inventory-demand-planning

name	inventory-demand-planning
description	Chooses retailer forecast families (moving average, Holt-Winters, causal, LightGBM), safety stock under lead-time risk, reorder/EOQ/min-max, and promo lift with cannibalization and post-promo dip. Trigger on demand forecast, safety stock, replenishment, or promo lift across stores and SKUs. Not for factory MRP, 3PL slotting, or warehouse WMS build-out.
version	1.0.1
risk	safe
source	https://github.com/ai-evos/agent-skills
date_added	2026-02-27

When to Use

Use this skill when you need to forecast demand and shape inventory policy across SKUs, stores, and vendors:

• Selecting and tuning forecasting methods, safety stock policies, and reorder logic for different demand patterns.
• Planning promotions, seasonal transitions, markdowns, and end-of-life strategies while balancing service, cash, and margin.
• Investigating chronic stockouts, excess inventory, or forecast bias and redesigning the planning process with clearer decision frameworks.

Prerequisites

• Context: Senior demand planner at a multi-location retailer (40–200 stores, 300–800 active SKUs).
• Systems: Demand planning suite (Blue Yonder, Oracle Demantra, Kinaxis), ERP (SAP, Oracle), WMS, POS data feeds, and vendor portals.

Procedure

1. Forecasting Method Selection

1. Moving Averages: Use for stable-demand, low-variability items. A 4-week simple moving average works for commodity staples. Weighted moving averages work better for slight drift. Never use moving averages on seasonal items — they lag trend changes by half the window length.
2. Exponential Smoothing: Single (SES, alpha 0.1–0.3) for stationary demand. Double (Holt's) for consistent growth/decline. Triple (Holt-Winters) for seasonal cycles. Optimize parameters on holdout data, never on fitting data.
3. Seasonal Decomposition: Use STL, classical, or X-13ARIMA-SEATS to isolate trend, seasonal, and residual components. Use when seasonal patterns shift year over year or before building promotional lift estimates.
4. Causal/Regression Models: Use when external factors drive demand. Encode promo flags with depth, display type, and media support. Regularize aggressively (Lasso/Ridge) and validate on out-of-time data.
5. Machine Learning: Justified for 1,000+ SKUs with 2+ years of weekly history. LightGBM/XGBoost outperforms simpler methods by 10–20% WAPE on promotional/intermittent items. Requires quarterly retraining minimum. Load references/decision-frameworks.md when you need detailed method selection trees, optimization models, or mapping demand patterns to primary/fallback methods.

2. Safety Stock Calculation

1. Standard Formula: SS = Z × σ_d × √(LT + RP) (Z = service level z-score, σ_d = demand std dev, LT = lead time, RP = review period). Works only for normally distributed, stationary demand.
2. Lead Time Variability: SS = Z × √(LT_avg × σ_d² + d_avg² × σ_LT²). Use when vendor lead times are uncertain (CV > 0.3).
3. Lumpy/Intermittent Demand: Use Croston's method for forecasting. Compute safety stock using a bootstrapped demand distribution.
4. New Products: Use analogous item profiling (3–5 most similar items). Add a 20–30% buffer for the first 8 weeks, then taper as own history accumulates.

3. Reorder Logic

1. Inventory Position: IP = On-Hand + On-Order − Backorders − Committed. Never reorder based on on-hand alone — you will double-order when POs are in transit.
2. Min/Max: Min = average demand during lead time + safety stock. Max = Min + EOQ. Order up to Max when IP drops to Min.
3. Reorder Point / EOQ: ROP = average demand during lead time + safety stock. EOQ = √(2DS/H). Round EOQ to vendor case packs, layer quantities, or pallet tiers.
4. Periodic Review (R,S): Review inventory every R periods, order up to target level S. Set R by vendor delivery schedule.
5. Vendor Tier Frequencies: A-vendors (top 10 by spend) get weekly review cycles. B-vendors (next 20) get bi-weekly. C-vendors get monthly.

4. Promotional Planning

1. Baseline Separation: Strip promotional volume from history before fitting baseline models. Keep a separate "promotional lift" layer that applies multiplicatively on top of the baseline.
2. Lift Estimation: Use YoY comparison, cross-elasticity models, or analogous item lift. Typical lifts: 15–40% for TPR only, 80–200% for TPR + display + circular, 300–500%+ for doorbusters.
3. Cannibalization: Estimate cannibalization at 10–30% of lifted volume for close substitutes.
4. Post-Promo Dip: Expect 1–3 weeks of below-baseline demand. Dip magnitude is typically 30–50% of incremental lift, concentrated 60/30/10 across the three post-promo weeks.

5. ABC/XYZ Classification

1. ABC (Value): A = top 20% of SKUs driving 80% of margin. B = next 30% driving 15%. C = bottom 50% driving 5%. Classify on margin contribution, not revenue.
2. XYZ (Predictability): X = CV < 0.5. Y = CV 0.5–1.0. Z = CV > 1.0. Compute on de-seasonalized, de-promoted demand.
3. Policy Matrix: AX items get automated replenishment with tight safety stock. AZ items need human review every cycle. CX items get automated replenishment with generous review periods. CZ items are candidates for discontinuation.

6. Seasonal Transition Management

1. Buy Timing: Commit 12–20 weeks before selling season. Allocate 60–70% of expected demand in initial buy, reserving 30–40% for reorder based on early-season sell-through.
2. Markdown Timing: Begin markdowns when sell-through drops below 60% of plan at season midpoint. Early shallow markdowns (20–30% off) recover more margin than late deep markdowns.
3. Season-End Liquidation: Set a hard cutoff date 2–3 weeks before next season's product arrives. Do not hold seasonal product into the next year.

7. Communication Patterns

• Vendor routine reorder: Transactional, brief, PO-reference-driven.
• Vendor lead time escalation: Firm, fact-based, quantifies business impact.
• Internal stockout alert: Urgent, actionable, leads with customer impact and estimated revenue at risk.
• Markdown recommendation: Data-driven, includes margin impact. Frame as "sell-through pace requires price action", not "we bought too much". Load references/communication-templates.md when you need full templates with variables and tone guidance for vendor or internal communications.

Pitfalls

1. Overfitting causal models: Overfitting on sparse promo history is the single biggest pitfall. Validate on out-of-time data.
2. Low-volume MAPE: MAPE breaks on low-volume items (division by near-zero). Use WMAPE instead.
3. Moving Averages on seasonal items: They lag trend changes by half the window length.
4. Ignoring post-promo dip: Failing to forecast the dip leads to excess inventory and markdowns.
5. Classifying ABC on revenue: Overinvests in high-revenue low-margin items. Use margin contribution.
6. Holding seasonal product into next year: Style items date, and warehousing cost erodes margin recovery.
7. Slow-mover lingering: Consumes shelf space, warehouse slots, and working capital. Set a hard exit date 8 weeks from first markdown.
8. Viral social media spike: Do not chase. Capture from existing inventory, issue allocation rules. Revise baseline only if sustained 4+ weeks post-spike.
9. Supplier lead time doubling: Recalculate SS immediately. Place emergency order for the delta, negotiate partial shipments.
10. Phantom inventory: Suspect when service level drops despite "adequate" on-hand. Conduct cycle counts.
11. Vendor MOQ conflicts: Consolidate with other items, negotiate lower MOQ, or accept overage if holding cost is lower than alternative supplier.
12. Holiday calendar shift: Align forecasts to "weeks relative to holiday" rather than calendar weeks.
13. Demand pattern regime change: Old model will fail silently. Monitor tracking signal weekly — when it exceeds ±4 for two consecutive periods, trigger model re-selection. Load references/edge-cases.md for the comprehensive edge case library with full resolution playbooks.

Verification

Track weekly and trend monthly to verify planning health:

Metric	Target	Red Flag
WMAPE (weighted mean absolute percentage error)	< 25%	> 35%
Forecast bias	±5%	> ±10% for 4+ weeks
In-stock rate (A-items)	> 97%	< 94%
In-stock rate (all items)	> 95%	< 92%
Weeks of supply (aggregate)	4–8 weeks	> 12 or < 3
Excess inventory (>26 weeks supply)	< 5% of SKUs	> 10% of SKUs
Dead stock (zero sales, 13+ weeks)	< 2% of SKUs	> 5% of SKUs
Purchase order fill rate from vendors	> 95%	< 90%
Promotional forecast accuracy (WMAPE)	< 35%	> 50%

Escalation Triggers:

• Projected stockout on A-item within 7 days: Alert demand planning manager + category merchant within 4 hours.
• Vendor confirms lead time increase > 25%: Notify supply chain director; recalculate all open POs within 1 business day.
• Promotional forecast miss > 40%: Post-promo debrief with merchandising and vendor within 1 week.
• Excess inventory > 26 weeks of supply on any A/B item: Markdown recommendation to merchandising VP within 1 week.
• Forecast bias exceeds ±10% for 4 consecutive weeks: Model review and re-parameterization within 2 weeks.
• New product sell-through < 40% of plan after 4 weeks: Assortment review with merchandising within 1 week.
• Service level drops below 90% for any category: Root cause analysis and corrective plan within 48 hours.

Escalation Chain: Level 1 (Demand Planner) → Level 2 (Planning Manager, 24 hours) → Level 3 (Director of Supply Chain Planning, 48 hours) → Level 4 (VP Supply Chain, 72+ hours or any A-item stockout at enterprise customer).