For a decade, Industry 4.0 has been gradually establishing itself in the manufacturing landscape, transforming modes of production through digital advancements. However, this upheaval is being slowed down by certain market players, notably data historians. Originally designed to capture data specific to operational technologies, these tools struggle with the new demands for harmonization between OT and IT systems. Although they allow for relevant real-time and retrospective analyses, their often outdated structure limits their ability to integrate with modern platforms and support the required interoperability. Thus, the industry must contend with essential tools whose current frameworks hinder the complete optimization of data-driven operations, crucial for competitiveness in this new era.
The challenges of traditional data collection systems for Industry 4.0
At the heart of Industry 4.0 lies a crucial dependence on industrial data. Indeed, modern technologies require unobstructed access to real-time information to enable comprehensive analysis and rapid decision-making. However, traditional data historians, despite their capabilities to process high-frequency data, encounter interoperability issues. Most legacy systems lack robust APIs and struggle to integrate with modern IT platforms. Thus, they become a barrier for SMEs unable to quickly adapt to the new demands of the generalized digital age.
The evolution of the data historian market
In response to the limitations of traditional systems, the data historian market is evolving. Traditional vendors are modernizing their solutions to embrace the interoperability and scalability required by Industry 4.0. Some independents like AVEVA and dataPARC are adopting open interfaces that offer smoother integration with cloud-based systems. This promises better management of real-time data and responds to the growing demands for analysis and predictive maintenance. Nevertheless, the systemic transition requires significant investments and a considerable upskilling of operational teams.
Perspectives for optimizing supply chains
To effectively optimize supply chains, companies must overcome the problem of data cherry-picking presented by legacy systems. The adoption of more modern solutions, such as integration of time-series databases or even the use of open-source alternatives, allows for better capturing and utilization of complete data sets. By leveraging best practices for data collection and management systems, companies can unlock the true potential of Industry 4.0, thereby enhancing their operational and strategic agility.
