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Supply Chain Optimizer

  • 310 installs
  • 86 repo stars
  • Updated July 17, 2026
  • travisjneuman/.claude

supply-chain-optimizer is a Claude agent skill providing supply chain mapping, inventory optimization, logistics planning, vendor evaluation, and demand forecasting frameworks for developers building SCM analytics or ope

About

supply-chain-optimizer is a travisjneuman/.claude agent skill with comprehensive frameworks for supply chain analysis, inventory management, logistics optimization, and vendor evaluation. It includes an end-to-end supply chain map template tracing raw materials through suppliers, manufacturing, distribution, and customers with steps to identify nodes, material flows, and information flows such as orders and forecasts. Developers building inventory dashboards, procurement tools, or logistics planning features invoke supply-chain-optimizer when they need structured SCM analysis patterns instead of inventing frameworks from scratch. The skill supports demand forecasting workflows and vendor scoring criteria that inform backend data models and integration priorities for warehouse and ERP connectors. Reach for supply-chain-optimizer during validate-phase scoping of SCM products or when refactoring operations modules that must model multi-node supply networks. Outputs include supply chain maps, inventory optimization notes, and vendor evaluation matrices ready for engineering specs.

  • supply-chain-optimizer

Supply Chain Optimizer by the numbers

  • 310 all-time installs (skills.sh)
  • +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #1,321 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs310
repo stars86
Last updatedJuly 17, 2026
Repositorytravisjneuman/.claude

How do you model supply chain flows for software?

Use supply-chain-optimizer for development tasks

Who is it for?

Developers building inventory, procurement, or logistics software who need structured supply chain mapping and vendor evaluation frameworks before implementation.

Skip if: Pure software teams with no supply chain, inventory, or logistics domain requirements in the product scope.

When should I use this skill?

A developer asks to analyze supply chains, optimize inventory, evaluate suppliers, forecast demand, or plan logistics for operations software.

What you get

End-to-end supply chain map, inventory optimization framework, vendor evaluation matrix, and demand forecasting template for SCM feature specs.

  • Supply chain map
  • Vendor evaluation matrix
  • Demand forecasting template

Files

SKILL.mdMarkdownGitHub ↗

Supply Chain Optimizer

Comprehensive frameworks for supply chain analysis, inventory management, logistics optimization, and vendor evaluation.

Supply Chain Mapping Template

End-to-End Supply Chain Map

RAW MATERIALS → SUPPLIERS → MANUFACTURING → DISTRIBUTION → CUSTOMER

MAPPING STEPS:
1. Identify all nodes (suppliers, plants, warehouses, customers)
2. Map material flows between nodes
3. Map information flows (orders, forecasts, POs)
4. Map financial flows (payments, invoicing)
5. Record lead times at each stage
6. Identify bottlenecks and single points of failure

NODE DETAIL TEMPLATE:
| Node           | Type       | Location  | Lead Time | Capacity | Utilization |
| -------------- | ---------- | --------- | --------- | -------- | ----------- |
|                | Supplier   |           |           |          |             |
|                | Plant      |           |           |          |             |
|                | Warehouse  |           |           |          |             |
|                | DC         |           |           |          |             |

Inventory Optimization

Economic Order Quantity (EOQ)

EOQ = sqrt(2DS / H)

Where: D = Annual demand, S = Ordering cost/order, H = Holding cost/unit/year
Total Cost = (D/Q)S + (Q/2)H + DC

Example: D=10,000, S=$50, H=$5 → EOQ = 447 units, 22.4 orders/year

Safety Stock & Reorder Point

SAFETY STOCK: SS = z x sigma_dLT
REORDER POINT: ROP = (Avg Daily Demand x Lead Time) + Safety Stock

SERVICE LEVEL FACTORS:
| Service Level | z-Score | Use Case          |
| ------------- | ------- | ----------------- |
| 90.0%         | 1.28    | Basic coverage    |
| 95.0%         | 1.65    | Standard          |
| 99.0%         | 2.33    | High service      |
| 99.9%         | 3.09    | Critical items    |

Inventory KPI Dashboard

MetricFormulaTarget
Inventory TurnsCOGS / Average InventoryIndustry-specific
Days of SupplyAverage Inventory / (COGS / 365)Minimize
Fill RateOrders Filled Complete / Total Orders97%+
Stockout RateStockout Events / Total Demand Events< 2%
Carrying Cost %Holding Costs / Average Inventory Value15-30%
Dead Stock %No-movement Items / Total SKUs< 5%
Inventory AccuracyCorrect Counts / Total Counts99%+
GMROIGross Margin / Average Inventory Cost> 2.0

ABC-XYZ Analysis Framework

ABC CLASSIFICATION (Value):
A Items: Top 20% of SKUs = ~80% of annual consumption value
  → Tight control, frequent review, accurate forecasts
B Items: Next 30% of SKUs = ~15% of value
  → Moderate control, periodic review
C Items: Bottom 50% of SKUs = ~5% of value
  → Minimal control, simple replenishment rules

XYZ CLASSIFICATION (Demand Variability):
X: Coefficient of Variation < 0.5 → Stable, predictable demand
Y: CV between 0.5 and 1.0 → Some variation, trend/seasonal
Z: CV > 1.0 → Highly irregular, sporadic demand

COMBINED MATRIX:
| Class | AX         | AY           | AZ            |
| ----- | ---------- | ------------ | ------------- |
| Strat | JIT/Kanban | Forecast     | Order on demand|
| Class | BX         | BY           | BZ            |
| Strat | Reorder pt | Buffer stock | Min/Max       |
| Class | CX         | CY           | CZ            |
| Strat | Bulk buy   | Periodic rev | Eliminate?     |

Vendor Scorecard

Supplier Evaluation Matrix

CriteriaWeightScore (1-5)Weighted ScoreNotes
Quality (PPM)25%
Delivery (OTIF)20%
Pricing20%
Responsiveness10%
Financial Health10%
Innovation5%
Sustainability5%
Risk Profile5%
Total100%__ / 5.0
RATING SCALE:
4.5-5.0  Strategic Partner — expand relationship
3.5-4.4  Preferred Supplier — maintain, develop
2.5-3.4  Approved Supplier — improvement plan required
< 2.5    Probation / Exit — find alternative

Supplier Performance Tracking

KPITargetQ1 ActualQ2 ActualQ3 ActualQ4 ActualTrend
On-Time Delivery98%+
Quality (PPM)< 500
Lead Time (days)
Price Variance+/- 2%
Response Time (hrs)< 24
Corrective Actions< 2/qtr

Total Cost of Ownership (TCO)

TCO = Acquisition Costs + Operating Costs + Disposal Costs

ACQUISITION COSTS:
  Purchase price
+ Shipping / freight
+ Customs / duties / tariffs
+ Procurement labor
+ Quality inspection
+ Supplier qualification
= Total Acquisition

OPERATING COSTS (over useful life):
  Maintenance & repair
+ Inventory carrying cost
+ Warranty claims
+ Downtime cost (if component fails)
+ Training / support
+ Quality failures (scrap, rework)
= Total Operating

DISPOSAL COSTS:
  Decommissioning
+ Recycling / disposal fees
+ Environmental compliance
= Total Disposal

TCO = Total Acquisition + Total Operating + Total Disposal

TCO Comparison Template

Cost ElementSupplier ASupplier BSupplier C
Unit Price
Shipping
Duties / Tariffs
Quality Cost (est.)
Inventory Carry Cost
Lead Time Cost
Risk Premium
Total TCO/Unit
Annual TCO

Demand Forecasting Methods

MethodBest ForHorizonData Required
Moving AverageStable demandShort-term3-12 periods history
Exponential SmoothTrend detectionShort-termRecent weighted data
Holt-WintersSeasonal patternsMedium-term2+ years seasonal
Linear RegressionTrend with causal factorsMedium-termDemand + drivers
ARIMAComplex time seriesShort-medium50+ data points
Machine LearningMulti-variable patternsAnyLarge datasets
Delphi / ExpertNew products, disruptionsLong-termExpert panel

Forecast Accuracy Metrics

MAD (Mean Absolute Deviation):
MAD = (1/n) x SUM(|Actual - Forecast|)

MAPE (Mean Absolute Percentage Error):
MAPE = (1/n) x SUM(|Actual - Forecast| / Actual) x 100

BIAS (Tracking Signal):
Bias = SUM(Actual - Forecast) / MAD
Target: Between -4 and +4

ACCURACY BENCHMARKS:
| Forecast Horizon | Good MAPE | Acceptable MAPE |
| ---------------- | --------- | --------------- |
| 1 month          | < 15%     | < 25%           |
| 3 months         | < 25%     | < 35%           |
| 6 months         | < 30%     | < 45%           |
| 12 months        | < 35%     | < 50%           |

Logistics Optimization

Transportation Mode Selection

ModeCost/UnitSpeedCapacityBest For
TruckMediumFastMediumRegional, door-to-door
RailLowSlowVery HighBulk, long-distance domestic
OceanVery LowVery SlowVery HighInternational, bulk cargo
AirVery HighVery FastLowHigh-value, urgent, perishable
IntermodalLow-MedMediumHighLong-distance, cost-effective

Warehouse Layout Optimization

SLOTTING STRATEGY:
Fast movers (A items) → Near shipping dock, prime pick locations
Medium movers (B items) → Middle zones
Slow movers (C items) → High racks, far locations

LAYOUT PRINCIPLES:
1. Minimize travel distance for highest-velocity items
2. Group items frequently ordered together
3. Separate receiving and shipping areas
4. Reserve staging areas for cross-docking
5. Maintain clear aisle widths for equipment

Lead Time Analysis

TOTAL LEAD TIME:
Order Processing Time
+ Supplier Manufacturing Time
+ Transportation Time
+ Receiving / Inspection Time
+ Internal Processing Time
= Total Lead Time

LEAD TIME VARIABILITY:
Track actual vs. quoted lead times over 12+ orders
Calculate standard deviation
Use for safety stock calculations

LEAD TIME REDUCTION STRATEGIES:
| Strategy              | Typical Reduction | Effort    |
| --------------------- | ----------------- | --------- |
| Vendor-managed inv.   | 30-50%            | Medium    |
| Local sourcing        | 40-70%            | High      |
| Process automation    | 10-30%            | Medium    |
| Blanket POs           | 20-40%            | Low       |
| Consignment stock     | 50-80%            | Medium    |
| 3PL consolidation     | 10-25%            | Low       |

Supply Chain Risk Assessment

Risk Identification Matrix

Risk CategoryRisk EventLikelihoodImpactScoreMitigation
SupplySingle-source failureDual-source
DemandDemand spike/collapseBuffer stock, flex
LogisticsPort congestionAlternate routes
GeopoliticalTariffs, sanctionsNearshoring
Natural DisasterEarthquake, floodGeographic diversity
CyberSystem breachSecurity protocols
QualityBatch failureInspection, redundancy
FinancialSupplier bankruptcyFinancial monitoring
RISK SCORING:
Likelihood: 1 (Rare) to 5 (Almost Certain)
Impact: 1 (Negligible) to 5 (Catastrophic)
Risk Score = Likelihood x Impact

ACTION THRESHOLDS:
20-25  Critical — immediate action, executive attention
12-19  High — mitigation plan required within 30 days
6-11   Medium — monitor quarterly, contingency plans
1-5    Low — accept or monitor annually

KPI Dashboard Template

CategoryKPITarget
CostSC Cost as % of Revenue5-10%
CostCost per OrderMinimize
ServicePerfect Order Rate95%+
ServiceOn-Time In-Full (OTIF)98%+
ServiceCustomer Fill Rate97%+
EfficiencyInventory TurnsIndustry-specific
EfficiencyCash-to-Cash CycleMinimize
EfficiencyWarehouse Utilization85%
EfficiencyForecast Accuracy (MAPE)< 20%

See Also

  • Operations
  • Finance
  • Risk Management

Related skills

How it compares

Use supply-chain-optimizer for SCM domain modeling and vendor frameworks; use generic API skills when only CRUD endpoints without logistics domain logic are needed.

FAQ

What does supply-chain-optimizer map?

supply-chain-optimizer maps end-to-end supply chains from raw materials through suppliers, manufacturing, distribution, and customers. The travisjneuman/.claude skill documents material flows and information flows such as orders and forecasts for SCM software scoping.

When should developers use supply-chain-optimizer?

Developers should use supply-chain-optimizer when building inventory, procurement, or logistics features and needing structured analysis frameworks. The skill provides vendor evaluation and demand forecasting templates before backend SCM modules and ERP integrations are implement

Backend & APIsbackendintegrations

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