Need for analytics drives advanced machine learning

Additionally, emerging applications like energy management offer substantial opportunities for end users to benchmark and optimise their energy consumption rates.

The Internet of Industrial Things (IoIT), a concept derived from the ability to connect assets, business processes and people across the enterprise, has transformed the plant floor into a state of hyper-connectivity. However, with greater connectivity comes a drastic increase in the inflow of data. In addition to traditional structured data, the manufacturing sector is facing a spike in semi-structured and unstructured data from sensors, machines, web and social media. In the current setup, it is therefore imperative for end users to append new data management tools to store and process the large influx of data as well as utilise state-of-the-art analytical platforms to derive actionable insights for core operations in the facility.

Rahul Vijayaraghavan, Senior Research Analyst, Industrial Automation and Process Control, Frost & Sullivan, commented: “With respect to data storage and integration, cloud-based systems are most feasible for small and medium enterprises with fewer customisation requirements and distributed user locations. Businesses are also turning to hybrid solutions, which enable the storage and integration of specific data in public and private clouds depending on sensitivity and security.”

Post data capture and collation, the need to derive value from the data will push for advancements in analytical platforms. Proactive improvement in asset uptime and streamlining maintenance activities will particularly generate intense interest in predictive and prescriptive analytics. The demand for predictive and prescriptive solutions is expected to record a CAGR of 56.9% from 2014 to 2021.

“The current reactive approach recognises the cause of a failure post a breakdown. In contrast, applying complex statistical algorithms and machine learning techniques to evaluate historical and real-time sensor data will help end users identify potential equipment malfunctions well in advance,” added Rahul Vijayaraghavan.

In terms of the visualisation of these metrics, customisation is paramount. The creation of user-friendly, highly intuitive interfaces to analyse critical data based on individual personnel requirements is central to any successful big data deployment. Thus, strengthening the ability to seamlessly store, centrally integrate, proactively analyse and effectively depict end user critical data will continue to open doors for big data providers in the manufacturing industry.