⌘K
ScopePeriod

Workforce Intelligence

Headcount · attendance · productivity · overtime · training · labour planningAll operations
Human review mandatory — no automated employment decisions; analytics are aggregate decision support. Views are aggregated to site, role and pseudonymous crew level (e.g. EC-D1-G3). Individuals are not ranked or scored; any follow-up is handled by line management under HR policy and the collective labour agreement.
Headcount (in scope)
300
150 harvesters · 56 mill staff
Attendance (MTD)
93.0%
Target 92%
Harvester output
1,408kg/HK
−2.9% vs 3-mo baseline
Avg overtime
10.4h/mo
Policy threshold 24 h/mo
Training due
58
81% compliant
Safety observations
157
This month · 37% good practice

Harvest-season labour demand forecast

Harvesters needed vs available per day · next 8 weeks (peak crop Oct–Nov)

Forecast
Labour gap outlook
Confidence81%
Peak gap in W46 · 09 Nov: need 170 vs 135 harvesters/day (−35). Cumulative shortfall ≈ 1,146 HK over 8 weeks. Estate C drives most of the gap (attendance 86%).
Gap drivers (harvesters/day)
Seasonal peak crop
−17
Attendance below 92%
−6.0
Planned leave (Nov)
−1.2
Mechanised in-field transport
+1.6
◀ lowers predictionraises ▶
Recommended: redeploy 14 harvesters from Estate D Div 2 to Estate C for 6 days; pre-contract seasonal crews for W44–W47; hold 10-day harvest rounds.

Productivity anomaly detection

Aggregated at site / division / pseudonymous crew level

Anomaly
Requires reviewEstate C harvestersConfidence84%
Output vs baseline
−11%
Attendance
86%
kg / HK
1,291 / 1,450
Contribution to output deviation (pp)
Harvester attendance 86%
−4.6%
3 blocks on >12-day rounds
−2.9%
Wet-season path condition
−1.8%
Lower average bunch weight
−1.1%
Supervision ratio
−0.6%
◀ lowers predictionraises ▶
Crew (pseudonymous)Sizekg/HKvs baseAttend.
EC-D2-G441,132−21.9%90%
EE-D2-G431,175−19.0%95%
EA-D1-G231,223−15.7%94%
EC-D1-G3101,255−13.4%86%
EC-D1-G431,255−13.4%89%
Crews shown only with ≥3 members; operational causes first (rounds, access, supervision).

Headcount by site and role group

Active workforce, synthetic roster

Harvester output distribution

kg FFB per harvester-day (HK) · 150 harvesters · aggregate only

x-axis: tonnes per HK band · baseline 1,450 kg/HK
Estate A
1,472 kg+1.5%
Estate B
1,431 kg−1.3%
Estate E
1,424 kg−1.8%
Estate C
1,291 kg−11.0%
Estate D
1,424 kg−1.8%

Mill shift optimisation

Suggested operator / technician plan vs FFB arrival profile

ShiftOperatorsTechniciansRationale
Shift 1 · 06–149 → 84 → 3Low morning intake; release 1 operator to Shift 2
Shift 2 · 14–228 → 103 → 3Peak arrivals 12–18h; reduce FFB queue & restan
Shift 3 · 22–066 → 62 → 4ST-04 bearing job in window Wed 30 Sep, 22:00–04:00
Expected: FFB queue −22% at peak · overtime −18% · same headcount. 78%

Training recommendations

By role & competency — linked to current operational signals

Recommendation
RoleCompetency focusDuePriority
Sprayer
Chemical handling & PPE
Pesticide safety certification (3 h)
7High
Lab Analyst
Oil-loss sampling protocol
Lab QA/QC refresher (2 h)
2High
Mill Operator
Sterilizer cycle control & pressure hold
Sterilization SOP v4 + simulator (4 h)
9High
Maintenance Tech
Vibration analysis & bearing replacement
Condition monitoring L1 (6 h)
5High
Harvester
Ripeness criteria & loose-fruit collection
FFB grading field clinic (2 h)
25Medium
Admin
Data privacy & records
Data protection basics (1 h)
1Medium
Mill operator & technician modules prioritised by the Mill A OER / ST-04 root-cause findings.Group-level counts

Site scorecard

Aggregated by site — no individual ranking

SiteHCAttend.OT hTrain. due
Estate A5093.5%9.512
Estate B5194.1%8.113
Estate E3394.0%8.57
Estate C5489.7%8.18
Estate D5693.8%8.52
Mill A3593.2%20.112
Mill B2193.6%16.24

Overtime by role

Average hours / month · threshold 24 h

Maintenance Tech
24.8
Mill Operator
15.4
Lab Analyst
14.7
Admin
10.2
Sprayer
10.1
Field Supervisor
10.1
Security
9.5
Fertilizer Crew
8.7
Harvester
8.3
Driver
7.0

Mill A maintenance overtime elevated during ST-04 monitoring; see shift plan suggestion.

Training & safety observations

Compliance by role · observations this month

Sprayer
65% (7)
Lab Analyst
67% (2)
Mill Operator
70% (9)
Maintenance Tech
75% (5)
Harvester
83% (25)
Unsafe act
41
Unsafe condition
33
Near-miss report
25
Good practice
58
Open Safety AI monitoring
Synthetic workforce roster (300 records) · aggregate decision support · no individual-level scoring
V-TEKI — IT & Business ConsultingDeveloped by V-TEKI · IT & Business ConsultingPrime Agri AI Intelligence Platform — prototype. All operational records are synthetic; model outputs are simulated; financial impacts are scenario estimates; recommendations require human validation. No actual company confidential data is used.