Pick-Pack-Ship Latency Profile — Fulfillment Timing Analysis

Executive Summary

Once a fulfillment exists, it ships same-day almost everywhere. The latency — and the money — sits upstream of the pick and in a stale unfulfilled backlog.

94.6%

Same-day pick rate

227 of 240 fulfillments created the day the SO was entered

$1.93M

Shipped same-day

Order value flowing through with zero wait

$402K

Dollar-days of delay

13 delayed fulfillments × avg 91.4 days late × order value

$116K

Unfulfilled backlog

111 open SOs never fulfilled ($67.8K real + $48.3K future-dated)

$3,938

Stuck in WIP

9 fulfillments frozen at Picked/Packed, never shipped

The Pipeline: Where the Wait Lives

Stage 0 · Order → Pick

0–360 days

⚠ 100% of latency lives here. Median 0d, but a long tail of backorder waits — plus 111 orders that never enter the pipeline at all.

➜

Stage 1 · Picked

6 stuck

Fulfillments at status A. All but one are stale (900+ days).

➜

Stage 2 · Packed

3 stuck

Status B. One live: IF #240, Miami, packed 28 days ago.

➜

Stage 3 · Shipped

231 ✓

96% of all fulfillments completed. Pick→ship is effectively same-day.

Stage transitions measured from SO createddate → Item Fulfillment createddate (via transactionline.createdfrom), and fulfillment status (A=Picked, B=Packed, C=Shipped). This account keeps no system-note audit trail on fulfillments, so intra-warehouse pick→pack→ship durations are represented by current stage + dwell age.

Latency by Location order → pick, days

New York is the benchmark: 105 fulfillments, never a single day of lag. Miami and Chicago carry chronic multi-month waits. Lines with no location assigned are the worst cohort — unowned orders wait longest.

Location	Fulfillments	Avg days	Median	Max	% Same-day
(no location on lines)	36	33.0	0	360	64%
05: Miami	7	26.6	0	174	70%
03: Chicago	4	16.2	0	138	88%
Store #140 Boston	1	8.0	8	8	0%
01: San Francisco	120	1.9	0	360	97%
Store #100 San Francisco	1	0.0	0	0	100%
02: New York	105	0.0	0	0	100%
04: Denver	2	0.0	0	0	100%

🏆 New York ships 105/105 same-day. ⚠ Miami is the systemic laggard at every stage: slowest picks (avg 26.6d), the only live stuck shipment (IF #240, packed 28d), and the largest regional share of the unfulfilled backlog. San Francisco is fast on average (97% same-day) but produced the two worst outliers — two fulfillments created 360 days after their SOs (#23174/#23175), likely abandoned orders finally swept.

Latency by Order Size counter-intuitive, consistent

Order size	Count	Avg days	Max	% Same-day
🔴 Small (1–3 lines)	93	12.4	360	89%
🟡 Medium (4–8 lines)	74	0.3	19	97%
🟢 Large (9+ lines)	73	0.1	6	99%

💡 Every long-latency incident is a small order. Large orders get prioritized (likely B2B/wholesale flows with dedicated handling); 1–3-line orders fall through a crack — probably batch-released only when someone manually sweeps the queue.

Items That Wait stock-out signature

The high-volume electronics catalog (iPads, MacBooks, monitors — 34 fulfillments each) ships at 0.0 days, 100% same-day. The waits concentrate entirely in soft-goods / specialty SKUs — a classic backorder pattern, not a warehouse-labor problem.

Item	Fulfillments	Avg days	Max days	Signal
Salida Backpack BU	8	90.9	360	Chronic — repeat offender across 12 delayed lines
Volumizing Shampoo	1	360	360	One extreme abandoned-order event
XLR Precision Circular Saw	2	24.5	49	Recurring backorder
3D Cube Pro Printer	2	19.0	19	Recurring backorder
Salida Backpack GR	2	9.5	19	Same family as worst offender
Baja Rectangular Cocktail Table	2	3.0	6	Minor
Electronics catalog (19 SKUs: iPad, MacBook, monitors, cables…)	34 each	0.0	0	✓ Perfect — 100% same-day

Financial Impact what the waiting costs

$1,934,119

Value shipped same-day

227 fulfillments · zero wait — the healthy baseline

$11,365

Value shipped late

13 fulfillments · avg 91.4 days late

$401,882

Dollar-days in transit-wait

Σ (order value × days waited) — revenue recognition & cash conversion drag

Unfulfilled backlog by age — $116,094.68 total

Age bucket	Orders	Value	Share of value
0–30 days	1	$1,410	1%
31–90 days	12	$24,646	21%
91–365 days	9	$30,268	26%
Over 1 year	45	$11,520	10%
Future-dated (demo data)	44	$48,252	42%

Where the recoverable money is

💰 $54,913 — fresh & fulfillable (0–365d)

22 orders young enough that the customer is likely still waiting. Single largest item: SO #315 at $17,753 (91–365d bucket). Fulfill or communicate.

🧹 $11,520 — stale, close it (1yr+)

45 orders over a year old. These distort demand planning and committed-inventory calculations. Close or cancel.

📦 $3,938 — stuck WIP

9 picked/packed fulfillments. Inventory is committed but not moving — it's invisible to available-to-promise.

Stuck In-Process Fulfillments picked or packed, never shipped

IF #	Stage	Location	Created	Dwell (days)	Order value	Disposition
240	PACKED	05: Miami	2026-06-29	28	$86	⚡ Live — ship it or investigate carrier hand-off
229	PICKED	01: San Francisco	2024-02-07	901	$61	Stale — cancel/clean up
230	PICKED	01: San Francisco	2024-02-07	901	$353	Stale — cancel/clean up
226	PICKED	01: San Francisco	2023-02-12	1,261	$203	Stale — cancel/clean up
212	PICKED	03: Chicago	2022-01-27	1,642	$47	Stale — cancel/clean up
213	PICKED	02: New York	2022-01-27	1,642	$2,047	Stale — highest value of the stuck set
207	PACKED	04: Denver	2022-01-27	1,642	$915	Stale — cancel/clean up
211	PACKED	04: Denver	2022-01-27	1,642	$126	Stale — cancel/clean up
241	PICKED	Store #140 Boston	2027-09-04	future	$100	Future-dated demo record

Recommended Actions

NOWShip IF #240 (Miami).

Packed 28 days ago and sitting. The only genuinely live stuck shipment in the pipeline. Check carrier hand-off / tracking assignment.

WEEK 1Triage the $54.9K fulfillable backlog (22 orders, 0–365 days).

Start with SO #315 ($17,753). For each: fulfill, split-ship available lines, or proactively notify the customer.

WEEK 2Close the stale tail: 45 SOs over 1 year ($11.5K) + 8 stale stuck fulfillments.

These inflate committed inventory and pollute demand signals. A one-time bulk close restores data hygiene.

FIX ROOT CAUSEAdd a small-order release sweep & backorder escalation.

The at-risk profile is precise: 1–3-line orders containing specialty SKUs (Salida Backpacks, saws, printers) routed to Miami/Chicago or left with no location. A daily automated release for small orders + a reorder-point review on the 5 offending SKUs eliminates most of the tail. Replicate New York's process — 105/105 same-day — at Miami.

MEASUREEnforce line-level location on Sales Orders.

The "(no location)" cohort is the slowest (avg 33 days) and largest backlog segment — orders nobody owns. Make location mandatory at order entry so every order has a warehouse accountable for it.

Methodology. Population: 240 item fulfillments (231 Shipped / 3 Packed / 6 Picked) linked to originating sales orders via transactionline.createdfrom; 111 open SOs (status B/D/E) with no fulfillment lines. Latency = fulfillment createddate − SO createddate (calendar days). Order values from foreigntotal. Dollar-days = Σ(order value × days waited). This demo account contains future-dated transactions (SOs through 2028); they are flagged and excluded from latency conclusions. No system-note audit trail exists for fulfillment status transitions in this account, so pick→pack→ship internal durations are approximated by current stage + dwell age. Generated by Sonar AI · 2026-07-28.