supply-chain-optimizer — скилл для Claude Code: анализ цепочек
Скилл с готовыми шаблонами для анализа цепочек поставок, расчёта запасов, оценки поставщиков и прогноза спроса. Пригодится, когда нужно посчитать EOQ или собрать карту поставок.
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a claude-codeЧто делает supply-chain-optimizer
Скилл даёт агенту набор шаблонов и формул, которые обычно считают вручную в таблицах. Внутри — карта цепочки поставок от сырья до клиента с шагами, таблицей узлов и полями под время поставки и загрузку мощностей. Дальше идут расчёты запасов: формула EOQ, страховой запас, точка перезаказа и таблица z-оценок по уровням сервиса от 90.0% до 99.9%.
Есть разбор ABC-XYZ: SKU делятся по стоимости и по колеблемости спроса, для каждой из девяти комбинаций прописана стратегия — от JIT и Kanban до «заказывать по потребности». Отдельный блок про поставщиков: матрица оценки с весами по качеству, срокам и цене, шкала рейтинга от стратегического партнёра до выхода из отношений, таблица KPI по кварталам. Плюс расчёт совокупной стоимости владения с разбивкой на закупку, эксплуатацию и утилизацию.
Формулы и таблицы скелетные: пустые ячейки под ваши данные, конкретных цифр по отрасли нет. Значит, агенту придётся опираться на ваши вводные — годовой спрос, стоимость заказа, затраты на хранение. Если этих чисел нет, расчёт EOQ или страхового запаса превратится в подстановку нулей, так что готовьте данные заранее. Скилл ставится в Claude, Codex, Cursor, GitHub Copilot, Gemini CLI и OpenCode.
Какие задачи решает
- посчитать EOQ и точку перезаказа по своим данным
- разложить SKU по ABC-XYZ и выбрать стратегию пополнения
- сравнить поставщиков по взвешенной матрице критериев
- посчитать совокупную стоимость владения вместо цены за единицу
- найти узкие места и единые точки отказа в цепочке поставок
Примеры запросов агенту
- Построй карту цепочки поставок для моего производства и отметь узкие места
- Посчитай EOQ и страховой запас: годовой спрос 10000, стоимость заказа 50, хранение 5 за единицу
- Собери матрицу оценки трёх поставщиков по качеству, срокам и цене
Как установить supply-chain-optimizer
1Через Skills CLI (Vercel)
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a claude-code2Вручную: положить файлы
~/.claude/skills/supply-chain-optimizer/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/supply-chain-optimizer/
1Через Skills CLI (Vercel)
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a codex2Вручную: положить файлы
.agents/skills/supply-chain-optimizer/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a cursor2Вручную: положить файлы
.cursor/skills/supply-chain-optimizer/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install travisjneuman/.claude supply-chain-optimizer2Вручную: положить файлы
.github/skills/supply-chain-optimizer/SKILL.md1Через Skills CLI (Vercel)
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a gemini-cli2Командой платформы
gemini skills install https://github.com/travisjneuman/.claude --path skills/supply-chain-optimizerВручную: положить файлы
.devin/skills/supply-chain-optimizer/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
По шагам, с командами для каждой платформы: Как установить скилл в Claude Code: пять способов
Вопросы о supply-chain-optimizer
Как установить supply-chain-optimizer в Claude?
Выполните npx skills add travisjneuman/.claude --skill supply-chain-optimizer -a claude-code. Скилл появится в ~/.claude/skills/supply-chain-optimizer/SKILL.md.
Что делает supply-chain-optimizer?
Даёт агенту шаблоны и формулы для анализа цепочек поставок: карту потоков, EOQ, страховой запас, ABC-XYZ, матрицу оценки поставщиков и расчёт совокупной стоимости владения.
Для каких платформ подходит этот скилл?
Claude, Codex, Cursor, GitHub Copilot, Gemini CLI и OpenCode. Для каждой платформы своя команда установки и свой путь к файлу скилла.
Исходный текст
SKILL.md · MIT
--- name: supply-chain-optimizer description: Supply chain analysis, inventory optimization, logistics planning, vendor evaluation, and demand forecasting frameworks. Use when analyzing supply chains, optimizing inventory, or evaluating suppliers. --- # 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 | Metric | Formula | Target | | --------------------- | ---------------------------------------- | --------------- | | Inventory Turns | COGS / Average Inventory | Industry-specific | | Days of Supply | Average Inventory / (COGS / 365) | Minimize | | Fill Rate | Orders Filled Complete / Total Orders | 97%+ | | Stockout Rate | Stockout Events / Total Demand Events | < 2% | | Carrying Cost % | Holding Costs / Average Inventory Value | 15-30% | | Dead Stock % | No-movement Items / Total SKUs | < 5% | | Inventory Accuracy | Correct Counts / Total Counts | 99%+ | | GMROI | Gross 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 | Criteria | Weight | Score (1-5) | Weighted Score | Notes | | ------------------ | ------ | ----------- | -------------- | ----- | | Quality (PPM) | 25% | | | | | Delivery (OTIF) | 20% | | | | | Pricing | 20% | | | | | Responsiveness | 10% | | | | | Financial Health | 10% | | | | | Innovation | 5% | | | | | Sustainability | 5% | | | | | Risk Profile | 5% | | | | | **Total** | 100% | | **__ / 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 | KPI | Target | Q1 Actual | Q2 Actual | Q3 Actual | Q4 Actual | Trend | | ------------------- | ------- | --------- | --------- | --------- | --------- | ----- | | On-Time Delivery | 98%+ | | | | | | | 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 Element | Supplier A | Supplier B | Supplier 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 | Method | Best For | Horizon | Data Required | | ------------------- | -------------------------- | ------------ | --------------------- | | Moving Average | Stable demand | Short-term | 3-12 periods history | | Exponential Smooth | Trend detection | Short-term | Recent weighted data | | Holt-Winters | Seasonal patterns | Medium-term | 2+ years seasonal | | Linear Regression | Trend with causal factors | Medium-term | Demand + drivers | | ARIMA | Complex time series | Short-medium | 50+ data points | | Machine Learning | Multi-variable patterns | Any | Large datasets | | Delphi / Expert | New products, disruptions | Long-term | Expert 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 | Mode | Cost/Unit | Speed | Capacity | Best For | | -------- | --------- | --------- | --------- | ----------------------------- | | Truck | Medium | Fast | Medium | Regional, door-to-door | | Rail | Low | Slow | Very High | Bulk, long-distance domestic | | Ocean | Very Low | Very Slow | Very High | International, bulk cargo | | Air | Very High | Very Fast | Low | High-value, urgent, perishable| | Intermodal | Low-Med | Medium | High | Long-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 Category | Risk Event | Likelihood | Impact | Score | Mitigation | | ---------------- | ----------------------- | ---------- | ------ | ----- | ---------------------- | | Supply | Single-source failure | | | | Dual-source | | Demand | Demand spike/collapse | | | | Buffer stock, flex | | Logistics | Port congestion | | | | Alternate routes | | Geopolitical | Tariffs, sanctions | | | | Nearshoring | | Natural Disaster | Earthquake, flood | | | | Geographic diversity | | Cyber | System breach | | | | Security protocols | | Quality | Batch failure | | | | Inspection, redundancy | | Financial | Supplier bankruptcy | | | | Financial 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 | Category | KPI | Target | | ---------- | ------------------------ | --------------- | | Cost | SC Cost as % of Revenue | 5-10% | | Cost | Cost per Order | Minimize | | Service | Perfect Order Rate | 95%+ | | Service | On-Time In-Full (OTIF) | 98%+ | | Service | Customer Fill Rate | 97%+ | | Efficiency | Inventory Turns | Industry-specific| | Efficiency | Cash-to-Cash Cycle | Minimize | | Efficiency | Warehouse Utilization | 85% | | Efficiency | Forecast Accuracy (MAPE) | < 20% | ## See Also - [Operations](../operations/SKILL.md) - [Finance](../finance/SKILL.md) - [Risk Management](../risk-management/SKILL.md)
© travisjneuman · MIT · travisjneuman/.claude