Where CubeRight stands
Seven criteria decide this. Almost every product in the market is built to win one of them.
Scored 0–5 on the two axes of the problem — when the cube number exists, and what it is allowed to drive — plus the question every buyer asks and no capability chart shows. The dashed outline starts as the best score anyone achieves on each axis, combined into one shape that no vendor actually sells. Pick a real category below and the gap widens, not narrows — which is why that composite is the default.
Read the shape, not the scores.
Coverage is the claim. The dashed line is not a product — it is the best score anyone manages on each axis, and the colored ring shows which category holds it. Five different categories appear on that line, which is the finding: a specialist wins its axis because it gave up the others. An engine that scores well on cost awareness prices the box at the pack bench; one that installs in an afternoon has no downstream surface to install into.
Cost awareness is where we are level with the field, not ahead of it. A cartonization API built around carrier rate tables is genuinely good here — it is the closest anyone gets to us on this axis, and the two lines meet at 4. Neither of us is pricing the landed cost of a whole load; we are pricing more of it than a box chooser can, and we are not going to claim more than that.
Data foundation is the widest gap — 5 against 2. Every competing product assumes the item master is correct. Dimensional provenance, per-SKU confidence and a predicted-versus-actual reconciliation loop are the only assets in this category that compound with time, and they cannot be bought in.
Time to value is the one axis we lose, and it is held by the early-cubing specialists at 5 against our 4. Rate cards, routing guides and receiver height caps are real configuration, and standing CubeRight up in full is a project — we would rather you heard that from us than from your integrator. What keeps the gap to one point is that the first result waits for none of it: it comes off an order file, before any integration exists at all, and it is a measured delta on your own book rather than a box choice. One loss on a seven-axis chart is not an oversight. It is the axis we have not bought yet, and we would rather show you which one it is.
| Criterion | CubeRight | Early-cubing specialists |
Cartonization APIs |
Load-optimization engines |
Supply-chain platforms |
WMS / ERP modules |
|---|---|---|---|---|---|---|
| Temporal position when the cube number exists | 5 | 5 | 3 | 3 | 2 | 1 |
| Geometric depth carton → pallet → vehicle | 5 | 4 | 3 | 5 | 5 | 3 |
| Cost awareness landed cost, not a proxy | 4 | 3 | 4 | 1 | 3 | 1 |
| Shape coverage round, irregular, conformable | 5 | 2 | 2 | 4 | 3 | 1 |
| Downstream decision surface class, appointment, ASN, procurement | 4 | 1 | 1 | 1 | 4 | 3 |
| Data foundation provenance, confidence, reconciliation | 5 | 2 | 1 | 1 | 2 | 2 |
| Time to value first measured result, without replacing anything | 4 | 5 | 4 | 3 | 1 | 1 |
| Composite / 10 | 8.9 | 7.2 | 5.8 | 5.4 | 4.8 | 3.1 |
↔ Scroll the table to see every category
Composite = 0.65 × completeness (mean of criteria 1–6) + 0.35 × implementation ease, scaled to 10. Categories are scored on published capability across the products we surveyed in each group. The dashed line on the chart is the fairest test we know: the best score any category achieves on each axis, combined into one shape. No vendor is close to it — it is five different products' best days stitched together — and CubeRight meets or exceeds it on six of the seven axes, 8.9 against 8.7. Beating a shape nobody sells is a coverage claim rather than a superiority claim, and we state it that way on purpose: against any category you can actually buy, the gap is between 1.7 and 5.8 points. Where an axis is a tie, the ring is attributed to one holder: temporal position between the early-cubing and cartonization-API categories, cost awareness between the API category and us at 4, geometric depth between load-optimization engines and supply-chain platforms, and data foundation three ways at 2. This is our own scoring methodology and we publish the formula so you can disagree with it precisely rather than generally.