Details
| Original language | English |
|---|---|
| Title of host publication | Computer and Information Science 2021 - Summer |
| Editors | Roger Lee |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 61-75 |
| Number of pages | 15 |
| ISBN (electronic) | 978-3-030-79474-3 |
| ISBN (print) | 9783030794736 |
| Publication status | Published - 2021 |
| Event | 20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science, ICIS 2021 - Shanghai, China Duration: 23 Jun 2021 → 25 Jun 2021 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 985 |
| ISSN (Print) | 1860-949X |
| ISSN (electronic) | 1860-9503 |
Abstract
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.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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Computer and Information Science 2021 - Summer. ed. / Roger Lee. Springer Science and Business Media Deutschland GmbH, 2021. p. 61-75 (Studies in Computational Intelligence; Vol. 985).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - KnowGraph-PM
T2 - 20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science, ICIS 2021
AU - Ramzy, Nour
AU - Auer, Sören
AU - Chamanara, Javad
AU - Ehm, Hans
N1 - Funding Information: Acknowledgements This work has received funding from the EU Electronic Component Systems for European Leadership (ECSEL) Joint Undertaking within Integrated Development 4.0 project (idev40) and the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536).
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85111449199&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2205.07627
DO - 10.48550/arXiv.2205.07627
M3 - Conference contribution
AN - SCOPUS:85111449199
SN - 9783030794736
T3 - Studies in Computational Intelligence
SP - 61
EP - 75
BT - Computer and Information Science 2021 - Summer
A2 - Lee, Roger
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 June 2021 through 25 June 2021
ER -