KnowGraph-PM: a Knowledge Graph based Pricing Model for Semiconductors Supply Chains

TL;DR

This paper introduces KnowGraph-PM, a knowledge graph-based dynamic pricing model for semiconductor supply chains that leverages customer data and delivery speed to optimize prices and increase profits.

Contribution

It presents a novel semantic model and pricing algorithm that integrate customer profiles into revenue management for semiconductors using knowledge graphs.

Findings

Increased revenue through customer-specific pricing

Effective knowledge graph validation via competency questions

Semantic data integration enhances revenue management

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.