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  4. SCEDAS - Workforce Planning. Mathematically optimized
 
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2021
Study
Title

SCEDAS - Workforce Planning. Mathematically optimized

Abstract
Objectives:
SCEDAS® is a decision support system that supports shipping and maritime logistics as highly efficient and sophisticated systems. Many formerly very labor-intensive processes in maritime transport and cargo handling have now been digitized and automated. As a result, many jobs are completely different and many employees are now responsible for extensive and complex areas of responsibility. Coordinating them efficiently is the task of workforce planning 2.0.
The reduced personnel resources on the ships may give rise to risks for shipping companies in the area of compliance, for example in observing the prescribed rest periods. In addition, large handling volumes and short lay times of merchant ships have led to an increased workload for the crew in recent decades. These changed conditions, combined with legal regulations and administrative burdens, make it difficult to assess personnel requirements on the basis of knowledge and experience alone. Added to this is the special circumstance that the crew on board is responsible for both the operation and maintenance of the ship. Consequently, maintenance management is closely intertwined with manpower planning. The challenge is to consider the interdependencies between maintenance, operations and crew management in ship management.
Solution:
SCEDAS® is a decision support system that helps ship managers calculate detailed work schedules for each seaman - mathematically optimized with powerful algorithms, ready for use ashore and on board. In addition to the special requirements of a specific voyage for the crew and their qualifications, SCEDAS® can take into account and document legal requirements, supporting the complex task of personnel management ashore and on board. In the meantime, the crewing software SCEDAS® has been further developed so that free personnel resources are now also optimally used for maintenance and servicing tasks.
Author(s)
Rizvanolli, Anisa
Fraunhofer-Institut für Materialfluss und Logistik IML  
Bosma, Celien
Fraunhofer-Institut für Materialfluss und Logistik IML  
Publisher
Fraunhofer CML
Publishing Place
Hamburg
Link
Link
Language
English
Fraunhofer-Institut für Materialfluss und Logistik IML  
Keyword(s)
  • SCEDAS

  • workforce planning

  • decision support

  • Data Science

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