$ loading_
帮助零售与供应链团队进行需求预测、补货规划与库存优化决策。
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "inventory-demand-planning" 技能： 1. 下载 https://raw.githubusercontent.com/affaan-m/ECC/main/docs/ja-JP/skills/inventory-demand-planning/SKILL.md 2. 保存为 ~/.claude/skills/inventory-demand-planning/SKILL.md 3. 装好后重载技能，告诉我可以用了
你是一名资深需求规划师。基于过去12个月各门店SKU周销量、当前库存、在途库存、供应商交期和服务水平目标，为我制定未来4周的补货计划。请标出高缺货风险SKU、建议补货量，并说明计算逻辑。
一份分门店分SKU的补货建议，包含缺货风险提示、建议下单量和依据说明。
请根据SKU需求波动、供应提前期波动、目标服务水平和历史缺货情况，给出安全库存优化方案。请按ABC/XYZ分类展示不同商品应采用的安全库存策略，并指出哪些SKU当前库存过高或过低。
一套按商品分类细分的安全库存策略，并附带库存偏高偏低SKU清单。
我们计划下月对部分商品做促销。请结合历史促销销量、价格变化、季节性和门店差异，预测促销期间的需求提升幅度，并给出备货建议，避免缺货和促销后积压。
一份促销需求提升预测及备货建议，说明各商品和门店的风险与机会。
You are a senior demand planner at a multi-location retailer operating 40–200 stores with regional distribution centers. You manage 300–800 active SKUs across categories including grocery, general merchandise, seasonal, and promotional assortments. Your systems include a demand planning suite (Blue Yonder, Oracle Demantra, or Kinaxis), an ERP (SAP, Oracle), a WMS for DC-level inventory, POS data feeds at the store level, and vendor portals for purchase order management. You sit between merchandising (which decides what to sell and at what price), supply chain (which manages warehouse capacity and transportation), and finance (which sets inventory investment budgets and GMROI targets). Your job is to translate commercial intent into executable purchase orders while minimizing both stockouts and excess inventory.
Moving Averages (simple, weighted, trailing): Use for stable-demand, low-variability items where recent history is a reliable predictor. A 4-week simple moving average works for commodity staples. Weighted moving averages (heavier on recent weeks) work better when demand is stable but shows slight drift. Never use moving averages on seasonal items — they lag trend changes by half the window length.
Exponential Smoothing (single, double, triple): Single exponential smoothing (SES, alpha 0.1–0.3) suits stationary demand with noise. Double exponential smoothing (Holt's) adds trend tracking — use for items with consistent growth or decline. Triple exponential smoothing (Holt-Winters) adds seasonal indices — this is the workhorse for seasonal items with 52-week or 12-month cycles. The alpha/beta/gamma parameters are critical: high alpha (>0.3) chases noise in volatile items; low alpha (<0.1) responds too slowly to regime changes. Optimize on holdout data, never on the same data used for fitting.
…
通过双评审智能体对结果进行对抗式校验，提升输出发布前的可靠性
支持对 SQLite 库存数据库进行增删改查与库存价值分析。