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ColProLan: A DSML for Blockchain-Based Collaboration in Industry 4.0

2026 · IEEE Access · Vol 14, pp. 153255-153284 · 0 citations · 68 references

TL;DR

A Model-Driven (MD) solution that enables collaborating partners to configure a blockchain-based monitoring platform automatically and raises the level of abstraction from the technical to the business domain, allowing partners to specify models encapsulating each partner's expected behavior using only business domain-related concepts.

Abstract

In today’s world of fast-paced technological change driven by Industry 4.0, companies gain a competitive edge by adopting new technologies, innovating, and delivering small batches of customized products quickly. This has put Small and Medium-sized Enterprises (SMEs) at a disadvantage, as they often lack resources and expertise to keep up. To succeed in such a demanding market, SMEs are joining forces by forming collaborative production networks. Still, many SMEs are hesitant to join such networks as they lack trust in potential partners. Achieving trust between SMEs is a significant challenge that can be addressed by ensuring transparency in network operations. A blockchain-based collaborative production monitoring platform could be a solution to ensuring transparency, as blockchain offers a trustworthy way to exchange production data between collaborating partners. However, utilizing blockchain requires a high level of technical expertise that SMEs might not possess. To avoid the need for excessive technical expertise, we introduced a Model-Driven (MD) solution that enables collaborating partners to configure a blockchain-based monitoring platform automatically. The solution is centered on an innovative Domain-Specific Modeling Language (DSML) called ColProLan, presented in this paper. ColProLan raises the level of abstraction from the technical to the business domain, allowing partners to specify models encapsulating each partner’s expected behavior using only business domain-related concepts, while also enabling them to use the created models for automatically configuring a blockchain-based monitoring platform. We systematically evaluated the solution, confirming its potential to facilitate trustful collaboration within collaborative production networks.

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