Order Fulfilment Flow

Orders

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On-time %

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Work in progress

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Lead time · P50

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Bottleneck stage

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Cumulative flow — arrivals vs shipments

Cumulative orders received vs shipped, by day. Vertical gap = orders in the building (WIP); horizontal gap = lead time. The widening gap late in the quarter is the backlog building.

When orders arrive — weekday × hour

Order intake by weekday and hour of day · darker = busier. Illustrative of the arrival shape this data models (synthetic).

fewermore

On-time %

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Fragile on-times

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cleared with <10% SLA headroom

Late orders

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Lateness concentration

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Gini · higher = fewer clients own the misses

On-time rate by week — with control limits

Weekly on-time % against a p-chart centre-line and ±3σ limits (limits widen when a week ships fewer orders). Points outside the band are special-cause, not noise. 13 weeks is a short baseline — limits are indicative.

Client reliability — who we're actually failing

Each dot is a client · x = order volume (log) · y = on-time % · size = late orders · dashed = 90% target. Bottom-right = big clients missing SLA.

Carrier on-time vs target

On-time % per carrier (teal) inside a 100% track · navy tick = 90% target

Lead time · P50

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median receive → ship

P90

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slowest 1-in-10

P99

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worst 1-in-100 · the tail

Over 8 hours

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Stage dwell spread — P10 → P90

Per stage: light bar = P10–P90 range, teal = middle-half (P25–P75), navy tick = median. A wide bar is an unpredictable stage — the tail is where SLAs break.

Cycle-time distribution

Shipped orders by total time receive → ship · dashed lines mark P50 / P90 / P95

SLA headroom by priority — median slack vs cut-off

Median hours of slack (SLA − actual) per service level. Teal = comfortable; coral = median order already breaching. Zero line = on the SLA.

Bottleneck stage

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Its share of cycle

of median receive → ship time

Breach minutes

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of all SLA-breach time

Late orders analysed

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broken down by culprit stage

Where the time goes — cycle-time waterfall

Median dwell stacked stage by stage up to the total lead time. The bottleneck stage is highlighted.

What broke the late orders

For each late order, the stage whose dwell most exceeded its cohort's normal — share of breach-minutes by culprit stage. A decomposition of where misses diverge, not a causal claim.

Which stage separates late from on-time?

Lateness rate as each stage's dwell rises (deciles, fastest → slowest). The steepest-climbing line is the stage that most discriminates a late order. Association, not cause — the biggest, most variable stage naturally lifts hardest.

Pick productivity

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lines picked / labour-hour

Labour cost / order

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Understaffed weeks

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actual heads > roster

Backlog to clear

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at current pick rate

Heads on the floor — planned roster vs actual demand

Full-time-equivalents needed each week to clear the work (bars) vs the fixed roster (line), per stage total. Bars above the line = weeks you were short — where the backlog builds. DC-level; not affected by filters.

Productivity by stage — units per labour-hour

Throughput per labour-hour at each stage (Pick measured in lines). Navy tick = target. Below target = the stage soaking up hours.

Labour cost per order — by week

Weekly labour cost ÷ orders shipped · the unit-economics view of the floor · dashed = quarter average

Open orders

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Already breaching

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past SLA, still open

At risk · < 4 h left

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breach imminent

Most backed-up client

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SLA-at-risk — open orders by time to breach

Open orders bucketed by SLA time remaining (SLA − time already waited). Red = already past SLA; amber = breaching within 4 h. This is the firefighting queue.

Backlog age profile

Open orders by how long they've waited in the DC · darker = older

Backlog by client

Open orders still in the DC, worst 12 clients · click to drill

Cost to serve / order

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fulfilment cost

Revenue / order

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fee charged

Contribution / order

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Loss-making clients

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Client profit cliff — who pays, who costs

Clients ranked by total contribution (revenue − cost to serve). Bars = each client's contribution (teal = profit, coral = loss); line = running cumulative. The dip at the right is the loss-makers dragging total profit down.

Carrier scorecard — cost vs reliability

Each bubble is a carrier · x = cost to serve · y = on-time % · size = volume · top-left = best value

Margin by order profile

Contribution margin % for each priority × order-type cohort · teal = profitable, coral = loss · n shown; thin cells (n<50) greyed.

Detail breakdown

Aggregated on current filters · click headers to sort · click a row to filter

Group by

Group	Orders	On-time %	Avg cycle	Cost/order	Rev/order	Contribution ▼	Backlog