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Planogram Lab

Retail & Category Management

Allocate physical shelf facings across eye-level, reach, and bottom zones while balancing case-pack holding capacity, restock intervals, and historical placement bias.

👨‍🏫 Professor Mode: Guided Learning Experience

New to retail category management? Professor Mode walks you through shelf physics, days of supply, and how to audit historical sales data for placement bias.

OVERVIEW & WHAT THIS TOOL SOLVES

🛒 NEW TO RETAIL ANALYTICS?

What Is a Planogram? (A 60-Second Visual Guide)

A planogram is the visual shelf map that retail stores use to decide which products go on which shelf and how many columns wide each product gets. Every convenience store cooler, grocery aisle, and pet supply rack is built from a planogram to balance customer visibility against keeping items in stock between delivery trucks.

↔️ 1. Horizontal Facings (Width)

A facing is one front-facing column of a product visible to shoppers. Giving an item 2 or 3 facings makes it easier to spot and holds more inventory, but uses up shelf width that could hold another product.

↕️ 2. Vertical Shelf Zones (Height)

Shoppers look at Eye Level (1.25× sales boost) and Reach Level (1.00×) first. Products placed on the Top (0.85×) or Bottom (0.75×) shelves get less natural attention.

📦 3. Depth & Days of Supply

Each front column has a deep lane of cans behind it (e.g., 10 cans deep). A 2-facing block holds 2 × 10 = 20 cans. If those cans sell out before the next delivery truck arrives (every 4 days), the shelf sits empty and loses sales!

Retail category managers rarely see true customer demand—they only see last year's cash register scans, which are distorted by where items sat on the shelf and how often they ran out of stock between delivery trucks. Planogram Lab lets you rearrange a real-scale 2D cooler or store aisle, fix stockout bottlenecks with a live health coach, and test your shelf across a simulated 13-week quarter.

🎯 Questions It Answers

  • Which products on the shelf will run empty during a 4-day or 7-day gap between delivery trucks—or can't even fit one full warehouse delivery box?
  • Did last year's #1 brand sell well because shoppers truly prefer it, or because it sat on the best eye-level shelves all year?
  • Should you accept a supplier's $900 cash slotting check in exchange for giving their brand half the cooler?

📂 What Data You Need

  • Shelf Fixture Dimensions: Cooler or aisle width and depth (in inches), shelf heights, and vertical zones (top, eye, reach, bottom)
  • Product List (SKUs): Package width, height, depth, warehouse case-pack size, retail price, and wholesale cost
  • 52-Week Sales History: Weekly units sold, facings, shelf zone, days in stock (out of 7), price, and promo weeks

💡 What You'll Discover

  • Live Stockout Traffic Lights: Instant 🟢 Green / 🟡 Yellow / 🔴 Red warnings on every product block as you edit the shelf
  • Hidden "Stockout Sleepers": Popular items stuck on the bottom shelf with 1 facing that sold out every week—hiding 40%+ of their true demand
  • Walkout Loyalty Traps: Niche items whose loyal buyers walk out of the store empty-handed if you remove them
🔍 Key Nuances & Common Traps to Watch For
  • The Case-Pack Trap: Even if a slow seller has 6 days of supply, if its shelf capacity is 10 cans and the distributor delivers in 12-can cases, a restock clerk cannot fit a full case on the shelf—creating backroom clutter and skipped restocks.
  • Stockout Censoring Hides Demand: A SKU that sells out every Monday morning before a Thursday restock records zero sales for 2–3 days each week. Naive weekly averages treat those zero-sale days as low customer interest.
  • Endogenous Placement Confounding: When last year's category manager gave the #1 brand four facings at eye level, its historical sales reflect both base appeal and a +25% eye-level visibility boost.
🤔 Try this: Load Cooler Wars (Quad Mart), inspect PL-02 (Plainleaf Zero Berry) on bottom shelf S5 at 1 facing, and check why its stockout light is Red even before you look at its 52-week stockout log.
📐 Under the Hood: Model Equations & Variable Definitions

1. Physical Shelf Capacity & Days of Supply (DOS)

\[ \text{capacity}_i = \text{facings}_i \times \text{stack}_i \times \left\lfloor \frac{\text{shelf depth}}{d_i} \right\rfloor, \qquad \text{DOS}_i = \frac{\text{capacity}_i}{\bar{u}_i / 7} \]

💬 Read it in plain English: A placed block's total unit capacity equals its horizontal facings times vertical stack height times how many packages fit front-to-back in the fixture depth; dividing capacity by the daily sales rate (\(\bar{u}_i / 7\)) gives how many days a full shelf lasts before running empty.

facingsi, stacki
Horizontal Facings & StackYOUR DATA ⓘ
Number of front-facing columns and vertically stacked units for SKU i on its assigned shelf.
⌊depth / di⌋
Units Deep per FacingOUTPUT ⓘ
Whole number of packages of depth d_i (in inches) that fit front-to-back on the shelf.
ūi
Historical Mean Weekly UnitsESTIMATED ⓘ
Average weekly units sold in historical POS data (or launch estimate when isNew = true).
DOSi
Days of SupplyINFERENCE ⓘ
Days a full shelf of SKU i lasts at average daily velocity; Green ≥ 1.5× restock gap, Amber ≥ 1.0×, Red < 1.0× (or capacity < casePack).

2. Space & Zone Demand Response

\[ D_{i,t} = \frac{\text{base}_i}{7} \times \text{season}_{w(t)} \times \left(\frac{\text{facings}_i}{\text{refFacings}}\right)^{\beta_i} \times \text{zoneMult}_{z(i)} \times \varepsilon_{i,t} \]

💬 Read it in plain English: Daily customer demand for SKU i scales with diminishing returns as facings increase (governed by space elasticity \(\beta_i\)) and shifts multiplicatively with vertical shelf zone (\(z(i)\)) and weekly seasonality.

βi
Space ElasticityESTIMATED ⓘ
Exponent measuring % demand lift per 1% increase in facings (typically 0.03–0.30 across retail categories).
zoneMultz(i)
Vertical Zone MultiplierYOUR DATA ⓘ
Relative attention multiplier for eye level (e.g., 1.25×), reach (1.00×), top (0.85×), and bottom (0.75×) shelves.

Step 1 DATA SOURCE

Load a pre-built retail category management case study—starting with Cooler Wars at Halbrook State's Quad Mart—or upload your own SKU assortment and 52-week POS history CSVs.

Use a Case Study

Upload Your Data

Drag & Drop weekly sales history CSV (.csv, .tsv, .txt)

Columns: week, sku, units, facings, zone, daysInStock, price, promo (continuous counts/prices + categorical SKU/zone)

No file uploaded.

Step 2 2D SHELF BUILDER & LIVE VISUAL DIAGNOSTICS

How to edit the shelf: Click + or − on any product block to add or remove columns (facings), drag products between shelves, or click a product in the tray and click a shelf to place it. Watch the traffic lights and the Live Shelf Health Coach below update in real time!

1
Try Your Intuition First: Tweak the shelf below how you think it should look, then click 📌 Lock "Your Gut" Plan to save your starting baseline.
2
Fix Red Warnings & Check Clues: Use the 🩺 Shelf Health Coach below to fix Red stockout items, then check the 3 Clues in Step 3.
3
Run the 13-Week Quarter: Click ▶ Run the 13-Week Quarter Simulation in Step 4 to see how your final shelf scores!
1. Products on Shelf (Listed vs. Tray)
17 / 18
1 delisted (VF-06 New Launch)
2. Shelf Width Used (Inches & %)
96.6 / 120.0 in
8.05 of 10.00 linear ft (80.5%)
3. Total Cans / Bags on Shelf
381 units
38 total facings across 5 shelves
4. Stockout & Case-Pack Health
11 Green · 2 Amber · 4 Red
Restock gap: 4 days (Mon & Thu)
UNSAVED GUT PLAN Why lock "Your Gut"? Save your first intuitive layout now so Step 4 can compare your starting intuition against your data-driven final shelf!
🔍 How to Read a Shelf Block: − 2f + = 2 Facings Wide (click +/− to resize) ×10 deep = 10 Cans Front-to-Back (2f × 10 = 20 cans) 🟢 6.5d = Safe (Lasts ≥6 days & fits 1 full box) 🟡 4.8d = Tight (4–6 days supply) 🔴 <1 case / 3.2d = Fix Me! Runs empty (<4d) or can't fit 1 delivery case
QUAD MART 1-DOOR COOLER (24" W × 26" D)
GRID: 0.5" SNAP 36°F LED
Drag Drop Cues: Green = Insert in Open Space Blue = Neighbor Slides Over Gold = Swap Two Products Orange = Replace to Tray

📊 Live Visual Shelf Charts: Profit per Foot ($/ft) & Stockout Runway (Days of Supply)

🟢 Safe Buffer · 🟡 Tight · 🔴 Runs Empty or <1 Case Pack

These two visual charts update every time you change the shelf. Left Chart: Which products generate the most weekly gross profit per foot of shelf space. Right Chart: Which products will run out of stock before the delivery truck arrives (or can't fit a full delivery case)—all 🔴 Red items stop in the Red zone on the left!

📈 View Retail Finance Ratios (Sales/ft, Profit/ft, GMROI) & Brand Fair-Share Chart Category KPIs & Brand Balance

Based on last year's sales at last year's placement.

What are projected weekly category sales?
$758.80 / wk
13-wk quarter projection: $9,864.44
What is category sales per linear foot?
$94.26 / ft
$75.88 / ft across 10.00 available ft
What is category gross profit per linear foot?
$40.48 / ft
Gross margin: $325.90 / wk ($4,236.69 / qtr)
What is the category GMROI?
51.3x
Annual margin ÷ $330.12 avg shelf inv at cost

Brand Fair-Share Index (Brand Share of Sales ÷ Brand Share of Shelf Width)

1.00 line = Balanced · > 1.08 Under-Spaced (Needs More Space) · < 0.92 Over-Spaced (Has Too Much Space)
📋 View Full 14-Column Physical Capacity, Productivity & Days-of-Supply Schedule Table Detailed SKU Table & Copy
Shelf / Zone SKU & Brand Pos X (in) Facings × Stack Width (in / ft) Depth ⌊D/d⌋ Capacity Case Pack Hist. Avg/Wk (ū) Sales/ft ($) Profit/ft ($) GMROI DOS (Days) Stockout Risk Light

Step 3 FIND THE HIDDEN CLUES IN LAST YEAR'S SALES DATA

100% Computed from Historical Log — Zero Hidden Model Leakage

Why shouldn't you just give all your shelf space to last year's #1 seller? Because last year's sales data has three hidden traps! Read the 30-Second Cheat Sheet below, or click any of the 5 visual tabs to explore the evidence before running the quarter simulation.

1. How Much Do Extra Facings Really Boost Sales? (Raw Illusion vs. True Lift)

The Data Trap: In raw store sales, popular brands already sat at Eye Level (`1.25×` boost) with 3–4 facings all year—making extra facings look twice as powerful as they really are! Once we separate out the Eye-Level boost and promotions, you can see the true diminishing returns of adding a 2nd, 3rd, or 4th facing.

Step 4 SIMULATE A 13-WEEK QUARTER & SCORE YOUR PLANOGRAM

Official Quarter Seed: 20261107 · 50-Seed Robustness Range

Ready to test your shelf? Choose your supplier deal stance below and click ▶ Run the 13-Week Quarter Simulation. We will simulate 91 days of customer shopping, delivery trucks, stockouts, and walkouts to score Your Final Plan against Your Gut, Last Year's Plan, an Automated Naive Optimizer, the Supplier Deal, and the Best Possible Benchmark.

⚙️ Advanced Simulation & Optimizer Settings (Automated Greedy Optimizers, RNG Seed & Literature Toggles) For Power Users & Instructors

🤖 Automated Greedy Marginal Optimizer Previews

Want to see how an algorithm packs the shelf? Preview the Naive Optimizer (fooled by eye-level history and stockouts) vs. the Controlled-β Optimizer (de-biased elasticity + stockout buffers).

🎲 Simulation Random Number Seed (CRN)

All plans are evaluated on identical daily customer arrivals (Common Random Numbers) plus a 50-seed robustness check. Change the official seed to test different demand draws.

📚 Published Retail Marketing Literature Toggles (6 Empirical Findings: Optimizer Beliefs vs. True Simulation World) 6 Academic Toggles

Toggle six published retail marketing findings as Optimizer Beliefs (what the greedy algorithm assumes when recommending a shelf) and as Simulation World (Truth) (what the 91-day simulator actually enforces).

Step 5 THE WRITE-UP: CAPACITY, BIAS & MERCHANDISING SUMMARY

EXPLORATORY QUESTIONS

Use the 2D cooler canvas, the five pre-commit diagnostic tools, and the 13-week quarter simulator above to test these merchandising questions:

  1. Where is the hidden stockout bottleneck? Look at PL-02 (Plainleaf Zero Berry) on bottom shelf S5 in last year's plan. At 1 facing (\(11\text{ cans}\) capacity) and a historical average of \(\sim 10.2\text{ units/week}\), what is its Days of Supply? Open the 52-Week History Viewer (Tab 4) and ABC / Pareto (Tab 3): why did it stock out almost every Monday and Tuesday, and what is its unconstrained weekly velocity?
  2. Why is Voltfang's naive space elasticity (\(\hat{\beta}\)) more than double its controlled \(\hat{\beta}\)? Open the Space Elasticity Estimator (Tab 1). Compare Naive Pooled OLS against Controlled Fixed-Effects OLS across brands. How much of Voltfang's historical sales per linear foot came from sitting on eye-level shelves S2 and S3 all year rather than from marginal facings?
  3. Should you take Voltfang's $900 Full Deal, the $450 Counteroffer, or build an independent plan? Open the Supplier Slotting Deal Calculator (Tab 5) and switch between Naive \(\hat{\beta}\) and Controlled \(\hat{\beta}\), then run the 13-Week Quarter Simulation (Step 6). How much category gross margin and walkout basket profit does the $900 Full Deal destroy when Plainleaf is crushed on S5?