Demand Predictability Evaluation for Supply Chain Processes Using Semantic Web Technologies Use Case
Nour Ramzy, Philipp Ulrich, Lancelot Mairesse, Hans Ehm · 2022
Semantic Web technologies provide the possibility of a common framework to share knowledge across supply chain networks. We explore Semantic Web technologies to evaluate processes' demand predictability through a use case. First, we create an ontology describing the relevant domain concepts and data and define competency questions based on the need of the use case. Then, we map the data to the ontology in a knowledge graph. We design a chain of SPARQL queries to retrieve and insert information from the knowledge graph to answer the competency questions. We calculate the underlying demand for supply chain processes using aggregations and the created semantic description. We successfully computed a pre-defined metric for demand predictability for different time scopes and process groups: the mean of yearly coefficient of variation. Using this approach, one could perform predictability evaluations for relevant indicators of end-to-end supply chains by further data integration in the semantic framework.