  Semiconductor supply chains are described by significant demand fluctuation
that increases as one moves up the supply chain, the so-called bullwhip effect.
To counteract, semiconductor manufacturers aim to optimize capacity
utilization, to deliver with shorter lead times and exploit this to generate
revenue. Additionally, in a competitive market, firms seek to maintain customer
relationships while applying revenue management strategies such as dynamic
pricing. Price change potentially generates conflicts with customers. In this
paper, we present KnowGraph-PM, a knowledge graph-based dynamic pricing model.
The semantic model uses the potential of faster delivery and shorter lead times
to define premium prices, thus entail increased profits based on the customer
profile. The knowledge graph enables the integration of customer-related
information, e.g., customer class and location to customer order data. The
pricing algorithm is realized as a SPARQL query that relies on customer profile
and order behavior to determine the corresponding price premium. We evaluate
the approach by calculating the revenue generated after applying the pricing
algorithm. Based on competency questions that translate to SPARQL queries, we
validate the created knowledge graph. We demonstrate that semantic data
integration enables customer-tailored revenue management.
