Which Simple KPIs Tell Me If Picking Is Working Well?
Table of contents
A line = one SKU within the order (with its quantity). Below you’ll find each KPI with: what it is, why it matters, how to calculate it, how to implement it, and how to track it — in a concise, actionable format.
The KPIs, One by One (Defined and Ready to Use)
1) Picking Accuracy
- What it is: % of lines without error (correct SKU/quantity).
- Why it matters: fewer returns and rework; protects margin.
- Formula: 1 − (error lines / total lines).
- Implementation: record cause of each error in quality control (sheet/WMS).
- Tracking: daily and weekly, by area/shift/category.
2) Orders per Hour (OPH)
- What it is: complete orders closed per hour (by team/shift).
- Why it matters: measures real output capacity.
- Formula: closed orders / worked hours.
- Implementation: log shift start/end and count closed orders.
- Tracking: daily and by time slot (regular vs campaign).
3) Lines per Hour (LPH)
- What it is: lines prepared per hour (regardless of order size).
- Why it matters: fine measure of work rhythm; complements OPH.
- Formula: prepared lines / worked hours.
- Implementation: extract from WMS or tally sheet by area.
- Tracking: daily; compare by method (order/batch/zone).
4) Order-to-Ship Time
- What it is: minutes from “order confirmed” to “ready for shipping.”
- Why it matters: direct impact on SLA and customer satisfaction.
- Formula: end timestamp − start timestamp.
- Implementation: timestamps in WMS or simple manual record.
- Tracking: daily with P50/P90 to see variance.
5) On-Time Shipments (Simple OTIF)
- What it is: % of orders shipped before the committed cutoff.
- Why it matters: fulfillment of the promise to customer/carrier.
- Formula: on-time orders / committed orders × 100.
- Implementation: define carrier cutoffs and tag each order.
- Tracking: daily/weekly; cross-check by carrier and channel.
6) Returns Due to Picking Error
- What it is: % of returns caused solely by picking mistakes.
- Why it matters: separates warehouse failures from catalog/customer issues.
- Formula: “picking error” returns / shipped orders.
- Implementation: standardized, mandatory return reason codes.
- Tracking: weekly/monthly; top 10 SKUs with most incidents.
7) Cost per Order (or per Line)
- What it is: labor + consumables cost of picking.
- Why it matters: links productivity with margin and investment decisions.
- Formula: (picking labor cost + consumables) / orders (or lines).
- Implementation: hours assigned to picking × cost/hour; standard consumables.
- Tracking: monthly; compare against previous months and peaks.
8) Labor Utilization
- What it is: % of the shift spent on productive picking tasks.
- Why it matters: reveals waiting times, bottlenecks, and coordination gaps.
- Formula: productive time / total time × 100.
- Implementation: quick sampling (5 min/hour) or WMS terminal status logs.
- Tracking: weekly; act on causes (routes, replenishment, packing).
9) Distance/Route per Order
- What it is: meters or minutes walked to complete a typical order.
- Why it matters: less walking = more orders/hour and less fatigue.
- Formula: sample with pedometer/app; alternative: picks/minute.
- Implementation: measure representative batches across shifts/zones.
- Tracking: biweekly; compare before/after layout changes.
10) Perfect Order Rate
- What it is: % of orders without error, damage, delay, or missing docs.
- Why it matters: “customer view” of operations (quality + timing).
- Formula: perfect orders / shipped orders × 100.
- Implementation: simple validation checklist at closing.
- Tracking: monthly; prioritize recurring issues.
Quick Adjustments by Picking Type
- By order: monitor OPH, LPH, walking distance; check SKU accuracy.
- By batch: include consolidation time and allocation errors.
- By zone: watch balance between areas and transfers.
- PTL/Put-to-Light: focus on accuracy, LPH, and training time.
- RF/voice: compare confirmation speed and rereads vs paper.
Looking Ahead
With these 10 KPIs and compact definitions, you’ll get a clear picture: quality, speed, compliance, and cost. Start with a short dashboard (Accuracy, LPH, OTIF, Picking Error Returns, Order-to-Ship) and keep formulas consistent. When the data shows a bottleneck, decide whether to automate just enough or adjust processes to free up capacity.