◆ Stop hoping, start knowing, before the shift
An afternoon packing surge the morning crew plan would miss.
Tomorrow's order forecast shows packing volume spiking after 2pm at DC-Atlanta, well past what the standing morning-heavy crew plan covers. Left alone, that is overtime and a late cut-off. The planner reads the order forecast the night before and recommends shifting hours into the afternoon, and the same numbers roll straight into the daily client-metrics view your clients see.
Recommended: move 5 packers to a 12pm to 8pm window and pull two pickers forward; the morning runs lean, the afternoon is covered, no overtime.
DC-ATLANTA · PACKING DEMAND vs. PLANNED CREWBY HOUR
Volume spikes after 2pm; standing plan is morning-heavy
◆ Tomorrow's plan · DC-Atlanta
| Function | Forecast driver | Units / head | Rec. | Sched. | Gap | Status |
|---|
↳ Click any function for the hour-by-hour curve behind the recommendation.
Overtime trend
DC-Atlanta OT · last 6 weeks
Two weeks ago480 hrs
Last week390 hrs
This week (proj.)300 hrs
Most overtime traced to afternoon surges the morning-heavy plan could not see.
One plan, two outputs
Staffing + client metrics
Per-shift staffing recbefore each shift
Feeds client-metrics viewsame numbers
Projected OT cut~25%
The throughput numbers that size the crew are the same ones clients see, so the floor and the report never disagree.
Plugs into the stack EXE already runs
read-only · works with your order forecast & time-clock data · ~4–6 week build
Order forecast / OMS
OMorder dataTime & attendance
TCtime-clockWMS / throughput
WMWMSClient metrics
CMclient view→ Tell us your exact systems and we map the connectors to them. It reads the order forecast and crew availability, hands back a per-shift plan, and feeds the client view. Nothing migrates.
A staffing recommendation before every shift, with overtime around 25% down.
Mockup by Instive AI for EXE Logistics · sample data
◆ The daily client view the staffing numbers feed
Summit Retail DAILY
Orders shipped5,120
On-time99.4%
Units packed18,900
Cut-off metyes
Northpeak DTC DAILY
Orders shipped3,640
On-time98.8%
Units packed11,200
Cut-off metyes
Why staffing and metrics share a source
One number, two uses
The throughput forecast that sizes tomorrow's crew is the same feed that fills today's client metrics. When the afternoon surge gets staffed, the cut-off gets met, and the client view shows it, without anyone pulling a separate report.
Afternoon surge staffedyes
Cut-offs met today9 / 9 clients
The staffing forecast and the client metrics read from one source.
Client-metrics view · Instive AI for EXE · sample data
◆ Functions across the network
DC-Atlanta
high-volume DTC
TomorrowPM surge
OT trenddown
Cut-offs met99%
DC-Dallas
retail replenishment
Tomorrowon plan
OT trendflat
Cut-offs met99%
DC-Reno
west-coast DTC
Tomorrowlight
OT trenddown
Cut-offs met98%
One planner, every DC
staffing + client metrics from the same feeds
Every DC reads from the same order forecast and time-clock feeds. A PM packing surge at Atlanta and a steady day at Dallas both turn into a per-shift plan the night before, and the same throughput rolls into the client view, so labor stops being the thing that gets hoped through the shift.
One AI layer · labor planning feeding staffing and client metrics.
Mockup by Instive AI for EXE Logistics · sample data