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This paper presents approaches to determine a network based pricing for 3Dprinting services in the context of a two-sided manufacturing-as-a-servicemarketplace. The intent is to provide cost analytics to enable service bureausto better compete in the market by moving away from setting ad-hoc andsubjective prices. A data mining approach with machine learning methods is usedto estimate a price range based on the profile characteristics of 3D printingservice suppliers. The model considers factors such as supplier experience,supplier capabilities, customer reviews and ratings from past orders, and scaleof operations among others to estimate a price range for suppliers' services.Data was gathered from existing marketplace websites, which was then used totrain and test the model. The model demonstrates an accuracy of 65% for USbased suppliers and 59% for Europe based suppliers to classify a supplier's 3DPrinter listing in one of the seven price categories. The improvement overbaseline accuracy of 25% demonstrates that machine learning based methods arepromising for network based pricing in manufacturing marketplaces. Conventionalmethodologies for pricing services through activity based costing areinefficient in strategically pricing 3D printing service offering in aconnected marketplace. As opposed to arbitrarily determining prices, this workproposes an approach to determine prices through data mining methods toestimate competitive prices. Such tools can be built into online marketplacesto help independent service bureaus to determine service price rates.

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