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.
New analysis from Frost & Sullivan, “Investing in the Currency of the Future: Big Data for the Manufacturing Domain”, finds that discrete industries – such as life sciences, automotive, aerospace, food and beverage, hi-tech and general manufacturing – account for 14 percent of global stored data, and hold tremendous promise for big data and analytic solutions. Additionally, emerging applications like energy management offer substantial opportunities for end users to benchmark and optimise their energy consumption rates
“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,” says Frost & Sullivan industrial automation and process control senior research analyst Rahul Vijayaraghavan. “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 compound annual growth rate of 56.9 percent from 2014 to 2021.
“The current reactive approach recognises the cause of a failure post a breakdown,” says Vijayaraghavan. “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.”
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.
Despite the obvious opportunities presented by predictive analytics in the manufacturing industry in South Africa, few companies have adopted effective machine learning systems. As data analytics becomes more commonplace, we can also expect real-time solutions to anomalies and problems that would otherwise require complete systems analysis. Companies, such as Isazi Consulting and Data Prophet, are pioneering innovative approaches that promise to bring both cost reduction and new opportunities that will breathe new life into local industries.
