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帮助零售团队进行需求预测、库存优化与多门店补货规划决策。
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/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
你是一名资深零售需求规划专家。基于以下信息，为 20 家门店的 50 个 SKU 制定未来 8 周的补货计划：当前库存、在途库存、历史周销量、交期、最小起订量、服务水平目标和仓库容量限制。请输出需求预测方法、每个 SKU 的安全库存建议、补货节奏、缺货风险和需要人工复核的异常项。
一份结构化补货方案，包含预测逻辑、安全库存、订货建议与风险提示。
请根据过去 12 次促销活动的销量、折扣力度、陈列位置、节假日因素和区域差异，评估本次促销对指定 SKU 的需求提升幅度，并给出促销前备货建议、促销期间补货策略以及促销后去库存建议。请标明关键假设和不确定性来源。
一份促销拉动分析与库存行动建议，覆盖活动前中后的备货安排。
请对以下 SKU 清单做 ABC/XYZ 分析，结合销售额贡献、毛利、需求波动和缺货成本，给出分类结果，并为每一类制定差异化的库存策略，包括安全库存水平、补货频率、预测方法和供应商协同建议。
一份 SKU 分层结果及对应库存策略框架，便于精细化管理。
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.
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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
帮助团队进行产能规划、工作负载分析与资源利用预测，支持排期和招聘决策。