Building a Dataspace for Manufacturing as a Service in Factory-X

2026-04-04

AI Summary

This study addresses the challenges faced by small and medium-sized manufacturers when integrating with Manufacturing-as-a-Service (MaaS) platforms—namely, low order conversion rates, difficulties in achieving high-quality small-batch production, and inefficient quotation processes. To overcome these issues, the authors propose a manufacturing data space architecture tailored for Factory-X environments, enabling end-to-end automation across the entire workflow from capability registration and request response to order execution and quality feedback. The architecture features a modular design that integrates core functionalities including manufacturing capability modeling, automated quotation, order management, and quality traceability. Prototype system validation demonstrates that the proposed approach significantly enhances the efficiency of handling high-concurrency requests for small manufacturers and effectively ensures product quality in small-batch production scenarios.

Technology Category

• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

One way to solve the challenge of small and medium-sized enterprise (SME) manufacturers of acquiring sufficient orders is by joining digital Manufacturing-as-a-Service (MaaS) platforms for on-demand manufacturing. However, joining such platforms brings about new challenges such as efficient quoting handling in the face of potentially low success rates and the need for high production quality for low lot sizes. Automating the complete interaction between manufacturers and MaaS platforms, from registering the manufacturer and its capabilities to handling incoming requests and managing offers, orders, and production quality reporting, helps to overcome these challenges. Thus, the increased number of requests can be handled efficiently, and the production quality can be maintained at a high level even for low lot sizes. This paper presents an architecture for automating the interaction and functional building blocks between manufacturers and MaaS platforms, along with a prototype implementation and evaluation of its effectiveness in addressing the challenges SME manufacturers are faced with.

Problem

• Manufacturing-as-a-Service
• SME manufacturers
• low lot sizes
• production quality
• quoting efficiency

Innovation

• Manufacturing-as-a-Service
• Dataspace
• Automation
• SME
• Quality Management