One dataset. Three answers about where you make money.
Everything below is computed live from the embedded operational data of a fictional EUR 63M wholesale distributor: drops, order lines, cases, minutes and capacity. Change the filters above and every engine recalculates.
Margin cascade: revenue to EBIT (TDABC view)
The method-divergence problem
Monthly trend: revenue, TDABC profit and transport utilisation
Traditional vs ABC vs TDABC: who is lying to you?
All three engines allocate the same operating cost pool. They just disagree, sometimes by 30 to 46%, about who caused it. Research on costing-system distortion reports errors of this magnitude on the extremes (IJISR).
How each engine thinks
Traditional: one blanket overhead percentage on revenue absorbs all operating cost; SG&A is spread the same way. Behaviour is invisible: a 190 EUR urban drop with a return pickup "costs" the same share of its invoice as a 3,000 EUR pallet delivery to a chain DC.
ABC: cost pools with drivers (per line picked, per drop, per order, per return, per visit). Better, but every driver is an average, and all capacity cost is pushed onto customers, so idle time silently inflates every rate.
TDABC: each resource group gets a capacity cost rate in EUR per minute; time equations estimate the minutes each order, drop, line and return actually consumes. Unused capacity is isolated instead of allocated.
Two real time equations from this model
+ 1.6 × order_lines
+ 0.11 × cases × bulk_factor (0.80 to 2.18 by category)
+ 3 if special_pack, per line
+ 12..60 by distance band A..D
+ 15 if urban_access_window
+ 25 if return_pickup
Cost and margin by engine
Traditional margin vs TDABC margin
Under the hood: the full model card
The whale curve: a few relationships carry everyone else.
Sort customers from most to least profitable (TDABC) and accumulate. The curve climbs far above 100%, then the tail gives it back. Kaplan's Kanthal case found 225% at the peak; HBS research typically finds the top 20% of customers generate 150 to 300% of profits while the bottom tail destroys 50 to 200%.
Cumulative profit curve (TDABC)
Detail: select a point on the curve
Hover or click any point on the whale curve.
Fixing the tail: the three levers
1. Process lever
Reduce the minutes: order portals instead of phone calls, consolidated delivery days per postcode, route resequencing, pre-agreed return windows instead of ad hoc pickups.
2. Relationship lever
Renegotiate behaviour: minimum drop values, two delivery days a week instead of daily top-ups, forecast sharing, returns discipline. Most customers change when shown their cost-to-serve.
3. Menu-based pricing lever
Price the menu, not the average: delivery fee below a minimum drop, urban window surcharge, returns handling fee, special-pack fee. Customers self-select and the cross-subsidy stops.
Unused capacity: the cost line almost nobody measures.
An IMA field study found only 3 of 63 companies measured the cost of unused capacity. TDABC makes it a standing line item per resource group, following the CAM-I capacity model: theoretical, practical, used, idle.
CAM-I capacity bridge by resource group
Utilisation of practical capacity by month
EUR of idle capacity, by resource group and month
What a consultant would circle in red.
Nine findings, ranked by annual EUR impact, each computed live from this model. Expand any card for the calculation trace, the lever and the first action.
Sum of the nine sized opportunities below. Not all are additive and not all are fully capturable; even the conservative half typically funds the costing programme many times over.
Ask the model. It computes, it does not improvise.
Type a question about profitability, methods, capacity or what-ifs. Answers are calculated live from the demo dataset on this page.
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See this with your data in 3 to 6 weeks
A CostCtrl pilot loads your ledger, routes, payroll and order lines into the same engines: time equations, whale curve, drop-size economics, unused capacity and all. No 6-month ABC project.