Supply chain teams often underestimate the cost of redundant or near-duplicate SKUs. When multiple SKUs represent the same — or nearly identical — product, forecasting loses precision, inventory balloons, and valuable labor gets caught in admin tasks.
The result? Operational clutter that drives up hard costs and chips away at profit margins.
Why Duplicate and Similar SKUs Hurt Your Bottom Line
Duplicate SKUs represent the same physical product under different identifiers. This often happens due to manual data entry, disparate systems, or siloed catalog management across ERPs, CRMs, subsidiaries, and suppliers.
Similar SKUs, while not identical, create confusion through minor distinctions: packaging variations, region-specific labeling, or inconsistent units of measurement. These near-duplicates may sometimes be intentional, but over time, they fuel unnecessary complexity and catalog bloat.
And the financial impact is real:
- One study found that 19% of SKUs in a hearing device manufacturer’s catalog were either duplicates or too similar to merit differentiation.
- Unilever faced a similar issue: 20% of SKUs in its UK and Ireland portfolio accounted for only 5% of sales. That insight led the company to streamline its product lineup.
Why Redundancy Persists in Modern Supply Chains
SKU duplication remains a persistent challenge in supply chain operations. Mergers and acquisitions often bring together fragmented catalogs with conflicting naming conventions. In global organizations, regional teams may create SKUs independently, unaware of centralized systems, resulting in duplicate entries across business units. On top of that, Indirect materials and MRO items often bypass central governance, with new SKUs created ad hoc in procurement systems, leading to bloated catalogs and fragmented spend visibility.
Manual data entry exacerbates this, especially in companies reliant on spreadsheets or disconnected tools to manage product information.
The problem often runs deeper. Without standardized taxonomy or clear governance, the same product may be entered multiple times, with slight modifications, renamed, or recreated under different supplier terms. These inconsistencies degrade data quality, distort inventory accuracy, and lead to misguided purchasing decisions.
What’s at Stake: Cost, Time, and Decision Confidence
The cost of inaction is staggering. Research shows that 60% of SKUs contain inaccuracies, contributing to over $1.77 trillion in global retail losses, from poor replenishment and excess stock to inventory write-offs. In e-commerce, inventory distortions drive more than $818 billion in annual losses due to a costly imbalance between overstock and stockouts.
Beyond financial damage, SKU duplication quietly undermines operational planning and reliability:
- Unreliable Demand Forecasting: Scattered sales and inventory data across multiple identifiers skews historical trends, making future demand harder to predict.
- Missed or Duplicate Orders: Procurement teams may place redundant orders or overlook legitimate demand due to fragmented SKU data.
- Inventory Imbalances: Warehouses may overstock one version of an SKU while underfilling another, leading to waste and missed sales.
Left unchecked, these issues erode decision-making, inflate operating costs, and weaken customer satisfaction.
A Practical Way to Identify and Eliminate Redundant SKUs
The first step in eliminating SKU duplication is improving visibility. Without a centralized view of product data, identifying overlap is nearly impossible. Organizations should start by consolidating product records from all relevant systems, such as ERPs, PIMs, supplier catalogs, and procurement tools. Applying consistent formatting to these records makes comparison far more effective.
Once visibility is in place, a structured process can help pinpoint and resolve redundancy:
- Standardize and Clean Data: Align naming conventions, descriptions, packaging details, and manufacturer codes to a uniform format.
- Group Potential Duplicates: Use logic-based matching and clustering to uncover SKUs with similar or overlapping attributes. This can reveal obvious redundancies along with borderline cases.
- Apply Similarity Scoring: Machine learning models can assess how closely related SKUs are by analyzing both structured and free-text fields, even when data is inconsistent or incomplete.
- Validate Through Expert Review: Internal teams determine whether flagged SKUs are truly redundant or serve unique operational or regulatory functions.
- Rationalize Based on Business Impact: Evaluate product performance with tools like ABC or velocity analysis. Consolidate or retire SKUs with low volume or margin.
Combining automation with human insight ensures both speed and accuracy. This balanced approach is especially valuable in high-SKU environments where subtle differences can matter.
How Deda Ai Brings Order to Product Data
Deda Ai’s Inventory Data Intelligence solution gives supply chain teams a practical way to identify and resolve duplicate, similar, and obsolete SKUs. By connecting fragmented product records and analyzing catalog-wide inconsistencies, it delivers clean, structured insights that support better decisions.
Here’s how the solution works:
- Data Collection and Analysis: Deda Ai securely accesses your inventory data and applies proprietary algorithms to detect patterns, relationships, and quality issues. This includes spotting duplicate SKUs, grouping similar items, and flagging incomplete or outdated entries.
- Insight Report Generation: A comprehensive report highlights key data problems, such as redundancy, missing attributes, and SKU-level anomalies. This gives teams a clear baseline for cleanup and a framework for tracking improvement over time.
- Targeted Recommendations: Through its Inventory Commentary, Deda Ai provides actionable recommendations, such as consolidating duplicates, enriching product details, or substituting SKUs to streamline operations and prevent fulfillment delays.
- Continuous Monitoring and Optimization: As inventory data changes, the solution supports ongoing monitoring to maintain high data quality. Teams gain timely insights that help them manage complexity and stay ahead of operational challenges.
By eliminating guesswork from catalog management, Deda Ai helps supply chain leaders align inventory with demand, reduce hidden costs, and strengthen confidence in the data behind every decision.
Clarity Isn’t Optional — It’s a Competitive Edge
Supply chains move quickly and so should your data decisions. Redundant SKUs drag operations down, distort inventory visibility, and limit your ability to plan and adapt.
Cleaning up your catalog doesn’t mean a complete overhaul—it just takes the right tools and a structured approach. With Deda Ai, organizations can gain clarity across product data, capture operational savings, and make inventory work as efficiently as the rest of the business.
Ready to take control of your SKU data? Explore Inventory Data Intelligence or schedule a demo today to see how Deda Ai can help.
