Authors:
Daniel Hilpoltsteiner
1
;
Stephanie Bäuml
2
and
Christian Seel
1
Affiliations:
1
Institute for Project Management and Information Modelling, Landshut University of Applied Sciences, Landshut and Germany
;
2
Technology Centre for Production and Logistics Systems, Landshut University of Applied Sciences, Landshut and Germany
Keyword(s):
Process Variability Modelling, Order Picking Process, Knowledge Management, Information Modeling, Adaptive Process Modeling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Best Practices & Communities of Practice
;
Communication, Collaboration and Information Sharing
;
Communities of Practice
;
Computer-Supported Education
;
Enterprise Information Systems
;
KM Strategies and Implementations
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Learning/Teaching Methodologies and Assessment
;
Society, e-Business and e-Government
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Information modelling is an established standard for knowledge representation in companies. However, small and medium-sized companies (SME) often lack the resource to use it for their own purpose. In this paper a solution to model business process variability in order picking processes is discussed. Therefore we did a knowledge extraction from different companies using a questionnaire, expert interviews and workshops with different experts from the field of production logistics in SME has been done. Based on their knowledge different variants of order picking processes in SME were defined and put together in an adaptive process model. Using configuration terms to enrich the adaptive process model allows the distinction between these different variants. Based on different influencing factors a specific process variant can be generated from the process model using element selection and further process optimizations including introducing new technologies can be made.