Set № 2026-03 61 702 pieces Ages 8–99 Build time · 72 h
Building
Instructions
for the Order Clock — a part-level demand engine that turns $10.4 B of toy revenue into 64 billion brick-level order signals, and shows where the shortages hide.
An independent case study on the real LEGO® catalogue · not affiliated with the LEGO Group
Sort the pieces
A brick is never
just a brick.
The world’s best-known toy is also one of its hardest supply-chain problems. Behind every boxed set sits a bill of materials that crosses eight very different procurement worlds — from commodity ABS pellets on a 4-week lead, to custom injection molds that lock design decisions 18 weeks out, to semiconductors and sensors that arrive 24 weeks after you order them.
Expansion into electronics, licensed collaborations and limited runs made the mix heavier at exactly the risky end. Leadership could see what was selling — but not when to act, or where the next shortage would materialise.
Figures computed from the model — Rebrickable part catalogue, LEGO annual-report revenue, and the tool’s lead-time parameters.
Read the clock backwards
December is decided
in June.
“We don’t ask what sells in December — we ask what needs to happen in June so December works.”
This is the Order Clock — the engine’s central move, a time-axis reframe: every chart runs on order dates, not sales dates. Take a category’s demand, shift it back by its lead time, and the December peak lands on the month you actually have to act.
Slide the delivery month — watch when each category’s order deadline falls.
From revenue to bricks
No one publishes unit sales.
So build them.
The LEGO Group reports revenue — DKK 74.3 B in 2024 — but never per-set volumes. The engine anchors on that public number and cascades it down through the real 26,350-set catalogue: proportional allocation to themes, inverse price elasticity within a theme, empirical seasonality by month, then a bill-of-materials explosion into eight procurement categories. Pick a stage; the arithmetic is right there.
Revenue: LEGO Group annual reports (DKK × 0.14). Catalogue: Rebrickable, CC0. Unit figures are modeled estimates, not reported actuals.
Find the red bricks
Shortages announce
themselves — early.
Every cell below is a month’s order-basis demand divided by its own trailing nine-month average. A ratio drifting past 2.2× means suppliers are being asked to produce far beyond their recent run-rate — the signature of a shortage, visible months before it lands. Flip the scenario and watch the board light up.
Monthly parts to order, by category · billions of parts · order month, lead-time shifted
Order-pressure heatmap · month demand ÷ rolling 9-month average
View the numbers as a table
Electronic components
637 unique parts — every one flagged at-risk by the model. At a 24-week lead, one disruption ripples through 4–6 months of production.
Custom injection molds
14,611 of 24,352 unique parts flagged at-risk. An 18-week lead means late design changes cascade straight into launch delays.
ABS plastic pellets
19,589 unique parts, only 10% flagged at-risk. A 4-week commodity lead — the calm end of the portfolio.
Risk scores are the model’s 0–100 weighted exposure index (lead time, part uniqueness, sourcing depth, volume) — modeled, not reported actuals. Heatmap ratios use a trailing 9-month baseline with 1.2/1.6/2.2× bins — the deck’s documented method; the sample tool ships a looser 12-month variant.
When the internet buys it all
One viral video is
a supply-chain event.
A set catches fire on TikTok and Q4 demand for its whole theme runs at 2.5× — with a 1.3× halo the rest of the year. The model treats virality as a first-class scenario: pick a theme, flip the switch, and the surge propagates through the BOM into every one of its procurement categories.
Monthly units, 2024–25 · thousands of units
Drill down — explode one set
BOM explosion · real Rebrickable bills of materials
Reinforce the base
$45M, aimed by
the model.
The point of demand visibility is not the dashboard — it’s the capital-allocation conversation it makes possible. The model flags 18 category-months of elevated order pressure in the coming 18-month window, and prices the exposure behind them: $2.1B of annual revenue flows through sets that need at least one electronic component, $8.7B through sets that need a custom mold. A single 24-week electronics disruption puts ~$970M of a season at risk. Each investment is sized as an insurance premium against that exposure — and the exposure moves with demand: flip one theme viral in the scenario above and the Q4 season grows from $4.2B to $4.6B.
Exposure = 2024 revenue of sets requiring ≥1 part from the category (real Rebrickable BOMs × modeled unit sales) × the category’s lead time as a fraction of the year. Investment amounts are the proposal’s planning figures, sized against that exposure.
The next set
Where margin
lives next.
Demand visibility prices the risk — the natural sequel prices the portfolio. 86% of unique part numbers appear in five sets or fewer, yet parts used in 500+ sets carry 78% of 2024 piece volume. Cost concentrates top-left, volume bottom-right — and the question a part-level cost model unlocks: which bespoke parts justify their tooling through set-level revenue, and which should be redesigned onto universal geometry?
Part cost vs reusability · all 4,738 parts shipped in 2024
Reusability and 2024 volume are real (Rebrickable catalogue × modeled unit sales). Cost is a modeled index — material base + tooling amortized over 2024 volume — not supplier pricing. Click a legend chip to isolate categories.
How this was built
Seventy-two hours,
start to finish.
- Day 1
A sketch and a spreadsheet
Hand-drawn dashboard mockup, then a working Excel concept: four quadrants, spike heatmap, Pareto — the whole idea at toy scale.
- Day 2
Real data, real engine
Rebrickable’s open catalogue into SQLite — 61,702 parts, 26,350 sets, 1.48M BOM rows — plus the revenue-anchored engine and the Order Clock’s lead-time shift, vectorised to run in a quarter of a second.
- Day 3
Deck and dashboard, shipped
A 15-slide executive narrative and the interactive platform — scenario modeling, drill-downs, BOM explosion — delivered as a consulting-interview case study. Interview the next morning.
About this study
Designed and built by Marios Z. Dardas — analytics & AI-solutions leader; formerly a global management consultancy, working with Fortune 500 and public-sector clients.
This is an independent, illustrative case study prepared for a consulting interview. It is not affiliated with, endorsed by, or sourced from the LEGO Group. Part and set data come from the Rebrickable open database (CC0); revenue anchors from published LEGO Group annual reports. All unit-level demand figures, supplier attributes, risk scores and investment figures are modeled estimates or illustrative constructs — not reported actuals. LEGO® is a trademark of the LEGO Group, which does not sponsor or authorise this study.