Core Insights - Salted Fish's revenue for the first half of 2025 reached 2.94 billion yuan, representing a year-on-year growth of 19.6% [1] - The company's net profit attributable to shareholders was 373 million yuan, an increase of 16.7% year-on-year [1] Revenue Breakdown - Spicy snacks generated 1.32 billion yuan in revenue, up 47.05% year-on-year [1] - Baked potato snacks reported revenue of 459 million yuan, down 18.42% year-on-year [1] - Dried fruits and jelly snacks achieved revenue of 428 million yuan, growing by 9.01% year-on-year [1] - Deep-sea snacks earned 363 million yuan, reflecting an 11.93% increase year-on-year [1] - Egg snacks generated 309 million yuan, with a year-on-year growth of 29.57% [1] Business Expansion and Strategy - The company has transitioned from regional to national market expansion, particularly in the savory snack and baked goods sectors, creating two growth curves [1] - Salted Fish has initiated a supply chain transformation and upgrade, promoting smart manufacturing and digital reform to enhance product strength and market competitiveness [1] - The company has strengthened R&D innovation for core product categories, with notable performance in konjac products in Southeast Asian markets, indicating initial success in its globalization strategy [1]

Core Insights - The article emphasizes the necessity for companies to adopt reliable and forward-looking sales forecasting methods to enhance operational efficiency and sales performance in a rapidly changing market environment [2][3]. Group 1: Sales Forecasting Methods - Sales forecasting is based on historical sales data, market trends, industry dynamics, and various internal and external factors, utilizing scientific methods and models to predict future sales [2][3]. - The sales forecasting process faces challenges such as data quality issues, changes in macroeconomic conditions, and the inadequacy of existing models to adapt to specific industry characteristics [4]. Group 2: Key Steps in Sales Forecasting - Data preparation and cleaning are foundational steps in the forecasting process, involving the correction or exclusion of anomalies and aligning data according to business logic [7]. - Statistical forecasting generation involves testing and adapting statistical models using cleaned data, optimizing parameters through machine learning algorithms, and continuously iterating to improve accuracy [8]. - Hypothesis testing is crucial, requiring alignment of initial assumptions with actual business dynamics and forming a closed loop of "forecast-validate-iterate" [9]. Group 3: Applications of Sales Forecasting - Demand forecasting for regular products utilizes various factors such as historical sales data and consumer behavior to predict future demand, helping companies optimize inventory and improve customer satisfaction [10]. - New product forecasting focuses on predicting sales for products that have not yet been launched, using market research and data analysis to identify potential demand and inform supply chain decisions [11]. - Promotion forecasting assesses the impact of promotional activities on sales, enabling companies to optimize promotional resources and enhance market competitiveness [12]. Group 4: Case Studies and Practical Applications - A case study of an energy company illustrates the effectiveness of a multi-faceted forecasting model that integrates over 100 indicators, achieving a monthly profit deviation rate within 5% and a 20% year-on-year increase in gross profit for a specific product [10]. - Another example highlights how a consulting team utilized over 20 influencing factors to build a predictive model, ultimately providing robust guidance for sales planning over the next 12 months [14]. Group 5: Conclusion - Effective sales forecasting is essential for companies to build agile supply chains, reduce costs, and respond to environmental changes, necessitating standardized management processes and adaptable forecasting models [14].