When bin locations become necessary.
Below roughly 1,000 SKUs and one or two pickers, bin locations are overhead with little benefit. The pickers know where everything is. The warehouse fits in their head. Adding bin discipline at this scale slows pickers down without measurable accuracy gain.
Past 2,000-3,000 SKUs, the pattern flips. Pickers cannot keep the layout in memory. New staff take weeks to train. Time per pick stretches from 30 seconds to 2-3 minutes for items they have to hunt for. Bin locations become essential — without them, throughput stops scaling and errors multiply.
How a bin coding system works.
A standard bin code identifies a specific position in the warehouse: Aisle - Bay - Level - Position. Example: A-04-3-B = Aisle A, Bay 4, Level 3, Position B. Aisle is a row of shelves. Bay is a section within the aisle. Level is the shelf height (1 = ground, 2-5 = up). Position is left/right or further sub-division.
Codes should be sequenced by walking order through the warehouse — A1-1-A is the first bin you reach from the entry, the next bin is A1-1-B, then A1-2-A, etc. This matters when you generate a pick list: the system sorts the list by bin code and the picker walks once through the warehouse instead of zigzagging. Throughput on properly sequenced codes is 2-3x raw scattered picking.
- Aisle: physical row identifier (A, B, C...)
- Bay: section within the aisle (numeric: 01, 02, 03...)
- Level: shelf height (1 = ground, ascending)
- Position: sub-bin within the level (A, B, C...)
- Sequence codes by physical walk order, not alphabetic alone
Assigning skus to bins.
Three policies are common. Fixed location: each SKU has one bin, always the same. Easy to memorize, lowest training time, fastest pick once known, but wastes bin space when stock is low. Dynamic location: SKUs go to whatever bin is empty when they arrive. Maximum space utilization, but pickers cannot work without scanning. Hybrid: fast-movers (A items) get fixed bins, slow-movers (B and C items) go dynamic.
For most growing warehouses, hybrid works best. Fixed bins for the top 200-300 fast movers means pickers do not need to scan for them — they walk to the bin and grab. Dynamic for the long tail keeps space efficient. The system tracks dynamic locations via scan-on-put-away and scan-on-pick.
A worked example: 8000-sku electronics warehouse.
A Frankfurt electronics importer runs a warehouse with 8,000 SKUs. Layout: 12 aisles, each with 8 bays, each bay 5 levels, 4 positions per level = 1,920 bin slots. Capacity allocation: 240 fixed bins for top-200 A items (with margin), 1,680 dynamic bins for everything else. Items go in dynamic bins on receipt; the system records the bin assignment.
Pick performance before bin system: 35 picks per picker per hour, 4% pick error rate, 3-day training to be productive. After bin system: 75 picks per hour, 0.8% error, half-day training. The labour productivity doubled and error rate dropped 80%, paying back the system rollout in 4 months. Inventory accuracy improved as a side effect because bin-level cycle counts surfaced location errors that aggregate counts had missed.
Designing the pick path.
A pick list of 30 items, scattered across 12 aisles, needs to be walked in a smart order. The system sorts by bin code (which encodes physical location) and produces a walking sequence — start at aisle A, work through to aisle L, no backtracks. The picker walks once. Compare to a list sorted by SKU alphabetically: the picker zigzags 8 times across the warehouse and takes 2x longer.
Better systems support multi-order batching: 5 customer orders combined into one pick list, sorted by bin, picked together, then sorted into customer bags at a packing station. Throughput goes up 30-50% on batch picking. Errors require careful sortation but are manageable with scan-confirm at packing.
Cycle counting at the bin level.
With bin locations, cycle counts get sharper. Instead of "count this SKU at this branch", you count "this bin" — which contains a known set of SKUs and quantities. Discrepancies surface as either missing/extra units of the expected SKU, or as a SKU in the wrong bin entirely (which is its own error worth investigating).
Bin cycle counts can run faster than SKU cycle counts because the counter walks the warehouse in physical order. A counter can count 200 bins in an hour with reasonable accuracy. SKU cycle counts in the same warehouse, scattered across bins, take much longer per item due to walk time. Bin counts also surface put-away errors that SKU counts cannot detect.
How nonari handles bin locations.
Nonari supports bin locations as sub-locations within a warehouse Stock Location. Each bin has a code, a parent warehouse, optional capacity attributes (volume, weight), and a current contents list. SKUs can be assigned to bins (fixed) or assigned dynamically on receipt with scan-confirm.
Pick lists generated for outbound transfers or sales sort by bin code per the configured walk sequence. Cycle counts can run at the bin level or SKU level. Bin-level reports surface specific bins with high error rates, slow turnover, or capacity issues — much sharper than aggregate inventory reports for warehouse operations management.