antigravity-skills-reference/skills/inventory-demand-planning/references/decision-frameworks.md

Decision Frameworks — Inventory Demand Planning

This reference provides the detailed decision logic, optimization models, method selection trees, and segmentation methodologies for inventory demand planning at multi-location retailers. It is loaded on demand when the agent needs to make or recommend nuanced planning decisions.

All thresholds, formulas, and cost assumptions reflect US multi-location retail operations managing hundreds of SKUs across grocery, general merchandise, seasonal, and promotional categories.

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1. Forecast Method Selection Trees

1.1 Primary Selection Algorithm

The goal is to match each SKU to the forecasting method that minimizes WMAPE on out-of-time holdout data. In practice, most organizations cannot afford per-SKU model optimization across hundreds of items. Instead, classify items into demand pattern archetypes and assign methods by archetype.

Step 1 — Classify the Demand Pattern

Compute the following statistics on the most recent 52 weeks of de-promoted demand data (remove promotional lift periods before computing):

Statistic	Formula	Purpose
Coefficient of Variation (CV)	σ_demand / μ_demand	Measures demand variability
Average Demand Interval (ADI)	Total periods / Number of non-zero demand periods	Measures intermittency
Trend Strength	R² of linear regression on 26-week trailing demand	Measures directional movement
Seasonal Strength	Autocorrelation at lag 52 (weekly) or lag 12 (monthly)	Measures repeating seasonal pattern
Zero-Demand Ratio	Count of zero-demand periods / Total periods	Measures how often demand is absent

Step 2 — Map to Demand Archetype

Archetype	CV	ADI	Trend R²	Seasonal AC	Zero Ratio	Example
Smooth	< 0.5	1.0–1.1	< 0.3	< 0.3	< 5%	Milk, bread, paper towels
Trending	< 0.7	1.0–1.2	≥ 0.3	< 0.3	< 10%	Growing brand, declining legacy item
Seasonal	0.3–1.0	1.0–1.3	any	≥ 0.3	< 15%	Sunscreen, holiday decor, grills
Trending-Seasonal	0.4–1.2	1.0–1.3	≥ 0.3	≥ 0.3	< 15%	Growing seasonal category
Erratic	≥ 0.7	1.0–1.5	< 0.3	< 0.3	< 30%	Fashion accessories, novelty items
Intermittent	any	≥ 1.5	any	any	≥ 30%	Spare parts, specialty ingredients
Lumpy	≥ 1.0	≥ 1.5	any	any	≥ 30%	Bulk wholesale items with sporadic orders

Step 3 — Assign Forecasting Method

Archetype	Primary Method	Parameters	Fallback
Smooth	Weighted moving average (4–8 week window, recent-weighted)	Weights: 0.4/0.3/0.2/0.1 for 4-week	Single exponential smoothing (α = 0.15–0.25)
Trending	Holt's double exponential smoothing	α = 0.2–0.4, β = 0.05–0.15	Linear regression on trailing 26 weeks
Seasonal	Holt-Winters (additive if stable amplitude, multiplicative if growing amplitude)	α = 0.1–0.3, β = 0.01–0.05, γ = 0.1–0.3, period = 52 weeks	STL decomposition + SES on residual
Trending-Seasonal	Holt-Winters (multiplicative)	α = 0.2–0.4, β = 0.05–0.15, γ = 0.15–0.3	X-13ARIMA-SEATS
Erratic	Damped trend exponential smoothing	α = 0.2–0.4, β = 0.05, φ = 0.8–0.95	Ensemble of 3 methods (median)
Intermittent	Croston's method or SBA	α_demand = 0.1–0.2, α_interval = 0.1–0.2	Bootstrap simulation (1000 draws)
Lumpy	SBA (Syntetos-Boylan Approximation)	Same as Croston's with bias correction	Aggregated to monthly then disaggregated

1.2 Model Switching Rules

Do not switch methods based on a single bad week. Models need time to prove or disprove themselves.

Condition	Action	Minimum Observation Period
WMAPE improves > 10% on holdout vs. current method	Switch to candidate method	8-week parallel test
Tracking signal exceeds ±4 for 2 consecutive periods	Trigger model review; re-estimate parameters first	2 periods (weeks)
Tracking signal exceeds ±6 for 1 period	Immediate model review; likely archetype change	1 period
Demand pattern archetype changes (e.g., smooth → trending)	Re-run selection algorithm from Step 1	Quarterly archetype reassessment
New product transitions from analog-based to own-history	Switch when 12+ weeks of own data available and own-data WMAPE < analog-based	12 weeks
Post-promotion baseline contamination detected	Refit baseline model excluding promo periods	Immediate

1.3 Parameter Optimization Protocol

For exponential smoothing methods, optimize parameters using grid search on time-series cross-validation (rolling origin, 1-step ahead forecast, 26+ origins).

Grid search ranges:

Parameter	Range	Step Size	Constraint
α (level)	0.05–0.50	0.05	—
β (trend)	0.01–0.20	0.01	β ≤ α
γ (seasonal)	0.05–0.40	0.05	—
φ (damping)	0.80–0.98	0.02	Only for damped methods

Optimization metric: Minimize WMAPE on the holdout origins. If two parameter sets produce WMAPE within 1 percentage point, prefer the set with lower α (more smoothing) for stability.

Overfitting guard: If the optimized model produces WMAPE on the holdout that is

5 percentage points better than on the fitting data, the model is likely overfit. Increase smoothing (lower α) until the gap narrows to <3 points.

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2. Safety Stock Optimization Models

2.1 Standard Safety Stock (Normal Demand, Fixed Lead Time)

When demand follows a roughly normal distribution and lead time is consistent:

SS = Z × σ_d × √(LT)

Where:

• Z = z-score for the target service level (see lookup table below)
• σ_d = standard deviation of demand per period (use same period as LT)
• LT = lead time in periods

Z-Score Lookup:

Service Level	Z-Score	Typical Application
85.0%	1.04	CZ items — minimal investment
90.0%	1.28	C-items, non-critical B-items
92.0%	1.41	Mid-range safety net
95.0%	1.65	Standard target for A/B items
97.5%	1.96	AX items — high value, predictable
99.0%	2.33	Critical items — stockout cost vastly exceeds holding
99.5%	2.58	Life-safety or contractual obligation items
99.9%	3.09	Rarely justified — extreme holding cost

2.2 Safety Stock with Lead Time Variability

When vendor lead times are uncertain (CV of lead time > 0.15):

SS = Z × √(LT_avg × σ_d² + d_avg² × σ_LT²)

Where:

• LT_avg = average lead time in periods
• σ_LT = standard deviation of lead time in periods
• d_avg = average demand per period

Practical note: Many planners underestimate lead time variability because they measure "vendor ship date to DC receipt" without accounting for receiving delays, quality holds, or weekend/holiday dead time. Measure lead time from PO release to "available to sell" — this is the operationally relevant metric.

2.3 Safety Stock with Review Period

For periodic review systems (review every R periods):

SS = Z × σ_d × √(LT + R)

The review period adds exposure time — between reviews, you cannot react to demand changes. Weekly review (R=1) on a 2-week lead time item needs safety stock for √3 weeks. Monthly review (R=4) on the same item needs safety stock for √6 weeks — 41% more.

2.4 Safety Stock for Intermittent Demand

Normal-distribution formulas fail when demand has many zero periods. Use empirical (bootstrapped) safety stock instead:

1. Collect the last 52 periods of demand data (include zeros).
2. Generate 10,000 bootstrap samples of length (LT + R) by random sampling with replacement from the historical demand.
3. Compute the sum of each bootstrap sample (= simulated demand during lead time + review).
4. The safety stock is the (service level)th percentile of the simulated demand totals minus the mean simulated demand total.

Example: For 95% service level, safety stock = P95 of bootstrap demand — mean of bootstrap demand. This captures the skewed, zero-inflated distribution that parametric formulas miss.

2.5 Safety Stock for New Products (No History)

When an item has < 8 weeks of own demand history:

1. Identify 3–5 analogous items matching on: category, price point (±20%), brand tier (national/private label), pack size, and target demographic.
2. Compute the average σ_d and CV across the analogs.
3. Apply a "new product uncertainty premium" of 1.25× to the analog σ_d.
4. Use the standard formula with the inflated σ_d: SS = Z × (1.25 × σ_d_analog) × √(LT + R).
5. Every 2 weeks, blend own-history σ_d with the analog σ_d. By week 8, use 70% own history and 30% analog. By week 12, use 100% own history.

2.6 Safety Stock Cost-Optimization

The naive approach is to set a service level target and compute SS. The sophisticated approach is to optimize the tradeoff between holding cost and stockout cost:

Optimal SL = 1 − (H / (H + S × D/Q))

Where:

• H = holding cost per unit per period
• S = stockout cost per unit (lost margin + customer goodwill + substitution cost)
• D = demand per period
• Q = order quantity

For most retailers, stockout cost on A-items is 3–5× the unit margin (including lost customer visits and substitution effects), which pushes optimal SL to 96–98%. For C-items, stockout cost is approximately equal to the unit margin, yielding optimal SL of 88–92%.

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3. Promotional Planning Frameworks

3.1 Promotional Lift Estimation Methodology

Promotional lift is always computed relative to the baseline forecast (the forecast that would have been generated without the promotion). Contaminating the baseline with promotional history is the #1 source of systematic forecast error in retail.

Step 1 — Establish Clean Baseline

Strip promotional periods from the demand history before fitting the baseline model. Flag weeks as "promotional" if any of the following were active:

• Temporary price reduction (TPR) > 5% off regular price
• Feature in circular, digital ad, or endcap display
• BOGO or multi-buy offer
• Cross-promotion in another category

After stripping, interpolate the gaps using the forecast model fitted to non-promotional periods. This creates a "what would have sold at regular price" baseline.

Step 2 — Compute Historical Lifts

For each historical promotional event on this SKU:

Lift Ratio = Actual Promo-Period Sales / Baseline Forecast for Promo Period

A lift ratio of 2.5 means the promotion drove 2.5× baseline volume (150% incremental).

Organize lift ratios by promotional mechanism:

Mechanism	Typical Lift Range	Key Drivers
TPR only (5–15% off)	1.15–1.40	Depth of discount, category elasticity
TPR (15–30% off)	1.40–2.00	Deeper discount creates sharper response
TPR + display	1.80–2.50	Display location (endcap > wing > inline)
TPR + circular feature	2.00–3.00	Circular reach and placement (front page > interior)
TPR + display + circular	2.50–4.00	Full support — this is the "A-level" promo
BOGO	2.50–5.00	Perceived value drives high response but heavy forward-buy
Doorbuster / loss leader	3.00–6.00+	Traffic driver; lift varies wildly by event

Step 3 — Apply Lift to Current Forecast

Promo Forecast = Baseline Forecast × Lift Ratio

When multiple promotional mechanisms are combined, do NOT multiply individual lifts. Use the combined-mechanism lift from historical data or the table above. The interaction effects are sub-additive (display alone = 1.5× and circular alone = 1.8× does not mean display + circular = 2.7×; it's typically 2.0–2.5×).

Step 4 — Model the Post-Promotion Dip

Post-Promo Demand = Baseline × (1 − Dip Factor × Decay)

Default dip factors by product type:

Product Type	Dip Factor (% of incremental lift)	Dip Duration	Decay Pattern
Shelf-stable pantry	40–60%	2–4 weeks	60/30/10 (Week 1/2/3)
Perishable / refrigerated	10–20%	0–1 week	Immediate recovery
Household consumables	30–50%	2–3 weeks	50/35/15
Personal care	25–40%	2–3 weeks	50/30/20
Seasonal	15–30%	1–2 weeks	70/30
Discretionary / general merch	10–25%	1–2 weeks	70/30

3.2 Cannibalization Estimation

When SKU A is promoted, substitutable SKU B loses sales. The cannibalization rate is:

Cannibalization Rate = ΔB_down / ΔA_up

Where ΔA_up is the incremental lift on A and ΔB_down is the volume loss on B during the same period.

Default estimates when no cross-elasticity data exists:

Substitutability	Cannibalization Rate	Example
Direct substitute (same brand, different size)	25–40%	12-oz promoted, 16-oz loses
Close substitute (different brand, same segment)	15–25%	National brand promoted, private label loses
Moderate substitute (same category, different segment)	5–15%	Premium promoted, value tier affected
Weak substitute (adjacent category)	0–5%	Chips promoted, crackers slightly affected

Important: Cannibalization is bidirectional across the category. When building the category-level promotional plan, sum the cannibalization effects across all substitutes to compute the true category-level lift (which is always less than the item-level lift).

3.3 Forward-Buy and Pantry Loading

Deep promotions cause customers to purchase ahead of their consumption schedule. Forward-buy volume is demand pulled from future periods, not incremental category demand.

Forward-buy estimation:

Forward-Buy Volume = Incremental Lift × Forward-Buy Factor

Promotional Depth	Product Shelf Life	Forward-Buy Factor
10–20% off	< 2 weeks (perishable)	0.05–0.10
10–20% off	2–12 weeks	0.10–0.20
10–20% off	> 12 weeks (shelf-stable)	0.20–0.35
20–35% off	< 2 weeks	0.05–0.15
20–35% off	2–12 weeks	0.20–0.35
20–35% off	> 12 weeks	0.35–0.50
35–50% off	< 2 weeks	0.10–0.20
35–50% off	2–12 weeks	0.30–0.45
35–50% off	> 12 weeks	0.50–0.70
BOGO / > 50%	Any	0.50–0.80

The forward-buy factor tells you what fraction of the incremental lift came from pantry loading rather than true consumption increase. This directly feeds the post-promo dip calculation — the dip is approximately equal to the forward-buy volume spread over its consumption period.

3.4 Promotional Calendar Planning

When planning the annual promotional calendar, apply these rules:

1. Minimum inter-promotion gap: 4 weeks between promotions on the same SKU. Shorter gaps train customers to only buy on deal, eroding brand equity and baseline velocity.
2. Maximum promotional frequency: 13 weeks per year (25%) for any single SKU. Beyond this, the "promotional price" becomes the reference price in consumers' minds.
3. Seasonal alignment: Promote seasonal items during the build phase (first 40% of the season), not during peak or decline. Promoting at peak wastes money on demand that would have occurred anyway. Promoting during decline is a markdown, not a promotion.
4. Cross-category coordination: Avoid promoting close substitutes simultaneously. Stagger promotions across substitutes by at least 2 weeks to avoid self-cannibalization.
5. Vendor funding alignment: Match promotional timing to vendor trade fund availability. Many CPG manufacturers operate on calendar quarters — funds not committed by quarter-end expire. Plan key promos in weeks 8–12 of each quarter when vendors are motivated to spend remaining funds.

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4. ABC/XYZ Segmentation Methodology

4.1 ABC Classification (Value)

ABC classification segments SKUs by their financial contribution. The classification drives differentiated investment in forecasting effort, safety stock, review frequency, and management attention.

Classification Procedure

1. Select the value metric. Options in order of preference:

   • Gross margin contribution (best — focuses investment on profit, not revenue)
   • Revenue (acceptable when margin data is unavailable)
   • Unit volume (use only for warehouse space planning, not financial investment)

2. Compute trailing 52-week value for each active SKU.

3. Sort descending by the value metric.

4. Compute cumulative % of total value and classify:

Class	Cumulative % of Value	Typical % of SKUs	Description
A	0–80%	10–20%	High-value items driving the business
B	80–95%	20–30%	Mid-value items providing assortment breadth
C	95–100%	50–70%	Long-tail items with minimal individual impact

5. Exception overrides:
   • New items (< 13 weeks) are auto-classified one tier higher than their data suggests until they have sufficient history. A new item computing as C is treated as B.
   • Items with contractual obligations (planogram commitment, vendor agreement) are classified minimum B regardless of current sales velocity.
   • Items flagged as strategic by merchandising (e.g., traffic drivers, competitive price match items) are classified minimum A.

Reclassification Schedule

Run ABC reclassification quarterly. Between quarters, items are reclassified only if they cross a threshold by >50% (e.g., an item must contribute >120% of the A/B boundary to move from B to A mid-quarter). This prevents oscillation at class boundaries.

4.2 XYZ Classification (Predictability)

XYZ classification segments SKUs by demand forecast difficulty. It drives differentiated forecasting method selection and safety stock strategies.

Classification Procedure

1. Compute de-seasonalized, de-promoted demand for each SKU over the trailing 52 weeks. Remove seasonal indices and promotional lift periods so that the variability metric reflects genuine demand uncertainty, not planned variation.

2. Compute the coefficient of variation (CV):

   CV = σ_demand / μ_demand
   

   Use the de-seasonalized, de-promoted demand series.

3. Classify:

Class	CV Range	Description	Forecast Difficulty
X	< 0.5	Highly predictable — demand varies little around its mean	Low — simple methods work well
Y	0.5–1.0	Moderately predictable — noticeable variability	Medium — requires good models and monitoring
Z	> 1.0	Erratic/lumpy — demand is highly variable or intermittent	High — no model will be highly accurate

4. Supplement with ADI (Average Demand Interval): Items with ADI > 2.0 (meaning demand occurs less than every other period) should be classified Z regardless of CV, because the intermittency itself creates forecast difficulty that CV alone doesn't capture.

4.3 Combined ABC/XYZ Policy Matrix

Segment	Forecast Method	Safety Stock	Review Frequency	Replenishment	Management Attention
AX	Exponential smoothing (automated)	Z = 1.96 (97.5%)	Weekly	Automated with exception alerts	Monthly review
AY	Holt-Winters or causal model	Z = 1.65 (95%)	Weekly	Automated with planner review	Bi-weekly review
AZ	Ensemble or manual override	Z = 1.41–1.65 (92–95%)	Weekly	Planner-managed; never fully automated	Weekly review
BX	Moving average (automated)	Z = 1.65 (95%)	Bi-weekly	Automated	Monthly review
BY	Exponential smoothing (automated)	Z = 1.65 (95%)	Bi-weekly	Automated with exception alerts	Monthly review
BZ	Croston's or damped trend	Z = 1.28 (90%)	Bi-weekly	Semi-automated with planner approval	Monthly review
CX	Simple moving average	Z = 1.28 (90%)	Monthly	Automated	Quarterly review
CY	Simple moving average	Z = 1.28 (90%)	Monthly	Automated	Quarterly review
CZ	Croston's or none	Z = 1.04 (85%)	Monthly	Manual or min/max	Quarterly — discontinuation candidate

4.4 Migration Tracking

Track SKU movement between segments quarterly. Key migration patterns to monitor:

Migration	Signal	Action
A → B	Revenue or margin declining	Investigate: is this category shrinkage, competitive loss, or assortment issue?
B → A	Revenue or margin growing	Upgrade forecasting method and review frequency. Validate safety stock.
X → Y or Z	Demand becoming less predictable	Check for demand pattern regime change. Review forecast model fit. Increase safety stock.
Z → X or Y	Demand stabilizing	Possible to simplify forecast model. Review safety stock for reduction.
Any → CZ	Low value + erratic	Strong discontinuation candidate. Run slow-mover kill decision.

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5. Vendor Management Decision Logic

5.1 Vendor Tier Classification

Classify vendors into tiers based on annual purchase volume, strategic importance, and supply risk profile:

Tier	Criteria	Count (typical)	Review Cadence
Strategic	Top 5 by spend, or sole-source for A-items	3–8	Monthly scorecards, quarterly business reviews
Preferred	Top 20 by spend, multiple A/B items	10–25	Quarterly scorecards
Approved	All remaining active vendors	30–100+	Annual review
Probationary	Vendors under corrective action	Variable	Weekly monitoring, monthly review

5.2 Vendor Scorecard Metrics

Score each vendor quarterly on a 0–100 scale across these dimensions:

Dimension	Weight	Metric	Target	Calculation
On-time delivery	30%	% of POs delivered within the agreed window (±1 day)	> 95%	Score = (Actual % / 95%) × 100, cap at 100
Fill rate	25%	% of ordered units actually shipped	> 97%	Score = (Actual % / 97%) × 100, cap at 100
Lead time consistency	20%	CV of actual lead time vs. stated lead time	CV < 0.15	Score = max(0, 100 − (CV − 0.15) × 500)
Quality	15%	% of received units passing QC inspection	> 99%	Score = (Actual % / 99%) × 100, cap at 100
Responsiveness	10%	Average response time to inquiries/issues (hours)	< 24 hours	Score = max(0, 100 − (Avg Hours − 24) × 2)

Composite score thresholds:

Score Range	Rating	Action
90–100	Excellent	Consider for volume increase, preferred terms
75–89	Good	Standard operations, no action needed
60–74	Needs Improvement	Issue corrective action request; 90-day improvement plan
< 60	Unacceptable	Immediate escalation; begin qualifying alternative suppliers

5.3 Vendor Lead Time Management

Lead time management is the demand planner's most underleveraged tool for reducing inventory investment. A 1-day reduction in lead time across all vendors can reduce aggregate safety stock by 5–8%.

Lead time decomposition:

Component	Typical Range	Planner Influence
Order processing at vendor	1–3 days	Low — vendor's internal process
Production/picking	2–10 days	Medium — negotiate priority tiers
Vendor ship preparation	1–2 days	Low
Transit time	1–14 days	Medium — carrier selection, mode choice
Receiving and put-away	1–3 days	High — internal process improvement
Quality hold (if applicable)	0–5 days	High — streamline QC process

Actions to reduce lead time:

1. For strategic vendors: negotiate VMI (vendor-managed inventory) where the vendor monitors your inventory and ships proactively. Eliminates order processing delay.
2. For all vendors: provide rolling 8-week forecasts to allow pre-positioning. Reduces production/picking time on non-stock items.
3. Internally: invest in receiving automation (ASN-enabled receiving, barcode scanning) to cut receiving from 2–3 days to same-day.
4. Negotiate consolidated weekly shipments vs. per-PO shipments to reduce transit frequency while maintaining fill rate.

5.4 MOQ (Minimum Order Quantity) Negotiation Framework

When a vendor's MOQ creates excess inventory, evaluate these options in order:

Option	When to Use	Expected Outcome
Negotiate lower MOQ	Annual spend > $50K with this vendor; you have leverage	MOQ reduced 20–40%
Consolidate with other SKUs	Multiple SKUs from same vendor; dollar minimum instead of unit minimum	Meet dollar MOQ without over-ordering individual SKUs
Accept higher price for lower MOQ	MOQ overage cost > price premium cost	Pay 3–8% more per unit but order only what you need
Negotiate consignment	Slow-moving items from strategic vendors	Vendor owns inventory until you sell it
Split orders with another buyer	Known network of retailers ordering from the same vendor	Share the MOQ and split the shipment
Accept the overage	Holding cost for the excess is < $500 and item is non-perishable	Order the MOQ and treat the overage as forward inventory

5.5 Vendor Negotiation for Lead Time Reduction

Preparation checklist before negotiating:

1. Document your current order volume and growth trajectory with this vendor.
2. Compute the cost of their current lead time to your business: excess safety stock carrying cost + stockout cost from lead time variability.
3. Identify what you can offer in return: longer-term commitments, higher volumes, fewer order frequency changes, rolling forecasts.
4. Know your BATNA (best alternative): have a qualified secondary supplier identified.

Negotiation structure:

1. Present the data: "Over the past 6 months, your average lead time has been X days with a standard deviation of Y. This variability costs us $Z annually in excess safety stock."
2. Propose the target: "We're requesting a committed lead time of X−2 days with a guarantee of CV < 0.15."
3. Offer the exchange: "In return, we can commit to rolling 8-week forecasts updated weekly, and we'll consolidate to 2 orders per week instead of daily."
4. Set the timeline: "Let's implement this for Q2 and review the scorecard at the end of Q2 QBR."

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6. Seasonal Buy and Markdown Timing Models

6.1 Seasonal Buy Planning

Seasonal categories require forward commitments because lead times exceed the selling season. The buy decision has two components: the initial buy (pre-season) and the in-season reorder (if the vendor supports it).

Initial Buy Calculation

Initial Buy = Season Forecast × Initial Commitment % − Carry-Over Inventory

Category Risk Profile	Initial Commitment %	Reserve for Reorder	Rationale
Low risk (staple seasonal, proven seller)	70–80%	20–30%	High confidence in forecast; reorder available
Medium risk (trend-influenced, moderate history)	55–65%	35–45%	Hedge against forecast error
High risk (fashion, new trend, first season)	40–50%	50–60%	Maximize flexibility; accept possible stockout
One-time buy (import, long lead, no reorder)	100%	0%	No reorder option; commit fully but forecast conservatively

In-Season Reorder Triggers

Monitor sell-through rate weekly starting from week 2 of the season:

Sell-Through Rate = Units Sold / (Units Sold + Units On-Hand + Units On-Order)

Weeks into Season	Sell-Through vs. Plan	Action
Weeks 1–2	> 120% of plan	Issue reorder immediately for 50% of reserve allocation
Weeks 1–2	80–120% of plan	Hold; too early to confirm trend
Weeks 3–4	> 110% of plan	Issue reorder for remaining reserve
Weeks 3–4	90–110% of plan	Issue conservative reorder (25% of reserve)
Weeks 3–4	70–89% of plan	Hold all reserve; prepare markdown contingency
Weeks 3–4	< 70% of plan	Cancel any open reorders; initiate early markdown
Weeks 5+	Any pace	Reorders unlikely to arrive in time; manage with markdowns

6.2 Markdown Timing and Depth Model

The markdown decision balances margin recovery against sell-through velocity. Every week of delay costs margin because holding costs accrue and the remaining selling window shrinks.

Markdown Decision Matrix

Weeks Remaining in Season	Weeks of Supply at Current Rate	Recommended Action
> 6 weeks	< 3	No markdown; possible reorder
> 6 weeks	3–6	Hold price; monitor weekly
> 6 weeks	7–10	First markdown: 20–25% off
> 6 weeks	> 10	Aggressive markdown: 30–40% off
4–6 weeks	< 3	No markdown needed
4–6 weeks	3–6	Consider 15–20% markdown
4–6 weeks	6–10	Markdown 25–35%
4–6 weeks	> 10	Markdown 40–50%; explore liquidation
2–4 weeks	< 3	No markdown
2–4 weeks	3–6	Markdown 30–40%
2–4 weeks	> 6	Markdown 50–60%; liquidation channels
< 2 weeks	Any remaining	Final clearance 60–75% off or liquidation

Markdown Velocity Curve

After applying a markdown, monitor the velocity response:

Markdown Depth	Expected Velocity Increase	If Not Achieved Within 1 Week
20% off	1.5–2.0×	Deepen to 30%
30% off	2.0–3.0×	Deepen to 40%
40% off	3.0–4.0×	Deepen to 50% or explore liquidation
50% off	4.0–6.0×	If still not moving, this is dead stock — liquidate

6.3 Season-End Liquidation Decision

When the selling season is ending and inventory remains:

Liquidation Net Recovery = (Liquidation Price × Remaining Units) − Logistics Cost
Hold-to-Next-Season Net = (Expected Sell Price × Sell-Through Estimate) 
                          − Holding Cost − Obsolescence Risk

Liquidation is preferred when:

• Hold-to-next-season sell-through estimate < 60% (style risk, trend change)
• Holding cost for 9–12 months > 15% of original cost (typical for most retailers)
• Warehouse space is constrained and the space has higher-value alternative use
• The product is trend/fashion and will be visually dated next season

Holding is preferred when:

• Product is a classic/carryover style with minimal fashion risk
• Hold-to-next-season sell-through estimate > 80%
• Warehouse space is available at low marginal cost
• Liquidation offers are below variable cost (you'd lose money selling)

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7. New Product Introduction Forecasting

7.1 Analogous Item Selection

The quality of a new product forecast depends almost entirely on the quality of the analogous items selected. Bad analogs produce bad forecasts regardless of the method.

Selection Criteria (rank by importance)

Criterion	Weight	How to Match
Category/subcategory	25%	Must be same subcategory (e.g., "premium yogurt" not just "dairy")
Price point	20%	Within ±20% of the new item's retail price
Brand tier	15%	National brand → national brand analog; private label → private label
Pack size / format	15%	Similar unit count, size, or weight
Target demographic	10%	Same customer segment (value, mainstream, premium)
Launch season	10%	Same quarter launch; seasonal patterns differ by quarter
Distribution breadth	5%	Similar initial door count (±25%)

Analog Scoring

Score each candidate analog on the criteria above (1–5 scale per criterion, weighted). Select the top 3–5 analogs with composite scores > 3.5. If no analogs score > 3.0, the new product is truly novel — use category average with a 40% confidence band.

7.2 New Product Lifecycle Curve

Most new products follow a lifecycle curve with four phases:

Phase	Duration	Demand Pattern	Description
Introduction	Weeks 1–4	Ramp-up, often trial-driven	Initial customer trial. Demand is unpredictable.
Growth	Weeks 5–12	Accelerating, repeat purchases begin	Repeat buyers emerge. Demand becomes more predictable.
Stabilization	Weeks 13–26	Plateaus to steady state	Item finds its "run rate." Baseline forecast is reliable.
Maturity	Weeks 27+	Stable or slowly declining	Standard demand planning applies.

Forecast by phase:

Phase	Method	Confidence Band
Introduction (1–4 weeks)	Analog average × 1.1 (trial bump)	±40–50%
Growth (5–12 weeks)	Blend: 40% analog + 60% own trajectory	±25–35%
Stabilization (13–26 weeks)	80% own history, 20% analog	±15–25%
Maturity (27+ weeks)	Standard method selection per demand pattern	Standard WMAPE target

7.3 New Product Safety Stock Protocol

Weeks of History	Safety Stock Approach	Uncertainty Premium
0–4	Analog σ_d with 30% premium	1.30×
5–8	Blended σ_d (50% own + 50% analog) with 20% premium	1.20×
9–12	Blended σ_d (75% own + 25% analog) with 10% premium	1.10×
13+	Own σ_d, standard formula	1.00×

7.4 New Product Kill Decision

Not every new product succeeds. The kill decision should be structured, not emotional:

Metric	Kill Threshold	Timeframe
Sell-through vs. analog-based plan	< 30% of plan	After 6 weeks
Repeat purchase rate (if measurable)	< 10% of trial purchasers	After 8 weeks
Velocity trend	Declining for 4 consecutive weeks after introduction	After 6 weeks
Category manager assessment	"Would not re-buy"	After 8 weeks

When a kill decision is made:

1. Cancel all open POs immediately.
2. Halt any planned promotions.
3. Mark down remaining inventory at 30% off for 3 weeks, then 50% for 2 weeks.
4. Liquidate any remainder after 5 weeks.
5. Document the post-mortem: why did the analog-based forecast fail? Was it the analogs, the product, the pricing, or the competitive context?

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8. Demand Sensing and Exception Management

8.1 Real-Time Demand Signal Monitoring

In addition to periodic forecast reviews, monitor for demand signals that require immediate attention between forecast cycles:

Signal	Detection Method	Threshold	Action
POS velocity spike	Daily POS > 3× trailing 4-week daily average	3× for 2+ consecutive days	Investigate cause; manual override if sustained
POS velocity drop	Daily POS < 0.3× trailing 4-week daily average	0.3× for 3+ consecutive days	Check for phantom inventory, display removal, or competitive action
Stockout cascade	3+ locations out of stock on same SKU within 48 hours	3 locations	Emergency replenishment from DC; allocate by sales velocity
Weather alert	NWS severe weather warning for region covering > 10% of stores	Forecast impact > 5% of category volume	Adjust forecasts for weather-sensitive categories
Competitive price move	Competitor price check shows > 15% lower on comparable SKU	Confirmed at 3+ competitor locations	Alert merchandising; prepare forecast downward revision
Social media spike	Monitoring tool shows > 500% increase in brand/product mentions	Sustained > 24 hours	Assess virality risk; prepare allocation plan

8.2 Forecast Override Governance

Manual overrides are necessary but dangerous. Ungoverned overrides introduce bias and degrade forecast accuracy over time.

Override rules:

1. All overrides must be documented with a reason code and quantitative justification.
2. Override authority by magnitude:
   • ±10%: Planner can override without approval
   • ±10–25%: Requires planning manager approval
   • ±25–50%: Requires director approval

   • ±50%: Requires VP approval (or planning committee)

3. Override accuracy tracking: Every override is tracked against actuals. If a planner's overrides have a WMAPE > 40% over a quarter, their override authority is reviewed.
4. Sunset rule: Overrides expire after 4 weeks. If the condition persists, a new override (with fresh justification) must be created. This prevents stale overrides from contaminating forecasts months later.
5. No "consensus" overrides: Overrides from demand review meetings where forecasts are adjusted to match sales team wishful thinking are the #1 source of positive bias. Require every meeting override to cite a specific, verifiable external signal.

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9. Inventory Health Diagnostics

9.1 Weeks of Supply Analysis

Weeks of supply (WOS) is the primary pulse-check metric for inventory health. Compute at the SKU level, aggregate to category, and review weekly.

WOS = On-Hand Inventory (units) / Average Weekly Demand (units)

Use the forward-looking forecast for the denominator, not trailing sales. Trailing sales understates demand when items have been out of stock (you can't sell what you don't have).

WOS Health Bands:

WOS Range	Status	Action
< 2 weeks	Critical low	Expedite replenishment; consider reallocation from low-velocity locations
2–3 weeks	Low	Verify next PO arrival; place emergency order if no PO in transit
4–8 weeks	Healthy	Standard operations
9–12 weeks	Elevated	Review forecast; defer or reduce next PO if demand hasn't increased
13–26 weeks	Excess	Initiate markdown or promotional sell-through plan
> 26 weeks	Critical excess	Flag for slow-mover kill decision; markdown or liquidate

9.2 Inventory Turns and GMROI

Inventory Turns:

Annual Turns = Annual COGS / Average Inventory at Cost

Category Type	Target Turns	Benchmark
Perishable grocery	30–52	1× per week
Center-store grocery	12–20	Every 2–4 weeks
General merchandise	6–12	Every 4–8 weeks
Seasonal (in-season)	8–15	Sell through in-season
Seasonal (annual)	2–4	Lower because of off-season zero sales

GMROI (Gross Margin Return on Inventory Investment):

GMROI = Gross Margin $ / Average Inventory at Cost

A GMROI of 2.0 means you earn $2 in gross margin for every $1 invested in inventory. Minimum acceptable GMROI varies by category but should generally exceed the company's cost of capital divided by the gross margin percentage. For a retailer with 8% cost of capital and 35% gross margin, minimum GMROI = 0.08 / 0.35 = 0.23. In practice, most retailers target GMROI > 1.5 for healthy categories.

9.3 Dead Stock and Obsolescence Identification

Dead stock is inventory with zero sales for a defined period. It is the most expensive form of excess inventory because it generates zero return while consuming warehouse space and working capital.

Dead stock tiers:

Tier	Definition	Action	Timeline
Aging	Zero sales for 8–12 weeks	Review — is this seasonal? New? Misplaced?	Investigate within 1 week
Dead	Zero sales for 13–26 weeks	Markdown 40–50% or move to clearance	Initiate within 2 weeks
Obsolete	Zero sales for > 26 weeks	Liquidate at any positive recovery or donate	Execute within 4 weeks
Write-off	Liquidation/donation uneconomical	Destroy and write off; recover warehouse space	Execute within 2 weeks

Root cause analysis for dead stock:

Run quarterly. Categorize dead stock by root cause to prevent recurrence:

Root Cause	% of Dead Stock (typical)	Prevention
Over-buying (forecast too high)	35–45%	Improve forecast accuracy; tighten override governance
Product failure (quality, customer rejection)	15–20%	Faster new product kill decisions
Seasonal carryover (missed markdown window)	15–25%	Enforce markdown timing model from §6.2
Assortment change (delisted but not sold through)	10–15%	Coordinate delist timing with sell-through
Phantom inventory (system says it exists but doesn't)	5–10%	Regular cycle counts on zero-velocity items

9.4 Allocation Logic for Multi-Location Retailers

When DC inventory is insufficient to fill all store-level demand, allocate using a priority framework rather than pro-rata distribution:

Priority 1: Prevent store stockout on A-items. Allocate first to stores with < 3 days of supply on A-items. Quantity = minimum of (days-to-next-DC-shipment × daily demand) to bridge until the next allocation cycle.

Priority 2: Match allocation to store-level forecast. For remaining inventory, allocate proportional to each store's forward weekly forecast (not historical sales, which penalizes stores that have been out of stock).

Priority 3: Minimum presentation stock. Every store receives at least the minimum display quantity regardless of forecast. An empty shelf signals "this item is discontinued" to the customer and destroys demand.

Priority 4: Cap allocation to shelf capacity. Do not send more than a store can merchandise. Excess units in the backroom create shrinkage, damage, and out-of-date risk (for perishables).

Allocation frequency:

• A-items: allocate with every DC-to-store shipment (typically 2–5× per week)
• B-items: allocate 1–2× per week
• C-items: allocate weekly or bi-weekly