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feat(mfg-data-agent): 添加HTML可视化仪表盘和优化项目配置

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- 新增6个HTML可视化仪表盘组件用于数据展示
* 人效产值损耗三维模型仪表盘
* 指标趋势分析与拐点预警仪表盘
* 一页式决策简报仪表盘
* 订单延迟预警分析仪表盘
* 供应链风险预警仪表盘
* 工单执行进度与异常节点仪表盘
- 添加VSCode工作区配置文件
- 更新businessQueries.json业务查询配置
- 优化api_client.py API客户端实现
- 更新pyproject.toml项目依赖版本
- 重组SQL查询文件结构
- 删除v2版本冗余文档配置
- 添加v2版本技能清单文档
- 更新日志文件记录
This commit is contained in:
yuanzhipeng
2026-01-14 11:56:43 +08:00
parent 118f1561f3
commit 3ea772c3be
29 changed files with 5255 additions and 1313 deletions
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lzwcai_mcpskills_mfg_data_agent/sql/一页式决策简报.sql Normal file
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-- =====================================================
-- 一页式决策简报SQL
-- One-Page Decision Brief
-- 数据库: PostgreSQL
-- =====================================================
-- =====================================================
-- 主查询：一页式决策简报
-- =====================================================
WITH
sales_summary AS (
SELECT
COUNT(*) AS total_orders,
SUM(deal_amount) AS total_sales_amount,
AVG(deal_amount) AS avg_order_amount,
SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_orders,
SUM(CASE WHEN payment_status = 'PARTIAL' THEN 1 ELSE 0 END) AS partial_orders,
SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_orders,
SUM(CASE WHEN payment_status = 'PAID' THEN deal_amount ELSE 0 END) AS paid_amount,
SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) AS unpaid_amount
FROM fact_sales_order
),
production_summary AS (
SELECT
COUNT(*) AS total_work_orders,
SUM(planned_qty) AS total_planned_qty,
SUM(completed_qty) AS total_completed_qty,
SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders,
SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders,
SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders
FROM fact_work_order
),
ar_summary AS (
SELECT
COUNT(*) AS receipt_count,
SUM(amount) AS total_receipt_amount,
AVG(amount) AS avg_receipt_amount
FROM fact_ar_receipt
),
ap_summary AS (
SELECT
COUNT(*) AS payment_count,
SUM(amount) AS total_payment_amount,
AVG(amount) AS avg_payment_amount
FROM fact_ap_payment
),
invoice_summary AS (
SELECT
COUNT(*) AS invoice_count,
SUM(invoice_amount) AS total_invoice_amount,
AVG(invoice_amount) AS avg_invoice_amount
FROM fact_invoice
),
return_summary AS (
SELECT
COUNT(*) AS return_count,
SUM(amount) AS total_return_amount,
AVG(amount) AS avg_return_amount
FROM fact_sales_return
),
shipment_summary AS (
SELECT
COUNT(*) AS shipment_count,
SUM(amount) AS total_shipment_amount,
AVG(amount) AS avg_shipment_amount
FROM fact_sales_shipment
),
purchase_summary AS (
SELECT COUNT(*) AS purchase_order_count
FROM fact_purchase_order
),
quality_summary AS (
SELECT
COUNT(*) AS inspection_count,
SUM(pass_qty) AS total_pass_qty,
SUM(fail_qty) AS total_fail_qty
FROM fact_quality_inspection
),
labor_summary AS (
SELECT
COUNT(DISTINCT worker_name) AS worker_count,
SUM(duration_minutes) AS total_work_minutes,
SUM(report_qty) AS total_output_qty
FROM fact_labor_report
),
scrap_summary AS (
SELECT COUNT(*) AS scrap_count
FROM fact_scrap
)
SELECT
'Decision Brief' AS report_title,
CURRENT_DATE AS report_date,
-- 销售板块
ss.total_orders AS sales_order_count,
ROUND(ss.total_sales_amount, 2) AS total_sales_amount,
ROUND(ss.avg_order_amount, 2) AS avg_order_amount,
ss.paid_orders AS paid_order_count,
ss.partial_orders AS partial_paid_count,
ss.unpaid_orders AS unpaid_order_count,
ROUND(ss.paid_orders * 100.0 / NULLIF(ss.total_orders, 0), 1) AS payment_completion_rate,
ROUND(ss.unpaid_amount, 2) AS receivable_amount,
-- 生产板块
ps.total_work_orders AS work_order_count,
ps.closed_orders AS completed_work_orders,
ps.started_orders AS in_progress_work_orders,
ps.open_orders AS pending_work_orders,
ROUND(ps.total_planned_qty, 0) AS planned_qty,
ROUND(ps.total_completed_qty, 0) AS completed_qty,
ROUND(ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0), 1) AS production_completion_rate,
-- 人效板块
ls.worker_count AS active_worker_count,
ROUND(ls.total_work_minutes / 60.0, 1) AS total_work_hours,
ROUND(ls.total_output_qty, 0) AS total_output,
ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 1) AS output_per_worker,
ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour,
-- 质量板块
qs.inspection_count AS qc_batch_count,
ROUND(qs.total_pass_qty, 0) AS pass_qty,
ROUND(qs.total_fail_qty, 0) AS fail_qty,
ROUND(qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS pass_rate,
ROUND(qs.total_fail_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0), 2) AS defect_rate,
scr.scrap_count AS scrap_record_count,
-- 财务板块
ar.receipt_count AS ar_receipt_count,
ROUND(ar.total_receipt_amount, 2) AS total_ar_amount,
ap.payment_count AS ap_payment_count,
ROUND(ap.total_payment_amount, 2) AS total_ap_amount,
ROUND(ar.total_receipt_amount - ap.total_payment_amount, 2) AS net_cash_flow,
inv.invoice_count AS invoice_count,
ROUND(inv.total_invoice_amount, 2) AS total_invoice_amount,
-- 物流板块
sh.shipment_count AS shipment_count,
ROUND(sh.total_shipment_amount, 2) AS total_shipment_amount,
pur.purchase_order_count AS purchase_order_count,
-- 售后板块
ret.return_count AS return_count,
ROUND(ret.total_return_amount, 2) AS total_return_amount,
ROUND(ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0), 2) AS return_rate,
-- 综合健康度评估
CASE
WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 95
AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 80
AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 5
THEN 'EXCELLENT'
WHEN (qs.total_pass_qty * 100.0 / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0)) >= 90
AND (ps.total_completed_qty * 100.0 / NULLIF(ps.total_planned_qty, 0)) >= 60
AND (ret.total_return_amount * 100.0 / NULLIF(ss.total_sales_amount, 0)) < 10
THEN 'GOOD'
ELSE 'WARNING'
END AS health_status
FROM sales_summary ss
CROSS JOIN production_summary ps
CROSS JOIN ar_summary ar
CROSS JOIN ap_summary ap
CROSS JOIN invoice_summary inv
CROSS JOIN return_summary ret
CROSS JOIN shipment_summary sh
CROSS JOIN purchase_summary pur
CROSS JOIN quality_summary qs
CROSS JOIN labor_summary ls
CROSS JOIN scrap_summary scr;
-- =====================================================
-- 补充查询1：本月 vs 上月对比分析
-- =====================================================
WITH
current_month AS (
SELECT
'current_month' AS period,
COUNT(*) AS order_count,
SUM(deal_amount) AS sales_amount
FROM fact_sales_order
WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE)
),
last_month AS (
SELECT
'last_month' AS period,
COUNT(*) AS order_count,
SUM(deal_amount) AS sales_amount
FROM fact_sales_order
WHERE DATE_TRUNC('month', order_date_utc::timestamp) = DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')
)
SELECT
period,
order_count,
ROUND(sales_amount, 2) AS sales_amount
FROM current_month
UNION ALL
SELECT * FROM last_month;
-- =====================================================
-- 补充查询2：客户贡献度TOP10
-- =====================================================
SELECT
c.customer_name,
COUNT(so.sales_order_id) AS order_count,
ROUND(SUM(so.deal_amount), 2) AS total_order_amount,
ROUND(SUM(so.deal_amount) * 100.0 / (SELECT SUM(deal_amount) FROM fact_sales_order), 2) AS contribution_rate,
SUM(CASE WHEN so.payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_order_count,
SUM(CASE WHEN so.payment_status = 'UNPAID' THEN so.deal_amount ELSE 0 END) AS receivable_amount
FROM fact_sales_order so
INNER JOIN dim_customer c ON so.customer_id = c.customer_id AND c.is_current = 't'
GROUP BY c.customer_name
ORDER BY total_order_amount DESC
LIMIT 10;
-- =====================================================
-- 补充查询3：产品类别业绩分析
-- =====================================================
SELECT
p.product_category,
COUNT(DISTINCT wo.work_order_id) AS work_order_count,
ROUND(SUM(wo.planned_qty), 0) AS planned_qty,
ROUND(SUM(wo.completed_qty), 0) AS completed_qty,
ROUND(SUM(wo.completed_qty) * 100.0 / NULLIF(SUM(wo.planned_qty), 0), 1) AS completion_rate,
COUNT(DISTINCT lr.worker_name) AS worker_count,
ROUND(SUM(lr.duration_minutes) / 60.0, 1) AS total_work_hours
FROM dim_product p
LEFT JOIN fact_work_order wo ON p.product_id = wo.product_id
LEFT JOIN fact_labor_report lr ON p.product_id = lr.product_id
WHERE p.is_current = 't'
GROUP BY p.product_category
ORDER BY completed_qty DESC;
-- =====================================================
-- 补充查询4：关键预警指标
-- =====================================================
SELECT
'warning_metrics' AS category,
'unpaid_order_amount' AS metric_name,
ROUND(SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END), 2) AS current_value,
50000 AS threshold,
CASE
WHEN SUM(CASE WHEN payment_status = 'UNPAID' THEN deal_amount ELSE 0 END) > 50000
THEN 'EXCEEDED' ELSE 'NORMAL'
END AS status
FROM fact_sales_order
UNION ALL
SELECT
'warning_metrics',
'defect_rate_pct',
ROUND(SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0), 2),
5,
CASE
WHEN SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty) + SUM(fail_qty), 0) > 5
THEN 'EXCEEDED' ELSE 'NORMAL'
END
FROM fact_quality_inspection
UNION ALL
SELECT
'warning_metrics',
'production_completion_rate',
ROUND(SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0), 2),
70,
CASE
WHEN SUM(completed_qty) * 100.0 / NULLIF(SUM(planned_qty), 0) < 70
THEN 'LOW' ELSE 'NORMAL'
END
FROM fact_work_order
UNION ALL
SELECT
'warning_metrics',
'return_rate_pct',
ROUND((SELECT SUM(amount) FROM fact_sales_return) * 100.0 /
NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0), 2),
5,
CASE
WHEN (SELECT SUM(amount) FROM fact_sales_return) * 100.0 /
NULLIF((SELECT SUM(deal_amount) FROM fact_sales_order), 0) > 5
THEN 'EXCEEDED' ELSE 'NORMAL'
END;
-- =====================================================
-- 补充查询5：月度趋势汇总
-- =====================================================
SELECT
TO_CHAR(DATE_TRUNC('month', order_date_utc::timestamp), 'YYYY-MM') AS month,
COUNT(*) AS order_count,
ROUND(SUM(deal_amount), 2) AS sales_amount,
ROUND(AVG(deal_amount), 2) AS avg_order_amount,
SUM(CASE WHEN payment_status = 'PAID' THEN 1 ELSE 0 END) AS paid_count,
SUM(CASE WHEN payment_status = 'UNPAID' THEN 1 ELSE 0 END) AS unpaid_count
FROM fact_sales_order
GROUP BY DATE_TRUNC('month', order_date_utc::timestamp)
ORDER BY month DESC
LIMIT 12;

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-- =====================================================
-- 人效-产值-损耗三维模型仪表盘SQL
-- Efficiency-Output-Loss 3D Model Dashboard
-- 数据库: PostgreSQL
-- 维度: 产品类别(化工/机械/电子)作为部门维度
-- =====================================================
-- 1. 部门级综合仪表盘（主查询）
WITH
labor_stats AS (
SELECT
p.product_category AS department,
COUNT(DISTINCT lr.worker_name) AS worker_count,
SUM(lr.duration_minutes) AS total_work_minutes,
SUM(lr.report_qty) AS total_output_qty,
COUNT(DISTINCT lr.work_order_number) AS work_order_count
FROM fact_labor_report lr
INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't'
GROUP BY p.product_category
),
work_order_stats AS (
SELECT
p.product_category AS department,
COUNT(*) AS total_work_orders,
SUM(wo.planned_qty) AS total_planned_qty,
SUM(wo.completed_qty) AS total_completed_qty,
SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders,
SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders,
SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders
FROM fact_work_order wo
INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't'
GROUP BY p.product_category
),
quality_stats AS (
SELECT
p.product_category AS department,
SUM(qi.pass_qty) AS total_pass_qty,
SUM(qi.fail_qty) AS total_fail_qty,
COUNT(*) AS inspection_count
FROM fact_quality_inspection qi
INNER JOIN dim_product p ON qi.product_id = p.product_id AND p.is_current = 't'
GROUP BY p.product_category
),
sales_stats AS (
SELECT
AVG(deal_amount) AS avg_order_amount,
SUM(deal_amount) AS total_sales_amount,
COUNT(*) AS order_count
FROM fact_sales_order
),
scrap_stats AS (
SELECT COUNT(*) AS total_scrap_count
FROM fact_scrap
)
SELECT
ls.department,
-- 人效维度
ls.worker_count,
ROUND(ls.total_work_minutes / 60.0, 2) AS total_work_hours,
ls.total_output_qty,
ROUND(ls.total_output_qty / NULLIF(ls.worker_count, 0), 2) AS output_per_worker,
ROUND(ls.total_output_qty / NULLIF(ls.total_work_minutes / 60.0, 0), 2) AS output_per_hour,
ROUND(
ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0),
2
) AS efficiency_index,
-- 产值维度
ws.total_planned_qty AS planned_qty,
ws.total_completed_qty AS completed_qty,
ROUND(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS plan_completion_rate,
ROUND(ws.total_completed_qty * ss.avg_order_amount / 100, 2) AS estimated_output_value,
ROUND(ws.total_completed_qty * ss.avg_order_amount / 100 / NULLIF(ls.worker_count, 0), 2) AS output_value_per_worker,
-- 损耗维度
qs.total_pass_qty AS qc_pass_qty,
qs.total_fail_qty AS qc_fail_qty,
ROUND(qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100, 2) AS defect_rate,
ROUND((ws.total_planned_qty - ws.total_completed_qty) / NULLIF(ws.total_planned_qty, 0) * 100, 2) AS production_loss_rate,
-- 综合评分
ROUND(
(ls.total_output_qty / NULLIF(ls.worker_count, 0) / NULLIF(ls.total_work_minutes / 60.0 / ls.worker_count, 0)) * 0.4 +
(ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) * 0.35 +
(qs.total_pass_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) * 0.25,
2
) AS performance_score,
-- 预警等级
CASE
WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 10
OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 50
THEN 'RED'
WHEN (qs.total_fail_qty / NULLIF(qs.total_pass_qty + qs.total_fail_qty, 0) * 100) >= 5
OR (ws.total_completed_qty / NULLIF(ws.total_planned_qty, 0) * 100) < 70
THEN 'YELLOW'
ELSE 'GREEN'
END AS warning_level
FROM labor_stats ls
LEFT JOIN work_order_stats ws ON ls.department = ws.department
LEFT JOIN quality_stats qs ON ls.department = qs.department
CROSS JOIN sales_stats ss
CROSS JOIN scrap_stats scr
ORDER BY performance_score DESC;
-- =====================================================
-- 2. 人员级明细报表
-- =====================================================
SELECT
lr.worker_name,
p.product_category AS department,
COUNT(DISTINCT lr.work_order_number) AS work_order_count,
SUM(lr.duration_minutes) AS total_work_minutes,
ROUND(SUM(lr.duration_minutes) / 60.0, 2) AS total_work_hours,
SUM(lr.report_qty) AS total_output,
ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour,
RANK() OVER (PARTITION BY p.product_category ORDER BY SUM(lr.report_qty) DESC) AS dept_output_rank
FROM fact_labor_report lr
INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't'
GROUP BY lr.worker_name, p.product_category
ORDER BY p.product_category, total_output DESC;
-- =====================================================
-- 3. 月度趋势分析
-- =====================================================
SELECT
TO_CHAR(DATE_TRUNC('month', lr.event_time_utc::timestamp), 'YYYY-MM') AS month,
p.product_category AS department,
COUNT(DISTINCT lr.worker_name) AS active_worker_count,
SUM(lr.duration_minutes) AS total_work_minutes,
SUM(lr.report_qty) AS total_output,
ROUND(SUM(lr.report_qty) / NULLIF(COUNT(DISTINCT lr.worker_name), 0), 2) AS output_per_worker,
ROUND(SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0), 2) AS output_per_hour
FROM fact_labor_report lr
INNER JOIN dim_product p ON lr.product_id = p.product_id AND p.is_current = 't'
GROUP BY DATE_TRUNC('month', lr.event_time_utc::timestamp), p.product_category
ORDER BY month DESC, p.product_category;
-- =====================================================
-- 4. 产品级损耗分析
-- =====================================================
SELECT
p.product_category AS department,
p.product_name,
p.product_code,
COALESCE(SUM(qi.pass_qty), 0) AS pass_qty,
COALESCE(SUM(qi.fail_qty), 0) AS fail_qty,
ROUND(
COALESCE(SUM(qi.fail_qty), 0) /
NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100,
2
) AS defect_rate,
CASE
WHEN COALESCE(SUM(qi.fail_qty), 0) /
NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 10
THEN 'HIGH'
WHEN COALESCE(SUM(qi.fail_qty), 0) /
NULLIF(COALESCE(SUM(qi.pass_qty), 0) + COALESCE(SUM(qi.fail_qty), 0), 0) * 100 >= 5
THEN 'MEDIUM'
ELSE 'LOW'
END AS loss_level
FROM dim_product p
LEFT JOIN fact_quality_inspection qi ON p.product_id = qi.product_id
WHERE p.is_current = 't'
GROUP BY p.product_category, p.product_name, p.product_code
ORDER BY defect_rate DESC NULLS LAST;
-- =====================================================
-- 5. 工单完成率分析
-- =====================================================
SELECT
p.product_category AS department,
wo.status,
COUNT(*) AS order_count,
SUM(wo.planned_qty) AS total_planned,
SUM(wo.completed_qty) AS total_completed,
ROUND(SUM(wo.completed_qty) / NULLIF(SUM(wo.planned_qty), 0) * 100, 2) AS completion_rate,
ROUND(AVG(wo.completed_qty / NULLIF(wo.planned_qty, 0) * 100), 2) AS avg_completion_rate
FROM fact_work_order wo
INNER JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't'
GROUP BY p.product_category, wo.status
ORDER BY p.product_category, wo.status;

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-- =====================================================
-- 供应链风险预警SQL
-- Supply Chain Risk Warning
-- 数据库: PostgreSQL
-- =====================================================
-- =====================================================
-- 1. 供应商历史交期表现分析
-- =====================================================
WITH supplier_delivery AS (
SELECT
po.supplier_id,
COUNT(*) AS order_count,
COUNT(pr.purchase_receipt_id) AS receipt_count,
AVG(
CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL
THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)
ELSE NULL END
) AS avg_delivery_days,
MAX(
CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL
THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)
ELSE NULL END
) AS max_delivery_days,
STDDEV(
CASE WHEN pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL
THEN EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)
ELSE NULL END
) AS stddev_delivery_days
FROM fact_purchase_order po
LEFT JOIN fact_purchase_receipt pr ON po.supplier_id = pr.supplier_id
GROUP BY po.supplier_id
),
supplier_quality AS (
SELECT
pr.supplier_id,
COUNT(pr.purchase_receipt_id) AS total_receipts,
SUM(pr.receipt_qty_total) AS total_qty,
SUM(pr.amount) AS total_amount
FROM fact_purchase_receipt pr
GROUP BY pr.supplier_id
),
supplier_returns AS (
SELECT
pret.supplier_id,
COUNT(*) AS return_count,
SUM(CASE WHEN pret.return_reason = '损坏' THEN 1 ELSE 0 END) AS damage_count
FROM fact_purchase_return pret
GROUP BY pret.supplier_id
),
supplier_quality_rate AS (
SELECT
sq.supplier_id,
sq.total_receipts,
sq.total_qty,
sq.total_amount,
COALESCE(sr.return_count, 0) AS return_count,
COALESCE(sr.damage_count, 0) AS damage_count,
CASE WHEN sq.total_receipts > 0
THEN (sq.total_receipts - COALESCE(sr.return_count, 0)) * 100.0 / sq.total_receipts
ELSE 100 END AS quality_rate
FROM supplier_quality sq
LEFT JOIN supplier_returns sr ON sq.supplier_id = sr.supplier_id
),
supplier_risk AS (
SELECT
s.supplier_id,
s.supplier_name,
s.supplier_category,
COALESCE(sd.order_count, 0) AS order_count,
COALESCE(sd.receipt_count, 0) AS receipt_count,
ROUND(COALESCE(sd.avg_delivery_days, 0), 1) AS avg_delivery_days,
ROUND(COALESCE(sd.max_delivery_days, 0), 1) AS max_delivery_days,
ROUND(COALESCE(sd.stddev_delivery_days, 0), 1) AS delivery_volatility,
COALESCE(sqr.total_receipts, 0) AS total_receipts,
COALESCE(sqr.return_count, 0) AS return_count,
ROUND(COALESCE(sqr.quality_rate, 100), 1) AS quality_rate,
CASE
WHEN COALESCE(sd.avg_delivery_days, 0) > 60 THEN 40
WHEN COALESCE(sd.avg_delivery_days, 0) > 45 THEN 30
WHEN COALESCE(sd.avg_delivery_days, 0) > 30 THEN 20
ELSE 10
END +
CASE
WHEN COALESCE(sd.stddev_delivery_days, 0) > 20 THEN 30
WHEN COALESCE(sd.stddev_delivery_days, 0) > 10 THEN 20
ELSE 10
END AS delivery_risk_score,
CASE
WHEN COALESCE(sqr.quality_rate, 100) < 80 THEN 50
WHEN COALESCE(sqr.quality_rate, 100) < 90 THEN 30
WHEN COALESCE(sqr.quality_rate, 100) < 95 THEN 15
ELSE 5
END AS quality_risk_score
FROM dim_supplier s
LEFT JOIN supplier_delivery sd ON s.supplier_id = sd.supplier_id
LEFT JOIN supplier_quality_rate sqr ON s.supplier_id = sqr.supplier_id
WHERE s.is_current = 't'
),
supplier_risk_level AS (
SELECT
*,
delivery_risk_score + quality_risk_score AS total_risk_score,
CASE
WHEN delivery_risk_score + quality_risk_score >= 80 THEN 'HIGH'
WHEN delivery_risk_score + quality_risk_score >= 50 THEN 'MEDIUM'
ELSE 'LOW'
END AS risk_level,
CASE
WHEN delivery_risk_score >= 50 AND quality_risk_score >= 30 THEN 'DELIVERY_AND_QUALITY'
WHEN delivery_risk_score >= 50 THEN 'DELIVERY_ISSUE'
WHEN quality_risk_score >= 30 THEN 'QUALITY_ISSUE'
ELSE 'NORMAL'
END AS risk_pattern
FROM supplier_risk
)
SELECT
supplier_name,
supplier_category,
order_count,
receipt_count,
avg_delivery_days,
max_delivery_days,
delivery_volatility,
total_receipts,
return_count,
quality_rate,
delivery_risk_score,
quality_risk_score,
total_risk_score,
risk_level,
risk_pattern
FROM supplier_risk_level
ORDER BY total_risk_score DESC, supplier_name;
-- =====================================================
-- 补充查询1：高风险订单预警清单
-- =====================================================
WITH supplier_risk_info AS (
SELECT
s.supplier_id,
s.supplier_name,
s.supplier_category,
COALESCE(
(SELECT AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po2.doc_date_utc::timestamp))
FROM fact_purchase_order po2
LEFT JOIN fact_purchase_receipt pr ON po2.supplier_id = pr.supplier_id
WHERE po2.supplier_id = s.supplier_id
AND pr.doc_date_utc IS NOT NULL), 0
) AS avg_delivery_days,
COALESCE(
(SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0
) AS return_count,
COALESCE(
(SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0
) AS receipt_count
FROM dim_supplier s
WHERE s.is_current = 't'
),
order_risk AS (
SELECT
po.purchase_order_number,
po.doc_date_utc::date AS order_date,
sri.supplier_name,
sri.supplier_category,
ROUND(sri.avg_delivery_days, 1) AS supplier_avg_days,
sri.return_count,
sri.receipt_count,
CASE WHEN sri.receipt_count > 0
THEN ROUND((sri.receipt_count - sri.return_count) * 100.0 / sri.receipt_count, 1)
ELSE 100 END AS quality_rate,
EXTRACT(DAY FROM CURRENT_TIMESTAMP - po.doc_date_utc::timestamp) AS days_since_order,
CASE
WHEN sri.avg_delivery_days > 45 AND sri.return_count > 0 THEN 'HIGH'
WHEN sri.avg_delivery_days > 45 OR sri.return_count > 0 THEN 'MEDIUM'
ELSE 'LOW'
END AS risk_level
FROM fact_purchase_order po
JOIN supplier_risk_info sri ON po.supplier_id = sri.supplier_id
)
SELECT
purchase_order_number,
order_date,
supplier_name,
supplier_category,
supplier_avg_days,
quality_rate,
days_since_order,
risk_level,
CASE
WHEN risk_level = 'HIGH' THEN 'IMMEDIATE_FOLLOWUP'
WHEN risk_level = 'MEDIUM' THEN 'MONITOR'
ELSE 'NORMAL'
END AS action_required
FROM order_risk
WHERE risk_level IN ('HIGH', 'MEDIUM')
ORDER BY
CASE risk_level WHEN 'HIGH' THEN 1 WHEN 'MEDIUM' THEN 2 ELSE 3 END,
days_since_order DESC;
-- =====================================================
-- 补充查询2：供应商类别风险分布
-- =====================================================
WITH supplier_stats AS (
SELECT
s.supplier_category,
COUNT(DISTINCT s.supplier_id) AS supplier_count,
COUNT(DISTINCT po.purchase_order_id) AS order_count,
COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count,
COUNT(DISTINCT pret.purchase_return_id) AS return_count,
SUM(pr.amount) AS total_amount
FROM dim_supplier s
LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id
LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id
LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id
WHERE s.is_current = 't'
GROUP BY s.supplier_category
)
SELECT
supplier_category,
supplier_count,
order_count,
receipt_count,
return_count,
CASE WHEN receipt_count > 0
THEN ROUND((receipt_count - return_count) * 100.0 / receipt_count, 1)
ELSE 100 END AS quality_rate,
ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount,
CASE
WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 'HIGH'
WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 5 THEN 'MEDIUM'
ELSE 'LOW'
END AS category_risk_level
FROM supplier_stats
ORDER BY return_count DESC;
-- =====================================================
-- 补充查询3：近期交期异常趋势
-- =====================================================
WITH monthly_delivery AS (
SELECT
DATE_TRUNC('month', pr.doc_date_utc::timestamp)::date AS month_start,
COUNT(*) AS receipt_count,
AVG(EXTRACT(DAY FROM pr.doc_date_utc::timestamp - po.doc_date_utc::timestamp)) AS avg_delivery_days
FROM fact_purchase_receipt pr
JOIN fact_purchase_order po ON pr.supplier_id = po.supplier_id
WHERE pr.doc_date_utc IS NOT NULL AND po.doc_date_utc IS NOT NULL
GROUP BY DATE_TRUNC('month', pr.doc_date_utc::timestamp)
),
monthly_returns AS (
SELECT
DATE_TRUNC('month', pret.doc_date_utc::timestamp)::date AS month_start,
COUNT(*) AS return_count
FROM fact_purchase_return pret
GROUP BY DATE_TRUNC('month', pret.doc_date_utc::timestamp)
),
monthly_combined AS (
SELECT
md.month_start,
md.receipt_count,
ROUND(md.avg_delivery_days, 1) AS avg_delivery_days,
COALESCE(mr.return_count, 0) AS return_count
FROM monthly_delivery md
LEFT JOIN monthly_returns mr ON md.month_start = mr.month_start
),
with_trend AS (
SELECT
*,
LAG(avg_delivery_days, 1) OVER (ORDER BY month_start) AS prev_avg_days,
LAG(return_count, 1) OVER (ORDER BY month_start) AS prev_return_count
FROM monthly_combined
)
SELECT
month_start,
receipt_count,
avg_delivery_days,
return_count,
CASE WHEN receipt_count > 0
THEN ROUND(return_count * 100.0 / receipt_count, 1)
ELSE 0 END AS return_rate,
CASE
WHEN prev_avg_days IS NULL THEN 'NONE'
WHEN avg_delivery_days > prev_avg_days * 1.1 THEN 'INCREASING'
WHEN avg_delivery_days < prev_avg_days * 0.9 THEN 'DECREASING'
ELSE 'STABLE'
END AS delivery_trend,
CASE
WHEN prev_return_count IS NULL THEN 'NONE'
WHEN return_count > prev_return_count THEN 'INCREASING'
WHEN return_count < prev_return_count THEN 'DECREASING'
ELSE 'STABLE'
END AS return_trend
FROM with_trend
ORDER BY month_start DESC;
-- =====================================================
-- 补充查询4：风险预警汇总看板
-- =====================================================
WITH risk_summary AS (
SELECT
s.supplier_id,
s.supplier_name,
COALESCE(
(SELECT COUNT(*) FROM fact_purchase_return pret WHERE pret.supplier_id = s.supplier_id), 0
) AS return_count,
COALESCE(
(SELECT COUNT(*) FROM fact_purchase_receipt pr WHERE pr.supplier_id = s.supplier_id), 0
) AS receipt_count
FROM dim_supplier s
WHERE s.is_current = 't'
),
risk_counts AS (
SELECT
SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count > 10 THEN 1 ELSE 0 END) AS high_risk_suppliers,
SUM(CASE WHEN receipt_count > 0 AND return_count * 100.0 / receipt_count BETWEEN 5 AND 10 THEN 1 ELSE 0 END) AS medium_risk_suppliers,
SUM(CASE WHEN receipt_count = 0 OR return_count * 100.0 / receipt_count < 5 THEN 1 ELSE 0 END) AS low_risk_suppliers,
COUNT(*) AS total_suppliers
FROM risk_summary
),
order_stats AS (
SELECT
COUNT(*) AS pending_orders,
COUNT(CASE WHEN EXTRACT(DAY FROM CURRENT_TIMESTAMP - doc_date_utc::timestamp) > 30 THEN 1 END) AS overdue_orders
FROM fact_purchase_order
),
return_stats AS (
SELECT
COUNT(*) AS total_returns,
COUNT(CASE WHEN doc_date_utc::timestamp >= CURRENT_DATE - INTERVAL '30 days' THEN 1 END) AS recent_returns
FROM fact_purchase_return
)
SELECT
'high_risk_suppliers' AS metric_name,
rc.high_risk_suppliers::text AS metric_value,
CASE WHEN rc.high_risk_suppliers > 0 THEN 'ATTENTION_NEEDED' ELSE 'NORMAL' END AS status
FROM risk_counts rc
UNION ALL
SELECT 'medium_risk_suppliers', rc.medium_risk_suppliers::text,
CASE WHEN rc.medium_risk_suppliers > 2 THEN 'MONITOR' ELSE 'NORMAL' END
FROM risk_counts rc
UNION ALL
SELECT 'low_risk_suppliers', rc.low_risk_suppliers::text, 'NORMAL'
FROM risk_counts rc
UNION ALL
SELECT 'pending_orders', os.pending_orders::text,
CASE WHEN os.pending_orders > 20 THEN 'BACKLOG' ELSE 'NORMAL' END
FROM order_stats os
UNION ALL
SELECT 'overdue_orders_30d', os.overdue_orders::text,
CASE WHEN os.overdue_orders > 5 THEN 'DELIVERY_WARNING' ELSE 'NORMAL' END
FROM order_stats os
UNION ALL
SELECT 'recent_returns_30d', rs.recent_returns::text,
CASE WHEN rs.recent_returns > 3 THEN 'QUALITY_WARNING' ELSE 'NORMAL' END
FROM return_stats rs;
-- =====================================================
-- 补充查询5：供应商风险排行榜
-- =====================================================
WITH supplier_metrics AS (
SELECT
s.supplier_id,
s.supplier_name,
s.supplier_category,
COUNT(DISTINCT po.purchase_order_id) AS order_count,
COUNT(DISTINCT pr.purchase_receipt_id) AS receipt_count,
COUNT(DISTINCT pret.purchase_return_id) AS return_count,
SUM(pr.amount) AS total_amount
FROM dim_supplier s
LEFT JOIN fact_purchase_order po ON s.supplier_id = po.supplier_id
LEFT JOIN fact_purchase_receipt pr ON s.supplier_id = pr.supplier_id
LEFT JOIN fact_purchase_return pret ON s.supplier_id = pret.supplier_id
WHERE s.is_current = 't'
GROUP BY s.supplier_id, s.supplier_name, s.supplier_category
),
ranked_suppliers AS (
SELECT
*,
CASE WHEN receipt_count > 0
THEN ROUND(return_count * 100.0 / receipt_count, 1)
ELSE 0 END AS return_rate,
ROW_NUMBER() OVER (ORDER BY
CASE WHEN receipt_count > 0 THEN return_count * 1.0 / receipt_count ELSE 0 END DESC,
return_count DESC
) AS risk_rank
FROM supplier_metrics
)
SELECT
risk_rank,
supplier_name,
supplier_category,
order_count,
receipt_count,
return_count,
return_rate,
ROUND(COALESCE(total_amount, 0), 2) AS purchase_amount,
CASE
WHEN return_rate > 10 THEN 'HIGH_RISK'
WHEN return_rate > 5 THEN 'MEDIUM_RISK'
WHEN return_count > 0 THEN 'LOW_RISK'
ELSE 'EXCELLENT'
END AS risk_assessment
FROM ranked_suppliers
ORDER BY risk_rank
LIMIT 20;

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lzwcai_mcpskills_mfg_data_agent/sql/工单执行进度与异常节点.sql Normal file
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-- =====================================================
-- 工单执行进度与异常节点SQL
-- 数据库: PostgreSQL
-- 实时拉取工单数据，动态映射订单各环节状态
-- =====================================================
-- =====================================================
-- 1. 工单执行进度主视图
-- =====================================================
WITH work_order_base AS (
SELECT
wo.work_order_id,
wo.work_order_number,
wo.product_id,
wo.status,
wo.planned_qty,
wo.completed_qty,
wo.event_time_utc::timestamp AS start_time,
wo.last_updated_utc::timestamp AS last_update,
wo.source_system
FROM fact_work_order wo
),
product_info AS (
SELECT
product_id,
product_name,
product_category
FROM dim_product
WHERE is_current = 't'
),
labor_summary AS (
SELECT
work_order_number,
COUNT(DISTINCT worker_name) AS worker_count,
SUM(report_qty) AS total_report_qty,
SUM(duration_minutes) AS total_minutes,
MAX(event_time_utc::timestamp) AS last_report_time
FROM fact_labor_report
GROUP BY work_order_number
),
quality_summary AS (
SELECT
work_order_number,
SUM(pass_qty) AS pass_qty,
SUM(fail_qty) AS fail_qty
FROM fact_quality_inspection
GROUP BY work_order_number
),
-- =====================================================
-- 2. 工单进度计算与状态映射
-- =====================================================
work_order_progress AS (
SELECT
wb.work_order_id,
wb.work_order_number,
p.product_name,
p.product_category,
wb.status AS raw_status,
-- 状态映射
CASE wb.status
WHEN 'OPEN' THEN '待生产'
WHEN 'STARTED' THEN '生产中'
WHEN 'CLOSED' THEN '已完成'
ELSE '未知'
END AS status_name,
wb.planned_qty,
wb.completed_qty,
-- 完成进度
CASE WHEN wb.planned_qty > 0
THEN ROUND(wb.completed_qty * 100.0 / wb.planned_qty, 1)
ELSE 0 END AS completion_rate,
-- 剩余数量
GREATEST(wb.planned_qty - wb.completed_qty, 0) AS remaining_qty,
wb.start_time,
wb.last_update,
-- 已用时间(小时)
ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wb.start_time)) / 3600, 1) AS elapsed_hours,
-- 报工信息
COALESCE(ls.worker_count, 0) AS worker_count,
COALESCE(ls.total_report_qty, 0) AS total_report_qty,
COALESCE(ls.total_minutes, 0) AS total_work_minutes,
ls.last_report_time,
-- 质检信息
COALESCE(qs.pass_qty, 0) AS qc_pass_qty,
COALESCE(qs.fail_qty, 0) AS qc_fail_qty
FROM work_order_base wb
LEFT JOIN product_info p ON wb.product_id = p.product_id
LEFT JOIN labor_summary ls ON wb.work_order_number = ls.work_order_number
LEFT JOIN quality_summary qs ON wb.work_order_number = qs.work_order_number
),
-- =====================================================
-- 3. 异常节点检测
-- =====================================================
anomaly_detection AS (
SELECT
*,
-- 进度异常：进行中但完成率过低
CASE
WHEN raw_status = 'STARTED' AND elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后'
WHEN raw_status = 'STARTED' AND elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后'
ELSE NULL
END AS progress_anomaly,
-- 质量异常：废品率过高
CASE
WHEN qc_pass_qty + qc_fail_qty > 0
AND qc_fail_qty * 100.0 / (qc_pass_qty + qc_fail_qty) > 10 THEN '质量异常'
ELSE NULL
END AS quality_anomaly,
-- 报工异常：长时间无报工
CASE
WHEN raw_status = 'STARTED'
AND last_report_time IS NOT NULL
AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞'
WHEN raw_status = 'STARTED'
AND last_report_time IS NULL
AND elapsed_hours > 24 THEN '无报工记录'
ELSE NULL
END AS labor_anomaly,
-- 效率异常：人效过低
CASE
WHEN total_work_minutes > 0
AND total_report_qty / (total_work_minutes / 60.0) < 5 THEN '效率偏低'
ELSE NULL
END AS efficiency_anomaly
FROM work_order_progress
)
-- =====================================================
-- 输出：工单执行进度明细
-- =====================================================
SELECT
work_order_number AS "工单号",
product_name AS "产品名称",
product_category AS "产品类别",
status_name AS "状态",
planned_qty AS "计划数量",
completed_qty AS "完成数量",
remaining_qty AS "剩余数量",
completion_rate AS "完成率(%)",
worker_count AS "参与人数",
ROUND(total_work_minutes / 60.0, 1) AS "累计工时(小时)",
elapsed_hours AS "已用时间(小时)",
-- 异常标记
COALESCE(progress_anomaly, '') ||
CASE WHEN progress_anomaly IS NOT NULL AND quality_anomaly IS NOT NULL THEN ',' ELSE '' END ||
COALESCE(quality_anomaly, '') ||
CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL) AND labor_anomaly IS NOT NULL THEN ',' ELSE '' END ||
COALESCE(labor_anomaly, '') ||
CASE WHEN (progress_anomaly IS NOT NULL OR quality_anomaly IS NOT NULL OR labor_anomaly IS NOT NULL) AND efficiency_anomaly IS NOT NULL THEN ',' ELSE '' END ||
COALESCE(efficiency_anomaly, '') AS "异常标记",
-- 风险等级
CASE
WHEN progress_anomaly = '进度严重滞后' OR quality_anomaly IS NOT NULL THEN '高'
WHEN progress_anomaly = '进度滞后' OR labor_anomaly IS NOT NULL THEN '中'
WHEN efficiency_anomaly IS NOT NULL THEN '低'
ELSE '-'
END AS "风险等级"
FROM anomaly_detection
ORDER BY
CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END,
CASE WHEN progress_anomaly IS NOT NULL THEN 0 ELSE 1 END,
completion_rate ASC;
-- =====================================================
-- 补充查询1：工单状态分布汇总
-- =====================================================
WITH status_summary AS (
SELECT
CASE status
WHEN 'OPEN' THEN '待生产'
WHEN 'STARTED' THEN '生产中'
WHEN 'CLOSED' THEN '已完成'
ELSE '未知'
END AS status_name,
status AS raw_status,
COUNT(*) AS order_count,
SUM(planned_qty) AS total_planned,
SUM(completed_qty) AS total_completed
FROM fact_work_order
GROUP BY status
)
SELECT
status_name AS "状态",
order_count AS "工单数",
ROUND(order_count * 100.0 / SUM(order_count) OVER (), 1) AS "占比(%)",
ROUND(total_planned, 0) AS "计划总量",
ROUND(total_completed, 0) AS "完成总量",
CASE WHEN total_planned > 0
THEN ROUND(total_completed * 100.0 / total_planned, 1)
ELSE 0 END AS "完成率(%)"
FROM status_summary
ORDER BY
CASE raw_status WHEN 'STARTED' THEN 1 WHEN 'OPEN' THEN 2 ELSE 3 END;
-- =====================================================
-- 补充查询2：异常工单预警清单
-- =====================================================
WITH work_order_anomaly AS (
SELECT
wo.work_order_number,
p.product_name,
wo.status,
wo.planned_qty,
wo.completed_qty,
CASE WHEN wo.planned_qty > 0
THEN ROUND(wo.completed_qty * 100.0 / wo.planned_qty, 1)
ELSE 0 END AS completion_rate,
ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1) AS elapsed_hours,
(SELECT MAX(lr.event_time_utc::timestamp)
FROM fact_labor_report lr
WHERE lr.work_order_number = wo.work_order_number) AS last_report_time
FROM fact_work_order wo
LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't'
WHERE wo.status = 'STARTED'
)
SELECT
work_order_number AS "工单号",
product_name AS "产品",
ROUND(planned_qty, 0) AS "计划数量",
ROUND(completed_qty, 0) AS "完成数量",
completion_rate AS "完成率(%)",
elapsed_hours AS "已用时间(小时)",
CASE
WHEN elapsed_hours > 48 AND completion_rate < 20 THEN '进度严重滞后'
WHEN elapsed_hours > 24 AND completion_rate < 30 THEN '进度滞后'
WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN '无报工记录'
WHEN last_report_time IS NOT NULL
AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '报工停滞'
ELSE '正常'
END AS "异常类型",
CASE
WHEN elapsed_hours > 48 AND completion_rate < 20 THEN '立即跟进，排查生产瓶颈'
WHEN elapsed_hours > 24 AND completion_rate < 30 THEN '关注进度，协调资源'
WHEN last_report_time IS NULL AND elapsed_hours > 24 THEN '确认工单是否已开工'
WHEN last_report_time IS NOT NULL
AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24 THEN '跟进报工情况'
ELSE '-'
END AS "处理建议"
FROM work_order_anomaly
WHERE elapsed_hours > 24 AND completion_rate < 50
OR (last_report_time IS NULL AND elapsed_hours > 24)
OR (last_report_time IS NOT NULL
AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - last_report_time)) / 3600 > 24)
ORDER BY
CASE
WHEN elapsed_hours > 48 AND completion_rate < 20 THEN 1
WHEN elapsed_hours > 24 AND completion_rate < 30 THEN 2
ELSE 3
END,
completion_rate ASC;
-- =====================================================
-- 补充查询3：产品类别执行进度汇总
-- =====================================================
SELECT
p.product_category AS "产品类别",
COUNT(*) AS "工单数",
SUM(CASE WHEN wo.status = 'OPEN' THEN 1 ELSE 0 END) AS "待生产",
SUM(CASE WHEN wo.status = 'STARTED' THEN 1 ELSE 0 END) AS "生产中",
SUM(CASE WHEN wo.status = 'CLOSED' THEN 1 ELSE 0 END) AS "已完成",
ROUND(SUM(wo.planned_qty), 0) AS "计划总量",
ROUND(SUM(wo.completed_qty), 0) AS "完成总量",
CASE WHEN SUM(wo.planned_qty) > 0
THEN ROUND(SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty), 1)
ELSE 0 END AS "整体完成率(%)"
FROM fact_work_order wo
LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't'
GROUP BY p.product_category
ORDER BY "工单数" DESC;
-- =====================================================
-- 补充查询4：工单执行时间线
-- =====================================================
WITH timeline AS (
SELECT
wo.work_order_number,
p.product_name,
wo.status,
wo.event_time_utc::timestamp AS start_time,
wo.last_updated_utc::timestamp AS last_update,
CASE WHEN wo.status = 'CLOSED'
THEN ROUND(EXTRACT(EPOCH FROM (wo.last_updated_utc::timestamp - wo.event_time_utc::timestamp)) / 3600, 1)
ELSE ROUND(EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600, 1)
END AS duration_hours,
wo.planned_qty,
wo.completed_qty
FROM fact_work_order wo
LEFT JOIN dim_product p ON wo.product_id = p.product_id AND p.is_current = 't'
)
SELECT
work_order_number AS "工单号",
product_name AS "产品",
CASE status
WHEN 'OPEN' THEN '待生产'
WHEN 'STARTED' THEN '生产中'
WHEN 'CLOSED' THEN '已完成'
END AS "状态",
TO_CHAR(start_time, 'YYYY-MM-DD HH24:MI') AS "开始时间",
TO_CHAR(last_update, 'YYYY-MM-DD HH24:MI') AS "最后更新",
duration_hours AS "持续时间(小时)",
ROUND(planned_qty, 0) AS "计划数量",
ROUND(completed_qty, 0) AS "完成数量",
CASE WHEN planned_qty > 0
THEN ROUND(completed_qty * 100.0 / planned_qty, 1)
ELSE 0 END AS "完成率(%)"
FROM timeline
ORDER BY start_time DESC
LIMIT 50;
-- =====================================================
-- 补充查询5：实时生产看板汇总
-- =====================================================
WITH current_stats AS (
SELECT
COUNT(*) AS total_orders,
SUM(CASE WHEN status = 'OPEN' THEN 1 ELSE 0 END) AS open_orders,
SUM(CASE WHEN status = 'STARTED' THEN 1 ELSE 0 END) AS started_orders,
SUM(CASE WHEN status = 'CLOSED' THEN 1 ELSE 0 END) AS closed_orders,
SUM(planned_qty) AS total_planned,
SUM(completed_qty) AS total_completed
FROM fact_work_order
),
anomaly_stats AS (
SELECT
COUNT(*) AS anomaly_count
FROM fact_work_order wo
WHERE wo.status = 'STARTED'
AND EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - wo.event_time_utc::timestamp)) / 3600 > 24
AND wo.completed_qty * 100.0 / NULLIF(wo.planned_qty, 0) < 30
),
today_stats AS (
SELECT
COUNT(*) AS today_completed
FROM fact_work_order
WHERE status = 'CLOSED'
AND last_updated_utc::date = CURRENT_DATE
)
SELECT
'总工单数' AS "指标",
cs.total_orders::text AS "数值",
'-' AS "状态"
FROM current_stats cs
UNION ALL
SELECT
'待生产',
cs.open_orders::text,
CASE WHEN cs.open_orders > 20 THEN '积压' ELSE '正常' END
FROM current_stats cs
UNION ALL
SELECT
'生产中',
cs.started_orders::text,
'进行中'
FROM current_stats cs
UNION ALL
SELECT
'已完成',
cs.closed_orders::text,
'正常'
FROM current_stats cs
UNION ALL
SELECT
'整体完成率',
ROUND(cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0), 1)::text || '%',
CASE
WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 80 THEN '良好'
WHEN cs.total_completed * 100.0 / NULLIF(cs.total_planned, 0) >= 50 THEN '正常'
ELSE '偏低'
END
FROM current_stats cs
UNION ALL
SELECT
'异常工单数',
ans.anomaly_count::text,
CASE WHEN ans.anomaly_count > 5 THEN '需关注' ELSE '正常' END
FROM anomaly_stats ans
UNION ALL
SELECT
'今日完成',
ts.today_completed::text,
'-'
FROM today_stats ts;

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lzwcai_mcpskills_mfg_data_agent/sql/指标趋势分析与拐点预警.sql Normal file
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-- =====================================================
-- 指标趋势分析与拐点预警SQL
-- Metric Trend Analysis and Turning Point Warning
-- 数据库: PostgreSQL
-- =====================================================
-- =====================================================
-- 1. 日度指标基础数据
-- =====================================================
WITH daily_metrics AS (
SELECT
DATE(lr.event_time_utc::timestamp) AS metric_date,
COUNT(DISTINCT lr.worker_name) AS worker_count,
SUM(lr.duration_minutes) / 60.0 AS total_hours,
SUM(lr.report_qty) AS total_output,
CASE WHEN SUM(lr.duration_minutes) > 0
THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0)
ELSE 0 END AS hourly_efficiency
FROM fact_labor_report lr
GROUP BY DATE(lr.event_time_utc::timestamp)
),
daily_quality AS (
SELECT
DATE(qi.event_time_utc::timestamp) AS metric_date,
SUM(qi.pass_qty) AS pass_qty,
SUM(qi.fail_qty) AS fail_qty,
CASE WHEN SUM(qi.pass_qty) + SUM(qi.fail_qty) > 0
THEN SUM(qi.fail_qty) * 100.0 / (SUM(qi.pass_qty) + SUM(qi.fail_qty))
ELSE 0 END AS defect_rate
FROM fact_quality_inspection qi
GROUP BY DATE(qi.event_time_utc::timestamp)
),
daily_production AS (
SELECT
DATE(wo.event_time_utc::timestamp) AS metric_date,
SUM(wo.planned_qty) AS planned_qty,
SUM(wo.completed_qty) AS completed_qty,
CASE WHEN SUM(wo.planned_qty) > 0
THEN SUM(wo.completed_qty) * 100.0 / SUM(wo.planned_qty)
ELSE 0 END AS completion_rate
FROM fact_work_order wo
GROUP BY DATE(wo.event_time_utc::timestamp)
),
combined_daily AS (
SELECT
COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) AS metric_date,
COALESCE(dm.worker_count, 0) AS worker_count,
COALESCE(dm.total_hours, 0) AS total_hours,
COALESCE(dm.total_output, 0) AS total_output,
COALESCE(dm.hourly_efficiency, 0) AS hourly_efficiency,
COALESCE(dq.defect_rate, 0) AS defect_rate,
COALESCE(dp.completion_rate, 0) AS completion_rate
FROM daily_metrics dm
FULL OUTER JOIN daily_quality dq ON dm.metric_date = dq.metric_date
FULL OUTER JOIN daily_production dp ON dm.metric_date = dp.metric_date
WHERE COALESCE(dm.metric_date, dq.metric_date, dp.metric_date) IS NOT NULL
),
numbered_data AS (
SELECT
*,
ROW_NUMBER() OVER (ORDER BY metric_date) AS day_seq
FROM combined_daily
),
-- =====================================================
-- 2. 移动平均计算 (7日移动平均)
-- =====================================================
moving_avg_step1 AS (
SELECT
metric_date,
day_seq,
worker_count,
total_output,
hourly_efficiency,
defect_rate,
completion_rate,
AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_efficiency,
AVG(total_output) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_output,
AVG(defect_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_defect_rate,
AVG(completion_rate) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7_completion_rate,
AVG(day_seq) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_x
FROM numbered_data
),
moving_avg AS (
SELECT
*,
LAG(ma7_efficiency, 1) OVER (ORDER BY metric_date) AS prev_ma7_efficiency,
LAG(ma7_output, 1) OVER (ORDER BY metric_date) AS prev_ma7_output,
LAG(ma7_defect_rate, 1) OVER (ORDER BY metric_date) AS prev_ma7_defect_rate
FROM moving_avg_step1
),
-- =====================================================
-- 3. 简化的趋势斜率计算
-- =====================================================
slope_calc AS (
SELECT
*,
(ma7_efficiency - LAG(ma7_efficiency, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_efficiency,
(ma7_output - LAG(ma7_output, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_output,
(ma7_defect_rate - LAG(ma7_defect_rate, 3) OVER (ORDER BY metric_date)) / 3.0 AS slope_defect
FROM moving_avg
),
-- =====================================================
-- 4. 趋势判断与异常检测
-- =====================================================
trend_analysis AS (
SELECT
metric_date,
hourly_efficiency,
total_output,
defect_rate,
completion_rate,
ROUND(ma7_efficiency, 2) AS ma7_efficiency,
ROUND(ma7_output, 2) AS ma7_output,
ROUND(ma7_defect_rate, 2) AS ma7_defect_rate,
ROUND(COALESCE(slope_efficiency, 0), 4) AS slope_efficiency,
ROUND(COALESCE(slope_output, 0), 4) AS slope_output,
ROUND(COALESCE(slope_defect, 0), 4) AS slope_defect,
prev_ma7_efficiency,
prev_ma7_output,
prev_ma7_defect_rate,
CASE
WHEN COALESCE(slope_efficiency, 0) > 0.3 THEN 'RISING'
WHEN COALESCE(slope_efficiency, 0) < -0.3 THEN 'FALLING'
ELSE 'STABLE'
END AS efficiency_trend,
CASE
WHEN COALESCE(slope_output, 0) > 3 THEN 'RISING'
WHEN COALESCE(slope_output, 0) < -3 THEN 'FALLING'
ELSE 'STABLE'
END AS output_trend,
CASE
WHEN COALESCE(slope_defect, 0) > 0.3 THEN 'RISING'
WHEN COALESCE(slope_defect, 0) < -0.3 THEN 'FALLING'
ELSE 'STABLE'
END AS defect_trend,
CASE
WHEN ma7_efficiency > 0 AND ABS(hourly_efficiency - ma7_efficiency) > ma7_efficiency * 0.3 THEN 'ANOMALY'
ELSE 'NORMAL'
END AS efficiency_status,
CASE
WHEN ma7_output > 0 AND ABS(total_output - ma7_output) > ma7_output * 0.3 THEN 'ANOMALY'
ELSE 'NORMAL'
END AS output_status,
CASE
WHEN defect_rate > ma7_defect_rate * 1.5 AND defect_rate > 5 THEN 'ANOMALY'
ELSE 'NORMAL'
END AS defect_status
FROM slope_calc
)
-- =====================================================
-- 输出：日度趋势分析明细
-- =====================================================
SELECT
metric_date,
ROUND(hourly_efficiency, 2) AS hourly_output,
ma7_efficiency AS efficiency_ma7,
slope_efficiency,
efficiency_trend,
efficiency_status,
CASE
WHEN prev_ma7_efficiency IS NOT NULL
AND ma7_efficiency > prev_ma7_efficiency AND slope_efficiency < 0 THEN 'TURNING_POINT'
WHEN prev_ma7_efficiency IS NOT NULL
AND ma7_efficiency < prev_ma7_efficiency AND slope_efficiency > 0 THEN 'TURNING_POINT'
ELSE 'NONE'
END AS efficiency_turning_point,
ROUND(total_output, 0) AS daily_output,
ma7_output AS output_ma7,
slope_output,
output_trend,
output_status,
CASE
WHEN prev_ma7_output IS NOT NULL
AND ma7_output > prev_ma7_output AND slope_output < 0 THEN 'TURNING_POINT'
WHEN prev_ma7_output IS NOT NULL
AND ma7_output < prev_ma7_output AND slope_output > 0 THEN 'TURNING_POINT'
ELSE 'NONE'
END AS output_turning_point,
ROUND(defect_rate, 2) AS defect_rate,
ma7_defect_rate AS defect_rate_ma7,
slope_defect,
defect_trend,
defect_status,
CASE
WHEN prev_ma7_defect_rate IS NOT NULL
AND ma7_defect_rate > prev_ma7_defect_rate AND slope_defect < 0 THEN 'TURNING_POINT'
WHEN prev_ma7_defect_rate IS NOT NULL
AND ma7_defect_rate < prev_ma7_defect_rate AND slope_defect > 0 THEN 'TURNING_POINT'
ELSE 'NONE'
END AS defect_turning_point
FROM trend_analysis
ORDER BY metric_date DESC;
-- =====================================================
-- 补充查询1：周度趋势汇总
-- =====================================================
WITH weekly_metrics AS (
SELECT
DATE_TRUNC('week', lr.event_time_utc::timestamp)::date AS week_start,
COUNT(DISTINCT lr.worker_name) AS worker_count,
SUM(lr.duration_minutes) / 60.0 AS total_hours,
SUM(lr.report_qty) AS total_output,
CASE WHEN SUM(lr.duration_minutes) > 0
THEN SUM(lr.report_qty) / (SUM(lr.duration_minutes) / 60.0)
ELSE 0 END AS hourly_efficiency
FROM fact_labor_report lr
GROUP BY DATE_TRUNC('week', lr.event_time_utc::timestamp)
),
weekly_quality AS (
SELECT
DATE_TRUNC('week', qi.event_time_utc::timestamp)::date AS week_start,
SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate
FROM fact_quality_inspection qi
GROUP BY DATE_TRUNC('week', qi.event_time_utc::timestamp)
),
weekly_combined AS (
SELECT
wm.week_start,
wm.worker_count,
wm.total_hours,
wm.total_output,
wm.hourly_efficiency,
COALESCE(wq.defect_rate, 0) AS defect_rate
FROM weekly_metrics wm
LEFT JOIN weekly_quality wq ON wm.week_start = wq.week_start
),
weekly_with_lag AS (
SELECT
*,
LAG(hourly_efficiency, 1) OVER (ORDER BY week_start) AS prev_efficiency,
LAG(total_output, 1) OVER (ORDER BY week_start) AS prev_output,
LAG(defect_rate, 1) OVER (ORDER BY week_start) AS prev_defect_rate
FROM weekly_combined
)
SELECT
week_start,
worker_count,
ROUND(total_hours, 1) AS total_hours,
ROUND(total_output, 0) AS total_output,
ROUND(hourly_efficiency, 2) AS hourly_output,
ROUND(defect_rate, 2) AS defect_rate,
CASE
WHEN prev_efficiency IS NULL THEN 'NONE'
WHEN hourly_efficiency > prev_efficiency * 1.1 THEN 'RISING'
WHEN hourly_efficiency < prev_efficiency * 0.9 THEN 'FALLING'
ELSE 'STABLE'
END AS efficiency_trend,
CASE
WHEN prev_output IS NULL THEN 'NONE'
WHEN total_output > prev_output * 1.1 THEN 'RISING'
WHEN total_output < prev_output * 0.9 THEN 'FALLING'
ELSE 'STABLE'
END AS output_trend,
CASE
WHEN prev_defect_rate IS NULL THEN 'NONE'
WHEN defect_rate > prev_defect_rate * 1.2 THEN 'RISING'
WHEN defect_rate < prev_defect_rate * 0.8 THEN 'FALLING'
ELSE 'STABLE'
END AS defect_trend,
ROUND((hourly_efficiency - COALESCE(prev_efficiency, hourly_efficiency)) / NULLIF(prev_efficiency, 0) * 100, 1) AS efficiency_wow_pct
FROM weekly_with_lag
ORDER BY week_start DESC;
-- =====================================================
-- 补充查询2：指标趋势汇总报告
-- =====================================================
WITH
recent_data AS (
SELECT
DATE(lr.event_time_utc::timestamp) AS metric_date,
SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency,
SUM(lr.report_qty) AS total_output
FROM fact_labor_report lr
WHERE lr.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days'
GROUP BY DATE(lr.event_time_utc::timestamp)
),
recent_quality AS (
SELECT
DATE(qi.event_time_utc::timestamp) AS metric_date,
SUM(qi.fail_qty) * 100.0 / NULLIF(SUM(qi.pass_qty) + SUM(qi.fail_qty), 0) AS defect_rate
FROM fact_quality_inspection qi
WHERE qi.event_time_utc::timestamp >= CURRENT_DATE - INTERVAL '14 days'
GROUP BY DATE(qi.event_time_utc::timestamp)
),
period_stats AS (
SELECT
AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_7d,
AVG(CASE WHEN rd.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_7d,
AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.hourly_efficiency END) AS avg_eff_prev7d,
AVG(CASE WHEN rd.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rd.total_output END) AS avg_output_prev7d
FROM recent_data rd
),
quality_stats AS (
SELECT
AVG(CASE WHEN rq.metric_date >= CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_7d,
AVG(CASE WHEN rq.metric_date < CURRENT_DATE - INTERVAL '7 days' THEN rq.defect_rate END) AS avg_defect_prev7d
FROM recent_quality rq
)
SELECT
'efficiency_per_hour' AS metric_name,
ROUND(ps.avg_eff_7d, 2) AS last_7d_avg,
ROUND(ps.avg_eff_prev7d, 2) AS prev_7d_avg,
ROUND((ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) * 100, 1) AS change_rate_pct,
CASE
WHEN ps.avg_eff_7d > ps.avg_eff_prev7d * 1.05 THEN 'RISING'
WHEN ps.avg_eff_7d < ps.avg_eff_prev7d * 0.95 THEN 'FALLING'
ELSE 'STABLE'
END AS trend,
CASE
WHEN ABS(ps.avg_eff_7d - ps.avg_eff_prev7d) / NULLIF(ps.avg_eff_prev7d, 0) > 0.2 THEN 'ANOMALY'
ELSE 'NORMAL'
END AS warning
FROM period_stats ps
UNION ALL
SELECT
'daily_output',
ROUND(ps.avg_output_7d, 0),
ROUND(ps.avg_output_prev7d, 0),
ROUND((ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) * 100, 1),
CASE
WHEN ps.avg_output_7d > ps.avg_output_prev7d * 1.05 THEN 'RISING'
WHEN ps.avg_output_7d < ps.avg_output_prev7d * 0.95 THEN 'FALLING'
ELSE 'STABLE'
END,
CASE
WHEN ABS(ps.avg_output_7d - ps.avg_output_prev7d) / NULLIF(ps.avg_output_prev7d, 0) > 0.2 THEN 'ANOMALY'
ELSE 'NORMAL'
END
FROM period_stats ps
UNION ALL
SELECT
'defect_rate_pct',
ROUND(qs.avg_defect_7d, 2),
ROUND(qs.avg_defect_prev7d, 2),
ROUND((qs.avg_defect_7d - qs.avg_defect_prev7d) / NULLIF(qs.avg_defect_prev7d, 0) * 100, 1),
CASE
WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.1 THEN 'RISING'
WHEN qs.avg_defect_7d < qs.avg_defect_prev7d * 0.9 THEN 'FALLING'
ELSE 'STABLE'
END,
CASE
WHEN qs.avg_defect_7d > 8 THEN 'THRESHOLD_EXCEEDED'
WHEN qs.avg_defect_7d > qs.avg_defect_prev7d * 1.3 THEN 'ANOMALY_RISING'
ELSE 'NORMAL'
END
FROM quality_stats qs;
-- =====================================================
-- 补充查询3：拐点预警汇总
-- =====================================================
WITH daily_eff AS (
SELECT
DATE(lr.event_time_utc::timestamp) AS metric_date,
SUM(lr.report_qty) / NULLIF(SUM(lr.duration_minutes) / 60.0, 0) AS hourly_efficiency
FROM fact_labor_report lr
GROUP BY DATE(lr.event_time_utc::timestamp)
),
with_ma_step1 AS (
SELECT
metric_date,
hourly_efficiency,
AVG(hourly_efficiency) OVER (ORDER BY metric_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma7
FROM daily_eff
),
with_ma AS (
SELECT
metric_date,
hourly_efficiency,
ma7,
LAG(ma7, 1) OVER (ORDER BY metric_date) AS prev_ma7,
LAG(ma7, 2) OVER (ORDER BY metric_date) AS prev2_ma7
FROM with_ma_step1
),
turning_points AS (
SELECT
metric_date,
hourly_efficiency,
ma7,
prev_ma7,
prev2_ma7,
CASE
WHEN prev2_ma7 IS NOT NULL
AND prev_ma7 < prev2_ma7 AND ma7 > prev_ma7 THEN 'UPWARD'
WHEN prev2_ma7 IS NOT NULL
AND prev_ma7 > prev2_ma7 AND ma7 < prev_ma7 THEN 'DOWNWARD'
ELSE NULL
END AS turning_type
FROM with_ma
)
SELECT
metric_date,
ROUND(hourly_efficiency, 2) AS daily_efficiency,
ROUND(ma7, 2) AS ma7_line,
turning_type,
CASE
WHEN turning_type = 'UPWARD' THEN 'MAINTAIN_CURRENT_MEASURES'
WHEN turning_type = 'DOWNWARD' THEN 'INVESTIGATE_AND_IMPROVE'
ELSE 'NONE'
END AS recommendation
FROM turning_points
WHERE turning_type IS NOT NULL
ORDER BY metric_date DESC
LIMIT 10;

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lzwcai_mcpskills_mfg_data_agent/sql/订单延迟预警分析.sql Normal file
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-- 订单延迟预警分析（最终优化版）
-- Order Delay Warning Analysis
WITH
-- 1. 历史生产周期统计（全局平均，仅统计已完成工单）
production_cycle_stats AS (
SELECT
COALESCE(
AVG(
GREATEST(0, EXTRACT(DAY FROM last_updated_utc - event_time_utc))
),
0
) AS avg_production_days
FROM fact_work_order
WHERE status = 'CLOSED'
AND last_updated_utc >= event_time_utc
),
-- 2. 物流延误统计（按客户维度）
logistics_delay_stats AS (
SELECT
customer_id,
AVG(
GREATEST(0, EXTRACT(DAY FROM event_time_utc - doc_date_utc))
) AS avg_logistics_delay_days,
SUM(
CASE WHEN EXTRACT(DAY FROM event_time_utc - doc_date_utc) > 3
THEN 1 ELSE 0 END
) AS delay_count
FROM fact_sales_shipment
WHERE doc_date_utc IS NOT NULL
AND event_time_utc IS NOT NULL
GROUP BY customer_id
),
-- 3. 质量问题统计（全局平均）
quality_issue_stats AS (
SELECT
COALESCE(
ROUND(
SUM(fail_qty) * 100.0 / NULLIF(SUM(pass_qty + fail_qty), 0),
2
),
0
) AS defect_rate_pct
FROM fact_quality_inspection
WHERE pass_qty IS NOT NULL
AND fail_qty IS NOT NULL
),
-- 4. 报废率（全局）
scrap_stats AS (
SELECT
COALESCE(
(SELECT COUNT(*) FROM fact_scrap) * 100.0 /
NULLIF((SELECT COUNT(*) FROM fact_work_order WHERE status = 'CLOSED'), 0),
0
) AS scrap_rate_pct
),
-- 5. 当前生产滞后风险（全局）
active_work_order_risk AS (
SELECT
COUNT(*) AS active_wo_count,
COALESCE(
SUM(
CASE
WHEN planned_qty > 0
AND (completed_qty / planned_qty) < 0.3
AND EXTRACT(DAY FROM NOW() - event_time_utc) > 7
THEN 1
ELSE 0
END
),
0
) AS lagging_wo_count
FROM fact_work_order
WHERE status IN ('OPEN', 'STARTED')
),
-- 6. 合并全局指标为单行
global_metrics AS (
SELECT
pcs.avg_production_days,
qis.defect_rate_pct,
ss.scrap_rate_pct,
awr.active_wo_count,
awr.lagging_wo_count
FROM production_cycle_stats pcs,
quality_issue_stats qis,
scrap_stats ss,
active_work_order_risk awr
)
-- 主查询
SELECT
so.sales_order_number AS order_number,
c.customer_name AS customer_name,
so.order_date_utc::DATE AS order_date,
so.deal_amount AS order_amount,
so.payment_status AS payment_status,
-- 风险指标
ROUND(gm.avg_production_days::NUMERIC, 1) AS avg_production_days,
ROUND(COALESCE(lds.avg_logistics_delay_days, 0)::NUMERIC, 1) AS avg_logistics_delay_days,
COALESCE(lds.delay_count, 0)::INT AS historical_delay_count,
ROUND(gm.defect_rate_pct::NUMERIC, 2) AS defect_rate_pct,
ROUND(gm.scrap_rate_pct::NUMERIC, 2) AS scrap_rate_pct,
gm.active_wo_count::INT AS active_work_order_count,
gm.lagging_wo_count::INT AS lagging_work_order_count,
-- 延迟概率计算
ROUND(
LEAST(100, GREATEST(0,
LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) +
LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) +
LEAST(25, gm.defect_rate_pct * 2.5) +
LEAST(20, gm.lagging_wo_count * 10)
))::NUMERIC, 1
) AS delay_probability_pct,
-- 预警等级
CASE
WHEN (
LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) +
LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) +
LEAST(25, gm.defect_rate_pct * 2.5) +
LEAST(20, gm.lagging_wo_count * 10)
) >= 60 THEN 'RED'
WHEN (
LEAST(25, GREATEST(0, gm.avg_production_days - 10) * 2.5) +
LEAST(30, COALESCE(lds.avg_logistics_delay_days, 0) * 6) +
LEAST(25, gm.defect_rate_pct * 2.5) +
LEAST(20, gm.lagging_wo_count * 10)
) >= 30 THEN 'YELLOW'
ELSE 'GREEN'
END AS warning_level,
-- 主要风险因素
CASE
WHEN gm.lagging_wo_count >= 2 THEN 'PRODUCTION_SEVERELY_DELAYED'
WHEN COALESCE(lds.avg_logistics_delay_days, 0) > 5 THEN 'HIGH_LOGISTICS_DELAY_RISK'
WHEN gm.avg_production_days > 15 THEN 'LONG_PRODUCTION_CYCLE'
WHEN gm.defect_rate_pct > 10 THEN 'QUALITY_ISSUES'
ELSE 'NORMAL'
END AS primary_risk_factor
FROM fact_sales_order so
LEFT JOIN dim_customer c
ON so.customer_id = c.customer_id
AND c.is_current = 't'
CROSS JOIN global_metrics gm
LEFT JOIN logistics_delay_stats lds
ON so.customer_id = lds.customer_id
ORDER BY delay_probability_pct DESC, so.order_date_utc DESC;