3 research outputs found
BPM Adoption at Bilfinger
Big size corporate companies that opt for Business ProcessManagement (BPM) adoption invest a lot in BPM initiatives with theprimary focus on the identification and standardization of best practicesin the different phases of the BPM lifecycle. The business processes de-signed are usually seen as the standard way of executing the processesand tend not be adapted to specific customers' need or changing condi-tions. Furthermore, the acceptance of a paradigm shift by the end usersis an added challenge. This case introduces a success story on BPMadoption in complex environments where different organizational unitswith different needs are involved. The projects executed in different unitsrespond to specific customers’ requirements, which affects the set of pro-cesses to be designed and executed within them. We developed a novelapproach inspired by the Cynefin framework and used it to define processarchitectures and the respective business process models for a subset ofthe units. To ensure the applicability and acceptance of the new paradigmwe followed a number of well-known methodologies and practices (e.g.SCRUM and gamification). As a result, we managed to move from thetraditional function orientation to BPM orientation taking into consid-eration the flexibility needs, and we received very positive feedback fromour end users
BPM Adoption at Bilfinger
Big size corporate companies that opt for Business Process
Management (BPM) adoption invest a lot in BPM initiatives with the
primary focus on the identi fication and standardization of best practices
in the different phases of the BPM lifecycle. The business processes de
signed are usually seen as the standard way of executing the processes
and tend not be adapted to specifi c customers' need or changing condi
tions. Furthermore, the acceptance of a paradigm shift by the end users
is an added challenge. This case introduces a success story on BPM
adoption in complex environments where different organizational units
with different needs are involved. The projects executed in different units
respond to specifi c customers' requirements, which affects the set of pro-
cesses to be designed and executed within them. We developed a novel
approach inspired by the Cyne fin framework and used it to defi ne process
architectures and the respective business process models for a subset of
the units. To ensure the applicability and acceptance of the new paradigm
we followed a number of well-known methodologies and practices (e.g.
SCRUM and gami fication). As a result, we managed to move from the
traditional function orientation to BPM orientation taking into consid
eration the
exibility needs, and we received very positive feedback from
our end users
From Planning Stage Towards FAIR Data: A Practical Metadatasheet For Biomedical Scientists
Abstract Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation