# Saisca – offline supply chain risk analyzer (Excel/CSV → insights)

> Source: <https://github.com/cayincoorts-hue/saisika>
> Published: 2026-06-06 12:42:46+00:00

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.

| Output | Answers the Question |
|---|---|
Risk Trend Chart |
Is risk worsening or improving over time? |
Risk Distribution Chart |
Where is risk concentrated across the supply chain? |
Propagation Timeline |
Where does risk come from and spread to? |
High-Risk Node Table |
Which nodes need immediate action, in what order? |
Data Confidence |
How reliable is this analysis? What data is missing? |
Domain Insights |
Bullwhip effect, VMI, QR patterns detected? |

Each 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).

Download the latest `.dmg`

from [GitHub Releases](https://github.com/cayincoorts-hue/saisika/releases) → drag to Applications → launch Saisca. (First launch: right-click → Open.)

**Now free and open — no activation code required.**

```
git clone https://github.com/cayincoorts-hue/saisika.git
cd saisika
pip install -r requirements.txt
python run.py
# Open http://localhost:8000
```

`demo_data/demo_scenario/`

contains a purpose-built 10-node supply chain simulation (26 weeks × 5 metrics):

| File | Description |
|---|---|
`Sales Order.csv` |
Downstream demand (long format: date, node_id, value) |
`Production.csv` |
Factory output |
`Delivery To Distributor.csv` |
Distribution center deliveries |
`Factory Issue.csv` |
Supplier shipments |
`Inventory.csv` |
Node inventory levels (S002 sharp drop in last 10 weeks) |
`Nodes.csv` |
Node attributes (name, type, tier, region) |
`Edges.csv` |
Supply chain relationships (risk links marked) |
`Node Types.csv` |
Node type descriptions |
`README.md` |
Scenario design notes |

**Engineered risk characteristics:**

- S002 East China Parts Supplier → inventory crash + delivery disruption →
**high-risk node** - P001 Shenzhen Factory → upstream volatility amplification →
**bullwhip effect** - D001 South China DC → volatility significantly lower than peers →
**VMI pattern** - R002 Shanghai Retailer → high-frequency small-batch stable demand →
**QR pattern**

```
User uploads Excel/CSV
  ↓
excel_adapter      Read file → detect role (fact/node/edge/metadata)
  ↓
field_mapper       Wide-table melt → column mapping → tri-state identification
  ↓
data_merger        Cross-scenario merge → mark duplicate measurement bases
  ↓
graph_builder      Build supply chain network → infer tier hierarchy
  ↓
risk_engine        Compute risk scores → annotate risk_causes
  ↓
decision_engine    Classify actions → generate action_type + justification
  ↓
analysis_engine    Assemble 6 result objects (including domain_insights)
  ↓
prompt_builder     Generate text conclusions (reads annotations, never raw numbers)
  ↓
result_exporter    Output JSON + HTML report
```

| Layer | Technology |
|---|---|
| Frontend | React + TypeScript + Vite + ECharts + GSAP |
| Backend | Python + FastAPI + pandas + openpyxl |
| Graph Layout | Three.js + react-force-graph-3d |
| LLM (optional) | Ollama (explanation layer only, not risk engine) |
| Packaging | PyInstaller + Electron + electron-builder |
| Theory | SCOR / Bullwhip Effect / VMI / QR / SC-BSC |

The system accepts two fact-table formats:

**Long format** (recommended): `date, node_id, value`

```
date,node_id,value
2026-01-05,S001,99.3
```

**Wide format**: rows as dates, columns as node codes

```
Date,SOS008L02P,SOS005L04P
2026-01-05,1355.0,890.2
```

Node tables need: `node_id, node_name, node_type`

. Edge tables need: `source, target`

.

- All data stored locally in
`data/`

directory - No internet connection, no telemetry, no cloud dependency
- Backend compiled to binary for code logic protection

MIT License. See [LICENSE](/cayincoorts-hue/saisika/blob/main/LICENSE) for details.

## 中文说明

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。

| 结果 | 回答的问题 |
|---|---|
| 风险趋势图 | 风险是在加剧还是缓解？ |
| 风险分布图 | 整体风险集中在哪一层？ |
| 风险传播时序图 | 风险从哪里来、往哪里扩散？ |
| 高风险节点表 | 哪些节点需要立即处理、按什么顺序？ |
| 数据可信度 | 结论有多可信、缺了什么数据？ |
| 供应链领域洞察 | 是否存在牛鞭效应、VMI、QR 等经典模式？ |

从 [GitHub Releases](https://github.com/cayincoorts-hue/saisika/releases) 下载 `.dmg`

→ 拖入 Applications → 右键打开。

**现已免费开放，无需激活码。**

`demo_data/demo_scenario/`

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。

全部数据存本地 `data/`

目录。不联网，无遥测，无云端依赖。

MIT License。详见 [LICENSE](/cayincoorts-hue/saisika/blob/main/LICENSE)。