Case study · Manufacturing
Shift planningon skills, not on memory.
A leading global manufacturer of shipping containers and a major supplier of logistics and energy equipment, with over 300 member enterprises and 4 listed companies across Asia, North America, Europe and Australia, serving customers in more than 100 countries. In 2024 the group recorded sales revenue of approximately $24 billion with over 50,000 employees. Its refrigerated container production division scheduled by hand and by experience, and uneven scheduler skill produced persistent mismatches between what production needed and what people could do.
Constraints and answers
The schedule decides the shift, and experience was writing it.
One standard, and the agent that applies it
No standard framework for skill matching
With no standard skill-matching framework, experience-based judgment on skills and positions varied by scheduler and delayed production.
Answer
Intelligent scheduling agent
Matches positions to production needs and skill rankings on one standard, replacing experience-based judgment with data-driven matching.
Multi-dimensional data organized by hand
Multi-dimensional data organized by hand made scheduling slow, and emergency readjustment delayed the production response further.
Answer
Automated scheduling engine
Integrates employee data across dimensions, selects staff automatically and generates synchronized schedules, compressing the job from hours to minutes.
No global view of resource allocation
Without global resource optimization, allocation was irrational: skills went unused, positions ran short or over, and satisfaction suffered.
Answer
Global manpower optimization
Analyzes workforce capacity across the whole operation, closing position gaps and surpluses instead of balancing them locally.
No tools for integration or conflict resolution
There were no tools for data integration or conflict resolution, and no automatic scheduling to meet flexible production needs.
Answer
Built-in conflict resolution
Detects scheduling conflicts and fills vacancies for leave or absence automatically, so adjustments happen without manual work.
The schedule stopped depending on who was writing it.
The results
Scheduling in minutes, production efficiency up 20%.
- 20%
- HIGHER PRODUCTION EFFICIENCY
- Position gaps and surpluses closed.
- 95%
- FASTER RESPONSE TIME
- Conflict resolution and auto-vacancy fill.
- SCHEDULING TIME
- Automated scheduling engine.
- MANPOWER MATCHING
- Rational schedules, lower risk.
- EMPLOYEE SATISFACTION
- Fair, skill-based matching.
- PRODUCTION SUPPORT
- Changing production needs met across the group.
Six outcomes, and the two that matter to the floor are the 20% and the satisfaction.
The stack
What was deployed
Multi-dimensional employee data including skills, certifications, availability and performance, brought together for scheduling.
Integration with production planning, HR systems and manufacturing execution platforms for unified workforce management.
Enterprise-grade controls and labor compliance, protecting employee data and holding to regulation.
Configurable scheduling rules and constraints, so different production lines, shifts and requirements are handled on their own terms.
역량에 관한 노트
본 사이트 전반에 소개된 역량, 자격, 배포 레퍼런스는 Amanah 핵심 엔지니어링 팀의 총체적인 작업을 반영합니다. 일부 프로젝트는 이전 또는 계열 법인 하에 수행되었습니다. 소버린 배포는 규정상 기밀이므로 개별 고객명은 공개되지 않습니다. 기반이 되는 레퍼런스는 NDA 하에 검증 가능합니다.
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