3 Ways Cloud, Big Data Analytics and AI Will Redefine Manufacturing in 2017

By Vinay Nathan -CEO, Co-Founder, Altizon Systems

Vinay Nathan -CEO, Co-Founder, Altizon Systems

Past few years, pretty much every industry has realized the value of Big data. It has largely influenced the executive level decision making right from shop floor to top floor (boardrooms). The enterprise of today looks at the data as a silver bullet  to help solve all their problems and achieve key objectives; be it to improve overall efficiency, or achieve cost effectiveness, or to find how to build a competitive advantage.  

“Big data is not about the data. The data is plentiful and easy to collect, the real value is in the analytics” ~ Gary King, Harvard University

The rise of data storage and analytics is not sudden rather it is a gradual process with boom of several technologies that served as a driving factor.  The rise of cloud has led to creation of large data repositories of data, that are accessible to people and software systems anywhere, anytime. This followed by the major strides made in big data technology, has enabled processing large amounts of data in a standardized way rapidly. The last piece of the puzzle is advanced neural networks driven AI systems that exponentially enhance the possibilities for machines to learn and apply that learning to software systems. According to Gartner report, the applied AI and advanced ML and IoT are amongst the top technology trends in 2017-18, which means it is going to be an exciting ride for the data enthusiasts.

Here is how the industry will be redefined in 2017 by this triumvirate of forces:

1. Benchmarking: Analytics has ceased to be an intra-organizational affair. In the age of collaborative manufacturing, Enterprises want to know how their customer is doing and the supplier of their supplier also. Thus, CXOs need to benchmark not just machines of the same plant against each other, but also between plants of the same enterprise as well as with the metrics of their competitors and overall industry. On the other hand, with component outsourcing becoming a norm in manufacturing, often the product itself is an assembly of diverse types of components, outsourced from disparate vendors. In such situations, the quality and effectiveness of manufacturing processes of all components play an important part in the performance of the final product. In these cases, it is not a competition between one product vs other, but between one eco system to another. To stay ahead of the curve, top level executives of enterprises need a technology that brings the quality of factors onto a single platform by the virtue of realtime & error free data that enables benchmarking to avail real-time and deeper insights for proactive & agile decision making across the entire supply chain.

2. Artificial Intelligence on cloud: The Cloud has come a long way from being a dumb repository for data storage. 2016 saw the beginning of intelligent cloud, powered by next generation technologies like AI & Machine learning, thereby arming it with huge computing power. Enterprises like Microsoft, IBM & Google have been making significant strides by offering cognitive services like image processing, language comprehension, translation etc. 2017 will see the cloud technology moving beyond the conventional functionalities of data storage & computing to foray into unventured areas like data mining, predictive intelligence. It will create and access various intelligent services. The new age cloud, armed with in stream analytics and AI capabilities will be able to integrate diverse types of data seamlessly and enable AI powered decision making. On the shop floor, this means the cloud will now be able to provide ability to filter patterns, anticipate & relate relevant information, and point out anomalies in the information generated by IIoT systems, further refining and enhancing productivity. The Holy Grail for AI is not only to predict disruptions in the supply chain but also take automated corrective actions that balance their impact.

3. Edge Computing: Lastly, it is noticed that the CXOs are many a times get pulled in for post several of critical situations that arise from a seemingly minor irregularities on shop floor. Ex: production of out-of-spec components, equipment downtime, worker injury, hazardous incidents, etc. An analysis would reveal that local data aggregations across diverse data sets, with analytics closer to the source of the anomalies would in many cases have helped prevent these situations from escalating. Edge computing is a promising remedy for this issue. Essentially, it moves the computing technology right to next to the sources of data i.e a shop floor or facility. Thus, it works on empowering the local staff to take these decisions by relying on intelligent gateways that do some of the heavy lifting that would otherwise have been performed on the cloud. By effectively utilizing the recency-to-value of the data (fresh data analyzed in real time), avoiding network bottlenecks and reducing service latency, enterprises can dramatically improve response time to local disruptions and create actionable insights locally. In turn, it gives more bandwidth to the CXO to instead focus on the strategic goals for the enterprise. Applications like predictive maintenance, equipment health monitoring & safety control (poka yoke) be then performed by utilizing this power of edge computing. . All these applications can be enhanced by distributing the compute optimally between the edge gateway and the cloud. Such a system would be a continuously learning system that benefits from the immediacy of local action and the learning distilled from the global know that the cloud computing system possess.

Industry 4.0 is widely touted as the future of manufacturing. Enterprises adopting the value of Cloud, Big Data Analytics and AI in 2017 will be best positioned to the manufacturing powerhouses of tomorrow.

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