria. NIPES-J. Sci. Technol. Res. 2025, 7, 44-58. DOI: 10.37933/nipes/7.2.2025.3

[32] Terradillos E, Matias J, Navas HVG, Costa O, Mendes D. A Conceptual Lean-Sustainability Model for Industrial SMEs. Sustainability 2026, 18, 3826. Doi: 10.3390/su18083826

[33] Onyema CE, Obiukwu OO, Osueke GO. Reduction of machine breakdown in the brewery manufacturing company: A case study of Brewery “A” in Eastern Nigeria. Asian J. Appl. Sci. Technol. (AJAST) 2023, 7, 65-74. DOI: 10.38177/ajast.2023.7307

[34] Paprocka I. Evaluation of the Effects of a Machine Failure on the Robustness of a Job Shop System Proactive Approaches. Sustainability 2019, 11, 65. DOI: 10.3390/su11010065

[35] Mohamed A, Ben J, Muduli K. Implementation of Autonomous Maintenance and its Effect on MTBF, MTTR, and Reliability of a Critical Machine in a Beer Processing Plant. Int. J. Pareto Stud. Allied Sci. Technol. 2022. Available online: https://www.researchgate.net/publication/359061752 (accessed on 24 January 2026)

[36] Inyiama GK, Oke SA. Maintenance downtime evaluation in a process bottling plant. Int. J. Qual. Reliab. Manag. 2021, 38, 229-248. DOI: 10.1108/IJQRM-12-2018-0340

[37] Martínez Monseco FJ, Gómez Lázaro E, Martín Martínez S. Implementation of the RCM Methodology as a Technical Analysis for Maintenance and Innovation for Hydroelectric Power Plants. Energies 2026, 19, 1394. DOI: 10.3390/en19061394

[38] Ezeaku NI, Ekpechi DA, Chiabuotu CC, Nwankwo EI, Olum RI, Jerome FN. Application of Quantitative Models for Enhancing Supply Chain Efficiency and Mitigating Risks in Water Tank Manufacturing Industry of Nigeria. Eng. Sci. Technol. 2024, 389-404. DOI: 10.37256/est.5220244554

[39] Drljača M, Petar S, Brannan GD, Štimac I. Quality Models for Preventing the Impact of Supply Chain Disruptions in Future Crises. Sustainability 2025, 17, 3293. DOI: 10.3390/su17083293

[40] Nettesheim PM, Burggräf P, Steinberg F. Enhancing brewery logistics with smart kegs: A simulation study. J. Transp. Supply Chain. Manag. 2024, 18, 1073. DOI: 10.4102/JTSCM.v18i0.1073

[41] Duarte Soliani R, Tiradentes Terra Argoud AR, Santiago F, Batista de Camargo Junior J, Gomes de Freitas C, Pedroza Lobão MS. Impacts of collaborative logistics: A Brazilian brewing sector case study. Int. J. Ind. Eng. Manag. 2022, 13, 99-109. DOI: 10.24867/IJIEM-2022-2-304

[42] Wolmarans I, Niemann W. Internal supply chain integration during disruption recovery: A case study in the South African liquor industry. Acta Commer. 2023, 23, 16. DOI: 10.4102/ac.v23i1.1150

[43] Hinkosa Dinsa J. Effect of supply chain management practice on operational performance in the case of Bedele Brewery Share Company. J. Bus. Econ. Dev. 2023, 8, 94-107. DOI: 10.11648/j.jbed.20230803.13

[44] Arimany-Serrat N, Sgorla AF. Financial and ESG Analysis of the Beer Sector Pre- and Post-COVID-19 in Italy and Spain. Sustainability 2024, 16, 7412. DOI: 10.3390/su16177412

[45] Arimany Serrat N, Sgorla AF. Analysis of the Financial and Non-Financial Indicators of the Beer Sector: Italy and Spain. 2024, preprints. DOI: 10.20944/preprints202404.1886.v1

[46] Korálová E, Taušl Procházková P. Sustainable finance and carbon neutrality in the brewery industry: Challenges and opportunities. ACC J. 2025, 31, 43-58. DOI: 10.2478/acc-2025-0009

[47] Chuku U, Uche OJ, LongJohn TB. Management Accounting Strategies and Financial Performance of Listed Brewery and Food Product Manufacturing Companies in Nigeria. Repos. Antis Publ. 2022, 694021. Available online: https://www.neliti.com/publications/694021/management-accounting-strategies-and-financial-performance-of-listed-brewery-and (accessed on 12 February 2026).

[48] Obembe OJ, Jacob MS, Edheku OJ. Corporate Social Responsibility Accounting and Financial Performance of Breweries in Nigeria. Eur. J. Account. Audit. Financ. Res. 2022, 10, 58-70. Available online: https://eajournals.org/wp-content/uploads/Corporate-Social-Responsibility-Accounting-and-Financial.pdf (accessed on 12 February 2026).

[49] Agbata AE, Osingor AS, Ezeala G. Effect of financial ratios on firm performance: Study of selected brewery firms in Nigeria. Int. J. Trend Sci. Res. Dev. (IJTSRD) 2021, 5, 1664-1671. Available online: https://www.researchgate.net/profile/George-Ezeala/publication/355070875_Effect_of_Financial_Ratios_on_Firm_Performance_Study_of_Selected_Brewery_Firms_in_Nigeria_of_the_Creative_Commons_Attribution_License_CC_BY_40/links/615c4c9f5a481543a87ff2c5/Effect-of-Financial-Ratios-on-Firm-Performance-Study-of-Selected-Brewery-Firms-in-Nigeria-of-the-Creative-Commons-Attribution-License-CC-BY-40.pdf (accessed on 12 February 2026).

[50] Banabo KC, Aganaba D. The impact of capital structure and financial effectiveness listed brewery firms in Nigeria. BW Acad. J. 2024, 105-127. Available online: https://bwjournal.org/index.php/bsjournal/article/view/1984 (accessed on 12 February 2026).

[51] Török Á, Szerletics Á, Jantyik L. Factors Influencing Competitiveness in the Global Beer Trade. Sustainability 2020, 12, 5957. DOI: 10.3390/su12155957

[52] Williams A. Exploring the impact of legislation on the development of craft beer. Beverages 2017, 3, 18. DOI: 10.3390/beverages3020018

[53] Kalak T. Sustainable Utilization of Brewer’s Spent Grains for Energy Production: Technologies, Challenges, and Development Prospects. Energies 2026, 19, 1828. Doi: 10.3390/En19081828

[54] Dumbili EW. The Politics of Alcohol Policy in Nigeria: A Critical Analysis of How and Why Brewers Use Strategic Ambiguity to Supplant Policy Initiatives. J. Asian Afr. Stud. 2014, 49, 473-487. DOI: 10.1177/0021909613490137

[55] Odo YE, Nnamani E, Uzoamaka E. Environment and Organizational Growth of Brewery Industry in Nigeria. Adv. J. Bus. Entrep. Dev. 2023, 7. Available online: https://aspjournals.net/ajbed/index.php/ajbed/article/view/44 (accessed on 12 February 2026).

[56] Okafor CN, Udejinta NB, Obi-Anike HO. Impact of Economic Environment on the Performance of Nigeria Breweries Industry. Eur. J. Bus. Soc. Sci. 2017, 5, 63-80. Available online: https://europub.co.uk/articles/-A-10863 (accessed on 12 February 2026).

[57] Egwu O. The Impact of Government Regulatory Policies on the Performance of Business Organization (A Study of Nigerian Breweries, Aba): Egwu,Progress O. 2023. Available online: https://repository.mouau.edu.ng/work/view/the-impact-of-government-regulatory-policies-on-the-performance-of-business-organization-a-study-of-nigerian-breweries-aba-egwu-progress-o-7-2 (accessed on 12 January 2026).

[58] Poelmans E, Dove JA, Taylor JE. The politics of beer: Analysis of the congressional votes on the beer bill of 1933. Public Choice 2018, 174, 81-106. DOI: 10.1007/s11127-017-0493-1

[59] Hawkins B, Durrance-Bagale A, Walls H. Co-regulation and alcohol industry political strategy: A case study of the Public Health England-Drinkaware Drink Free Days campaign. Soc. Sci. Med. 2021, 285, 114175. DOI: 10.1016/j.socscimed.2021.114175

[60] Ji X, Hettiarachchige RO, Littman ALE, Lavery NL, Piovesan D. Prevent Workers from Injuries in the Brewing Company via Using Digital Human Modelling Technology. Appl. Sci. 2023, 13, 3593. DOI: 10.3390/app13063593

[61] Šipilova V, Aleksejeva L, Homutiņins A. Decline in Labor Force and the Affecting Factors: Insights from System Dynamics, PEST, and SWOT Analysis in Latvia. Soc. Sci. 2025, 14, 718. DOI: 10.3390/socsci14120718

[62] Osisioma EH, Audu YP. Performance management practices and productivity of employees of Nigeria Brewery and Guinness Plc in South-West Nigeria. Int. J. Public Adm. Manag. Res. 2022, 7, 28-45 https://www.researchgate.net/profile/Ekpechi-Daniel?ev=hdr_xprf (accessed on 17 March 2026).

[63] Akpan AU. The Effect of Unethical Recruitment Practices on Labour Turnover: A Case Study of Brewery Industry in South West Nigeria. AKSU J. Adm. Corp. Gov. (AKSUJACOG) 2021, 1, 149-157. Available online: https://aksujacog.org.ng/articles/21/12/the-effect-of-unethical-recruitment-practices-on-labour-turnover-a-case-study-of-brewery-industry-in-south-west-nigeria/ (accessed on 17 March 2026).

[64] Adewumi SA. Employment casualisation and trade union survival strategies in the beverage sector in Lagos, Nigeria. J. Public Aff. 2020, 21, e2256. DOI: 10.1002/pa.2256

[65] Ikwuka PC, Ikon MA, Onwuchekwa FC. Employee Resourcing Strategies and Performance of Brewery Companies in South-East, Nigeria. Mod. Econ. Manag. 2022, 1, 8. DOI: 10.53964/mem.2022008

[66] Okonkwo E, Okafor N, Essell O. The effect of human resource planning on organizational performance: A study of selected breweries in the South-East, Nigeria. Int. J. Manag. Entrep. Res. 2022, 4, 13-25. DOI: 10.51594/ijmer.v4i1.280

[67] Sak T, Mylko I, Ivashko O, Chulipa I, Wołowiec T, Sokołowska-Woźniak J. How the Digital Economy Is Revolutionizing Marketing Management and Driving Sustainable Development. Sustainability 2025, 17, 1130. DOI: 10.3390/su17031130

[68] He Z, Ni S, Jiang X, Feng C. The Influence of Demand Fluctuation and Competition Intensity on Advantages of Supply Chain Dominance. Mathematics 2023, 11, 4931. DOI: 10.3390/math11244931

[69] Etuk A, Basil G, Bassey I. Supply and Demand Chain Management and Operational Efficiencies of Brewerying Firms in Nigeria: A Study of Champion Breweries Uyo. AKSU J. Manag. Sci. (AKSUJOMAS) 2016, 1, 91-103. Available online: https://aksujomas.org.ng/articles/16/12/supply-and-demand-chain-management-and-operational-efficiencies-of-brewerying-firms-in-nigeria-a-study-of-champion-breweries-uyo/ (accessed on 17 March 2026).

[70] Ike NA, Ogbodo OC. Market Risk Effects and Financial Performance of Listed Brewery Firms in Nigeria. J. Glob. Account. 2023, 9, 182-213. Available online: https://journals.unizik.edu.ng/joga/article/view/2661/2162 (accessed on 17 March 2026).

[71] Ugboya AP, Odiamenhi M. Prospects of the Nigeria Brewery Industry. J. Adv. Sci. Eng. 2019, 2, 17-24. DOI: 10.37121/jase.v2i1.43

[72] Wojtaszek H. Energy Transition 2024-2025: New Demand Vectors, Technology Oversupply, and Shrinking Net-Zero 2050 Premium. Energies 2025, 18, 4441. DOI: 10.3390/en18164441

[73] Li J. New Infrastructure Construction, Institutional Pressure, and Sustainable Development Performance: Empirical Evidence from Chinese Manufacturing Enterprises. Sustainability 2025, 17, 8551. DOI: 10.3390/su17198551

[74] Mathew SU, Aban TL. Design and Cost Optimization of Heat Exchangers Network System in a Typical Brewery Plant. Eur. J. Eng. Technol. Res. 2020, 5, 245-251. DOI: 10.24018/ejeng.2020.5.3.1748

[75] Mtebwa MH, Ichwekeleza WY. Assessment of energy efficiency in beverage industry. Tanzan. J. Eng. Technol. 2022, 41, 171-177. DOI: 10.52339/tjet.v41i2.790

[76] Noah OO, Obanor AI, Audu ML. Energy audit of a brewery: A case study of Vitamalt Nig. Plc, Agbara. Energy Power Eng. 2012, 4, 137-143. DOI: 10.4236/epe.2012.43019

[77] Nydrioti I, Grigoropoulou H. Optimizing Water Footprint and Energy Use in Industry: A Decision Support Framework for Industrial Wastewater Treatment and Reuse Applied to a Brewery. Water 2025, 17, 1179. DOI: 10.3390/w17081179

[78] Brewery Collapses, Doors Close Forever. 2024. Available online: https://www.news.com.au/finance/business/retail/multiple-breweries-collapse-as-alcohol-industry-brought-to-its-knees/news-story/15fffe9945fce3dfb878f7a34be6a65e (accessed on 10 January 2025).

[79] Courier Mail. Liquidation Looms as Brewery’s Frothy Debts Revealed. 2024. Available online: https://www.couriermail.com.au/topics/company-collapses-qld/page/32 (accessed on 10 January 2025).

[80] The Sun. Major Beer Brand Placed into Administration & Staff ‘Made Redundant’ in Move to ‘Protect Firm from Market Pressures’. 2024. Available online: https://www.thesun.ie/money/13933183/major-beer-brand-placed-into-administration/ (accessed on 10 January 2025).

[81] Rocha PA. What to Know About Alamo Beer Co. as Brewery Files for Bankruptcy. 2025. Available online: https://www.mysanantonio.com/business/article/alamo-beer-company-bankruptcy-20151250.php (accessed on 10 January 2025).

[82] The Scottish Sun. Popular Beer Brand to File for Administration Leaving Drinkers Gutted. 2024. Available online: https://www.thescottishsun.co.uk/money/14136567/popular-beer-brand-to-file-for-administration/ (accessed on 10 January 2025).

[83] Wikipedia. Flying Fish Brewing. 2023. Available online: https://en.wikipedia.org/wiki/Flying_Fish_Brewing (accessed on 10 January 2025).

[84] Vanguard News. Nigerian Breweries Posts N106bn Loss, Shuts 2 Plants. 18 April 2024. https://www.vanguardngr.com/2024/04/nigerian-breweries-posts-n106bn-loss-shuts-2-plants/ (accessed on 10 January 2025).

[85] The Guardian. Last Orders: South Sudan’s Only Brewery to Shut as Civil War Causes Forex Shortage. 15 January 2016. Available online: https://www.theguardian.com/global-development/2016/jan/15/south-sudan-only-brewery-shuts-civil-war-forex-shortage (accessed on 10 January 2025).

[86] Nastasi G. Mathematical Models and Simulations, 2nd Edition. Axioms 2025, 14, 892. DOI: 10.3390/axioms14120892

[87] Dudek G. Advances in Machine Learning: Emerging Methods and Real-World Applications. Appl. Sci. 2026, 16, 443. DOI: 10.3390/app16010443

[88] Dawid L, Barańska AM, Baran P. Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs. Appl. Sci. 2025, 15, 6297. DOI: 10.3390/app15116297

[89] Wang D, Wu H, Zheng W, Zhao Y, Tian G, Wang W, et al. A Mixed-Integer Linear Programming Model for Addressing Efficient Flexible Flow Shop Scheduling Problem with Automatic Guided Vehicles Consideration. Appl. Sci. 2025, 15, 3133. DOI: 10.3390/app15063133

[90] Kim K, Kwon S, Choi M. Optimization of Production Scheduling for the Additive Manufacturing of Ship Models Using a Hybrid Method. J. Mar. Sci. Eng. 2024, 12, 1961. DOI: 10.3390/jmse12111961

[91] Vicente JJ. Optimizing Supply Chain Inventory: A Mixed Integer Linear Programming Approach. Systems 2025, 13, 33. DOI: 10.3390/systems13010033

[92] Chansombat S, Pongcharoen P, Hicks C. A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry. Int. J. Prod. Res. 2018, 57, 61-82. DOI: 10.1080/00207543.2018.1459923

[93] Taghizadeh H, Hajipirlu MZ. Application of non-linear modelling with uncertain resource limitations in the optimization of manufacturing and production dimensions based on a fuzzy approach. Res. Sq. 2023. DOI: 10.21203/rs.3.rs-2835107/v1

[94] Fadaeefath AM, Bordbari MJ, Haghighat F, Nasiri F. Dynamic Maintenance Cost Optimization in Data Centers: An Availability-Based Approach for K-out-of-N Systems. Buildings 2025, 15, 1057. DOI: 10.3390/buildings15071057

[95] Lopez-Landeros CE, Valenzuela-Gonzalez R, Olivares-Benitez E. Dynamic optimization of a supply chain operation model with multiple products. Mathematics 2024, 12, 2420. DOI: 10.3390/math12152420

[96] Mitrai I, Palys MJ, Daoutidis P. A Two-Stage Stochastic Programming Approach for the Design of Renewable Ammonia Supply Chain Networks. Processes 2024, 12, 325. DOI: 10.3390/pr12020325

[97] Wang Z, Hao S, Yuan L, Hao K. A Three-Stage Stochastic Model to Improve Resilience with Lateral Transshipment in Multi-Period Emergency Logistics. Systems 2024, 12, 73. DOI: 10.3390/systems12030073

[98] Liu Y, Tian G, Zhang X, Jiang Z. Multi-Objective Remanufacturing Processing Scheme Design and Optimization Considering Carbon Emissions. Symmetry 2025, 17, 266. DOI: 10.3390/sym17020266

[99] Zhao Y, Du S, Tu M, Ma H, Shang J, Xiang X. Multi-Objective Scheduling Optimization of Prefabricated Components Production Using Improved Non-Dominated Sorting Generic Algorithm II. Buildings 2025, 15, 742. DOI: 10.3390/buildings15050742

[100] Tomar V, Bansal M, Singh P. Metaheuristic Algorithms for Optimization: A Brief Review. Eng. Proc. 2023, 59, 238. DOI: 10.3390/engproc2023059238

[101] Mahmudy WF, Widodo AW, Haikal AH. Challenges and Opportunities for Applying Meta-Heuristic Methods in Vehicle Routing Problems: A Review. Eng. Proc. 2024, 63, 12. DOI: 10.3390/engproc2024063012

[102] Onokwai AO, Olusanya OO, Onifade MK, Anyaegbuna BE, Onoriode A, Agholor D. Comparative analysis of response surface methodology and adaptive neuro-fuzzy inference system for predictive fault detection and optimization in beverage industry. Front. Mech. Eng. 2024, 10, 1428717. DOI: 10.3389/fmech.2024.1428717

[103] Kucharczyk K, Żyła K, Tuszyński T. Optimization of Fermentation Parameters in a Brewery: Modulation of Yeast Growth and Yeast Cell Viability. Processes 2025, 13, 906. DOI: 10.3390/pr13030906

[104] Ugwuanyim G, Amuji H, Ebiringa O, Onwuegbuchunam D. Marketing Mix Optimization in Nigeria’s Brewing Industry: A Regression and Geometric Programming Approach (Case Study of Nigerian Breweries PLC). Open J. Stat. 2025, 15, 170-198. DOI: 10.4236/ojs.2025.152010

[105] Schmid L, Roidl M, Kirchheim A, Pauly M. Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation Study. Entropy 2025, 27, 25. DOI: 10.3390/e27010025

[106] Martinović M, Dokic K, Pudić D. Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach. Appl. Sci. 2025, 15, 3636. DOI: 10.3390/app15073636

[107] Dudek G, Tomczyk A. Advances in Computational Intelligence and Machine Learning Models and Applications. Electronics 2026, 15, 196. DOI: 10.3390/electronics15010196

[108] Amzil K, Saidi R, Cherif W. Enhancing Critical Industrial Processes with Artificial Intelligence Models. Eng. Proc. 2025, 112, 75. DOI: 10.3390/engproc2025112075

[109] Horr AM, Drexler H. Real-Time Models for Manufacturing Processes: How to Build Predictive Reduced Models. Processes 2025, 13, 252. DOI: 10.3390/pr13010252

[110] Rahman MA, Shahrior MF, Iqbal K, Abushaiba AA. Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration. Automation 2025, 6, 37. DOI: 10.3390/automation6030037

[111] Tabassum M, De Rouck G, De Brabanter K. Predictive modeling using artificial intelligence for sensory panel assessments of customized beer production. Int. J. Gastron. Food Sci. 2025, 42, 101311. DOI: 10.1016/j.ijgfs.2025.101311

[112] Sáenz-Navajas M-P, Ferreira C, Bastian SEP, Jeffery DW. Bagging and boosting machine learning algorithms for modelling sensory perception from simple chemical variables: Wine mouthfeel as a case study. Food Qual. Prefer. 2025, 129, 105494. DOI: 10.1016/j.foodqual.2025.105494

[113] Cheng W, Yu J, Li N, Pan T, Li R, Lan W. Machine-Learning Discrimination and Prediction of Flavor in Qingxiang-Xing Baijiu Fermented with Kiwi Fruit Residue. J. Am. Soc. Brew. Chem. 2025, 83, 511-523. DOI: 10.1080/03610470.2025.2527456

[114] Chen Z. Wine quality prediction with ensemble trees: A unified, leak-free comparative study. arXiv 2025, arXiv.2506.06327. DOI: 10.48550/arXiv.2506.06327

[115] Nettesheim P, Burggräf P, Steinberg F. Applications of machine learning in the brewing process: a systematic review. Discov. Artif. Intell. 2024, 4, 80. DOI: 10.1007/s44163-024-00177-6

[116] Liu M, Hu J, Wu C, Sun Q, Zhao X. Hybrid Mathematical Modeling and Optimization Framework for Branch Flow Estimation at Y-Intersections: A Constraint-Aware Approach with Minimal Sensing Requirements. Symmetry 2025, 17, 2052. DOI: 10.3390/sym17122052

[117] Liu X, Qi H, Jia S, Guo Y, Liu Y. Recent Advances in Optimization Methods for Machine Learning: A Systematic Review. Mathematics 2025, 13, 2210. DOI: 10.3390/math13132210

[118] Du S, Jin L, Huang Z, Wan X. Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes. Energies 2025, 18, 1939. DOI: 10.3390/en18081939

[119] Khan R, Goyal A, Kanyal HS, Parashar D, Sharma SK, Iqbal M. Improved machine learning framework with feature engineering and SHAP analysis for predicting wine quality. Discov. Appl. Sci. 2026, 8, 27. DOI: 10.1007/s42452-025-07999-8

[120] dos Santos WQ, McDermott O, Trubetskaya A. Lean Six Sigma 4.0 application in the food & beverage industry: A case study. IEEE Eng. Manag. Review 2025, 53, 110-130. DOI: 10.1109/EMR.2025.3538268

[121] Kulina H, Yaneva P, Gocheva-Ilieva S. Modeling the beer market in a competitive environment with principal component analysis and machine learning techniques: A case study. In Proceedings of the ICoMS 24: Proceedings of the 2024 7th International Conference on Mathematics and Statistics, Amarante, Portugal, 23-25 June 2024; pp. 114-120. DOI: 10.1145/3686592.3686612

[122] Ma BJ, Jackson I, Huang M, Villegas S, Macias-Aguayo J. A data-driven and context-aware approach for demand forecasting in the beverage industry. Int. J. Logist. Res. Applications 2025, 1-28. DOI: 10.1080/13675567.2025.2566806

[123] Saetae D. Machine learning-based prediction of microbial growth and acidification in yogurt fermentation at industrial temperatures. LWT 2025, 231, 118326. DOI: 10.1016/j.lwt.2025.118326

[124] Dazzarola C, Tighe R, Pérez-Correa JR, Saa PA. Toward a digital twin for beer quality control: Development of a digital model integrating industrial process data and model-based fermentation descriptors. J. Food Eng. 2025, 392, 112726. DOI: 10.1016/j.jfoodeng.2025.112726

[125] Grand T, Jenkins D, Maskell D, Zhuang S. Valorisation of Carbon Dioxide from Fermentation in Craft Brewing: Potential Technologies, Brewer Interviews, and Implication for a ‘Three-Level Valorisation System’. J. Am. Soc. Brew. Chem. 2025, 83, 248-259. DOI: 10.1080/03610470.2024.2417133

[126] Schreurs M, Piampongsant S, Roncoroni M, Cool L, Herrera-Malaver B, Vanderaa C, et al. Predicting and improving complex beer flavor through machine learning. Nat. Commun. 2024, 15, 2368. DOI: 10.1038/s41467-024-46346-0

[127] Diniz DP, Carvalho M. Environmental Repercussions of Craft Beer Production in Northeast Brazil. Sustainability 2024, 16, 4566. DOI: 10.3390/su16114566

[128] Osifeko M, Munda J. Scenario-Based Stochastic Optimization for Renewable Integration Under Forecast Uncertainty: A South African Power System Case Study. Processes 2025, 13, 2560. DOI: 10.3390/pr13082560

[129] van der Walt R, van Vuuren G, Larney J, Verster T, Raubenheimer H. Combining E-Scores with Scenario Analysis to Evaluate the Impact of Transition Risk on Corporate Client Performance. Climate 2024, 12, 107. DOI: 10.3390/cli12070107

[130] Wang Z, Zhang Y, Zhang Z. Scenario Analysis of Carbon Reduction Potential Through Forest Carbon Sink Mechanisms in the Beijing-Tianjin-Hebei Region, China. Sustainability 2025, 17, 7992. DOI: 10.3390/su17177992

[131] Lugo-Niebla JR, Lagarda-Leyva EA, Acosta-Quintana MPG, Portugal-Vásquez J, Naranjo-Flores AA, Bueno-Solano A. System Dynamics Modeling and Multicriteria Analysis Methods for Selecting Scenarios in a Harness Assembling Plant. Systems 2025, 13, 936. DOI: 10.3390/systems13110936

[132] Gathani S, Li K, Thind R, Zeng S, Xu M, Haas PJ, et al. PRAXA: A framework for what-if analysis. arXiv 2025, arXiv:2510.09791. DOI: 10.48550/arXiv.2510.09791

[133] Saretzky F, Andersen L, Engel T, Ansari F. Integrating causal foundation model in prescriptive maintenance framework for optimizing production line OEE. arXiv 2025, arXiv:2512.00969. DOI: 10.48550/arXiv.2512.00969

[134] Enebechi CT. Leveraging machine learning to optimize production processes and capacity in the brewery industry. SADI Int. J. Sci. Eng. Technol. (SIJSET) 2025, 12, 34-48. DOI: 10.5281/zenodo.15591176

[135] Pekarcikova M, Trebuna P, Kliment M, Kronova J, Dic M. Master Production Schedule in the Consumer Product Goods Industry: Benefits of APS Applications. Appl. Sci. 2025, 15, 1642. DOI: 10.3390/app15031642

[136] Li W, Yang H, Sun J, Lou L, Zhong J, Ouyang Z. Simulation of Beer Fermentation Combining CFD and Fermentation Reaction Models. Symmetry 2025, 17, 2025. DOI: 10.3390/sym17122025

[137] Atieh AA, Abu Hussein A, Al-Jaghoub S, Alheet AF, Attiany M. The Impact of Digital Technology, Automation, and Data Integration on Supply Chain Performance: Exploring the Moderating Role of Digital Transformation. Logistics 2025, 9, 11. DOI: 10.3390/logistics9010011

[138] Espina-Romero L, Gutiérrez Hurtado H, Ríos Parra D, Vilchez Pirela RA, Talavera-Aguirre R, Ochoa-Díaz A. Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis. Sci 2024, 6, 60. DOI: 10.3390/sci6040060

[139] Serviss MT, Van Hout D, Britton SJ, MacIntosh AJ. Brewing for the Future: Balancing Tradition and Sustainability. J. Am. Soc. Brew. Chem. 2026, 84, 121-139. DOI: 10.1080/03610470.2025.2509059

[140] Lehnhardt F, Schneiderbanger H, Messner C, Gastl M. Impact of Brew House Parameters on Beer Aging: Mapping the German Brewery Landscape. J. Am. Soc. Brew. Chem. 2026, 84, 84-95. DOI: 10.1080/03610470.2025.2524658

[141] Chiscop F, Vlase AI, Cazacu C-C, Popa CL, Cotet CE. Manufacturing Productivity Improvement by Integrating Digital Tools Illustrated in the Optimization of a Hub Assembly Line. Machines 2025, 13, 849. DOI: 10.3390/machines13090849

[142] Ionescu A-M, Ionescu A-C. Exploring the Future of Manufacturing: An Analysis of Industry 5.0’s Priorities and Perspectives. Sustainability 2025, 17, 7842. DOI: 10.3390/su17177842

[143] Antony Jose S, Tonner A, Feliciano M, Roy T, Shackleford A, Menezes PL. Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions. Materials 2025, 18, 2255. DOI: 10.3390/ma18102255

[144] Bernard G, Budde L, Hänggi R, Friedli T. Driving Continuous Improvement with Industry 4.0 Technologies: Lessons from Multiple Use Case Analysis. Appl. Sci. 2025, 15, 2191. DOI: 10.3390/app15042191

[145] Andriani DP, Rahmani IV, Aini APN, Wijayanti F. Manufacturing industry performance appraisals: Multi-criteria decision-making model. Int. J. Integr. Eng. 2022, 14, 28-37. DOI: 10.30880/ijie.2022.14.06.003

[146] Jaskó S, Ruppert T. The Future of Manufacturing and Industry 4.0. Appl. Sci. 2025, 15, 4655. DOI: 10.3390/app15094655

[147] Polo S, Rubio EM, Ayllón J, de Agustina B. Emerging Advances in Sustainable Manufacturing. Processes 2025, 13, 1549. DOI: 10.3390/pr13051549

[148] Bobitan N, Dumitrescu D, Popa AF, Sahlian DN, Turlea IC. Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations-A Foresight Analysis Using the Scenario Method. Electronics 2024, 13, 2198. DOI: 10.3390/electronics13112198

[149] Garzón AG, Pavón Y, Aquino ME, Drago SR. Valorization of brewing by-products: Obtaining flavor-enhancing and antioxidant compounds from spent yeast. Sustain. Food Technol. 2026, 4, 1728-1737. DOI: 10.1039/D5FB00499C

[150] Krenicky T. Advanced Manufacturing Technologies: Development and Prospect. Appl. Sci. 2025, 15, 4597. DOI: 10.3390/app15094597