The Application of Data Analysis and Statistical Methods in Process Optimization and Supply Demand

In traditional supply chain management, there are often problems such as poor process optimization and inaccurate demand forecasting. This paper introduces data analysis and statistical methods to improve supply chain efficiency, optimize processes, and accurately predict market demand, thereby enhancing the market responsiveness and resource utilization efficiency of enterprises. First, descriptive statistical analysis is used to summarize and identify patterns in historical data to reveal bottlenecks and potential problems in each link of the supply chain. A forecasting model is established through regression analysis of production processes and supply and demand data. Then, time series analysis (ARIMA (Autoregressive Integrated Moving Average Model) model) is applied to make short-term forecasts of future demand, and the key factors affecting demand fluctuations are modeled in combination with the multivariate linear regression model to achieve accurate demand forecasting. Finally, Monte Carlo simulation and optimization algorithms (combined with genetic algorithms) are used to optimize the supply chain process and explore the optimal inventory level and production scheduling strategy to minimize costs and maximize efficiency. After optimization, inventory costs have dropped significantly compared to before optimization, and the response speed ranged from 46 to 50 hours before optimization to 32 to 36 hours after optimization. Data analysis and statistical methods have important application value in process optimization and supply demand forecasting, which can significantly improve supply chain efficiency, reduce operating costs, optimize production scheduling, and improve the accuracy of market demand forecasting.