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Situation

A global leader that has been a dominant player in the industrial product supply sector, providing a vast array of machinery, equipment, and tools to businesses worldwide, recognized the need to transform itself into a competitive e-commerce company by improving its catalog to meet the changing demands of its customers.It was challenged with an intricate and unstructured product taxonomy spanning over 100,000 records. This led to inefficiencies in inventory management, procurement, and supply chain processes, causing lost opportunities, additional costs, and palpable financial strains.

Solution

Routine is now used to autonomously identify, analyze, and help clean data issues across
the Catalog. This is leveraged to improve the quality of the inventory data, lower
costs, improve procurement decisions & increase company profitability. Some of the major
issues which were identified within the catalog are as follows-


When items are not consistently categorized, it becomes challenging for customers to find what they are looking for. For example, a pair of running shoes might be classified under “Footwear,” “Athletic Shoes,” or even “Sports Gear,” causing confusion and frustration.


Incomplete taxonomy often means that essential attributes or details about products are missing. This could include product specifications, dimensions, material, or color options, leaving customers with inadequate information for making informed purchasing decisions.


Incorrect Manufacturer Part Numbers (MPNs), the presence of special characters in MPNs, and inconsistent Manufacturer Names can lead to significant issues in the supply chain and procurement processes.


It leads to ambiguity, miscommunication, inventory management challenges, supplier issues, data management problems, quality control difficulties, cost overruns, and compliance/documentation concerns. Standardizing part descriptions is essential to improve efficiency, reduce errors, and minimize costs, involving clear naming conventions, inventory systems, supplier collaboration, data management tools, quality control processes, compliance adherence, and employee education.


Incomplete taxonomy directly affects the effectiveness of search functionality. Without a comprehensive structure in place, search results can be inaccurate, and customers may struggle to find the items they desire.


Incomplete taxonomy limits the ability to recommend related or complementary products. This results in missed opportunities to increase average order value and enhance the overall shopping experience.

At RoutineAI, we understand the importance of a well-organized taxonomy and
have developed a solution to address the challenges associated with incomplete item
taxonomy. The steps taken were in phases and are as follows:

Catalog Cleanup Approach

Catalog Assessment

  • Catalog Assessment of the existing taxonomy.
  • Tailoring the taxonomy design to each client’s unique industry,
    product range, and operational processes.
  • Through research for every line item to define clear taxonomy
    categories and hierarchies.
  • Building of the library of all the existing taxonomies.

Refine Taxonomy

  • Assessment of the existing item attributes
  • Identification of Data Sources: Manufacturers, Vendors, external
    databases, industry-specific resources,etc
  • Data Enhancement: Enhance the existing attributes by adding
    missing details or supplementing them with more comprehensive
    information. This could include additional technical
    specifications, product features, or performance data.

Clean

  • Clean up Strategy
  • Cleanse Catalog data – Manufacturer names,Manufacturer Part
    Numbers,etc.
  • Re-run analysis to check progress
  • Finalize Master Records
  • Create/ Update Data Policies (as applicable)

Rule Book Implementation

  • Define rules and guidelines for attribute naming and formatting.
  • Standardize product attributes and descriptions to ensure
    consistency within the taxonomy.

Monitor

  • Automate daily monitoring
  • Configure & Schedule Notifications
  • Final report out to Project exec team
  • Presentation to CXOs + IT leadership team

Results

  • Taxonomy Transformation: Using Routine’s advanced algorithms, a
    methodical audit of the records was initiated, resulting in a coherent, Nth
    level-tiered taxonomy, optimizing 100,000 items in just 10-12 weeks.
  • Procurement Prowess: The clarity introduced by the new taxonomy
    facilitated smarter buying decisions, slashing inefficiencies by 11%.
  • Supply Chain Synchronization: Mispick incidences were reduced
    dramatically to 2%, and lead times were enhanced by 8%, driving supply chain
    efficiency.
  • Operational Excellence: Decision-making processes, backed by the
    restructured taxonomy, became 30% quicker, paving the way for agile operations.
  • Economic Efficiency: Addressing the core issues in taxonomy led to
    a 9% reduction in both inventory-related and overarching operational expenses.
  • Supply Chain Automation: Automated the supply chain through the
    development of a comprehensive taxonomy for items which enhanced the efficiency and
    accuracy.
Sample Data Comparison: Before vs. After Routine Deployment –

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