Inventory Management
Inventory Data Architecture Turnaround
Clean master data, actionable ageing reports, and SKU rationalization reduced slow-movers and stockouts.
Engagement at a glance
- Industry
- Manufacturing
- Focus
- Inventory Management
- Systems and work involved
- Tally Prime (ODBC Port 9000) · Microsoft Excel · Power BI
On this page
The business context
A single-site manufacturer held significant inventory value but paradoxically faced frequent stockouts on critical items. The store team blamed procurement, procurement blamed sales forecasts, and management lacked visibility into what was actually happening on the ground.
The challenge
Master Data Chaos
The same item appeared under 3-4 different names. Units of measure were inconsistent. There was no single item master.
Invisible Slow-Movers
Non-moving and slow-moving inventory was building up quietly because no one had a clear ageing report.
Over-Assortment
The SKU count had grown organically to over 2,000 items, many of which were near-duplicates or obsolete.
What we found
What we changed
Master Data Cleanup
Created naming rules: Category-SubCategory-Specification-Size. Merged duplicates. Standardized units of measure. Each item got a unique code and single owner.
Tally ODBC Extraction
Used Python scripts connecting to Tally on port 9000 to extract stock registers, receipt registers, and issue registers into flat files for analysis.
Ageing Dashboard
Built a Power BI dashboard showing ageing buckets (0-30, 31-60, 61-90, 90+ days) with drill-down by category and individual SKU.
Weekly Watchlist
Automated generation of two lists: items approaching reorder point, and items not moved in 60+ days.
Implementation
Week 1: Audited existing item master and documented inconsistencies.
Week 2-3: Cleaned and rationalized master data. Merged 400+ duplicates.
Week 4: Set up Tally ODBC extraction and built initial dashboard.
Week 5-6: Trained store and procurement teams on weekly review process.
Week 7-8: Monitored and refined thresholds based on actual consumption patterns.
Outcomes
| Area | Result described in this case |
|---|---|
| Aged Inventory (90+ days) | Directional reduction of approximately 35% in first quarter |
| Stockout Incidents | Reduced by approximately 60% on A-class items |
| SKU Count | Rationalized from 2,100 to 1,400 active SKUs |
The practical lesson
This material is general information. Apply it to your business only after checking the relevant facts, source documents and requirements.