Driving Profit Through Precision in Inventory Management

Leveraging Routine’s data quality engine to optimize inventory, slash costs, and boost procurement and supply chain performance.

Background:

A top-tier global producer of commercial and industrial goods was facing persistent issues in managing inventory and procurement data. Widespread inconsistencies, incomplete records, and duplicate entries inflated inventory costs, disrupted supply chain operations, and triggered flawed purchasing decisions—threatening both profitability and operational agility.

The Challenge:

The company’s outdated data landscape created critical barriers:

• ❌ Incomplete Inventory Data: Nearly 40% of records had missing values for primary fields like Manufacturer and Part Numbers.

• ❌ Lack of Standardization: Inconsistent naming and formatting across key columns like Part Description and Primary MFR.

• 🔁 Duplicate Records: Thousands of duplicate entries bloated systems and distorted procurement visibility.

• 🧩 Multiplicity Issues: Mismatched many-to-one relationships across suppliers, part numbers, and pricing led to confusion and inefficiencies.

• ⚠️ Poor Synchronization Across Tables: Misalignment between procurement, finance, and logistics systems.

Solution: AI-Driven Inventory Data Cleansing with Routine

The company partnered with us to lead a structured, multi-phase cleanup of its inventory and procurement master data.

Define

• Project kick off

• Project Objective and Timelines

• Team Members – Roles & Responsibilities

• Project Plan

• Governance Plan

Analyze

• Data extract to Routine with critical attributes

• Analysis of Inventory:

  
  

  • Profile

  • Standardization / Consistency

  • Pattern checks

  • Duplicates

  • Multiplicity issues

  • Synchronization check across tables

  • Custom checks

• Continuous Reviews with project team

• Weekly Review with Project Exec team

Clean

• Clean up Strategy

• Cleanse Inventory data

• Re-run analysis to check progress

• Finalize Master Records

• Create/ Update Data Policies (as applicable)

Upload/ Datamart

• Upload into current database/ Create a Datamart* as single source of truth

• Reconciliation checks

Monitor

• Automate daily monitoring

• Configure & Schedule Notifications

• Final report out to Project exec team

• Presentation to CXOs + IT leadership team

Results:

📈 Data Completeness Boosted by 80%

Over 10,000+ records updated with previously missing MFR and part numbers.

🧾 Standardization Improved in 2,100+ Descriptions

E.g., “KIT ORING” and “KIT O RING” standardized across systems.

🔍 Duplicates Removed (2,784 entries)

Across multiple levels using advanced attribute matching.

🔄 Multiplicity Errors Resolved (4,960+ cases)

Ensured one-to-one mappings for accurate vendor and pricing data.

💸 Inventory Cost Reduction: 12%

Thanks to clean, trusted data guiding stock decisions.

📦 Procurement Efficiency Up by 16%

Improved sourcing and pricing accuracy.

🚚 Supply Chain Agility Gained by 7.5%

Quicker decisions, cleaner data, faster fulfillment.

Conclusion:

This inventory transformation proves that dirty data is more than an IT problem—it’s a profit problem. By leveraging Routine’s autonomous checks and powerful reconciliation capabilities, this industrial giant now operates with cleaner, faster, and more reliable inventory intelligence.

From diagnostics to daily monitoring, Routine delivers lasting improvements across inventory, procurement, and supply chain operations.

Ready to Simplify Your Inventory Data? Schedule a Demo with Discover Alpha and take the first step towards procurement excellence.