Inventory Demand Planning
Codified expertise for demand forecasting, safety-stock optimization, replenishment planning, and promotional-lift estimation — the working knowledge of a senior multi-location retail demand planner managing hundreds of SKUs.
A forecast that's off by 10% doesn't just miss a number — it either strands cash in dead stock or empties shelves during your best-selling week, and most planning teams find out which one only after it's already happened.
Who it's for: demand planners at multi-location retailers, inventory managers setting safety stock and reorder points, merchandising teams planning seasonal buys and promotions, supply chain analysts evaluating forecast accuracy, anyone building or auditing a replenishment process
Example
"We're running a 3-week BOGO promotion on a top-20 SKU" → A promotional lift estimate from historical elasticity, a forward-buy calculation for the vendor PO, cannibalization modeling for close substitutes, and an explicit post-promo dip forecast so the following weeks' below-baseline sales don't get mistaken for a demand collapse
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add inventory-demand-planning# Inventory Demand Planning
Codified expertise for demand forecasting, safety-stock optimization, replenishment planning, and promotional-lift estimation at multi-location retailers — the working knowledge of a senior demand planner managing 300–800 active SKUs across 40–200 stores.
## When to Use
- Generating or reviewing demand forecasts for existing or new SKUs
- Setting safety-stock levels based on demand variability and service-level targets
- Planning replenishment for seasonal transitions, promotions, or new-product launches
- Evaluating forecast accuracy and deciding whether to adjust a model or override it
- Making buy decisions under supplier MOQ constraints or a lead-time change
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What This Does
Distills the working knowledge of a senior multi-location retail demand planner into a forecasting-method decision table (moving average, exponential smoothing, Croston's for intermittent demand, causal regression for promotions), a safety-stock methodology that goes beyond the textbook formula to handle lead-time variability and lumpy demand, and a full promotional-planning workflow covering lift estimation, cannibalization, forward-buy, and the post-promo dip that catches unprepared planners off guard.
It also covers ABC/XYZ classification tied to a concrete replenishment policy matrix, seasonal transition timing (buy commitment, markdown triggers, hard liquidation cutoffs), and a set of key edge cases — a viral demand spike, an overnight vendor lead-time doubling, phantom inventory, a holiday calendar shift — each with the specific reasoning that keeps a standard playbook from producing a bad call in that situation.
Quick Start
Step 1: Create a Project Folder
mkdir demand-planning && cd demand-planning
Step 2: Download the Template
Click Download above, then:
mv ~/Downloads/CLAUDE.md ./
Step 3: Work Through a Planning Decision
claude
Describe the situation — a forecast to build, a safety-stock level to set, a promotion to plan, a markdown decision to make — and Claude will apply the relevant framework and core knowledge to it.
Tips & Best Practices
- Document every new-product forecast's assumptions explicitly (which analogs were used, price point, distribution, competitive window) so a post-mortem can audit exactly what was assumed rather than reconstructing it from memory.
- Frame a markdown recommendation to merchandising around sell-through pace and margin math, never as "we bought too much" — the framing changes how the recommendation lands even when the number is identical.
- Watch the tracking signal weekly, not just WMAPE monthly — a demand-pattern regime change (after a reformulation or a competitor's entry) fails a model silently, and the tracking signal is what catches it early.
Limitations
- Planning methodology, not a forecasting engine — pair it with whatever demand-planning suite, ERP, or spreadsheet actually holds the SKU-level data.
- Dollar figures, lift percentages, and thresholds are illustrative benchmarks from a mid-size multi-location retail context — recalibrate against your own category's actual promo history and margin structure.
- Assumes reasonably clean POS and sell-through data feeding the forecast; garbage-in problems (phantom inventory, miscoded promotions) will silently degrade every downstream number.