The food industry must constantly improve its products and marketing strategies to meet changing market and consumer demands. Modern methods of exploratory data analysis (EDA) provide powerful tools for making informed decisions to enhance production efficiency and strengthen competitive positions.

EDA enables companies to extract valuable insights that can aid in identifying successful strategies, forecasting demand, and improving production efficiency. This article outlines the key steps in conducting EDA and demonstrates how to implement data-driven changes in business.

In the context of the food industry, EDA can offer valuable information about production processes, consumer behaviour, product quality, and other aspects. Here are several steps involved in conducting EDA:

1. Data Examination: This process begins with defining the task, identifying business metrics, and determining project success criteria. Data is collected from various sources to aggregate the necessary values influencing the target metrics.
2. Statistical Analysis: To understand the data properly, it is essential to know the statistical indicators of key variables affecting business efficiency, such as mean, median, and standard deviation. Studying distributions helps capture major trends in the data and identify anomalies.
3. Correlation Analysis: This allows the evaluation of relationships between different variables using correlation coefficients.
4. Data Visualization: Visual representation of data helps in understanding complex data structures and relationships.
5. Grouping and Aggregation: Data is grouped by various categories (e.g., product types, regions). Statistical analysis for each group reveals patterns specific to different categories, enabling a differentiated approach to the main task.
6. Machine Learning and Forecasting: Machine learning methods help solve production volume planning tasks considering seasonality and sales trends.

The results of EDA allow identifying key areas for improving production quality, adjusting sales strategies based on well-founded conclusions. Based on data analysis, a strategy for conducting A/B testing can be selected to increase the company's profit.

EDA plays a crucial role in understanding the market, consumer preferences, optimizing production, and ensuring product safety. Here are examples of tasks in this industry:
1. Consumer Demand Analysis and Forecasting:

• Collecting sales data for various products and analyzing customer behaviour.
• Using machine learning methods to identify consumer groups with similar preferences.
• Developing targeted promotion strategies for new products based on consumer preferences.
• Identifying trends and seasonal changes in consumer preferences to make more accurate demand forecasts.

2. Production Process Analysis and Monitoring:

• Monitoring quality parameters such as sugar, fat, protein content, and other components.
• Using statistical methods to detect anomalies in the production process.
• Automatic process monitoring provides timely alerts about identified issues.
• Incident data analysis helps identify patterns preceding emergency situations.

3. Production Optimization:

• Analyzing production parameters like temperature, pressure, pH, humidity, and other factors to select optimal conditions for achieving maximum production efficiency.

EDA is an integral part of the development strategy for food industry producers. A research-driven approach to business development helps make well-founded decisions, increasing the chances of success in a dynamic and competitive industry.