Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplace (1907.07673v1)

Abstract: This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.

• The paper introduces a network-based pricing model that leverages machine learning to classify supplier listings into seven price categories with accuracies of 65% in the US and 59% in Europe.
• The paper employs detailed supplier profiles—including experience, capabilities, and customer ratings—to establish objective, data-driven pricing ranges that outperform subjective methods.
• The study demonstrates that integrating systematic machine learning approaches can replace inefficient traditional costing methods, enhancing competitive pricing in 3D printing service marketplaces.

The paper discusses innovative approaches to network-based pricing for 3D printing services within a two-sided manufacturing-as-a-service (MaaS) marketplace. It aims to transition service bureaus from subjective, ad-hoc pricing models to more objective, data-driven strategies. This shift is achieved through advanced cost analytics to enhance competitive advantage in the marketplace.

The core methodology involves using data mining and machine learning techniques to estimate pricing ranges. This is based on detailed profiles of 3D printing service suppliers, incorporating variables such as:

• Supplier experience
• Capabilities
• Customer reviews and ratings
• Operational scale

Data for modeling was collected from existing marketplace websites, serving as the foundation for training and testing the machine learning model. The model achieved classification accuracies of 65% for suppliers based in the US and 59% for those in Europe when categorizing a supplier’s 3D printer listing into one of seven price categories. This performance significantly surpasses a baseline accuracy of 25%, highlighting the efficacy of machine learning in this context.

The paper criticizes traditional pricing methodologies like activity-based costing for their inefficiency in the dynamic and connected marketplace of 3D printing services. Instead, it proposes that using data-driven models can provide more strategic and competitive pricing solutions. Such methods can potentially be integrated into online marketplaces, assisting independent service bureaus in setting competitive pricing rates. This framework paves the way for more systematic pricing strategies, enhancing market competitiveness and efficiency.