The traditional method for collecting and managing manufacturing data involves retrieving raw data from shop floor equipment and then processing each piece of data through a configuration process to transform that data into information that is useful for further processing by software applications. Once configured, the transformation process itself is automatic. However, the configuration process that defines the rules for the transformation is a significant issue in most software systems.
There is an information systems technology that has been finding its way into the manufacturing world that addresses many of the issues associated with the costs and complexities associated with the configuration process. This technology is called “semantic data models.”
Semantic data models are used to enhance data produced from shop floor equipment and other manufacturing processes by incorporating information (semantics) such that a software system can read the data and fully understand its meaning and how each piece of data relates to the manufacturing process.
