# Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplace

## TL;DR

This paper introduces a machine learning-based network pricing model for 3D printing services in a manufacturing marketplace, enabling more strategic and competitive pricing by analyzing supplier and market data.

## Contribution

It develops a data-driven pricing approach using machine learning to classify service prices into categories, improving over traditional ad-hoc methods in a two-sided marketplace.

## Key findings

- Achieved 65% accuracy for US suppliers and 59% for European suppliers in price classification.
- Demonstrated a 40% improvement over baseline accuracy with machine learning methods.
- Proposed a practical tool for online marketplaces to set competitive service prices.

## 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.

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Source: https://tomesphere.com/paper/1907.07673