How to Detect Correlations in Sales and Inventory Data Using EDA

Exploratory Data Analysis (EDA) is an essential step in understanding relationships within sales and inventory data. Detecting correlations through EDA can uncover hidden patterns, improve forecasting, reduce overstock or stockouts, and optimize supply chain decisions. By applying statistical summaries, visualizations, and feature engineering, businesses can gain actionable insights into how sales and inventory interact.

Understanding the Nature of Sales and Inventory Data

Sales and inventory data typically include timestamps, product identifiers, quantities sold, stock levels, pricing, promotions, and regional or store-based breakdowns. These datasets often span across several time intervals such as daily, weekly, or monthly. The aim of EDA in this context is to understand how variables such as stock availability, product category, time of year, and promotions correlate with sales trends.

Data Cleaning and Preparation

Before performing EDA, the dataset must be cleaned to ensure accuracy:

• Remove Duplicates: Ensure each transaction or inventory record is unique.

• Handle Missing Values: Use imputation methods or remove rows/columns with excessive null values.

• Standardize Units: Ensure all quantities and prices are in consistent units.

• Create Time Features: Extract features like day of the week, month, quarter, or holidays to assess seasonality.

• Merge Datasets: Join sales and inventory data on product ID and date for a holistic view.

Univariate Analysis of Key Metrics

Start by examining individual features using descriptive statistics:

• Sales Volume: Analyze average, median, and standard deviation of daily sales.

• Inventory Levels: Understand typical stock levels and their variability.

• Stockout Frequency: Calculate the number of times inventory dropped to zero.

• Sales per Product Category: Aggregate sales by category to identify top-performing segments.

Use histograms, boxplots, and density plots to visualize distributions and identify outliers.

Bivariate Analysis: Uncovering Relationships

To detect correlations, bivariate analysis explores the relationship between two variables:

1. Sales vs. Inventory Levels:
   Use scatter plots to visualize how stock levels affect sales. A positive trend may indicate higher sales with more stock availability. However, diminishing returns can occur if excess stock doesn’t boost sales.

2. Time Series Correlation:
   Plot time series for both sales and inventory. Use rolling means or smoothing techniques like exponential moving averages to highlight trends. Use cross-correlation functions (CCF) to identify lagged relationships—for example, sales might peak a few days after inventory is restocked.

3. Sales vs. Pricing:
   Line or scatter plots can show how changes in price impact sales. Correlation matrices and regression lines can quantify sensitivity to pricing.

4. Sales vs. Promotions:
   Promotions often create short-term spikes in sales. Use box plots to compare sales during promotions vs. non-promotions and calculate correlation coefficients to quantify relationships.

5. Heatmaps and Correlation Matrices:
   Calculate Pearson or Spearman correlation coefficients between numerical variables such as price, inventory levels, and sales volume. Display them using heatmaps to easily identify strong linear or monotonic relationships.

Multivariate Analysis for Deeper Insight

1. Pair Plots:
   Generate pairwise scatter plots for several variables to detect clusters or nonlinear relationships.

2. Principal Component Analysis (PCA):
   Reduce dimensionality to identify key combinations of variables that explain the most variance in the data. PCA can help simplify complex datasets while preserving relationships.

3. Clustering:
   Use clustering algorithms like K-means to group products or stores with similar sales-inventory dynamics. This helps detect patterns at a segment level rather than in individual products.

4. Regression Models:
   Fit linear regression models with sales as the dependent variable and inventory, pricing, and time-based features as independent variables. The coefficients indicate how much each feature influences sales.

Time-Based Correlation Analysis

Sales and inventory are time-dependent, so traditional correlation measures might not capture dynamic relationships.

• Autocorrelation Plots:
  Analyze how current values of sales and inventory are correlated with their past values. This reveals seasonality and repeated patterns.

• Lag Features:
  Create lag variables (e.g., inventory level one week ago) to examine their predictive power on current sales.

• Causal Impact Analysis:
  Identify how changes in inventory or pricing cause changes in sales. This is particularly useful when a change in supply chain policy or stocking method is introduced.

Advanced Visualization Techniques

1. Dual Axis Plots:
   Plot sales and inventory over time on dual axes to detect co-movements or inverse relationships.

2. Animated Time Series:
   Visualizing changes over time can help identify anomalies or structural breaks.

3. Geo-Mapping:
   When regional data is available, visualize inventory and sales across locations to uncover regional trends or disparities.

Segment-Level Correlation

Analyzing correlations across different segments provides granular insights:

• Product Categories:
  Seasonal products may have stronger correlation with inventory availability compared to staple items.

• Store Types:
  Correlations may vary between urban vs. rural locations due to differing consumer behavior and supply chain dynamics.

• Customer Segments:
  High-frequency customers may react differently to inventory levels than occasional buyers.

Key Metrics and KPIs to Track

• Sell-Through Rate:
  Measures how quickly inventory turns into sales. A high rate indicates strong demand correlation.

• Days of Inventory Outstanding (DIO):
  Measures how long inventory stays before being sold. Can be used in correlation with sales to optimize stock levels.

• Stockout Rate:
  Helps detect if lost sales are due to insufficient inventory.

• Inventory Turnover Ratio:
  High turnover often correlates with strong sales and efficient inventory management.

Tools and Technologies for EDA

Several tools make it easier to perform EDA and detect correlations:

• Python Libraries:

  • Pandas for data manipulation

  • Matplotlib and Seaborn for visualization

  • Scikit-learn for regression and clustering

  • Statsmodels for time series and statistical tests

• Business Intelligence Tools:

  • Tableau and Power BI for interactive dashboards

  • Looker and Qlik for multidimensional analysis

Best Practices

• Segment Analysis: Always analyze by segment for clearer, actionable patterns.

• Rolling Windows: Use moving averages and windows to smooth time series and detect meaningful patterns.

• Test Hypotheses: Use statistical tests (e.g., t-tests or chi-square) to validate correlation hypotheses.

• Iterative Exploration: EDA should be an iterative process; revisit assumptions as new insights emerge.

• Feature Engineering: Create meaningful variables (e.g., stock ratio, price deviation from average) to enhance analysis.

Conclusion

Detecting correlations in sales and inventory data using EDA is vital for effective demand forecasting and stock optimization. Through statistical analysis, visual techniques, and multivariate methods, businesses can identify key drivers of sales performance and align inventory management strategies accordingly. The ultimate goal is to move from reactive to proactive decision-making, reducing costs while maximizing customer satisfaction.