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Situation

A global producer of commercial and industrial goods was grappling with major discrepancies in inventory and procurement records. This resulted in escalated inventory costs, disruptions in the supply chain, and flawed purchasing decisions. Such hurdles were taking a toll on the company’s top line and the bottom line, highlighting the pressing need for an effective solution to mend their data issues.

Solution

The client utilized Routine to meticulously scrutinize their inventory and procurement classification data. The steps taken were in phases and are as follows:

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

* Timelines may change based on requirements for the Datamart

Data Health Check Number of Records
Impacted 		Observations Examples
Completeness Null Check 12,920 (39.7%) Critical Data Elements missing: Primary MFR, MFR Part No,
Supplier Part No		 Primary MFR: NULL, Supplier Part No: NULL
Standardization 2,915 (8.9%) Need for value standardization in columns like Part Description,
Primary MFR 		Part Description: “KIT O RING” vs. “KIT ORING”
Duplicates Levels
– Level 1 2,820 Basic duplicate checks on Part Description Two entries with similar Part Descriptions
– Level 2 310 Advanced checks on Part Description, Location, MFR Part No, Supplier Part No Duplicate entries varying in Location or Supplier Part No
– Level 3 145 Advanced checks excluding Part No Duplicate entries with matching descriptions but different Part Nos
Multiplicity
– Check 1 2,540 Part Description, Primary MFR 🡪 MFR No Multiple MFR Nos for a single Part Description
– Check 2 1,110 Part Description, MFR Part No, Supplier Part No 🡪 Last Amount Multiple amounts for the same MFR Part No
– Check 3 2,550 Part Description, Primary Supplier 🡪 Supplier No Multiple Supplier Nos for a single Part Description

Results

Following the deployment of Routine, the leading global producer experienced transformative enhancements across
inventory, supply chain, and procurement functions, leading to significant improvements and tangible outcomes:

  • Enhanced Data Completeness:

    • Achieved an improvement in data completeness by approximately 80%.
    • This translated to enhanced precision in about 10,336 records that previously lacked essential data components.

  • Uniformity & Consistency:

    • Attained standardization in approximately 2,186 product descriptions that formerly lacked uniformity.
    • This improvement reinforced data accuracy and accessibility, aiding streamlined data recovery processes.

  • Duplicate Record Management:

    • Successfully detected and resolved around 2,784 duplicate entries, optimizing database storage and enhancing system performance.

  • Multiplicity Resolutions:

    • Remedied about 4,960 instances of erroneous multiple record associations, ensuring smoother data relationships and usage.

  • Cost Efficiency and Performance Enhancement:

    • Realized an estimated reduction of about 12% in inventory expenses due to more accurate inventory management.
    • Experienced an improvement in procurement efficacy by approximately 16%, thanks to more informed purchasing decisions.
    • Enhanced supply chain agility by around 7.5%, leading to quicker and more reliable delivery schedules.

These results not only underscore the pivotal role of high-quality data management in operational efficiency but also highlight the direct impact of Routine’s implementation on cost savings and performance metrics.

Standardization Example –
COLUMN NAME VALUE VALUE COUNT SUGGESTED VALUE CORRECT VALUE COUNT
`Part DESCRIPTION` KIT ORING 2 KIT O RING 3
`Part DESCRIPTION` LUBRICATOR 650-B 5 LUBRICATOR #650-B 15
`Part DESCRIPTION` ORING 53569-00 3 ORING 053569-00 4
`Part DESCRIPTION` RING LANTERN SPLIT 4 RING LANTERN, SPLIT 7
`PRIMARY MFR` OIL SAFE 1 OILSAFE 8
`PRIMARY MFR` CHECK ALL 1 CHECK-ALL 6
`PRIMARY MFR` EMTECHNIK 1 EMTECNIK 2
`PRIMARY MFR` SEIMENS 2 SIEMENS 146
`PRIMARY MFR` JACOB TARB 4 JACOBY TAR 6
`PRIMARY MFR` NESTECH(SE) 1 NESSTECH 199
`PRIMARY MFR` SPRAYOIN 1 SPRAYON 2
Duplicate Example –
NOUN Part_DESCRIPTION LOCATION QOH PRIMARY_MFR MFR_PART_NO PRIMARY_SUPPLIER SUPPLIER_PART_NO 2022 2021 2020 2019 MAX MIN LAST$
BEARING BEARING BALL INBOARD 6207 8050-20760 168 2SH2-04-03-04-03 1 S*** VD-BXCQ001-UW-PKG-DWG-EUN721-0702 6625 VD-BXCQ001-UW-PKG-DWG-EUN721-0 0 0 1 0 1 0 20.6
BEARING BEARING BALL INBOARD 6207 8050-20760 168 2SH2-04-03-04-03 1 S*** VD-BXCQ001-UW-PKG-DWG-EUN721-0702 6625 VD-BXCQ001-UW-PKG-DWG-EUN721-0 0 0 0 0 1 0 127.7
BEARING BEARING BALL INBOARD 6207 8050-20760 168 2SH2-04-03-04-03 1 S*** VD-BXCQ001-UW-PKG-DWG-EUN721-0702 6625 VD-BXCQ001-UW-PKG-DWG-EUN721-0 0 0 0 0 1 0 127.7
COUPLING COUPLING FOR CLOTH CORRECTION DEVICE SH08-02-02-02 0 E***A 7057 8483.9 0 1 0 0 0 0 660
COUPLING COUPLING FOR CLOTH CORRECTION DEVICE SH08-02-02-02 1 SH08-02-02-02 7057 8483.9 0 0 0 0 4 2 660
GASKET GASKET 3″ T#1890-MT FOR MODEL:3889 SH2-06-02-01-05 2 MO****** 3″ T#1890-MT FOR MODEL:3889 7054 3″ T#1890-MT FOR MODEL:3889 0 0 0 0 1 0 0.01
GASKET GASKET 3″ T#1890-MT FOR MODEL:3889 SH2-06-02-01-05 4 MO****** 3″ T#1890-MT FOR MODEL:3889 7054 3″ T#1890-MT FOR MODEL:3889 0 0 0 0 2 1 370
Multiplicity Example –

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