Overall SC Health
0
/ 100
Demand Planning
0
/ 100
▲ +2.1
GCC avg: 74
Inventory Mgmt
0
/ 100
▲ +4.3
GCC avg: 70
Supplier Performance
0
/ 100
▼ -1.5
GCC avg: 68
Logistics & Service
0
/ 100
▲ +5.0
GCC avg: 72
Demand Planning
Forecast accuracy, bias control, and planning cycle efficiency
82
Score
Forecast Accuracy (1 – MAPE)
87.3%
Target: ≥90% | World class: 95%
2.7 pts below GCC target — Ramadan spikes key driver
Forecast Bias
+2.1%
Target: ±3% | World class: ±1.5%
Within target — slight over-forecasting, manageable
SKU Forecast Coverage
96.4%
Target: ≥95% | World class: 99%
Exceeds GCC target — 3.6% uncovered are C-class tails
Planning Cycle Time
3.2 days
Target: ≤5 days | World class: ≤2 days
Beats GCC target by 36% — powered by automated data pulls
Consensus Forecast Adoption
78%
Target: ≥85% | World class: 95%
Sales team overriding stats model for 22% of SKUs
New Product Forecast Err.
31.4%
Target: ≤25% | World class: ≤15%
NPD pipeline accelerating — analogue-based model needed
Key Insight: Forecast accuracy is strong for core SKUs but weak at new product launch. Implementing an analogue model (mapping new SKUs to historical launch curves) can close the gap by 8–12 pts, directly improving initial stocking decisions and reducing first-90-day write-offs. Priority: High.
Inventory Management
Turns, fill rate, days of supply, ABC-XYZ segmentation, and excess & obsolete
78
Score
Inventory Turnover Ratio
8.4×
Target: ≥7× | World class: 12×
Above GCC benchmark — A-class SKUs turning 14×
Line Fill Rate
94.5%
Target: ≥95% | World class: 98.5%
0.5 pts below target — 14 SKUs drive 80% of shortfalls
Days of Supply (DOS)
43.5 days
Target: 40–50 days | World class: 30–35
Within optimal band — China supplier lead times buffered
Excess & Obsolete (%)
3.1%
Target: ≤5% | World class: ≤1.5%
Well within GCC target — mid-2025 NPD launches are at-risk
ABC-XYZ A/X Coverage
99.1%
Target: ≥98% | World class: 99.5%
Near world-class — top revenue SKUs always in stock
Safety Stock Efficiency
71%
Target: ≥80% | World class: 90%
Over-buffered in C-class SKUs — locking AED 1.2M working capital
Key Insight: Inventory performance is solid at the top of the range but safety stock is over-calibrated for C-class SKUs — a legacy of manual buffer rules. Dynamic safety stock recalibration using statistical service-level targets (Z × σ × √LT) can release AED 1.2M–1.8M in working capital with no service degradation. Priority: High ROI.
Supplier Performance
On-time delivery, quality, lead time compliance, and cost variance across China-MENA-GCC supply base
71
Score
Supplier On-Time Delivery
88.6%
Target: ≥92% | World class: 97%
Red Sea re-routing extended China-to-Jebel Ali by 9 days avg
Quality Acceptance Rate
97.8%
Target: ≥97% | World class: 99.5%
Meets target — rejections concentrated in 2 Chinese suppliers
Lead Time Variance
±18 days
Target: ≤±10 days | World class: ≤±5
Critical gap — Suez/Red Sea disruptions doubling variability
Purchase Price Variance
+3.1%
Target: ≤±2% | World class: ≤±1%
USD freight surcharge + CNY shift contributing +1.8%
Supplier Scorecard Adoption
62%
Target: ≥80% | World class: 95%
38% of suppliers not yet on formal scorecard framework
Dual-Source Coverage
34%
Target: ≥60% | World class: 80%
66% of strategic SKUs single-sourced — highest risk dimension
Critical Gap: Only 34% dual-source coverage on strategic SKUs against a 60% target. With Red Sea disruption adding 9-day average delays and lead-time variance at ±18 days, a single major supplier delay can trigger stockouts on A-class revenue lines. Priority: Urgent. Initiate regional backup sourcing qualification for top-20 SKUs.
Logistics & Customer Service
Perfect order rate, OTD, freight cost, OTIF, and returns
80
Score
Perfect Order Rate
89.4%
Target: ≥92% | World class: 97%
Wrong-item picks account for 47% of imperfect orders
On-Time In-Full (OTIF)
93.1%
Target: ≥93% | World class: 97%
Exactly on target — positive trend from last 2 months
Freight Cost per Unit
AED 8.20
Target: ≤AED 8.50 | World class: ≤AED 6.50
3.5% below target — route consolidation savings landing
Order Cycle Time
1.8 days
Target: ≤2 days | World class: ≤0.5 days
Within target — same-day dispatch achieved on 71% of orders
Returns Rate
1.7%
Target: ≤2.5% | World class: ≤0.8%
Well below target — near-expiry product returns improving
3PL SLA Compliance
96.2%
Target: ≥95% | World class: 99%
Exceeds target — Q3 3PL contract renegotiation created penalties
Key Insight: Logistics performance is improving (OTIF just hit target), but the Perfect Order Rate gap is a pick-accuracy issue, not a delivery issue. Wrong-item picks account for 47% of failures. A WMS barcode-scan verification step at pick (zero-capital change) typically closes this gap by 4–6 pts within 30 days of rollout.
Performance Radar
Current vs GCC Benchmark vs World Class — all 4 dimensions
6-Month Score Trend
Overall SC Health Score — monthly progression
Strategic Action Priorities
5 Initiatives · Q1–Q2 2025
1
Dual-Source Qualification: Top-20 Strategic SKUs
Identify and qualify regional backup suppliers (Turkey, India, UAE local) for the top-20 A-class SKUs currently single-sourced from China. Red Sea risk + ±18-day lead time variance creates stockout exposure on highest-revenue lines. Target: bring dual-source coverage from 34% → 60% within 90 days.
Urgent
+8 pts
Supplier Score
AED 3.2M
Stockout risk mitigated
2
Dynamic Safety Stock Recalibration — C-Class SKUs
Replace flat-rule safety stock buffers on C-class SKUs with statistical service-level model (Z × σ_demand × √lead_time). Current over-buffering locks AED 1.2M–1.8M working capital with no service benefit. Recalibrate using 12-month demand history and current lead-time distributions.
High ROI
+6 pts
Inventory Score
AED 1.5M
Working capital released
3
Analogue Forecasting Model for New Product Launches
New product forecast error at 31.4% vs 25% target is the biggest single drag on Demand Planning score. Build an analogue model library: map each NPD to 3 historical comparable launches by category, price tier, and channel — weight actuals to generate initial forecast curves. Reduce NPD error to <22% within 2 launch cycles.
Medium
+5 pts
Demand Planning
-12%
NPD write-off risk
4
WMS Pick-Verification: Scan-to-Confirm at Pick Stage
47% of imperfect orders are wrong-item picks — a WMS configuration change (barcode scan confirmation before pick completion) eliminates this category of errors at near-zero cost. Most WMS platforms support this natively. A 30-day pilot typically shows 4–6 pt improvement in Perfect Order Rate, pushing OTIF above 95%.
Quick Win
+5 pts
Logistics Score
~AED 0
Implementation cost
5
Formal Supplier Scorecard — Extend to 100% of Supply Base
38% of suppliers operate without a formal scorecard. Standardise the existing scorecard template (OTD, quality, lead-time variance, PPV) across all suppliers. Conduct quarterly business reviews for strategic suppliers (top-30 by spend). Scorecard visibility alone drives 5–8% OTD improvement via supplier self-correction in comparable GCC implementations.
Strategic
+7 pts
Supplier Score
6–8%
OTD improvement