Data Quality Improvement and Item Hierarchy Consolidation & Redefinition post-acquisition for a major US discount retailer

ML-driven item rationalization & re-classification

The Client

The client is a leading discount retailer in North America and is part of the Fortune 500 list. It deals in consumables, variety, and seasonal merchandise which are offered at discounted prices. It acquired another multi-billion dollar discount retailer and wanted to realize operational synergy through process, data, and system consolidation.

The Challenges

Due to the sheer complexity of such a big merger which had a large number of categories and 1.5 Million SKUs, the teams struggled with:-

1. Lack of a unified version of the truth

2. Heavy manual effort to fix data issues like:

  • Dependency on category merchants to validate duplicates​.
  • Lack of clarity in active/inactive, parent/child, and dummy SKUs​.

3. Delayed reporting and insights due to data inconsistency

4. Non-standard hierarchy and inaccurate SKU names:-

  • Hierarchy names were not according to the natural classification of material, but rather by merchandising teams, buying vendor type, supply chain type, etc.
  • Huge percentage of non-identifiable duplicates through either name or description
  • Non-standard SKU classification (Ex Baby Care was a separate category rather than in apparel, toys, and personal care)

The Objective

JK Tech and its client shared a common objective to orchestrate and execute a comprehensive item data deduplication, standardization, and consolidation program as part of an enterprise-wide people, process, and technology transformation program.

To enable the synergy of its IT operations, the customer wanted to migrate two different merchandising applications to a single instance. As part of this initiative, 1.5 million SKUs across the 2 banners were to be merged into a single list, names were to be standardized, and two different item hierarchies consolidated into one, to streamline the merchandising process across all categories.

The Solution

JK Tech delivered a best-in-class Master Data Cleansing and Standardization Platform ​powered by AI & ML and delivered the project with 100% accuracy within 3 months. This helped to reduce manual data integration, harmonize data from multiple banners/sources, reduce errors and duplicates, and speed up data-driven decision-making.

By using a master data cleansing and standardization platform, the client was able to efficiently harmonize data from various sources, eliminate errors and duplicates, and significantly accelerate data-driven decision-making.

JK Tech executed the following activities as part of the program –

Data Quality Improvement and Item Hierarchy Consolidation & Redefinition post-acquisition for a major US discount retailer

The client’s challenging master data and item consolidation issues were converted into a single deduplicated list at the UPC level. As a result of the stronger and dependable data management, the client was able to harmonize multiple data sources into a single, unified view which facilitated data-driven decision-making. This helped the creation of a stronger data governance framework, and processes and paved the way for actionable insights and BI reporting.

The Benefits

Master data Cleansing & Standardization Platform benefits: –

  • Our ​platform provided 250+ Out-of-the-box tests to identify typical item data quality validation.
  • ​Platform provided links to Nielsen, IMRB, and Kantar Category hierarchies that allowed seamless identification of non-standard hierarchies and SKUs.
  • The client was able to significantly reduce manual effort and errors via JK Tech’s AI-enabled, ML-based automation, market connectors.
  • 80% of the item list was cleaned up, resulting in an extremely accurate list.
  • The platform continuously operated and monitored to eliminate duplication, along with a monitoring and cleansing mechanism to create a material item-based hierarchy.
  • Consolidated standardized hierarchy was established to help create common item nomenclature and supplier collaboration.
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