Bin scoring

jai.reddy

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).

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