Quantitative decision-making methods include linear programming, sensitivity analysis, other mathematical models, data mining and so on.

1, linear programming method: linear programming method is an optimization method, which maximizes or minimizes an objective function by establishing a mathematical model based on given constraints. This method can be applied to resource allocation, industrial economy and other fields, including production, transportation and inventory. Through the optimization of mathematical model, enterprises can maximize profits or minimize costs with limited resources.

2. Sensitivity analysis: Sensitivity analysis is a commonly used quantitative analysis method in the fields of finance and investment. By changing the parameters in the decision model, the influence of this change on the decision results is evaluated. This method can help investors evaluate investment risks and formulate corresponding strategies.

3. Other mathematical models: Besides linear programming, there are other mathematical models, such as nonlinear programming, multi-objective decision-making, decision analysis, time series analysis and regression analysis. These methods are mainly used to achieve more goals, evaluate decision-making risks and predict future situations.

4. Data mining: Data mining uses artificial intelligence and machine learning technology to analyze data, find patterns and extract useful information. Through data mining, enterprises can optimize the decision-making process, identify trends, reduce costs and improve efficiency, thus improving the accuracy and efficiency of decision-making.

Quantitative decision-making significance

Quantitative decision-making refers to the process of making decisions by using mathematical and statistical methods. Compared with qualitative decision-making, quantitative decision-making pays more attention to the accuracy, repeatability and scientificity of data-based analysis and calculation. Using linear programming, data mining, risk analysis and other mathematical methods can improve the accuracy of decision-making and reduce the bias and subjectivity of decision-making.

Quantitative decision-making can be widely used in different fields, industries and stages. For example, in the financial field, the forecast of income and assets is usually based on risk management and balance sheet statistics. In supply chain and logistics management, quantitative technology can realize reasonable inventory control and material demand planning. In marketing, quantitative technology can be used to analyze market segmentation, product pricing and promotion effect.