Bin Scoring System for Warehouse Module
A “Bin scoring” system is a feedback loop that learns from every stock movement, giving each bin a score profile based on how well it has historically suited certain SKUs, volumes, or operational patterns. This provides a way to suggest bins in the future that better match real-world usage rather than static rules.
1. Core Idea
Every time stock is put into or moved out of a bin, you accumulate signals about how suitable that bin was. Over time, those signals form a score that can be used to recommend bins for new stock.
2. Dimensions to Score
You don’t want just one “global score,” but a combination of weighted metrics:
SKU Affinity
Does this bin commonly store this SKU (or related SKUs, e.g. same category)?
Accumulate a higher score if the SKU stays without problems (no overflow, no forced moves).
Penalize if stock is quickly moved out again (wrong placement).
Utilization & Fit
Track how full the bin is when this SKU is placed.
If the bin consistently holds “just right” quantities, give it positive points.
If it overflows or sits half-empty, deduct points.
Movement Efficiency
How easy was it to pick from this bin (short walking distance, ergonomic position)?
If warehouse operators move stock in/out smoothly, add points.
If items are frequently reallocated, subtract.
Stability / Retention
If stock remains in a bin until consumption/dispatch without mid-cycle moves, reward it.
Penalize bins where items are frequently relocated before use.
Task Correlation
Track whether the bin’s contents aligned with outbound demand (e.g., fast-moving SKUs near dispatch lanes score better).
Bins that are used repeatedly for similar tasks gain affinity.
3. Accumulator Mechanics
Think of this like a ledger of events that updates a scorecard per bin (and optionally per SKU-bin pair):
On Stock Inbound (putaway)
Increase bin_sku_score[bin][sku] by +N (baseline “fit” attempt).
On Stock Outbound (pick/ship)
Add +1 to the bin’s pick_success score.
On Forced Move (relocation due to bad fit/overflow)
Deduct points (e.g., -5).
On Repeated Usage (same SKU returns to same bin)
Add bonus multiplier for “stickiness.”
On Inactivity (bin holds stock but never used for long period)
Apply decay (reduce score slowly over time).
This produces an evolving score matrix:
bin_sku_score[bin][sku] = weighted sum of (putaway events, picks, forced moves, stability, etc.)
bin_score[bin] = aggregate across all SKUs
4. Suggestion Logic
When suggesting a bin for a new SKU inbound:
Filter by eligible bins (size, temperature, zone).
Rank by bin_sku_score[bin][sku] (highest first).
If no history for that SKU, fall back to:
bin_score[bin] (general suitability)
Or similarity (e.g., bins with same SKU category).
5. Long-Term Benefits
Creates self-learning warehouse flow – bins get better over time.
Reduces human decision fatigue (“where do I put this?”).
Can be tuned by adjusting weights:
e.g., prioritize picking efficiency vs space utilization.
Supports future automation (ML model can replace rule weights).