📦 Resilient Supply Chain Network — Monte Carlo Report

Generated: 04-Jul-2026 10:11 | Python engine

14400

Total Simulations

400

Replications/Combo

52 wk

Simulation Horizon

Factor Combinations

27 s

Total Compute Time

Executive Summary

✅ Best strategy for service: Conservative achieves the highest mean service level of 73.8% across all scenarios.
❌ Crisis impact: Calm→Crisis reduces service level from 67.3% to 55.7% (Δ=11.6pp) and increases lost sales from €1.54M to €2.78M.
🔗 Cost–Service trade-off: Correlation r = 0.586. Higher service levels come with higher total costs.
📈 Distributed resilience: In crisis, service levels are 46.8% (Centralized), 62.3% (Distributed), 58.0% (Hybrid).

Jump to full analysis ↓

▶ Watch the animated simulation

A frame-by-frame canvas animation of this scenario, side-by-side under contrasting operating policies.

1. Design of Experiments

A full-factorial design explores how inventory strategy, network configuration, and disruption scenario interact to shape supply chain resilience.

Factors

Factor	Levels	Description
Inventory Strategy	3	Conservative (3wk safety), Balanced (2wk), Lean/JIT (1wk)
Network Config	3	Centralized (80% via 1 DC), Distributed (even), Hybrid (regional+overflow)
Disruption Scenario	4	Calm (2%), Moderate (5%), Volatile (10%), Crisis (20%)

Network Topology

Node	Type	Key Parameters
🌎 Asia-Primary	Supplier	Lead 4wk, Rel 92%, Cap 5k/wk, €8/unit
🇪🇺 Europe-Secondary	Supplier	Lead 2wk, Rel 97%, Cap 3k/wk, €12/unit
🇺🇸 Americas-Backup	Supplier	Lead 3wk, Rel 95%, Cap 2k/wk, €10/unit
🏭 Plant-East / West	Manufacturing	Cap 4k–3.5k/wk, Yield 93–95%
📦 DC-North / Central / South	Distribution	Storage 15k–25k units, Holding €0.50/unit/wk
👤 5 Customer Regions	Demand	Seasonal, Trending, Volatile, Stable, Growing

2. Key Performance Indicators (across all runs)

61.6%

Service Level (±16.5%)

80.4%

Fill Rate (±14.4%)

5.33

Total Cost [€M] (±0.93)

6.0

Avg Lead Time [wk] (±0.2)

2.11

Lost Sales [€M] (±1.59)

0.11

Holding Cost [€M] (±0.07)

1.31

Transport Cost [€M] (±0.25)

49.4%

Backorder Rate (±27.1%)

4.0k

Avg Inventory [units] (±2.7k)

141.8

Inventory Turnover (±231.0)

316

Disruption Days (±202)

21.4

Recovery Time [wk] (±18.0)

3. Monte Carlo Summary Dashboard

20-panel overview showing distributions, heatmaps, time series, Pareto fronts, and factor effects across all runs.

3.1 Service & Cost Overview

Service-level and total-cost distributions segmented by sourcing strategy, disruption profile, and network configuration.

3.2 Trade-off Analysis

Cross-metric scatter plots showing how lost sales, costs, backorders, and lead times relate to service performance.

3.3 Convergence & Interaction Heatmaps

Monte Carlo convergence of service level, plus heatmaps showing strategy–disruption interaction effects on SL and cost.

3.4 Factor Effects & Pareto

Main-effect decomposition, cost density curves, the cost-vs-service Pareto frontier, and disruption recovery times.

3.5 Disruption & Financial Risk

Disruption-specific service-level impacts, perfect-order rates, cumulative cost paths, and cash-flow-at-risk exposure.

4. Key Insights

✅ Best strategy for service: Conservative achieves the highest mean service level of 73.8% across all scenarios.

❌ Crisis impact: Calm→Crisis reduces service level from 67.3% to 55.7% (Δ=11.6pp) and increases lost sales from €1.54M to €2.78M.

🔗 Cost–Service trade-off: Correlation r = 0.586. Higher service levels come with higher total costs.

📈 Distributed resilience: In crisis, service levels are 46.8% (Centralized), 62.3% (Distributed), 58.0% (Hybrid).

💨 Worst case: Lean/JIT under Crisis achieves only 38.8% service level with €3.43M lost sales.

5. Network Configuration Analysis

💡 Why Centralized Often Outperforms:

Inventory pooling effect — a single large DC requires less safety stock than multiple small DCs serving the same total demand (√n pooling).
Reduced lateral transshipments — with centralized inventory, demand variability across regions is averaged out.
Lower total holding costs — fewer units needed system-wide.

However, centralized networks carry higher single-point-of-failure risk, which becomes apparent in Crisis scenarios where the central DC can become overwhelmed.

Network Config	Mean SL [%]	Mean Cost [€M]	Mean Fill Rate [%]	Mean Lead Time [wk]	Mean Backorder [%]
Centralized	49.4	4.23	62.0	6.1	82.7
Distributed	68.2	5.87	89.3	5.9	31.9
Hybrid	67.2	5.89	89.8	5.9	33.6

📊 Best network: Distributed delivers the highest average service level of 68.2%.

6. Disruption Resilience Analysis

Scenario	Mean SL [%]	Mean Cost [€M]	Lost Sales [€M]	Disruption Days	Recovery [wk]
Calm	67.3	5.05	1.54	92	21.2
Moderate	63.8	5.25	1.85	209	21.1
Volatile	59.6	5.44	2.28	370	21.4
Crisis	55.7	5.59	2.78	591	21.9

⚠️ Crisis scenario: Average disruption duration of 591 days with recovery taking 21.9 weeks. Total financial exposure: €8.37M.

7. KPI Correlation Analysis

💡 Correlation Insights

What these supply-chain relationships reveal — with logistics theory and APICS/ASCM context.

KPI Pair	r	Strength	Actionable Insight
Holding Cost ↔ Avg Inventory	1.000	Strong	Holding Cost and Avg Inventory show a positive relationship (r=1.00). Changes in one tends to reinforce the other.
Total Cost ↔ Transport	0.996	Strong	Total Cost and Transport show a positive relationship (r=1.00). Changes in one tends to reinforce the other.
Fill Rate ↔ Lost Sales	-0.982	Strong	Fill Rate and Lost Sales show a negative relationship (r=-0.98). Improving one may come at the expense of the other.
Fill Rate ↔ Backorder	-0.937	Strong	Fill Rate and Backorder show a negative relationship (r=-0.94). Improving one may come at the expense of the other.
Lost Sales ↔ Backorder	0.905	Strong	Lost Sales and Backorder show a positive relationship (r=0.91). Changes in one tends to reinforce the other.
Backorder ↔ Recovery	0.889	Strong	Backorder and Recovery show a positive relationship (r=0.89). Changes in one tends to reinforce the other.
Service Level ↔ Avg Inventory	0.872	Strong	Service Level and Avg Inventory show a positive relationship (r=0.87). Changes in one tends to reinforce the other.
Service Level ↔ Holding Cost	0.872	Strong	Service Level and Holding Cost show a positive relationship (r=0.87). Changes in one tends to reinforce the other.

8. Full Statistical Summary

KPI	Mean	Std	Min	Median	Max	95% CI
Service Level [%]	61.599	16.527	8.788	60.909	96.970	[59.979, 63.218]
Fill Rate [%]	80.380	14.402	45.088	85.604	101.704	[78.968, 81.791]
Total Cost [€M]	5.332	0.932	3.801	5.438	7.978	[5.241, 5.424]
Avg Lead Time [wk]	5.960	0.192	5.470	5.943	6.769	[5.941, 5.979]
Lost Sales [€M]	2.113	1.595	0.000	1.563	7.962	[1.956, 2.269]
Holding Cost [€M]	0.105	0.071	0.001	0.105	0.294	[0.098, 0.112]
Transport Cost [€M]	1.311	0.254	0.878	1.350	2.047	[1.286, 1.336]
Backorder Rate [%]	49.364	27.050	2.726	44.373	95.831	[46.713, 52.014]
Avg Inventory	4043.327	2724.756	51.836	4026.030	11323.370	[3776.300, 4310.353]
Inv Turnover	141.784	231.048	14.891	56.738	5091.009	[119.141, 164.426]
Disruption Days	315.723	202.323	0.000	287.000	847.000	[295.896, 335.551]
Recovery Time [wk]	21.421	18.047	1.500	11.667	51.000	[19.652, 23.190]
Cash Flow at Risk [€M]	2.427	1.538	0.337	1.895	8.168	[2.277, 2.578]
Perfect Order Rate [%]	61.597	16.529	8.788	60.909	96.970	[59.977, 63.217]
Bullwhip Ratio	7.938	10.216	0.031	4.326	131.496	[6.937, 8.940]
Cash-to-Cash [days]	21.142	4.396	15.060	20.428	35.684	[20.711, 21.573]

9. Methodology & Model Description

9.1 Architecture

Discrete-time supply chain simulation with weekly time steps over 52 weeks. Enhanced with bidirectional demand shocks (surge & recession), cross-DC transshipment, and incoming quality inspection (5% rejection). Self-contained engine running inside ProcessPoolExecutor for embarrassingly parallel Monte Carlo execution.

9.2 Network Model

3 Suppliers (Asia, Europe, Americas) with differentiated lead times, reliability, capacity and unit costs. 2 Manufacturing Plants with yield rates. 3 Distribution Centres with storage capacity limits. 5 Customer Regions with distinct demand patterns (seasonal, trending, volatile, stable, growing).

9.3 Disruption Model

Three disruption types: supplier failure (reduces capacity to 0–30% for 1–8 weeks), transport delay (adds 1–4 weeks), demand shock (multiplies demand by 1.5–3.0× for 2–6 weeks). Probabilities scaled by scenario from 2% (Calm) to 20% (Crisis) per week per node.

9.4 Monte Carlo Design

3 strategies × 3 networks × 4 disruption scenarios = 36 combinations × 400 reps = 14400 runs. 52 weeks each. Executed with 8 CPU cores in 27.5s.

10. Interactive Analysis

11. Strategic Insights by Persona

Data-driven recommendations derived from the simulation results, tailored for four key stakeholder groups.

🌐

Network Design Impact

Distributed network achieves 68.2% SL vs Centralized 49.4% and Hybrid 67.2%. Under crisis, distributed shows highest resilience at 62.3%.

🏆

Optimal Strategy Selection

Conservative delivers 73.8% SL at €5.72M cost. The cost premium over Lean is €0.83M.

📊

Service Level Optimisation

Best achievable SL (Conservative+Calm): 78.3%. Service–inventory correlation: r = 0.872. Safety stock is the primary lever.

📦

DC Capacity Planning

Mean inventory across all runs: 4.0k units. Inventory turnover: 141.8×/yr. Conservative strategy reduces turnover but improves fill rate to 84.5%.

✅

Strategic Recommendation

Deploy Conservative with Distributed network for optimal resilience. This combination yields 81.8% SL across all disruption scenarios.

🛡

Disruption Preparedness

Conservative strategy maintains 68.3% SL even in crisis, vs Lean at 38.8%. Safety stock is the best disruption insurance.

⏰

Recovery Metrics

Mean recovery time: Conservative 18.9 wk, Balanced 21.1 wk, Lean 24.3 wk. Faster recovery = less cumulative lost revenue.

💰

Financial Exposure

Cash flow at risk: Calm €1.84M vs Crisis €3.10M. Crisis increases exposure by 1.7×.

🌋

Worst-Case Scenarios

Lean + Centralized + Crisis: 31.2% SL, €5.30M lost. This is the riskiest combination.

🛠

Risk Mitigation

Distributed network acts as insurance: crisis SL is 15.4pp higher than centralized. The premium is €1.64M extra cost in calm times.

🌎

Supplier Reliability Impact

3-supplier network provides redundancy. Asia-Primary (92% rel, €8) vs Europe-Secondary (97% rel, €12). Mean disruption days: 316 across all scenarios.

💰

Cost Optimisation

Procurement: €1.95M, Manufacturing: €1.14M, Transport: €1.31M. Procurement is the largest cost driver at 36% of total.

🔄

Multi-Sourcing Benefits

Triple-sourcing means even when one supplier fails (up to 8 weeks), the other two can cover 139% of base demand. Cost is only 11.2 €/unit avg from backups.

⏲

Lead Time Management

Average lead time: 6.0 weeks. Europe-Secondary at 2wk provides fastest response; Asia-Primary at 4wk is cheapest. Lead time variability is the key driver of safety stock requirements.

📈

Inventory-Cost Trade-off

Conservative holds 6.9k units (holding: €0.18M) vs Lean 1.1k units (holding: €0.03M). But lost sales offset holding savings.

⚖️

Strategic Trade-offs

The cost-service Pareto frontier shows diminishing returns above 95% SL. Conservative costs €5.72M for 73.8% SL, while Lean saves €0.83M but drops to 46.1% SL.

💸

Investment Priorities

Investing in safety stock (Conservative → +€0.83M) yields +27.7pp SL. Investing in distributed network adds €1.64M but provides crisis resilience.

💪

Competitive Resilience

Under crisis conditions, our best configuration maintains 77.2% SL. Lean competitors would drop to 31.2%.

📈

Financial Summary

Mean annual supply chain cost: €5.33M. Mean annual lost sales: €2.11M. Perfect order rate: 61.6%. Total exposure: €2.43M.

🎯

Executive Recommendation

Deploy Conservative strategy with Distributed network. Accept the €5.72M annual cost for 73.8% service. This is the optimal point on the cost-resilience frontier.

12. Disruption Recovery Analysis

Time-to-recovery definition: Weeks from service level dropping below 85% to sustained recovery above 95%.

Disruption	Network	Mean Recovery [wk]	P50 [wk]	P90 [wk]
Calm	Centralized	47.48	49.00	51.00
Moderate	Centralized	45.12	49.00	51.00
Volatile	Centralized	43.38	49.00	51.00
Crisis	Centralized	42.28	49.00	51.00
Calm	Distributed	7.89	7.25	12.00
Moderate	Distributed	8.64	7.75	13.00
Volatile	Distributed	9.53	8.25	14.67
Crisis	Distributed	10.38	9.00	16.00
Calm	Hybrid	8.37	8.00	12.33
Moderate	Hybrid	9.64	8.50	15.50
Volatile	Hybrid	11.33	9.75	20.00
Crisis	Hybrid	13.02	11.00	22.50

⏰ Recovery Insight: Under crisis conditions, distributed networks recover 31.9 weeks faster than centralized (P90: 16.0 vs 51.0 weeks).

13. Cost Component Analysis

Strategy / Network	Procurement [€M]	Manufacturing [€M]	Transport [€M]	Holding [€M]	Operating [€M]	Lost Sales [€M]
Conservative / Centralized	1.50	0.90	1.03	0.14	0.83	3.88
Conservative / Distributed	2.39	1.37	1.57	0.20	0.83	0.57
Conservative / Hybrid	2.39	1.39	1.59	0.19	0.83	0.60
Balanced / Centralized	1.46	0.88	1.02	0.07	0.83	4.03
Balanced / Distributed	2.23	1.28	1.47	0.13	0.83	1.18
Balanced / Hybrid	2.23	1.30	1.48	0.13	0.83	1.05
Lean/JIT / Centralized	1.39	0.84	0.97	0.01	0.83	4.17
Lean/JIT / Distributed	1.97	1.15	1.34	0.04	0.83	1.76
Lean/JIT / Hybrid	1.96	1.15	1.34	0.04	0.83	1.78

💰 Hidden Cost: Lost sales represent €2.11M (28.4% of total landed cost) — the hidden cost not captured in traditional accounting. Lost sales are computed as backorder quantity × €50/unit sell price.

14. Bullwhip Effect Analysis

The bullwhip effect occurs when order variability amplifies as we move upstream from customer to supplier. A bullwhip ratio > 1 indicates order amplification; values near 1 indicate stable ordering.

14.45

Conservative Bullwhip Ratio

7.27

Balanced Bullwhip Ratio

2.10

Lean/JIT Bullwhip Ratio

📈 Bullwhip Insight: Conservative strategy (bullwhip ratio = 14.45) amplifies orders more than Lean/JIT (2.10). Higher safety stock absorbs demand variability, reducing order amplification upstream.

15. Cash-to-Cash Cycle Analysis

Cash-to-Cash (C2C) measures the time between paying suppliers and receiving payment from customers: C2C = DIO + DSO − DPO.

Component Definitions

Component	Formula	Value
Days Inventory Outstanding (DIO)	avg_inventory / daily_demand	Varies by strategy
Days Payable Outstanding (DPO)	Supplier payment terms	30 days
Days Sales Outstanding (DSO)	Customer collection period	45 days

C2C by Strategy

Conservative [days]

Balanced [days]

Lean/JIT [days]

💰 Working Capital: Lean/JIT achieves 8.8 days lower C2C than Conservative, freeing approximately €0.26M in working capital.

16. Multi-Echelon Inventory Analysis

Analysis of inventory performance at each echelon level of the supply chain network.

🌎 Supplier Echelon

Supplier	Reliability	Capacity	Unit Cost	Lead Time
Asia-Primary	92%	5,000/wk	€8	4 wk
Europe-Secondary	97%	3,000/wk	€12	2 wk
Americas-Backup	95%	2,000/wk	€10	3 wk

🏭 Plant Echelon

Plant	Capacity	Yield	Unit Cost
Plant-East	4,000/wk	95%	€5
Plant-West	3,500/wk	93%	€6

📦 DC Echelon

DC	Max Capacity	Avg Inventory	Utilisation	Op. Cost/wk
DC-North	20,000	1,473	7.4%	€5,000
DC-Central	25,000	1,289	5.2%	€7,000
DC-South	15,000	1,388	9.3%	€4,000

🔗 Weakest Link: DC-Central shows the lowest average utilisation (5.2%), suggesting potential over-provisioning or demand imbalance. Rebalancing demand allocation or reducing capacity at this node could improve overall cost efficiency.

🌎 KPI Glossary & Definitions

Detailed explanation of every metric used in this analysis, with industry benchmarks and authoritative sources to help all stakeholders interpret results.

KPI	Definition	Why It Matters	Industry Benchmark
Service Level [%]	Fraction of demand fulfilled on time	The primary logistics performance indicator. Below 95% signals systematic issues.	Class A: ≥95% (APICS/ASCM)
Fill Rate [%]	Fraction of ordered quantity actually delivered	Order completeness measure. Distinct from service level (which is binary per order).	≥97% for Class A
Total Cost [€M]	Holding + transport + procurement costs	Total supply chain operating cost. Trade-off with service level.	Benchmark: 5–8% of revenue
Lead Time [wk]	Order-to-delivery time including delays	Drives safety stock requirements: SS = z × σ × √LT.	Simchi-Levi, 2008
Lost Sales [€M]	Revenue lost due to stockouts	Direct revenue impact + customer lifetime value erosion. Hard to recover.	Target: <2% of revenue
Backorder Rate [%]	Fraction of demand backordered	Systematic backorders signal structural capacity/inventory issues.	<5% for responsive supply chains
Avg Inventory	Mean units held across all DCs	Holding cost driver. Balance against stockout risk.	Optimize via EOQ models
Inv Turnover	Annual demand / average inventory	Higher = leaner. But very high turnover increases stockout risk.	8–12 manufacturing; 20+ retail
Disruption Days	Total days of supply disruption	Resilience metric. Geopolitical risk has increased this 3× since 2019.	Ivanov et al., 2017
Recovery Time [wk]	Weeks to restore full operations post-disruption	Resilience speed. Distributed networks recover faster.	Target: <4 weeks for critical paths
Cash at Risk [€M]	Weighted cash flow exposure under disruption	Combines inventory value, lost sales, and emergency costs.	Stress-test against 2× worst-case
Perfect Order Rate [%]	Orders delivered complete, on-time, undamaged	The "holy grail" metric combining service level, fill rate, and quality.	Best-in-class: ≥90%
Bullwhip Ratio	Var(DC orders) / Var(customer demand)	Measures order amplification upstream. Values >1 indicate the bullwhip effect; higher values mean more volatility in supply chain.	<2.0 is well-managed (Lee et al., 1997)
Cash-to-Cash [days]	DIO + DSO − DPO	Days between paying suppliers and receiving customer payment. Lower C2C frees working capital and improves cash flow.	Best-in-class: <30 days

📚 Sources & References

Industry standards, academic research, and data sources backing the model assumptions and benchmarks cited in this report.

#	Source	Used For
1	APICS/ASCM — Supply Chain Operations Reference (SCOR)	Service level, fill rate, and perfect order rate definitions and benchmarks
2	Simchi-Levi, D. (2008). Designing and Managing the Supply Chain	Safety stock formulas, lead-time variability, network design
3	Lee, Padmanabhan & Whang (1997). Management Science	Bullwhip effect: demand signal amplification through supply chain tiers
4	Ivanov, Sokolov & Dolgui (2017). IJPE	Supply chain resilience, disruption recovery, ripple effect
5	Supply Chain Quarterly	Inventory turnover benchmarks by industry
6	Ho et al. (2015). EJOR	Multi-criteria supplier evaluation and lateral transshipment models