Real-time data and its management are pertinent to multiple subdomains in manufacturing. IIoT data management facilitates innumerable combinations among these subdomains. Manufacturers can use data to quickly and more effectively align with external and internal dependencies like market demand or raw material availability, among others.
The transformation towards a connected manufacturing ecosystem like connected factories has emphasized the importance of IoT data management. An IoT enabled industrial data management system is a natural complement to technologies powered by artificial intelligence, machine learning, and big data. Let us take a look at the benefits and challenges in IoT data management for industrial IoT.
IIoT data management is responsible for improving operating costs and facility management. The multiple data forms involved in operations like inventory levels, purchase pipeline, and capacity utilization are covered here. Appropriately planned and executed data management system enables better decision making for cost optimization and resource utilization.
An IIoT data management system further ensures that the benefits of identifying leading indicators of quality and efficiency have a more significant positive impact. It also helps reduce, if not eliminate, the effect of lagging indicators.
IIoT data management is a critical driver for meeting and exceeding production targets. Data management ensures the quality of data being collected and processed by internal stakeholders, dependent processes, and data processing tools. Data management creates a seamless flow of valid and clean data between these multiple interlinked systems is made possible.
The advantages of a connected manufacturing system are amplified through data management. Helping increase the effectiveness of predictive quality, asset utilization, and in decreasing wastage.
Data management in an industrial manufacturing set up involves:
• Standardization of data formats, file types, and sources
• Definition of rules for data access, transport, and analysis
• Compliance management for operations and safety regulations
These steps work in close cohesion to identify and process data much faster. In the process, it also helps reduce the time required to predict incidents or its precursors. The reaction time involved in handling handle exigent circumstances can also be considerably reduced.