ABC Pareto Analysis
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Often companies spend a lot of time managing items/entities that have a low contribution to the profit margin. Every item/entity inside your shop does not have equal value - some of them cost more, some are used more frequently, and some are both. This is where the ABC Pareto analysis steps in, which helps companies to focus on the right items/entities.
ABC analysis is a classification method in which items/entities are divided into three categories, A, B, and C.
Category A is typically the smallest category and consists of the most important items/entities ('the vital few'),
while category C is the largest category and consists of least valuable items/entities ('the trivial many').
To create analysis you need to define 2 parameters:
ID column This represents the unique identifier or description of each entity being analyzed, such as a product ID or product name.
Numeric column - This is a measurable value used to categorize items into A, B, or C classes based on their relative importance. Common metrics include total sales volume, revenue, or usage frequency.
Since ABC inventory analysis divides items into 3 categories, let's analyze these categories by checking the Model Results. The results consist of 4 tabs: Overview, ABC Summary, Pareto Chart, and Details Tabs.
In the Overview tab provides an actionable summary that supports data-driven decision-making by focusing on high-impact areas within the dataset. You’ll find a structured breakdown of entities within a chosen dimension (e.g., product_id) categorized based on a specific metric (e.g., price). This analysis highlights the contributions of different entities, focusing on the most impactful ones.
Key highlights in the Overview tab include:
• Category Breakdown: The dimension is divided into three categories:
• Category A: Top contributors representing few entities with a large share of the total metric.
• Category B: Mid-range contributors with moderate impact and growth potential.
• Category C: The largest group with the least individual impact.
• ABC Analysis Process: Explanation of sorting entities, calculating cumulative totals, and dynamically determining category boundaries based on cumulative contributions.
• Benefits and Next Steps: Highlights key points of the analysis. Encourages reviewing the Pareto Chart for visual insights, exploring detailed metrics, and identifying high-impact entities for strategic action.
• The left chart shows the percentage of entities in each category (A, B, and C), illustrating how they are divided within the selected dimension (product_id).
• The right chart highlights each category’s contribution to the total metric (freight_price), showing how a smaller portion of entities (Category A) accounts for the majority of the impact, while the larger portion (Category C) has a lesser effect.
Together, these charts emphasize the purpose of ABC Analysis: to identify the “vital few” entities (Category A) that drive the most value, supporting targeted decision-making.
In the picture above, we can see that 33.77% of the items belong to category A and they represent 50.55% of the total value, meaning the biggest profit comes from the items in category A!
The ABC analysis, also called Pareto analysis, is based on the Pareto principle, which says that 80% of the results (output) come from 20% of the efforts (input). The Pareto Chart is a combination of a bar and a line graph - it contains both bars and lines, where each bar represents an item/entity in descending order, while the height of the bar represents the value of the item/entity. The curved orange line represents the cumulative percentage of the item/entity.
The Details tab provides a granular view of the dataset resulting from the ABC Analysis. Each row represents an entity along with the following key details:
• The metric used for categorization, indicating each entity’s contribution (.
• The category assigned to each entity (A, B, or C) based on its relative impact.
• The cumulative percentage contribution of each entity to the total freight price, showing its share within the dataset.
This detailed breakdown allows users to identify specific high-impact entities in Category A, moderate contributors in Category B, and lower-impact entities in Category C, supporting data-driven prioritization and decision-making.
There is a long list of benefits from including ABC analysis in your business, such as improved inventory optimization and forecasting, reduced storage expenses, strategic pricing of the products, etc. With Graphite, all you have to do is upload your data, create the desired model, and explore the results.
