Pick List Template (Excel Spreadsheet)

A pick list tells the picker exactly what to grab and where to find it. Without a structured list, pickers rely on memory or unsequenced order printouts. That means wasted travel time and missed items. This spreadsheet covers wave setup, pick sequencing, scan verification, and exception logging for batch and wave picking workflows.

According to the Georgia Tech Supply Chain and Logistics Institute, order picking accounts for roughly 55% of total warehouse operating costs. A good pick list is the cheapest way to bring that number down.

It plugs straight into a pick pack ship workflow as the operational document that drives the first stage.

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Wave header: 4 fields

Each wave or batch starts with a header block that identifies the pick run.

Field	Purpose	Example
Wave ID	Unique identifier for this pick batch	W-2026-0226-01
Release time	Time the wave was released to the floor	10:15
Picker	Assigned picker name	Alex
Priority	Wave priority level (Rush, Standard, Low)	Standard

Wave sizing matters. Over 50 lines and pickers wear out — mistakes creep in. Under 5 lines and you are burning walk time for almost nothing. Aim for 15-30 lines per wave for single-picker operations.

For teams processing over 200 orders per day, consider splitting waves by priority tier: rush orders in their own 5-10 line waves released immediately, standard orders batched into 20-30 line waves released every 60-90 minutes.

Pick lines: 8 columns

Sorting pick lines by bin location cuts travel time by 30-50%

Column	Type	Purpose
Sequence	Auto/manual	Pick order optimized by bin location to minimize walk time
Order ID	Pre-filled	Customer order number for this line
SKU	Pre-filled	Product variant to pick
Bin	Pre-filled	Storage location (e.g. A-03-02)
Qty to pick	Pre-filled	Units required for this order line
Qty picked	Manual	Actual units picked (should match qty to pick)
Scan pass?	Manual	Yes/No: confirms barcode scan matched expected SKU
Exception code	Dropdown	Only filled if pick could not be completed normally

Sort pick lines by bin location before printing or assigning. Walking the warehouse in bin order instead of order-ID order cuts picker travel by 30-50% in most layouts. This is the single biggest efficiency win in manual picking.

The scan verification column is critical. Teams that skip barcode verification at pick time see error rates 3-5x higher than those that scan every item. Even a quick visual barcode check against the printed list catches most wrong-SKU errors before they reach packing.

4 exception codes

What are the most common pick errors in a warehouse?

When a pick goes wrong, log the exception immediately. Do not continue and try to remember later:

• SP: short pick (bin has fewer units than requested — most common exception, typically 60-70% of all exceptions)
• WB: wrong bin (stock found in a different location than system shows)
• WS: wrong SKU (bin contains a different product than expected)
• DMG: damaged (item present but not shippable — needs quarantine)

Short picks (SP) are the number-one exception in most warehouses. If the same SKU keeps coming up short, you almost certainly have a receiving or cycle count problem upstream. Dig there first before blaming the picker.

Wrong-bin (WB) exceptions point to a slotting discipline problem. When stock gets put away in the wrong location, every future pick for that bin becomes unreliable. Consistent warehouse slotting practices prevent the majority of WB exceptions.

Exception rate benchmarks

Track your exception rates weekly to spot trends before they compound:

Exception type	Acceptable rate	Investigation threshold	Common root cause
Short pick (SP)	Under 1% of lines	Over 3%	Receiving errors, count inaccuracy
Wrong bin (WB)	Under 0.5% of lines	Over 1.5%	Put-away discipline, slotting drift
Wrong SKU (WS)	Under 0.2% of lines	Over 0.5%	Similar packaging, mislabeled bins
Damaged (DMG)	Under 0.3% of lines	Over 1%	Storage conditions, handling practices

An overall exception rate above 5% per wave is a red flag that warrants stopping and diagnosing the root cause before releasing the next wave.

Handoff block: 3 fields

Sloppy handoffs between picking and packing are where errors slip through.

Field	Purpose
Picker sign-off	Picker confirms all lines picked or exceptions logged
Pack station	Assigned packing station number
Handoff time	Time pick tote was transferred to pack station

The handoff block creates a paper trail. When a packing error gets traced back, the handoff time and picker sign-off tell you exactly where things broke. WERC benchmarking data shows top warehouses hit 99.5%+ pick accuracy, and clear handoffs are a big part of how they get there.

If you want SOP-level controls around this handoff, pair it with the pick pack ship SOP template for formal stage-gate rules between picking and packing.

Optimizing pick list performance

How many lines should be on a pick list?

A few practical tweaks that improve pick list efficiency without changing your warehouse layout:

• Batch similar orders: group single-item orders by SKU so one trip to a bin fills multiple orders
• Limit wave size to 30 lines: beyond 30, picker fatigue increases error rates by an estimated 12-18% according to internal benchmarks from mid-size ecommerce operations
• Print bin locations in large font: pickers scanning a sheet while walking need to read locations at a glance, not squint at 8pt text
• Pre-sort by aisle, then shelf, then bin: A-01-03 before A-02-01 before B-01-01 creates a natural serpentine path
• Flag multi-unit picks: any line requesting more than 5 units of the same SKU should be visually highlighted to prevent under-picking

When to replace paper pick lists with software

This template works well for operations processing up to 150-200 orders per day. Past that:

• Printing, distributing, and collecting paper pick lists becomes a bottleneck
• You cannot dynamically re-sequence picks when a rush order enters the queue mid-wave
• Exception data stays on paper and never makes it into a system for trend analysis
• There is no real-time visibility into which waves are in progress, completed, or stalled

Warehouse management software with barcode scanning generates pick lists digitally, sequences them by optimal walk path, and captures every scan event automatically. Pick accuracy, throughput, and exception data feed straight into your KPI dashboard without manual entry.

Quick Reference

• A wave header needs 4 fields: wave ID, release time, picker, and priority.
• Each pick line has 8 columns: sequence, order ID, SKU, bin, qty to pick, qty picked, scan pass, exception code.
• Use 4 exception codes: SP (short pick), WB (wrong bin), WS (wrong SKU), DMG (damaged).
• Optimal wave size is 15-30 lines per picker to balance efficiency and error rate.
• Sort pick lines by bin location to reduce walk time by 30-50%.
• Order picking accounts for roughly 55% of total warehouse operating costs.
• Short picks represent 60-70% of all pick exceptions in typical warehouses.
• Waves over 50 lines increase error rates significantly due to picker fatigue.

Metric	Target	Red flag threshold
Pick accuracy	99.5%+	under 98%
Short pick rate	under 1%	over 3%
Lines per hour per picker	40-60	under 25
Wave completion time	under 45 min	over 90 min
Exception rate per wave	under 2%	over 5%
Scan verification compliance	100%	under 95%