Inventory Shrink Analysis & Root Cause
Perpetual inventory with RFID and ML root cause analysis achieving 0.7-1.5% shrink versus 1.5-3.0% annual physical with 50% shrink reduction and actionable root cause identification.
Why This Matters
What It Is
Perpetual inventory with RFID and ML root cause analysis achieving 0.7-1.5% shrink versus 1.5-3.0% annual physical with 50% shrink reduction and actionable root cause identification.
Current State vs Future State Comparison
Current State
(Traditional)1. Store conducts annual physical Inventory Management: closes store, counts all products over 8-12 hours, enters data into Inventory Management Management over 2-4 weeks. 2. Inventory Management shrink calculated: compares system book Inventory Management to physical count finding $150,000 shrink (1.5-3.0% of sales) - 'missing Inventory Management' discovered. 3. Root cause analysis limited: total shrink known but causes unknown (external theft? internal theft? receiving errors? damaged goods?). 4. LP investigates high-shrink categories: identifies 'Electronics 5% shrink, Health & Beauty 4% shrink' but cannot determine specific causes. 5. LP implements broad corrective actions: increases camera coverage, adds EAS tags, retrains employees, with limited effectiveness since root causes unknown. 6. Shrink continues at similar rate next year: 1.5-3.0% ongoing because underlying causes not addressed. 7. Annual detection cycle too infrequent: shrink issues persist 365 days before measurement and intervention.
Characteristics
- • ERP systems for real-time inventory tracking
- • Cycle counting software for scheduled inventory counts
- • Data analytics platforms for pattern recognition
- • Spreadsheet applications (Excel) for data analysis
- • Email systems for cross-departmental communication
Pain Points
- ⚠ Data accuracy issues due to manual entry errors and system integration gaps.
- ⚠ Fragmentation of processes across departments leading to delays and incomplete analyses.
- ⚠ Resource constraints impacting the depth of investigations.
- ⚠ Detection lag due to infrequent full inventory counts.
- ⚠ Systemic gaps in inventory processes leading to recurring shrinkage.
- ⚠ Challenges in addressing shrinkage occurring in the supply chain outside direct control.
Future State
(Agentic)1. Shrink Analysis Agent maintains perpetual Inventory Management using RFID: tracks all products entering/leaving store in real-time vs annual count approach. 2. Agent detects shrink daily by category: identifies 'Electronics shrink 0.2% yesterday - 5 AirPods missing from shelf location' vs annual total only. 3. Root Cause Agent analyzes shrink patterns using ML: correlates RFID exit events (product left store), POS transactions (product sold?), video footage identifying root causes. 4. Agent determines shrink attribution: '3 AirPods sold but not scanned at POS = cashier error, 2 AirPods RFID exit no POS transaction = external theft'. 5. Agent generates actionable insights: 'AirPods 40% shrink from cashier scanning errors, 60% from external theft - corrective actions: retraining for cashiers, display locking cases for theft prevention'. 6. Agent tracks corrective action effectiveness: measures shrink reduction after interventions showing 'AirPods shrink reduced from 5% to 1.5% after display lock installation'. 7. 50% shrink reduction (0.7-1.5% vs 1.5-3.0%) through daily visibility, ML root cause analysis, and targeted corrective actions vs annual broad interventions.
Characteristics
- • RFID tag read events showing product movement (receiving, shelf, exit)
- • POS transaction data with scanned product sales
- • Inventory Management Management system with perpetual Inventory Management balances
- • Video footage correlated with RFID exit events (product leaving store)
- • Historical shrink patterns by product, category, store, time period
- • Corrective action tracking system (interventions and effectiveness)
- • ML models for shrink attribution (theft vs errors vs damages)
Benefits
- ✓ 50% shrink reduction (0.7-1.5% vs 1.5-3.0%) through root cause targeting
- ✓ Daily shrink visibility vs annual 365-day delay (perpetual inventory)
- ✓ ML root cause analysis identifies specific causes (theft vs errors vs damage)
- ✓ Actionable insights enable targeted corrective actions vs broad interventions
- ✓ Corrective action effectiveness measured (close the loop)
- ✓ $45,000-$135,000 annual shrink savings per store ($90K-180K to $45K-90K typical)
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Strong ROI potential based on impact score
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Inventory Shrink Analysis & Root Cause if:
- You're experiencing: Data accuracy issues due to manual entry errors and system integration gaps.
- You're experiencing: Fragmentation of processes across departments leading to delays and incomplete analyses.
- You're experiencing: Resource constraints impacting the depth of investigations.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Shrink & Loss Prevention
Reduces inventory loss through AI video analytics, real-time exception detection, and continuous monitoring achieving significantly lower shrink rates.
What to Do Next
Related Functions
Metadata
- Function ID
- function-inventory-shrink-analysis-root-cause