Manufacturing and IoT Analytics: The Data is Reaching Critical Mass

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In our recent blog on the impact and capabilities that IoT Analytics brings to supply chain management, we talked about a number of dynamic themes emerging at the nexus of IoT and supply chain management.

Since we published that blog, we have also seen a significant number of interesting articles and research reports in the manufacturing market around IoT and IoT Analytics. The supply chain momentum is closely related to and contributing to significant activity in manufacturing.

One manufacturing and IoT article that caught our attention appeared in Enterprise Innovation and focuses on the various IoT use cases and scenarios where integration of previously disaggregated islands of data can help manufacturers become much smarter. Ashish Pujari makes a persuasive case for leveraging IoT to monitor real-time conditions and look beyond the limits of individual factories. He cites an SCM World study which indicates that by 2020, only 10% of manufacturing companies will be optimizing production at the individual factory level. Executing at that level will demand new capabilities in analytics – especially predictions around demand and resource requirements.

Manufacturers have also been applying predictive analytics with IoT to some of their most pressing management challenges. There are a number of areas where predictive analytics can drive value, but learning more about how complex manufactured products are actually being used in the field is among the most interesting. In this recent piece in Predictive Analytics World, Bala Deshpande interviews Richard Semmes, the Senior Director of R&D at Siemens PLM. Richard specifically highlights scenarios where predictive analytics “serves to find issues with products we did not know existed.” Also, he talks about the importance of external environmental issues and how that data can help predict and resolve problems by leveraging that data. This kind of integrated approach to IoT analytics shows how manufacturers are indeed reaching critical mass in their sophistication with IoT.

While there are a number of areas where manufacturers are accelerating progress with IoT and Analytics, the complexity of manufacturing and IoT still presents a number of challenges. In a recent article on Advancedmanufacturing.org, John Younes writes about the gap in industrial IoT stack around network edge performance, legacy connectivity, security, and other adoption hurdles. While none of these issues are news, together they do present a considerable challenge. He goes on to write about his company’s particular solution to these challenges, but the generalized point he makes is broadly important – to consider the challenge as a whole vs. partial or point solutions. So, for example, if connectivity issues can be resolved at the edge, this facilitates the processing of data and advanced analytics there as well – which enhances overall performance by as much as 40% according to John.

Finally, we also caught an article in Machine Design, which discussed the growth of artificial intelligence in manufacturing. Nancy Friedrich hosted a panel at the recent Industrial Design & Engineering Show. The panels warns about some of the possible pitfalls in applying AI to manufacturing, but concludes with the optimistic view that careful use of AI and machine learning can turn large volumes of data into a proactive management tool to drive manufacturing efficiency and faster-decentralized decision-making.

Summary

Manufacturing and IoT are undoubtedly experiencing a major growth phase in 2017. Manufacturers of all types are leveraging IoT and advanced analytics to drive value in a wide variety of applications in their business. Many are progressing past simple measurement and monitoring to advanced techniques with predictive analytics and machine learning. But, some major issues and challenges remain – including security, network performance, and integration with legacy factory floor technologies. Nevertheless, the growing recognition of the power of analytics, use of external data, and integration across wider business units is driving more value for manufacturers who have been working hard on IoT adoption.

 

 

 

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