Optimization decisions in the brewery industry – Maximize Total Sales & Quality Score

For a brewery, how to improve the quality and sales of beer through optimization and supply chain management is important. Supply chain management plays a critical role in ensuring product quality stability and optimizing production output. Effective management of various stages within the supply chain makes it possible to achieve high-quality, high-yield production objectives. This paper employs four common regression models: Linear Regression, Lasso Regression, Ridge Regression, and Support Vector Regression (SVR) to fit and predict the ‘Quality Score’ and ‘Sales’ of the brewery, identifying ‘Gravity’ and ‘Alcohol Content’ as the most important features. Additionally, the introduction of a new variable, ‘Assess’, ensures that both ‘Quality Score’ and ‘Sales’ receive equal weight. By constructing a quadratic convex function based on the new variable and the two important features, the optimal solution is that the alcohol density is 1.03 and the alcohol content is 4.5%, achieving a dynamic balance between maximizing taste scores and sales. In essence, when equal emphasis is placed on both beer quality and sales, optimizing the critical factors of ‘Gravity’ and ‘Alcohol Content’ to achieve an optimal solution can enable the brewery to maximize both product quality and sales performance.