Decentralized Decision Making in Two Sided Manufacturing-as-a-Service Marketplaces

TL;DR

This paper develops decentralized decision-making tools for manufacturing-as-a-service marketplaces, focusing on pricing and matching mechanisms to improve transparency, stability, and efficiency in platform operations.

Contribution

It introduces data-driven pricing methods and three innovative matching mechanisms, including reverse auctions, stable matching, and online stochastic matching, for decentralized MaaS platforms.

Findings

Data mining-based pricing improves service provider competitiveness.

Stable matching mechanisms enhance platform stability and performance.

Online matching adapts to dynamic supply and demand conditions.

Abstract

Advancements in digitization have enabled two sided manufacturing-as-a-service (MaaS) marketplaces which has significantly reduced product development time for designers. These platforms provide designers with access to manufacturing resources through a network of suppliers and have instant order placement capabilities. Two key decision making levers are typically used to optimize the operations of these marketplaces: pricing and matching. The existing marketplaces operate in a centralized structure where they have complete control over decision making. However, a decentralized organization of the platform enables transparency of information across clients and suppliers. This dissertation focuses on developing tools for decision making enabling decentralization in MaaS marketplaces. In pricing mechanisms, a data driven method is introduced which enables small service providers to price services based on specific attributes of the services offered. A data mining method recommends a network based price to a supplier based on its attributes and the attributes of other suppliers on the platform. Three different approaches are considered for matching mechanisms. First, a reverse auction mechanism is introduced where designers bid for manufacturing services and the mechanism chooses a supplier which can match the bid requirements and stated price. The second approach uses mechanism design and mathematical programming to develop a stable matching mechanism for matching orders to suppliers based on their preferences. Empirical simulations are used to test the mechanisms in a simulated 3D printing marketplace and to evaluate the impact of stability on its performance. The third approach considers the matching problem in a dynamic and stochastic environment where demand (orders) and supply (supplier capacities) arrive over time and matching is performed online.