Three On-Time Delivery Numbers in the Same Monthly Pack
Metro Supply Co. (industrial distributor, 41,000 orders per year) reports three on-time delivery figures in the same monthly pack: 94% (orders shipped by the date entered at order entry), 78% (shipped by the customer's original requested date), and 61% (delivered to the customer by the date they asked for). Sales commissions are tied to the 94% figure. The customer survey ranks delivery as the worst attribute for the fourth consecutive quarter. The VP of Sales says the 61% 'includes customers who changed their dates.' The ERP stores only the commit date; requested dates live in free-text order-entry notes, and 12% of orders have no requested date recorded at all.
1. Which of the three numbers is the real metric, and how do you operationalize it so it can be defended to Sales and to customers?
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The real metric is customer-perceived delivery against the date the customer originally asked for - the 61% is closest to truth, but it is not yet a number anyone should be measured on, because it is uncontrolled.
Operational definition, written before any target is set:
- Start/stop: clock starts at the customer's original requested date (or the agreed date at order acknowledgement, whichever the customer specified); stops at confirmed receipt at the customer's dock.
- Tolerance: zero days late for top-tier accounts; a documented window for accounts under contract terms. The window must be contractual, not convenient.
- Exclusions, with an audit trail: customer-requested date changes, customer-caused holds, carrier force majeure. Each exclusion needs a coded reason in the system and a monthly audit - 'the customer changed it' is a claim, not a data point.
- The 12% missing requested date is a process defect, not a caveat. Make requested date a required field at order entry, and meanwhile run a 200-order audit to bound the true rate rather than let a quarter of the population vanish.
Then build a hierarchy: primary external metric = OTD to original request; internal process metrics = ship-by-commit adherence and order-to-ship cycle time. The 94% becomes an internal measure of promise-keeping, which is useful but is not the customer's experience.
2. Sales is paid on the 94% metric. How do you change the measurement without creating a fight or a payout shock?
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Treat this as a change-management problem with a financial model attached.
- Quantify before proposing. Model the commission impact of moving to the 61% basis at current performance. If payouts would drop 20%, an overnight switch guarantees sabotage of the new metric and of the data-capture fix.
- Transition period with a floor. Move to the new definition on a published date, with a hold-harmless floor for one or two quarters so no one loses pay because the metric changed.
- Two-metric scorecard during transition. Keep a commit-date adherence measure (process accountability) alongside OTD-to-request (customer outcome). Drop the loose 94% only after the new measure is stable.
- Add a counter-metric: date-change rate. If the 61% improves because date changes spike, you will see it. Every metric needs a partner metric that makes gaming visible.
- Close the data gap in parallel. Required field at order entry plus reason codes; otherwise the new metric is arguable every month.
Balance with quality and cost measures so delivery is not optimized by expediting freight - check freight cost per order in the same review.
3. The leadership team wants a target of 95% OTD by year end. How do you respond?
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A target should be derived, not declared, and 95% from 61% in twelve months is a number that will be missed and quietly abandoned.
Build the target from evidence:
- Best-in-class internal comparison: the best-performing branch or region, and what its process does differently. If the best branch is at 88%, 95% is not a stretch goal, it is fiction.
- Entitlement: what the process could deliver with known fixes - required-field capture, commit-date discipline, pick-pack cycle-time reduction. Run a quick gap analysis to estimate attainable performance.
- External benchmark from industry data or a comparable distributor, adjusted for order profile.
Then set a staged path: Year 1 target from the entitlement study (realistically 78–84% depending on the audit of the 12% missing dates), with 95% as a multi-year destination conditional on structural changes.
Present it as: current true baseline ± measurement uncertainty → entitlement → gap → what investment closes the gap. Leadership can then choose to fund the gap or accept a lower target, but the choice is explicit rather than a number someone liked.