CostCtrl Demo: Logistics 3PL Profitability Dashboard

One dataset. Three answers about where you make money.

Everything below is computed live from the embedded operational data of a fictional EUR 38M third-party logistics operator: shipments, departures, dock minutes and fleet capacity. Change the filters above and every engine recalculates.

Margin cascade: revenue to EBIT (TDABC view)

Where 100 cents of revenue go. Unused capacity is shown as its own block, not buried in product costs.

Practical capacity set at 85% of theoretical, following Kaplan and Anderson's TDABC convention, so product costs are not inflated by the cost of idle resources.

The method-divergence problem

Same plant, same ledger, different truths.

Monthly trend: revenue, TDABC profit and capacity utilisation

Seasonality is real: an August freight trough and a November-December e-commerce peak. Fleet and warehouse cost, though, is fixed every month.

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

The plain-language version.

Traditional: one blended cost per shipment: total operating cost divided by total shipments handled; SG&A is spread as a flat % of revenue. Complexity is invisible: a full truckload with 4 hours of driving "costs" the same as a parcel picked in 3 minutes.

ABC: cost pools with drivers (per handling minute, per departure, per delivery minute, per booking line). Better, but every driver is an average: dock wait and failed deliveries are spread across everyone, and all capacity cost is pushed onto shipments.

TDABC: each resource group gets a capacity cost rate in EUR per minute; time equations estimate the minutes each shipment, departure and booking actually consumes, including wait at client docks and redelivery after failed attempts. Unused fleet and warehouse capacity is isolated instead of allocated.

Two real time equations from this model

This is what replaces allocation keys. Every coefficient is minutes.

line_haul_min = driving_min × shipments (0.9 to 210 by service line)
+ (15 + client_dock_wait) × departures (wait 10 to 75 min by client)
+ (15 + client_dock_wait) × extra_departures if urgent

last_mile_min = 4.0..9.0 × shipments (by service line)
+ 15..30 × route_staging per departure
+ 11 × failed_first_attempts (redelivery)
+ 4 × failed_first_attempts (reschedule, customer service)

Rates used here: Warehouse & cross-dock 0.76 EUR/min, Line-haul & fleet 0.83, Last-mile 0.61, Customer service 0.98, Billing & claims 0.79 (resource cost / practical capacity minutes).

Cost and margin by engine

Unit cost and margin under each engine. Arrows show rank moves from Traditional to TDABC profitability.

Traditional margin vs TDABC margin

On the diagonal, the methods agree. Off it, your price list was negotiated on fiction. Points below the line are subsidised by the ones above it.

Why they differ: Traditional sees only a blended cost per shipment, so minute-heavy work (truckloads, reefer, long dock waits, failed home deliveries) is subsidised by parcels and pallets that barely touch the network. ABC catches departure and handling effects but averages away wait time and redelivery. TDABC prices the minutes.

Under the hood: the full model card

Every parameter behind the three engines. In an engagement these come from your GL, payroll, TMS shipment file and WMS extracts.

Capacity cost rate = resource cost / practical minutes (85% of theoretical, Kaplan & Anderson). Hub dock turns: 10 to 55 min by service line; urgent bookings break the consolidation plan and trigger extra part-loaded departures (0.8 per urgent booking, spread over the client's mix), which is why chaos is expensive and invisible to a blended cost per shipment.

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)

Hover a point for its P&L. Click to pin the card and see the three Kaplan levers for the tail.

Detail: select a point on the curve

Full TDABC P&L of the hovered or pinned item.

Hover or click any point on the whale curve.

Fixing the tail: the three levers

Kaplan's playbook for loss-making customers. Firing them is the last resort: their volume still absorbs capacity cost.

1. Process lever

Reduce the minutes: EDI or portal bookings instead of phone-and-email, consolidation to fuller departures, booked dock slots at chronic-wait clients, address quality and PUDO options on home delivery.

2. Relationship lever

Renegotiate behaviour: a free-time window at the dock and discipline beyond it, delivery windows agreed with e-commerce shippers, forecast sharing before peak. Most clients change when shown their cost-to-serve.

3. Menu-based pricing lever

Price the menu, not the average: detention per started half hour, redelivery fee, urgent-booking surcharge, manual-billing fee, customs documentation fee. Clients 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 department, following the CAM-I capacity model: theoretical, practical, used, idle.

CAM-I capacity bridge by department

Theoretical minutes, the 15% practical haircut (breaks, maintenance, training), productive use, and paid-for idle time.

Only 3 of 63 companies in the IMA capacity study measured the cost of unused capacity. Everyone else pays for it and then hides it inside product costs.

Utilisation of practical capacity by month

Warehouse and last-mile tighten in the November-December e-commerce peak; support functions carry structural slack all year.

EUR of idle capacity, by resource group and month

Resource cost is fixed monthly; idle EUR = (practical minutes - used minutes) x capacity cost rate. Darker cells burn more cash.

Decision menu for idle capacity: sell it (backhauls, spot capacity, overflow for other shippers), shift maintenance and training into it, defer the next tractor or van purchase, or accept it as growth headroom, but price services as if it did not exist.

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.

Identified in this model

EUR 0

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.

Try these

How this works: this assistant computes answers from the demo model embedded in this page. It is deterministic, not a live AI. In an engagement, the same questions run against your data.

Overview

One dataset. Three answers about where you make money.

Margin cascade: revenue to EBIT (TDABC view)

The method-divergence problem

Monthly trend: revenue, TDABC profit and capacity utilisation

Traditional vs ABC vs TDABC: who is lying to you?

How each engine thinks

Two real time equations from this model

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.

Cumulative profit curve (TDABC)

Detail: select a point on the curve

Fixing the tail: the three levers

1. Process lever

2. Relationship lever

3. Menu-based pricing lever

Unused capacity: the cost line almost nobody measures.

CAM-I capacity bridge by department

Utilisation of practical capacity by month

EUR of idle capacity, by resource group and month

What a consultant would circle in red.

Ask the model. It computes, it does not improvise.

Try these

See this with your data in 3 to 6 weeks