Cut Picking Errors by 57% &Cycle Time by 24%in 12 Weeks
How Lean Six Sigma root cause analysis transformed a 120,000 sq ft 3PL distribution center, improving order accuracy from 89% to 97% while reducing operational costs.
Executive Summary
A 120,000 sq ft 3PL distribution center faced critical operational challenges with low order accuracy (89%) and extended pick-to-ship cycle time (6.8 hours). Through a comprehensive Lean Six Sigma root cause analysis (RCA) and DMAIC approach, Yalgar implemented targeted, low-cost interventions that delivered exceptional results.
Context & Strategic Challenge
Order picking represents over 55% of warehouse operating costs, with 50% of picker time consumed by travel between picks. The facility needed to improve performance without heavy capital expenditure—maximizing ROI through lean process improvement.
Key Performance Gaps
- • Order accuracy at 89% (industry benchmark: 95%+)
- • Pick-to-ship cycle time of 6.8 hours (target: <5 hours)
- • High error-related costs impacting customer satisfaction
- • Inefficient resource utilization during peak periods
Methodology & Root Cause Analysis
Define & Baseline Metrics
Order Accuracy Formula
Order Accuracy = 1 – (errors ÷ total orders)
Cycle Time Definition
Pick-to-Ship = avg time pick released → shipped (APQC standard)
Analyze
Pareto Analysis Results
74% of mis-picks tied to 6 SKUs in 2 zones
Root Cause Categories (Fishbone + 5 Whys)
Pilot & Improve
Implemented Interventions
- • Slotting redesign for high-error SKUs
- • Mandatory scan validation implementation
- • Enhanced visual signage and color coding
- • Optimized replenishment windows
- • Micro-SOP development and training
- • Zone-specific pilot testing
Outcomes (12-Week Snapshot)
| Metric | Before | After | Impact |
|---|---|---|---|
| Order Accuracy | 89% | 97% | Error rate ↓ 57% |
| Pick-to-Ship Time | 6.8 hrs | 5.2 hrs | –24% |
| Throughput (orders/shift) | — | +18% | +18% |
| Dock-to-Stock | 12.3 hrs | 8.0 hrs | –35% |
Cost Impact Analysis
Hypothetical Savings Calculation
Volume: 5,000 orders/day
Accuracy Improvement: +8 percentage points (89% → 97%)
Daily Error Cost Avoidance: ~$4,800/day
Annual Savings: ~$1.2M
Key Success Factors
Data-Driven Approach
Pareto analysis focused efforts on highest-impact areas
Rapid Implementation
Low-cost interventions with immediate impact
Team Engagement
Front-line involvement in solution development
Sustain with Controls
Implemented Control Systems
KPI Dashboards & SPC Monitoring
- • Real-time accuracy tracking by zone and picker
- • Statistical process control thresholds
- • Automated alerts for performance deviations
- • Daily performance scorecards
Visual Management & Governance
- • Visual performance boards in work areas
- • Weekly operations huddles
- • Standardized problem-solving protocols
- • Continuous improvement suggestion system
Sustainability Framework
Standard Operating Procedures
Documented micro-SOPs for each critical process step with visual aids and decision trees.
Training & Competency
Structured training program with competency assessments and refresher cycles.
Performance Management
Individual and team performance metrics tied to recognition and development programs.
Continuous Improvement
Monthly review cycles with root cause analysis for any performance degradation.
Long-term Impact Indicators
External Validation
Our approach aligns with documented industry success stories:
- • A Lean Six Sigma DMAIC case in a service-parts warehouse reduced picking/packing errors by ~40% in 8 months
- • Manual order picking accounts for ~55% of warehouse cost and 50% of picker travel time (industry standard)
- • Root cause analysis methodologies consistently deliver 20-40% improvement in operational metrics
Why It Sticks
Lean-First, Automation-Later
Maximizes ROI with minimal capital expenditure by optimizing existing processes before considering technology investments.
Sustainable Controls
KPI dashboards, SPC thresholds, visual standards, and team governance ensure lasting performance improvements.
Benchmarked & Credible
Methodology aligned with documented industry case studies and ready for enterprise-level validation.
Rapid Implementation
12-week timeline from analysis to measurable results, minimizing disruption to ongoing operations.