What’s the best way to quantify the cost of manual exceptions in order-to-cash?

[Savannah78]

I’ve been mapping our order-to-cash cycle and hit a wall trying to quantify the actual cost of each manual exception. We track the time spent, but I’m struggling to assign a true dollar value to the delays and rework, especially when it causes a ripple effect into other departments. How have others approached this specific measurement?

[Andrew_W]

We started with ABC. Mapped each exception to drivers like data entry, validation, approvals. Put an hourly rate on the people handling it, then tried to capture the ripple in downstream teams by counting SLA breaches and extra handoffs. It helped orient leadership, but it took weeks to keep the model fresh and it still felt rough.

[Savannah.T]

We track delays as opportunity value. Estimate revenue-at-risk per hour of delay using average order value and typical margins, then factor probability of downstream penalties. Not perfect, but it gave a sense of which exception types sting most. We still argue about what counts as downstream though.

[EvelynC]

Do you actually need the precise dollar figure, or would a clear bottleneck metric tied to cycle time serve the business better? I ask because sometimes the measurement feels like the tail wagging the dog.

[Michael.M]

We tried a shadow-labor approach: allocate an hourly rate and assign it to each exception; pulled data from finance monthly; found the heavy part was rework from data quality, with smaller chunks from approvals.

[Paisley.L]

We standardized with cross-functional daily standups and a shared tracker. That reduced the ripple somewhat, and the team could see which exception patterns repeated. Still hard to get a stable value across teams.

[Luke45]

Time to resolve matters too. Sometimes measuring how fast we clear an exception is more actionable than pinning a monetary number on it. If we shrink cycle time, the money impact tends to shrink too, but the numbers stay noisy and inconsistent.