Solution design · all figures are example or default values
Parcel Hub WMS + WCS + Digital Twin: Solution Design
A full design for a platform that plugs into every hub's existing sorters, systems and cameras with minimal load, mirrors each floor as a live digital twin, tracks every parcel, bag, person and machine from gate-in to dispatch, and lets you configure WMS and WCS logic safely. All numbers in this document are example or default values, configurable per site, not real data.
1. Executive overview and goals
The platform sits on top of what each hub already runs. It reads events from sorters, scanners, DWS, handhelds, cameras and existing WMS and TMS without loading them, builds one live model of the floor, and turns it into control: who should be where, which chute serves which destination, and what each parcel category costs in time and money.
Goal
What it means
Example target
Visibility
Every vehicle, bag, parcel, person and machine located and timed at every stage
Scan-event coverage of 99.5% or more of parcels at every mandatory stage
Throughput
Raise effective site TPT without new hardware by removing bottlenecks
Effective PPH up 10 to 20% versus baseline
Turnaround
Cut TOT inbound-to-dispatch per category, hit every connection cutoff
Connection adherence 98% or more
Workforce
Right person at the right station at the right hour
People utilisation 80 to 88%, idle under 10%
Quality
Fewer missorts, no-reads, damages and shortages
Sort accuracy 99.9% or more, no-read under 1%
Security
Camera-based detection of pilferage, intrusion and rough handling
Every HV-cage incident with a video clip in under 60 s
Cost
Lower cost per parcel by stage and by site
Cost per parcel down 8 to 15%
Control
One studio to configure WMS process logic and WCS device logic, tested on the twin first
Zero untested config pushed to a live sorter
Low intrusion
Integrate with existing sorters, systems and cameras without loading them
Under 2% added CPU or IO on any source system
Design principle. Observe first, then advise, then control. Every site starts read-only; WCS control is switched on per site only after shadow-mode certification.
2. System architecture
Layer
Responsibilities
Typical technology
Note
L0 Site systems
Existing sorter PLC and WCS, scan tunnels, DWS, handhelds, legacy WMS and TMS, HRMS, badge readers, CCTV
Customer-owned
Untouched; read-first
L1 Edge and integration
Protocol adapters, CDC agents, file watchers, edge AI for video, local store-and-forward, time sync, device registry
Parcel categories and their effect on TOT, TPT and PPH
Category
Default threshold (configurable)
Handling path
Effect on PPH and TPT
Effect on TOT (example)
Flyer / polybag
Up to 0.5 kg, flat, max side 400 mm
Machine sort, bagged
Highest PPH; recirculation risk on some sorters
Low
Small box
Up to 2 kg, max side 450 mm
Machine sort, bagged
High PPH
Low
Standard
2 to 10 kg, within sorter max dims
Machine sort, bagged
Baseline
Baseline
Large
10 to 25 kg or a side 600 to 900 mm
Machine sort if within OEM limits, else manual
Slower induct, fewer per carrier
+15 to 30 min
Bulky
25 to 50 kg or a side over 900 mm
Manual sort, loose load
Manual PPH 3 to 5 times lower
+30 to 90 min
Non-conveyable
Irregular, rolls, tyres, liquids, over 50 kg, outside sorter limits
Manual, MHE needed
Lowest PPH; 2-person or MHE
+60 to 180 min
Fragile
Flag from client or label
Manual or low-drop chute
Lower PPH, damage risk
+10 to 30 min
High-value
Declared value over INR 10,000
Cage, dual control
Extra scans
+15 to 45 min
COD
Payment type COD
Normal path, COD tracking
Neutral
Neutral
Dangerous goods
DG flag or UN number
DG zone, segregated bag
Manual, certified staff
+30 to 120 min
Express / priority
Service type
Priority induct and early bag close
Neutral
Shorter, by rule
RTO
RTO flag
RTO lane or chute
Neutral
Separate SLA
Thresholds and time effects are example defaults; each site sets them from its sorter's OEM limits and its own time studies. A parcel can hold one size category plus several flags (fragile, HV, COD, DG, express, RTO).
How mix changes site throughput
Effective site PPH = 1Σ ( share of category ÷ PPH of category )
Because the slow paths sit in the denominator, a small rise in bulky or non-conveyable share pulls site PPH down sharply.
Scan or task event in the last 60 s (example), or camera sees a work activity at own station
Productive time
Idle
Logged in, no event over 60 s, camera sees idle pose, work available at station
Coachable
Starved
No event, station queue empty
Planning issue: redeploy
Blocked
No event, downstream full or machine fault
Machine or flow issue
Travel
Moving between stations (camera track crosses zones)
Layout or task issue
Break
Scheduled break window or break check-in
Planned
Off-floor
Attended but not checked in and not seen in any work zone
Leakage
Formulas
Required heads = Peak hourly load at stationStd PPH per person × Efficiency
Utilisation = Active minutesAttended minutes
PPH per person = Parcels handledDeployed hours
Labour cost per parcel = Σ (hours × cost rate)Parcels processed
Peak hourly load = daily volume ÷ operating hours × peak factor × stage share of volume. Daily heads = heads per shift × number of shifts, plus an absenteeism buffer (example 8 to 12%).
Demand-based redeployment logic
Forecast loadPer station, per 15 min, from pre-alerts, yard queue, trips ETA, and in-floor WIP
Compute needRequired heads per station from load, std PPH, efficiency and category mix
CompareGap = required minus deployed (camera headcount cross-checks logins); flag stations over plus or minus 1 head for 2 intervals
MatchPick donors from starved or over-staffed stations with the required skill, shortest walk
GuardrailsMinimum heads per station, max 2 moves per person per shift, no move within 20 min of last move
RecommendShift lead gets a card: move 3 from bagging to de-bag for 40 min, expected TOT gain
LearnTrack accepted moves and actual effect to tune the model
Where people are most utilised
Utilisation heatmap: station by 15-min interval, coloured by active share
Marginal value per head: TOT minutes saved by adding one person at each station, ranked
Skills coverage: stations where fewer than 2 certified people are on shift
Contract vs permanent: PPH, error rate, attrition and cost per parcel by employment type and vendor
Person scorecard: PPH vs standard, accuracy, attendance, stations worked
Availability = Running timePlanned production time
Performance = Items inductedRunning time × Design throughput
Quality = Correctly sorted itemsItems inducted
Planned production time excludes planned maintenance and scheduled breaks; starved time is reported separately so a sorter waiting for parcels is not blamed on the machine.
WCS configuration and sort plans
Item
Description
Sort plan
Destination to chute mapping per wave or time window; versioned; compiled to edge cache
MeasureArrival rate and capacity for every stage each 15 min
UtilisationStage with highest utilisation and growing queue is the active constraint
Root causeSplit capacity loss into people (idle, absent), machine (fault, starved, jam) and mix (bulky share)
QuantifyMinutes of TOT added and parcels at risk of missing cutoff
ActRecommend redeploy, chute swap, induct priority, or dock change
10. Computer vision and camera layer
Uses the cameras each hub already has. Edge AI boxes on site read the NVR streams, run the models locally and send only small events (and short clips for alerts) to the cloud, so the WAN, the NVR and the cameras carry almost no extra load.
People: headcount, activity, time in zone
Capability
How it works
Event fields
Frequency
Live headcount per zone
Person detection plus tracking inside zone polygons; count is unique tracks present in the last 10 s (example)
zone_id, count, confidence
Every 5 s
Activity recognition
Pose and action model per track: unloading, de-bagging, inducting, scanning, manual sorting, bagging, loading, carrying, pushing cage, MHE driving, idle standing, phone use, walking
track_id, activity, confidence, duration_s
On change
Time in zone
Entry and exit of each track per zone; dwell per visit and cumulative
track_id, zone_id, enter_at, exit_at
On exit
Anonymised tracking
Random track ID per visit, re-identification across cameras inside the site only, no face stored; IDs rotate every shift
track_id, x, y
1 s
Badge linking (optional)
Fuse track with BLE tag, scanner login at station or badge tap to attach employee_id
track_id, employee_id, link_method
On link
Face ID (optional, opt-in)
Only with explicit consent, only at entry points, templates encrypted on edge; off by default
employee_id, match_score
On match
Crowding and flow
Density per m² and walking paths heatmap
zone_id, density
1 min
Live headcount by zone (example view)
Zone
Seen on camera
Planned
Logged in
Gap
Inbound docks
2
3
3
1 short
De-bag
6
6
5
1 present without login
Induction
4
4
4
OK
Secondary sort
5
4
4
1 extra
Bagging
3
4
4
1 short
Outbound docks
3
3
2
1 present without login
Exception desk
1
2
2
1 short
Illustrative numbers only. The gap column is what drives redeploy cards and login-gap alerts.
Parcel tracking by vision
Status or detection
Vision trigger
Event
Effect in WMS
Auto inscan
Parcel passes profiler or DWS; camera track plus label OCR or barcode read
VISION_INSCAN
Status inscan (vision); confirmed when scan matches
Primary sort done
Parcel seen leaving sorter into chute
VISION_PRIMARY_SORT
Status primary sort done for that chute's destination
Secondary sort
Parcel arrives in the secondary sort area or put wall
VISION_SECONDARY_SORT
Status secondary sort in progress
De-bagging
Bag opened and emptied at station; parcel count estimate
VISION_DEBAG_DETECTED
Compare with bag manifest count
Bagging
Parcels dropped into bag at chute; bag closed and lifted
VISION_BAGGING_DETECTED
Compare with scans into bag
Load and unload count
Bags crossing dock tripwire in or out
VISION_DOCK_COUNT
Compare with load or unload scans
Chute jam or pile-up
Fill level estimate over 90% or no movement for 60 s while divert continues
VISION_CHUTE_JAM
Alert bagger and maintenance
Conveyor jam or gap
Parcels stationary on running belt, or long empty gaps at induct
VISION_CONVEYOR_JAM
Alert and induct coaching
Floor parcel
Parcel lying outside any valid zone over 5 min
VISION_STRAY_PARCEL
Pick-up task
Damage
Crushed, torn or leaking parcel classifier
VISION_DAMAGE
Damage desk with image
Camera vs scan reconciliation
Case
Rule (example)
Outcome
Seen but not scanned
Vision event with no matching scan within 120 s (example) at the same stage
Missed scan task to the station; counts in scan compliance
Scanned but not seen
Scan with no vision event at a covered stage
Check camera coverage or ghost scan (scan without physical handling)
Count mismatch
Vision count for bag, dock or chute differs from scan count by more than tolerance (example 1 or 2%)
Recount or audit task
Location mismatch
Scan says chute A; vision sees parcel at chute B
Possible missort; hold bag before seal
Matching keys
Time window, location, OCR text, size and colour signature, sorter carrier ID
Fusion confidence score 0 to 1
Security and suspicious activity
Behaviour
Detection
Zones
Default confidence
Severity
Pilferage gestures
Action model: concealment into clothing or bag, opening a parcel, hand-to-pocket after handling
All work zones
0.80
High
Loitering near HV cage
Track dwell inside cage buffer over 60 s (example) without a cage scan
HV cage
0.70
High
Two-person rule breach
Cage open with fewer than 2 people
HV cage
0.75
Critical
Non-dock exit
Person or parcel crossing a tripwire at a non-dock door or fence
Perimeter, side doors
0.70
Critical
Restricted zone intrusion
Person in DG, cage or server room zone without access role (badge link) or after hours
Secure zones
0.70
High
Rough handling
Throw, drop from height, kicking, stepping on parcels; parcel velocity and pose
Docks, sort, bagging
0.75
Medium
Missing PPE
No safety shoes, high-vis vest or helmet where required
Docks, MHE areas
0.70
Medium
Unsafe MHE
Forklift speed or person too close to moving MHE
MHE areas
0.75
High
Camera tamper
Blocked, moved or defocused view
All cameras
0.90
High
Smoke or fire
Smoke and flame classifier
All
0.80
Critical
Every security alert carries a clip (10 s before, 20 s after, example), a still, the zone and the confidence. A human reviews it before any action against a person.
Models
Model
Task
Approach
Default threshold
Latency per frame
Person detector
Detection
YOLO-class detector, fine-tuned on hub footage
0.50
under 20 ms
Multi-object tracker
Tracking
ByteTrack or BoT-SORT
n/a
under 5 ms
Cross-camera re-ID
Anonymous re-identification
Appearance embedding, site-local, shift-scoped
0.60 similarity
under 10 ms
Activity and pose
Action recognition
Pose keypoints plus temporal classifier (2 to 4 s window)
0.65
under 80 ms per clip
Parcel and bag detector
Detection and segmentation
Detector with parcel, bag, roll cage, pallet classes
0.55
under 20 ms
Label OCR and barcode
Recognition
Text detector plus recogniser; barcode decoder
0.80
under 40 ms
Chute fill estimator
Segmentation
Fill % from segmented parcel area
n/a (threshold 90% fill)
under 30 ms
Damage classifier
Classification
Crushed, torn, wet, leaking
0.75
under 15 ms
PPE detector
Detection
Vest, helmet, shoes
0.70
under 20 ms
Suspicious action
Action recognition
Concealment, throwing, kicking
0.75 to 0.80
under 100 ms per clip
Smoke and fire
Classification
Smoke, flame
0.80
under 20 ms
Camera placement per zone
Zone
Camera type
Mount
Count rule
Resolution
FPS
Models
Gate and yard
Bullet, ANPR at gate
4 to 6 m
1 per gate lane plus 1 per 40 m of yard
1080p
5 to 10
Vehicle, ANPR, person
Inbound and outbound docks
Dome or bullet facing dock door
4 to 5 m
1 per dock (1 per 2 docks minimum)
1080p
10
Person, activity, bag count, PPE
De-bag stations
Overhead or angled dome
3.5 to 5 m
1 per 2 to 4 tables
1080p
10 to 15
Person, activity, parcel count
Profiler / DWS
Fixed box camera near tunnel exit
2 to 3 m
1 per DWS
1080p to 4 MP
15 to 25
Parcel track, OCR
Induction
Angled dome
3.5 to 4 m
1 per 2 inducts
1080p
10 to 15
Person, activity, belt gaps
Sorter chutes
Overhead along chute bank
4 to 6 m
1 per 6 to 10 chutes
4 MP
10
Fill level, jam, bagging
Secondary sort and put walls
Dome
4 to 6 m
1 per 150 to 250 m²
1080p
10
Person, activity, parcel
HV cage
2 angles, one overhead
3 to 4 m
2 per cage
4 MP
15
Person, loiter, gesture
DG zone and restricted rooms
Dome
3 to 4 m
1 to 2 per zone
1080p
10
Intrusion, PPE, smoke
Perimeter and side doors
Bullet
3 to 5 m
1 per door
1080p
10
Tripwire, intrusion
Runtime parameters
Parameter
Range or default
Note
fps_inference
5 to 25 fps
Per use case: headcount 5, activity 10, parcel at DWS 15 to 25 (example)
resolution
1080p default, 4 MP for chutes and cage
Sub-stream where enough
conf_threshold
0.50 to 0.90
Per model and per alert; tuned on site data
min_duration_s
2 to 60 s
Event must persist before firing (debounce)
zone_polygons, tripwires
Floor coordinates
Drawn in config studio on camera view
privacy_masks
Polygons
Blur or skip areas
clip_pre_s, clip_post_s
10 s and 20 s (example)
Alert clip window
event_latency_target
under 500 ms frame to event; under 3 s to alert
model_version
Semantic version
Rolled out like any config, shadow first
camera_health
online, fps achieved, blur score, tamper
Every 30 s
Edge hardware sizing (example)
Site size
Edge hardware
Capacity
Note
Small hub (up to 16 cameras)
1 edge box with an embedded GPU module (Jetson Orin NX class)
8 to 16 streams at 1080p, 5 to 10 fps
Example sizing
Medium hub (16 to 64 cameras)
2 to 3 edge servers, each with 1 inference GPU (L4 or T4 class)
24 to 32 streams per GPU at 1080p, 10 fps
Example sizing
Large hub (64 to 200 cameras)
Edge rack with 4 to 8 GPUs, N+1 redundancy
Scale by streams × fps × models
Example sizing
Storage on edge
4 to 16 TB for clips and buffers
Raw video stays on existing NVR
Network
Cameras on a separate VLAN; uplink 1 to 10 GbE
To cloud: kbps per camera for events, clips on demand
Power
UPS covering edge box and switches
Retention
Data
Retention (example)
Controls
Raw video
Existing NVR policy (often 7 to 30 days)
Not copied by the platform
Alert clips
30 to 90 days (example), longer if under investigation
Encrypted, watermarked, access-logged
Vision events (no images)
13 months hot, then aggregated
Pseudonymous track IDs
Track-to-employee links
Shift duration plus 30 days (example)
Only where badge linking is on
Face templates
Only if face ID is on; deleted on consent withdrawal or exit
Never leaves edge
Training data
Curated, blurred faces, opt-in sites only
Separate approval
Thresholds, stream counts and hardware are example starting points; each site is tuned during the vision pilot on its own footage and lighting.
Privacy and compliance (India)
Area
Design
Lawful basis
Use for safety, security and operations under the Digital Personal Data Protection Act, 2023 and its Rules: give notice, and rely on consent or on the employment-related legitimate uses allowed by the Act where they apply. Confirm the basis with legal counsel per client.
Notice
Clear signs at entry and in each covered zone in English, Hindi and the local language; written notice in the joining kit explaining purposes, data kept, retention, rights and grievance contact.
Consent
Separate, explicit, withdrawable consent for any optional feature such as face ID; a non-biometric alternative (badge) always offered; no penalty for refusal.
Purpose limitation
Each model tagged with its purpose; no use outside the listed purposes without a new notice.
Data minimisation
Events not video; anonymous tracks by default; privacy masks on rest and toilet areas; no audio.
Retention and erasure
Per the retention table; automatic deletion jobs with logs.
Rights of data principals
Access, correction, erasure and grievance handling through a named grievance officer, with response timelines tracked.
Security and breach
Encryption, role-based clip access, audit trail; breach notice to the Data Protection Board and affected people as the Act requires.
Fairness
Productivity scores never from vision alone; no automated discipline; human review of every security alert before action.
Worker engagement
Brief worker representatives and contractors before go-live; publish what is and is not monitored.
Assessment
Privacy impact assessment per site before enabling vision; repeat on new models.
Legal review. This is a design baseline, not legal advice. Have counsel confirm the lawful basis, notices, consent wording and retention under the DPDP Act and Rules, and any state labour rules, before switching on people analytics at a site.
How vision feeds the rest of the platform
Module
Integration
Digital twin
Live people dots and counts per zone, parcel sightings, chute fill, security events on the floor map; replay includes vision layers
Workforce
Activity time feeds active, idle, travel and off-floor states; camera headcount vs logins vs roster per zone each 5 min; time-in-zone by team
Parcel tracking
Vision statuses fill gaps between scans and raise seen-not-scanned tasks; dock and bag counts reconcile against scans
WCS
Chute jam and fill events feed overflow logic and maintenance; induct gap analysis
KPIs
Headcount accuracy, vision-scan match rate, seen-not-scanned rate, activity time shares, rough-handling per 10,000, PPE compliance, camera uptime
Alerts
Security, safety and flow alerts with a clip link, routed by severity (section 13)
Simulation
Real walking paths and activity times calibrate the twin's people model
Staffing and TOT calculator
Simplified planning estimate using example defaults; the twin's simulation gives the precise answer.
PPH per person by station (editable)
Result
Station
Peak load/h
PPH/person
Heads/shift
Heads/day
Utilisation
Over capacity. At least one stage is at or above 98% utilisation, so queues grow without limit and the TOT shown is a floor, not an estimate. Add heads, raise staffing level, or reduce the peak factor.
Method: heads = ⌈peak load ÷ (PPH × efficiency)⌉; stage time = base handling time + 5 min × ρ ÷ (1 − ρ), where ρ is utilisation at the staffing level entered. Base times (example): unload 15, de-bag 10, machine sort 8, manual sort 20, bagging 25, staging wait 30, loading 15 minutes.
11. KPI dictionary
Throughput
KPI
Definition
Formula
Granularity
Example target
TPT (site)
Parcels dispatched in a period
Σ parcels with VEHICLE_DEPARTED in period
Site, hour, shift, day
At or above planned hourly
Effective PPH (site)
Dispatched parcels per operating hour
parcels dispatched / operating hours
Site, shift
Plan value
Stage PPH
Parcels processed by a stage per hour
stage out-events / hours
Stage, hour
At or above rated
PPH per person
Productivity of a person
parcels handled / deployed hours
Person, team, station
Example 300 to 600 for induct
Team PPH
Productivity of a team
team parcels / team deployed hours
Team, shift
Std PPH
Category PPH
PPH for one category
category parcels / hours on that path
Category, stage
Per category std
Sorter TPT
Items diverted per hour
divert confirms / running hours
Sorter, hour
Over 85% of design
Induct rate
Items inducted per hour per induct
inducts / hour
Induct
Rated
Capacity headroom
Spare capacity
1 − load / capacity
Stage, hour
10 to 20%
Time
KPI
Definition
Formula
Granularity
Example target
TOT (site)
Hub turnaround, first inscan to dispatch
departed_at − first_inbound_scan_at
Parcel, category, client, site
Example under 4 h express
Stage TOT
Time in a stage
stage_out − stage_in
Stage, category
Per stage target
Dwell time
Time a parcel or bag sits idle
Σ waiting gaps between stages
Stage, zone
Under 30 min
Yard dwell
Gate-in to dock-on
docked_at − gate_in_at
Vehicle
Under 20 min
Dock turn time
Dock-on to dock-off
undocked_at − docked_at
Dock, vehicle type
Under 45 min
Bag fill time
Bag open to seal
sealed_at − created_at
Chute, destination
By rule
Exception MTTR
Mean time to resolve
avg(resolved_at − raised_at)
Type
Under 30 min
Cutoff buffer
Time between bag load and cutoff
cutoff_at − loaded_at
Connection
Over 15 min
Quality
KPI
Definition
Formula
Granularity
Example target
Sort accuracy
Parcels sorted to correct destination
1 − missorts / sorted
Sorter, person, site
99.9% or more
Missort rate
Wrong chute or bag
missorts / sorted
Sorter, chute
Under 0.1%
No-read rate
Tunnel failed to read
no-reads / scans
Sorter
Under 1%
No-data rate
Read but no shipment data
no-data / reads
Client
Under 0.3%
Recirculation rate
Parcels going round again
recirc events / inducted
Sorter
Under 3%
Reject rate
Parcels to reject chute
rejects / inducted
Sorter
Under 1%
Damage rate
Damaged parcels per 10,000
damages / parcels × 10,000
Stage, site
Under 5
Shortage / excess rate
Bags or parcels short or excess vs manifest
short / expected
Origin, lane
Under 0.05%
Scan compliance
Parcels with every mandatory scan
parcels with full scan chain / parcels
Stage
99.5% or more
Utilisation
KPI
Definition
Formula
Granularity
Example target
People utilisation
Share of attended time that is productive
active min / attended min
Person, team
80 to 88%
Idle %
Logged in without work done
idle min / attended min
Person, team
Under 10%
Starved / blocked %
Waiting on upstream or downstream
starved or blocked min / attended min
Station
Under 5%
Deployment adherence
Heads at station vs plan
actual heads / planned heads
Station, interval
95 to 105%
Attendance %
Showed up vs rostered
present / rostered
Team, vendor
Over 92%
Sorter OEE
Availability × performance × quality
A × P × Q
Sorter, shift
Over 75%
Dock utilisation
Share of time docks occupied
occupied min / available min
Dock
60 to 80%
Chute utilisation
Chute active and not full
volume share and full-time %
Chute
Balanced
MHE utilisation
Equipment in use
in-use min / available min
Asset
Over 60%
Vision
KPI
Definition
Formula
Granularity
Example target
Headcount accuracy
Camera count vs audited count
1 − |vision − audit| / audit
Zone
97% or more
Login gap
People seen in zone without a station login
vision headcount − logged-in heads
Zone, interval
0
Vision-scan match rate
Vision events matched to a scan
matched / vision events
Stage
99% or more
Seen-not-scanned rate
Parcels seen but not scanned
unmatched vision / vision events
Stage, station
Under 0.5%
Activity time share
Share of time per activity
activity seconds / attended seconds
Zone, team
Idle under 10%
Rough handling rate
Incidents per 10,000 parcels
incidents / parcels × 10,000
Zone, shift
Trend down
PPE compliance
People with required PPE
compliant detections / detections
Zone
98% or more
Security alert precision
Alerts confirmed true on review
true alerts / reviewed alerts
Model, site
Over 80%
Camera uptime
Cameras streaming at target fps
healthy camera min / total camera min
Site
99% or more
SLA and cost
KPI
Definition
Formula
Granularity
Example target
SLA adherence
Parcels dispatched before promised cutoff
on-time parcels / parcels
Client, service
98% or more
Connection adherence
Bags loaded on planned connection
bags on planned trip / bags planned
Route
98% or more
On-time departure
Vehicles leaving on schedule
on-time departures / departures
Route
95% or more
Cost per parcel
Total hub cost per parcel
(labour + MHE + energy + space + overhead) / parcels
Site, stage, category
Down vs baseline
Labour cost per parcel
Labour only
Σ(hours × rate) / parcels
Team, stage
Trend down
Overtime %
Overtime of total hours
OT hours / total hours
Team
Under 8%
Contract share
Contract heads of total
contract heads / total heads
Site
Policy
Vehicle fill
Load factor
loaded volume / vehicle capacity
Trip
Over 85%
Every KPI is computed in streaming form (rolling 15 min, hour, shift) and batch form (day, week, month), and can be sliced by site, stage, machine, team, person, client, category and route.
12. Configuration studio
Configurable in WMS
Area
What can be set
Process flows
Stages, mandatory scans, order, skip rules per site template
Sort rules
Pincode, route, client, service, category to destination; effective dates
Category rules
Thresholds for weight, dims, value, DG; handling path per category
Bag rules
Max weight, count, fill, time; forced close before cutoff; mother bag rules
All sites live map, TPT vs plan, TOT, connection risk, sorter health, alerts by severity
Region, network
Drill into any site twin
Site manager
Shift plan vs actual, PPH by team, cost per parcel, OEE, SLA, attendance, top bottlenecks
Site, shift, day
Approve configs, rosters
Shift lead
Live floor twin, camera headcount per zone, queues per stage, redeploy cards, cutoff countdown, exceptions
Live, 15 min
Accept redeploys, switch fallback
Supervisor / team
Team heads, active, idle, PPH per person, errors, seen-not-scanned
Live, hour
Coach, move
Security
Vision security alerts with clips, cage activity, perimeter, review queue
Live, day
Confirm or dismiss, escalate
Maintenance
Sorter state timeline, alarms, read rate, chute faults and jams, camera health, MTBF, MTTR
Live, shift
Acknowledge alarms
Integration health
Connector and camera freshness, lag, error rate, quarantine queue
Live
Replay, pause
Client portal
Own volumes, TOT, SLA, exceptions, shortages, damage, PODs
Day, week
Download, webhooks
Finance
Cost per parcel by stage, labour cost, OT, contract vendor billing
Day, month
Export
Floor TV
Hourly target vs actual, cutoff board, safety reminders
Live
Display only
16. Non-functional requirements
Area
Requirement
Note
Scale
Up to 500 sites per tenant, 50 M events per site per day at peak, 200 cameras per site, 5,000 concurrent users
Example sizing target
Latency
Event to twin p95 under 2 s; sort decision at edge under 30 ms; frame to vision event under 500 ms
See latency table
Availability
Cloud 99.9%; edge 99.99% with HA pair; site never depends on cloud to keep sorting
Durability
No event loss: replicated log, edge buffer 72 h
Retention
Raw events 13 months hot, 7 years cold (configurable); vision per section 10
Client and legal needs
Multi-tenancy
Tenant ID on every row and topic, row-level security, per-tenant keys, optional dedicated deployment
Security
SSO (SAML, OIDC), MFA for admins, mTLS edge to cloud, encryption at rest and in transit, secrets vault for camera and PLC credentials
OT safety
Writes to PLC only through WCS executor, interlocks, read-only default, IEC 62443 aligned zoning; cameras on own VLAN
Privacy and compliance
DPDP Act 2023 and Rules: notice, consent where needed, minimisation, retention, rights, breach reporting; data residency in India regions; immutable audit logs
Observability
Tracing per event, connector and camera SLAs, model drift monitoring, synthetic tests
Sort-plan compiler, edge executor, shadow then canary, manual fallback
Live sort plan from studio at pilot
7. Scale
Ongoing
Connector library per sorter OEM, full vision security suite, multi-site rollout, client portal, AI optimisation
10+ sites live
Next step. Share one real site's numbers (daily volume, sorter type and OEM, headcount by team, shift pattern, camera count) to calibrate the calculator and build a worked example.