{"slug": "saisca-offline-supply-chain-risk-analyzer-excel-csv-insights", "title": "Saisca – offline supply chain risk analyzer (Excel/CSV → insights)", "summary": "Saisca, a new open-source supply chain risk analysis tool, has been released on GitHub, allowing supply chain managers to import Excel or CSV files and receive risk insights, reasoning trails, and actionable recommendations entirely offline with no cloud dependency. The tool generates visual outputs including risk trend charts, propagation timelines, and high-risk node tables, with each high-risk node accompanied by a six-step reasoning trail and structured action recommendations. Saisca is now free and open under the MIT License, deployable locally via a downloaded `.dmg` file or through a cloned repository.", "body_md": "A locally-deployable supply chain risk analysis tool for supply chain managers. Import Excel/CSV, get risk insights, reasoning trails, and actionable recommendations — all offline, no cloud dependency.\n\n| Output | Answers the Question |\n|---|---|\nRisk Trend Chart |\nIs risk worsening or improving over time? |\nRisk Distribution Chart |\nWhere is risk concentrated across the supply chain? |\nPropagation Timeline |\nWhere does risk come from and spread to? |\nHigh-Risk Node Table |\nWhich nodes need immediate action, in what order? |\nData Confidence |\nHow reliable is this analysis? What data is missing? |\nDomain Insights |\nBullwhip effect, VMI, QR patterns detected? |\n\nEach high-risk node includes a full **6-step reasoning trail**, **risk cause details** (trigger metrics + threshold comparisons), and **structured action recommendations** (replenish / reroute / switch supplier / adjust logistics / investigate / monitor).\n\nDownload the latest `.dmg`\n\nfrom [GitHub Releases](https://github.com/cayincoorts-hue/saisika/releases) → drag to Applications → launch Saisca. (First launch: right-click → Open.)\n\n**Now free and open — no activation code required.**\n\n```\ngit clone https://github.com/cayincoorts-hue/saisika.git\ncd saisika\npip install -r requirements.txt\npython run.py\n# Open http://localhost:8000\n```\n\n`demo_data/demo_scenario/`\n\ncontains a purpose-built 10-node supply chain simulation (26 weeks × 5 metrics):\n\n| File | Description |\n|---|---|\n`Sales Order.csv` |\nDownstream demand (long format: date, node_id, value) |\n`Production.csv` |\nFactory output |\n`Delivery To Distributor.csv` |\nDistribution center deliveries |\n`Factory Issue.csv` |\nSupplier shipments |\n`Inventory.csv` |\nNode inventory levels (S002 sharp drop in last 10 weeks) |\n`Nodes.csv` |\nNode attributes (name, type, tier, region) |\n`Edges.csv` |\nSupply chain relationships (risk links marked) |\n`Node Types.csv` |\nNode type descriptions |\n`README.md` |\nScenario design notes |\n\n**Engineered risk characteristics:**\n\n- S002 East China Parts Supplier → inventory crash + delivery disruption →\n**high-risk node** - P001 Shenzhen Factory → upstream volatility amplification →\n**bullwhip effect** - D001 South China DC → volatility significantly lower than peers →\n**VMI pattern** - R002 Shanghai Retailer → high-frequency small-batch stable demand →\n**QR pattern**\n\n```\nUser uploads Excel/CSV\n  ↓\nexcel_adapter      Read file → detect role (fact/node/edge/metadata)\n  ↓\nfield_mapper       Wide-table melt → column mapping → tri-state identification\n  ↓\ndata_merger        Cross-scenario merge → mark duplicate measurement bases\n  ↓\ngraph_builder      Build supply chain network → infer tier hierarchy\n  ↓\nrisk_engine        Compute risk scores → annotate risk_causes\n  ↓\ndecision_engine    Classify actions → generate action_type + justification\n  ↓\nanalysis_engine    Assemble 6 result objects (including domain_insights)\n  ↓\nprompt_builder     Generate text conclusions (reads annotations, never raw numbers)\n  ↓\nresult_exporter    Output JSON + HTML report\n```\n\n| Layer | Technology |\n|---|---|\n| Frontend | React + TypeScript + Vite + ECharts + GSAP |\n| Backend | Python + FastAPI + pandas + openpyxl |\n| Graph Layout | Three.js + react-force-graph-3d |\n| LLM (optional) | Ollama (explanation layer only, not risk engine) |\n| Packaging | PyInstaller + Electron + electron-builder |\n| Theory | SCOR / Bullwhip Effect / VMI / QR / SC-BSC |\n\nThe system accepts two fact-table formats:\n\n**Long format** (recommended): `date, node_id, value`\n\n```\ndate,node_id,value\n2026-01-05,S001,99.3\n```\n\n**Wide format**: rows as dates, columns as node codes\n\n```\nDate,SOS008L02P,SOS005L04P\n2026-01-05,1355.0,890.2\n```\n\nNode tables need: `node_id, node_name, node_type`\n\n. Edge tables need: `source, target`\n\n.\n\n- All data stored locally in\n`data/`\n\ndirectory - No internet connection, no telemetry, no cloud dependency\n- Backend compiled to binary for code logic protection\n\nMIT License. See [LICENSE](/cayincoorts-hue/saisika/blob/main/LICENSE) for details.\n\n## 中文说明\n\n面向供应链管理者，可本地部署、支持 Excel/CSV 导入的风险分析桌面工具。不依赖云端，数据不出本地。\n\n| 结果 | 回答的问题 |\n|---|---|\n| 风险趋势图 | 风险是在加剧还是缓解？ |\n| 风险分布图 | 整体风险集中在哪一层？ |\n| 风险传播时序图 | 风险从哪里来、往哪里扩散？ |\n| 高风险节点表 | 哪些节点需要立即处理、按什么顺序？ |\n| 数据可信度 | 结论有多可信、缺了什么数据？ |\n| 供应链领域洞察 | 是否存在牛鞭效应、VMI、QR 等经典模式？ |\n\n从 [GitHub Releases](https://github.com/cayincoorts-hue/saisika/releases) 下载 `.dmg`\n\n→ 拖入 Applications → 右键打开。\n\n**现已免费开放，无需激活码。**\n\n`demo_data/demo_scenario/`\n\n含 10 节点供应链模拟数据（26 周 × 5 指标），刻意设计了牛鞭效应、VMI、QR 等经典风险特征。\n\n全部数据存本地 `data/`\n\n目录。不联网，无遥测，无云端依赖。\n\nMIT License。详见 [LICENSE](/cayincoorts-hue/saisika/blob/main/LICENSE)。", "url": "https://wpnews.pro/news/saisca-offline-supply-chain-risk-analyzer-excel-csv-insights", "canonical_source": "https://github.com/cayincoorts-hue/saisika", "published_at": "2026-06-06 12:42:46+00:00", "updated_at": "2026-06-06 13:17:57.781963+00:00", "lang": "en", "topics": ["ai-tools"], "entities": ["Saisca", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/saisca-offline-supply-chain-risk-analyzer-excel-csv-insights", "markdown": "https://wpnews.pro/news/saisca-offline-supply-chain-risk-analyzer-excel-csv-insights.md", "text": "https://wpnews.pro/news/saisca-offline-supply-chain-risk-analyzer-excel-csv-insights.txt", "jsonld": "https://wpnews.pro/news/saisca-offline-supply-chain-risk-analyzer-excel-csv-insights.jsonld"}}