供应链可视化

看不见的供应链无法管理——可视化是数字化转型的第一步。

供应链可视化架构

IoT 在供应链中的应用

供应链控制塔

"""
供应链控制塔仪表盘
"""
from dataclasses import dataclass, field
from datetime import datetime
@dataclass
class SupplyChainEvent:
timestamp: str
category: str     # logistics / inventory / production
severity: str     # info / warning / critical
message: str
location: str
@dataclass
class ControlTower:
"""控制塔"""
events: list[SupplyChainEvent] = field(default_factory=list)
def add_event(self, event: SupplyChainEvent):
self.events.append(event)
def dashboard(self) -> dict:
"""仪表盘摘要"""
by_severity = {}
by_category = {}
for e in self.events:
by_severity[e.severity] = (
by_severity.get(e.severity, 0) + 1
)
by_category[e.category] = (
by_category.get(e.category, 0) + 1
)
return {
"总事件数": len(self.events),
"按严重度": by_severity,
"按类别": by_category,
"紧急事件": [
e.message for e in self.events
if e.severity == "critical"
],
}
def kpi_snapshot(self) -> dict:
"""KPI 快照"""
return {
"订单履行率": "96.5%",
"准时交付率": "94.2%",
"库存周转率": "11.8次/年",
"供应链成本占比": "12.3%",
"完美订单率": "91.7%",
"现金周转天数": "28天",
}
# 演示
tower = ControlTower()
events = [
SupplyChainEvent(
"2024-03-15 08:30", "logistics", "critical",
"华东仓发货延误 — 暴雨封路", "上海"
),
SupplyChainEvent(
"2024-03-15 09:00", "inventory", "warning",
"SKU-A001 库存低于安全线", "深圳"
),
SupplyChainEvent(
"2024-03-15 10:15", "production", "info",
"SMT 产线 OEE 达 87%", "东莞"
),
SupplyChainEvent(
"2024-03-15 11:00", "logistics", "warning",
"跨境清关延迟 2 天", "香港"
),
]
for e in events:
tower.add_event(e)
print("=== 控制塔仪表盘 ===")
for k, v in tower.dashboard().items():
print(f"  {k}: {v}")
print("\n=== KPI 快照 ===")
for k, v in tower.kpi_snapshot().items():
print(f"  {k}: {v}")

区块链溯源

"""
简易区块链溯源
"""
import hashlib
from dataclasses import dataclass
from datetime import datetime
@dataclass
class TraceRecord:
timestamp: str
stage: str
location: str
operator: str
data: dict
prev_hash: str = ""
hash: str = ""
def __post_init__(self):
content = f"{self.timestamp}{self.stage}{self.location}{self.data}{self.prev_hash}"
self.hash = hashlib.sha256(content.encode()).hexdigest()[:16]
class ProductTrace:
"""产品溯源链"""
def __init__(self, product_id: str):
self.product_id = product_id
self.chain: list[TraceRecord] = []
def add_record(
self, stage: str, location: str,
operator: str, data: dict,
):
prev_hash = self.chain[-1].hash if self.chain else "0000"
record = TraceRecord(
timestamp=datetime.now().strftime("%Y-%m-%d %H:%M"),
stage=stage,
location=location,
operator=operator,
data=data,
prev_hash=prev_hash,
)
self.chain.append(record)
def trace_report(self) -> list[dict]:
"""溯源报告"""
return [
{
"阶段": r.stage,
"地点": r.location,
"操作方": r.operator,
"数据": r.data,
"哈希": r.hash,
}
for r in self.chain
]
# 演示 — 食品溯源
trace = ProductTrace("FOOD-2024-00158")
trace.add_record("原料采购", "云南昆明", "农场A", {"品种": "阿拉比卡", "重量": "500kg"})
trace.add_record("加工", "昆明工厂", "加工厂B", {"工艺": "日晒", "批次": "B240315"})
trace.add_record("质检", "昆明", "SGS", {"检测": "合格", "证书": "SGS-2024-001"})
trace.add_record("运输", "昆明→上海", "物流C", {"方式": "冷链公路", "温度": "18-22°C"})
trace.add_record("零售", "上海", "门店D", {"上架": "2024-03-20"})
print(f"=== 溯源报告: {trace.product_id} ===")
for i, r in enumerate(trace.trace_report(), 1):
print(f"\n{i}. {r['阶段']} @ {r['地点']}")
print(f"   操作方: {r['操作方']}")
print(f"   数据: {r['数据']}")
print(f"   哈希: {r['哈希']}")

数字孪生应用

行动清单

• [ ] 梳理当前供应链可见性盲区：哪些环节"不知道货在哪、状态是什么"
• [ ] 评估 3 个 IoT 场景的 ROI：GPS 车辆追踪、冷链温度监控、RFID 仓库盘点
• [ ] 对食品、药品等高溯源要求商品，调研区块链溯源的合规要求和市场预期
• [ ] 建立基础控制塔看板（即使是 Excel/BI 工具）：每日显示在途货物数量、延迟预警
• [ ] 定义 5 类预警规则（库存低于安全线、发货延迟 > N 天等），并指定响应责任人
• [ ] 评估数字孪生对仓库布局或运输路线的优化价值（需有足够历史数据积累）

下一节：02-供应链控制塔实施 — 从概念到实施的控制塔建设指南。