Using advanced analytics and AI to drive value in Manufacturing.

The value of data in today’s industries is greater than ever providing businesses information about their internal operations, customers and product delivery status. In the manufacturing space, data is the linchpin of manufacturing processes as it notifies manufacturers to look upon which machine is performing accurately, where maintenance is required and so on. But to get the most out of data, companies require advanced analytics, artificial intelligence and other technologies. 

A majority of manufacturers have already started implementing such and other emerging digital technologies to transform production and supply chains. However, many face challenges in applying and managing data effectively and maximizing their ROI. Hence, by sharing data across companies, manufacturers can reap additional value and expedite innovation. According to a WEF’s white paper, the potential value of data sharing simply by focusing on manufacturing process optimization has been estimated at over US$100 billion.

Data sharing in Manufacturing

There is no denying fact that data is the new oil for businesses today. And the ability to share the data with others can generate huge value for the companies. In the manufacturing ecosystem, data sharing can deliver much value as this process can enhance asset optimization, product visibility of value chains, process conditions tracing along the value chain, exchanging of digital product characteristics, and provenance verification. 

In a recent BCG global survey, nearly three-quarters of manufacturing managers globally considered data sharing to improve their operations. Innovative companies use shared data not only to advance existing applications of technology but also to deploy applications that would otherwise not be possible.

Data sharing simply happens when manufacturers make their data available to their selective companies and organizations. It starts with strategic collaboration and to make collaboration on data sharing to be successful, stakeholders need a good understanding of how to promote value together, the WEF white paper noted. This collaboration can be established between manufacturers through a third-party solution provider, between direct suppliers and manufacturers in a supply chain, or between manufacturers through a machine supplier. Moreover, the report identified three factors that can promote success including a clear value proposition and rationale for data sharing, mutually beneficial agreements, and the use of secure technologies and common standards.

Besides advantages, interoperability and data collection are the among crucial challenges of data sharing.

Data Challenges in Manufacturing

Today, manufacturing supply chains have been challenged to meet ever-growing product demand. This is making productivity gains more critical to gain a competitive edge in this vertical. One of the main challenges is the distributed nature of industrial data sets. As data is generated from different sources, it usually ends up being structured and presented in totally inconsistent ways, creating impediments in industrial data access, integration and sharing.

Though the extensive integration of cost-effective sensors and their connection to the internet will drive benefits, they also generate massive amounts of data that need to be processed and analyzed effectively. Industrial data storage capacity is another challenge for manufacturers that can derive relevant, meaningful insights. 

Thus, by implementing intelligent data analytics tools and techniques that are able to glean and process data, manufacturers can succeed in their data processing practices. They must leverage devices that understand the current status or health of machines, communicate with other systems and devices, and be able to react to configuration or operational changes securely.