Making Data Smarter with Semantics

As production processes are getting increasingly complex and demanding regarding quality and efficiency, the role of data in decision-making, process optimization, and innovation is unparalleled. However, the true power of data lies not just in its collection but in how data is interpreted, brought into context and utilized. Semantics, or the meaning behind data, plays an important role in transforming raw data into actionable insights. A cornerstone of this transformation is metadata.

Metadata can be described as “data about data.” It provides contextual information about data, such as its origin, the time it was generated, the format, the context, and structure. In essence, metadata transforms raw information into structured, searchable, and useful knowledge.

In industries like plastics processing, metadata serves as the backbone for understanding and efficiently using data. For instance, in injection molding, metadata can include details about products, lot sizes, machinery, raw materials, environmental conditions, and production parameters. This data, when contextualized, helps operators and managers make informed decisions, optimize production, and reduce waste.

Why do we need metadata?
By collecting and organizing metadata, businesses can automatically operations like calculating key performance indicators (KPIs) like overall equipment efficiency (OEE), scrap rates, and energy efficiency. This automation not only saves time but also ensures accuracy and consistency in performance evaluations. Furthermore, it is critical in production, especially in industries like plastics processing, where maintaining quality and meeting compliance standards are paramount.
In the long run, metadata forms the foundation for artificial intelligence (AI) applications. Machine learning models can use metadata to identify patterns, collect connected information and derive insights as well as actions.

How to reliably acquire metadata?
For metadata to be useful, its acquisition must be systematic, reliable, and user-friendly. Strategies to robustly acquire meta data are systematically defined regulations or rules, which enforce (mostly by manual work) metadata acquisition, System-enforced mechanisms, which only allow certain actions or processes, when all necessary information are filled out, ergonomic measures to encourage data input and automized acquisition from connected systems, which include systems like ERP or MES systems.

The OSPHIM Approach: Metadata in Injection Molding
The OSPHIM approach embodies the principles of effective metadata utilization tailored for the plastics processing industry. It combines web-based interfaces for ergonomic and flexible user access as well as maximum device compatibility with purpose specific data-structures to acquire all necessary contextual information along with real data. Furthermore, OSPHIM focusses on injection molding to minimize effort and maximize ergonomics for the user. Furthermore, OSPHIM focusses on achieving maximum connectivity to real production equipment as well as production software systems.

Making data smarter with semantics involves understanding the critical role of metadata, ensuring its reliable acquisition, and optimizing its storage and usage. In industries like plastics processing, approaches like OSPHIM highlight the importance of tailoring semantic systems to specific needs, enhancing productivity, traceability, and innovation. By prioritizing semantics and metadata, businesses can unlock the full potential of their data, paving the way for a smarter, more efficient future.

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