LifeSeasons Warehouse Batching Optimization

Part 1 — Section 01

What Is Cart Assignment?

Before a single pick happens, every order must be assigned to a cart. The cart determines how many orders get picked together, what physical container they go in, and which packing lane they end up at. Getting the cart right is the foundation — if an order is on the wrong cart, everything downstream is wrong.

Step 1

Cart Assignment

Which cart does this order go on?

Step 2

Batch Optimization

Which orders share a cart? (Part 2)

Today

Sherry does Step 1 manually

Nobody does Step 2 — that is our opportunity

Why this comes first: Our Part 2 optimization algorithms batch orders within the same cart type. If an order is classified into the wrong cart type, it gets grouped with the wrong orders — and all improvement numbers become unreliable. Cart assignment must be confirmed before optimization results can be trusted.

Part 1 — Section 02

Cart Types & Capacities

LifeSeasons uses six cart types. Confirmed capacities come from the operations meeting. Estimated capacities are our best guess and need to be confirmed.

Mailer Cart

80 orders

120 for pre-kitted. Gray rolling open cart. For NeuroQ (NQ prefix), Symmetry (SKU 9085), My Best Heart bulk (>50 orders, <5 line items). (partially confirmed)

Pack-One Cart

~20 orders

Every bottle in the order is the same product. 1–12 bottles. Fastest pick — grab the whole box. (capacity estimated)

Green Cart

~20 orders

Small bag orders, 2–3 items. Uses auto-bagger. Cannot include restricted SKUs (272, 284, 503, 307, 317, 369). (capacity estimated)

Blue Cart

28 orders

Up to 28 bottles standard. Also used vertically (4 orders × 7 bins) for larger single orders. FIFO ordering. (confirmed)

Yellow Cart

variable

My Best Heart when volume is high enough. If low volume, goes on blue or mailer instead. (capacity not confirmed)

Red Cart

10 bins

Large orders, 29–80 regular-sized bottles. 10 bins per cart. Extra bottom bins can absorb overflow. (confirmed)

Auto-Bagger Exclusions — Confirmed List

These SKUs cannot go through the auto-bagger (too large, boxed, or crushable). They must be routed to blue or red carts instead of green. Confirmed by Ben:

SKU 272 SKU 284 SKU 503 SKU 307 SKU 317 SKU 369 SKU 505 — to confirm

Part 1 — Section 03

Sherry's Current Assignment Process

Sherry runs through these filters every morning in order. Each step pulls a group of orders and assigns them to a cart type. Whatever remains after all filters goes through size-based rules at the end.

Assignment happens in this exact sequence — order matters

Each step removes orders from the pool. Later steps see only what remains.

1

SLA / Priority Check

DTC orders (Shopify store in CustomField4) that came in before 3pm must ship today — processed first, every morning. After 3pm DTC goes next business day. Wholesale always goes next business day.

2

Brand / Channel Routing

NeuroQ (NQ order prefix, SKUs 9136/7/8) → mailer cart. Symmetry (SKU 9085) → mailer cart. My Best Heart (SKU 505/503) → yellow if high volume, else blue. NGVC → grouped together (Speed Cell 3). HC → grouped together, never with NGVC. Distributor orders → manually assigned separately.

3

Remove Restricted SKUs

SKUs 272, 284, 503, 307, 317, 369 cannot go in the auto-bagger. Pulled out before green cart assignment. SKUs 272 and 284 (large boxes) go on blue carts in vertical 4×7 configuration.

4

Pack-One Check

Every bottle in the order is the same product → pack-one cart (1–12 bottles). Fastest pick type — the worker grabs a full pre-prepped box from the pallet rather than hunting individual shelves.

5

Size-Based Assignment (remaining orders)

2–3 items, fits small bag → Green cart. Up to 28 bottles → Blue cart. 29–80 bottles → Red cart. >80 bottles → Mailer cart (bulk fallback). Blue can go vertical (4 orders × 7 bins) for larger individual orders. FIFO ordering on blue — oldest orders ship first.

Sherry also tries to create full carts wherever possible — a partial cart is inefficient because a worker walks the same distance for fewer orders. At end of day, leftover orders may go on any available cart rather than waiting.

Part 1 — Section 04

What We've Classified From the Data

Using the rules above (with estimated thresholds where values aren't confirmed), we classified all 930 usable orders from the ~10-day sample:

Pack-One Cart

483 orders (52%)

Proxy: 1 unique shelf location, qty ≤12. Capacity 20 (estimated)

Green Cart

329 orders (35%)

2–3 locations, no restricted SKUs. Capacity 20 (estimated)

Blue Cart

102 orders (11%)

Up to 28 bottles. Capacity 28 (confirmed)

Red Cart

13 orders (1.4%)

29–80 bottles. Capacity 10 bins (confirmed)

Mailer Cart

3 orders (<1%)

>80 bottles (fallback only). NeuroQ/Symmetry/My Best Heart SKUs not in sample. (partial)

Yellow Cart

0 orders

My Best Heart SKUs not in current 10k sample. Rule is coded but never triggered.

Important caveat: Pack-one detection is a proxy — we check if an order picks from exactly one shelf location. The real rule is whether every bottle is the same product, which is not always the same. Green cart thresholds and all estimated capacities need validation from LifeSeasons before this classification is reliable.

What We Have ✔ vs What We're Missing

What We Have (Solid)	Status
Blue cart threshold (28 bottles)	Confirmed by LifeSeasons
Red cart threshold (10 bins)	Confirmed by LifeSeasons
Auto-bagger SKU exclusions (272, 284, 503, 307, 317, 369)	Confirmed by Ben
D2C priority detection (hyphenated order number)	Confirmed — matches CustomField4=OrderLogix in data
Brand separation (HC / NGVC / LS never mixed)	Confirmed
Green cart blocks auto-bagger-excluded SKUs	Implemented correctly

What We Don't Have (Gaps)	Impact
Mailer channel criteria (NQ prefix, SKU 9085 / 505)	Not testable — none in 10k sample. NeuroQ orders may be misclassified as green/blue.
Green cart capacity	We use 20 (estimated). If it's 15 or 25, batching is wrong.
Pack-one capacity	We use 20 (estimated). Same issue.
Pack-one = “same product” check	We proxy via “1 shelf location.” An order with 2 products at the same location would be misclassified.
Green = “2–3 items” vs “2–3 locations”	4 items at 2 locations: we say green, Sherry might say blue.
Yellow cart rules	Sherry mentioned it — no criteria given. We don't use it.
SLA timestamp (order creation time)	We capture pick time, not creation time — can't enforce the 3 pm same-day cutoff.
SKU 505 confirmation	Ben said 505 goes to mailer, but 503 is on the exclusion list — same product family may need separate handling.

Brand Breakdown

Detected from item number prefixes — no guessing. Orders from different brands are never grouped into the same batch.

Health Concerns

44 orders

Items start with HC. In our sample all are wholesale (UPS Ground). Ben listed HC as a D2C channel — needs clarification (see Q3 below).

NGVC / Private Label

54 orders

Items start with PL-NGVC. Natural Grocers Vitamin Cottage. Stored on Speed Cell 3. Never mixed with HC.

LifeSeasons / Other

832 orders

All other brands (Femitry, Inner Eco, standard LS). Can share batches with HC or NGVC per Ben, but HC and NGVC never share a batch.

Priority Breakdown

Wave 1 — Urgent (DTC)

570 orders

Hyphenated order numbers (e.g. 4046858-3990825). All carry OrderLogix in CustomField4 in current data. Need updated export with Shopify store names.

Wave 2 — Standard

360 orders

Plain numeric order numbers. Wholesale (Sage) or blank CustomField4. UPS Ground shipping.

Part 1 — Section 05

Questions for LifeSeasons

1

Can the export include the order creation time?
Ben confirmed that D2C orders placed before 3 pm ship same day; after 3 pm they ship next business day. Right now the export only has the pick time — when a picker scanned the item — which can be hours after the order was placed. Without creation time, we cannot separate “must go out today” orders from “can go tomorrow” orders, which affects how we prioritize batches.
2

Can you send a more recent data export?
Ben mentioned that D2C orders can be identified by the Shopify store name in CustomField4 (LifeSeasons Shopify, NeuroQ Shopify, My Best Heart Shopify, etc.). The current sample is from 2021 and shows OrderLogix instead — the store-level breakdown is not there. A more recent export would let us identify D2C orders by the exact store rather than just the order number format.
3

Are Health Concerns (HC) orders D2C or wholesale?
Ben listed “Health Concerns” as one of the D2C Shopify stores. But every order with an HC-prefix item number in our data is shipped wholesale via UPS Ground. It is possible that “Health Concerns” the Shopify store and HC-prefix items are two separate things. We need to know whether HC orders should ever be treated as urgent D2C, or always as standard wholesale.
4

Is SKU 505 excluded from the auto-bagger?
Ben’s confirmed exclusion list is: 272, 284, 503, 307, 317, 369. The operations meeting also mentioned SKU 505 (My Best Heart) as too crushable for the auto-bagger. If 505 should be on the exclusion list, any order containing it must be routed away from the green cart. Right now we are not excluding it.
5

What is the exact rule for assigning an order to a mailer cart?
We know mailer carts handle bulk single-item orders for NeuroQ, Symmetry, and My Best Heart. But we do not know the exact trigger — is it the order number prefix (NQ…), specific SKUs (9085, 505, 9136–9138), a minimum order quantity, or some combination? None of these orders appear in the current 10k sample, so we cannot validate the routing without a data export that includes them.
6

How many orders fit on a pack-one cart, and how many on a green cart?
Blue cart (28 orders) and red cart (10 bins) are confirmed. For pack-one and green we do not have a confirmed number, so we are using 20 as a placeholder. The capacity directly controls how many orders get grouped into each batch — if the real number is different, our batch sizes are wrong.
7

What is the exact rule that separates a green cart order from a blue cart order?
The meeting described green as “2–3 items.” It is unclear whether “items” means distinct products, individual bottles, or unique shelf locations picked. For example: an order with 4 distinct products totalling 10 bottles — does that go green or blue? Getting this wrong means a meaningful portion of orders land on the wrong cart type entirely.
8

What is the yellow cart used for?
Sherry mentioned a yellow cart during the operations meeting but no rules or criteria were given for it. We currently do not assign any orders to it. If yellow handles a specific order type, those orders are being misrouted to a different cart right now.

Part 2 — Section 01

What Is Batch Optimization?

Once orders are assigned to a cart type (Part 1), there is a second decision: within that cart type, which specific orders get grouped into one batch? For example, if you have 100 blue cart orders, you need roughly 4 batches of 28. Which 28 go together in each batch?

Sherry does not currently optimize this. She assigns orders to batches based on what fits — not on which orders are physically closest on the shelves. This is the gap our algorithms fill. Grouping orders that pick from nearby shelf locations means the worker walks a shorter total distance per batch.

930

Orders tested

~10 days of real LifeSeasons data

1,563

Shelf locations

Mapped with pick sequence numbers

4

Algorithms tested

vs. WMS and random baseline

Part 2 — Section 02

Route Cost Model

We do not have the physical warehouse floor plan yet. As a proxy, route cost is:

Route Cost = 2 × max pick sequence in batch Walk from depot (seq = 0) to the farthest shelf location and back. Applied identically to all methods including WMS.

Known limitation: Pick sequence numbers do not capture cross-aisle travel or real warehouse geometry. The relative ranking of algorithms is reliable. The absolute improvement percentages will change once we have real walking distances from the floor plan.

Part 2 — Section 03

The 4 Algorithms We Tested

Each algorithm runs independently on orders of the same cart type, brand, and priority. Green cart orders are never mixed with blue cart orders. HC orders are never mixed with NGVC orders.

Baseline

Jaccard Greedy

Seed batch with farthest-location order
Fill by shared location overlap score
Fast, simple, good comparison baseline

Enhanced

RAG — Routing-Aware Greedy

Same seed as Jaccard
Screen top-15 by Jaccard similarity
Pick the one that adds the least route cost

Winner

Clarke-Wright Savings (CW)

Calculate savings from merging every pair of orders
Merge highest-savings pairs first
Industry-standard vehicle routing algorithm
Best performance across all metrics

Variant

CW + Consolidation

Run CW, then merge small batches to fill carts
Slightly worse than CW alone with smaller cart sizes
Not recommended for current capacity profile

Part 2 — Section 04

Results

All numbers use real LifeSeasons data (930 orders, ~10 days). Per-cart-type capacities applied. Brand and priority grouping enforced. Lower average route cost = less walking = better.

Average Route Cost Comparison

Lower bar = less walking = better. Scaled to Random baseline (52,823).

Random (no system)52,823

Baseline

no optimization — random grouping within cart types

WMS (current system)51,901

+1.7% vs Random

current production — barely better than random grouping

Jaccard Greedy38,915

+26.3% vs Random | +25.0% vs WMS

location-overlap grouping

RAG (Routing-Aware Greedy)38,915

+26.3% vs Random | +25.0% vs WMS

marginal cost minimization

Clarke-Wright Savings Best31,774

+39.8% vs Random | +38.8% vs WMS

pairwise savings merging — clear winner

CW + Consolidation33,884

+35.9% vs Random | +34.7% vs WMS

slightly worse than CW alone — consolidation merges orders from different shelf areas

Method	Avg Route Cost	vs Random	vs WMS	Status
Random (no system)	52,823	baseline	—	No optimization
WMS (current)	51,901	+1.7% better	baseline	In production
Jaccard Greedy	38,915	+26.3% better	+25.0% better	Tested
RAG (Routing-Aware Greedy)	38,915	+26.3% better	+25.0% better	Tested
Clarke-Wright Savings	31,774	+39.8% better	+38.8% better	Top Performer
CW + Consolidation	33,884	+35.9% better	+34.7% better	Tested

Clarke-Wright is the clear winner — 38.8% better than the current WMS on average route cost. Note that the WMS was free to mix priority 1 and priority 2 orders in the same batch, while our algorithms are constrained to never mix priorities. Despite this handicap, CW still wins by 38.8%, making the result stronger than it appears.

Part 2 — Section 05

Limitations & What's Next

What We Need for Part 2 to Be Exact

1
Warehouse Dimensions
- How long is one aisle (shelf row)?
- How wide is the gap between two adjacent aisles?
- Where does a picker start and end their route?
2
Warehouse Floor Plan or Map
- Even a rough hand-drawn layout would let us calculate real aisle-level distances.
- This is the single most impactful input for making optimization numbers exact and giving LifeSeasons a confirmed dollar figure on labor savings per day.

Current State vs After Answering Open Questions

Right Now

— Pick-sequence proxy for distance

— ~10 days of historical data

— Pack-one / green / mailer capacity estimated

— Cart assignment rules partially confirmed

✓ CW already 38.8% better than WMS

After Open Questions Answered

✓ Confirmed cart classification for all order types

✓ Exact capacities for every cart type

✓ Accurate D2C / SLA detection with order creation time

✓ Real warehouse geometry for exact distances

✓ Confirmed dollar figure on labor savings per day

What Is Cart Assignment?

Cart Types & Capacities

Auto-Bagger Exclusions — Confirmed List

Sherry's Current Assignment Process

What We've Classified From the Data

What We Have ✔ vs What We're Missing

Brand Breakdown

Priority Breakdown

Questions for LifeSeasons

What Is Batch Optimization?

Route Cost Model

The 4 Algorithms We Tested

Jaccard Greedy

RAG — Routing-Aware Greedy

Clarke-Wright Savings (CW)

CW + Consolidation

Results

Limitations & What's Next

The Distance Problem

What We Need for Part 2 to Be Exact