Augmented Analytics - Shaping Tomorrow’s Manufacturing Industry
Augmented Analytics- The next stride in AI: Artificial Intelligence is a hot button topic in the field of data and analytics technology with significant disruptive potential. An AI-enabled system processes and sorts through copious amounts of information to identify patterns and predict outcomes. But determining causal relationships is where Augmented Analytics, the next frontier in AI’s evolution, comes into play.
In 2017, Gartner introduced the concept of Augmented Analytics as part of its annual edition on “Hype Cycle for Emerging Technologies”. According to the Research Firm, Augmented Analytics is an approach that automates insights using machine learning and natural-language generation (NLG). The global Augmented Analytics market garnered USD 4.09 billion in 2017 and is expected to reach USD 29.86 billion by 2025, registering a CAGR of 28.4 percent from 2018 to 2025. It is majorly driven by factors like, the need to increase productivity and efficiency, the increasing need to democratize analytics, etc., Let’s decode the definition by understanding what the terms — Machine Learning and NLG mean.
Machine Learning: According to McKinsey, Machine Learning is based on algorithms that can learn from data without relying on rules-based programming. Much like us humans who attain proficiency in a field with experience, machines also perfect certain tasks by learning and processing a ton of data.
Natural-Language Generation: NLG translates a machine’s findings into words and phrases that humans can comprehend.
Both these technologies come together in Augmented Analytics to facilitate organizations to automate the labor-intensive process of data analysis and help convey important findings and insights in understandable formats.
To sum up the concept of Augmented Analytics, let’s take the example of the automobile industry. The automobile is a complex structure and comprises more than a thousand parts that contribute to its proper functioning. But it is not necessary that a user knows and understands the functioning of all these parts to use the automobile. Similarly, Augmented Analytics enables people with little or even no knowledge about data to carry out complex analysis by taking the complexity out of the equation.
Like every other sector, the manufacturing sector has also caught the wave that this disruptive technology has managed to build.
Source: modefinance.com
Application of Augmented Analytics in the Manufacturing Sector :
- Plant maintenance and Preventing Unexpected Downtimes: Solution providers in the manufacturing sector, specializing in analytics are leveraging the power of Augmented Analytics to understand the variables driving their Key Performance Indicators (KPIs). Augmented Analytics, in this case, can automatically find meaningful relationships between a given KPI and countless business variables, and generate visualizations and dashboards that narrate the KPI’s behavior in a simplified manner that can be comprehended by non-technical business users too. For asset maintenance teams, the goal could be to monitor and predict failures of a potential asset well in advance to prevent the failure from taking place. More than that, modern analytics can help prescribe the best response to prevent such failures. The power of Augmented Analytics can be reaped to track the cost of asset downtime, to find out how much to invest in replacement parts, to monitor overall equipment efficiency and energy consumption.
- Asset Repairs through NLG: Personal digital assistants aided by NLG can listen and respond to voice commands. Technicians and foremen working on the site of operation or repairing assets on the shop floor can use this technology. Personal digital assistants can also benefit from a semantic layer for appropriate responses
- Customized performance measurements: Plant operators can be given the option to control the variables of a machine or a set-up in order to define personalized priorities rather than relying upon the pre-set performance measurements
- Asset availability: Application of Augmented Analytics in the metals industry, a process-oriented sector where precision is paramount, can differ from the other manufacturing industries. The most probable areas of application of the technology in this industry are asset availability, ensuring equipment reliability, consistency, etc. that contribute to the continuous production process. Tracking and predicting quality and learning from historical data can prevent production disruption and process variability, ultimately ensuring precision in the final product
- Identifying defects: The application of Augmented Analytics in the manufacturing sector can also extend to unearthing defects in a process or a product. This aspect of the technology can help the operator visualize defects by the use of imaging/video technologies and identify the problem using intelligent machines before the final product reaches the shelves
- Miscellaneous variables impacting asset performance: Other miscellaneous variables like geographic location, weather, product specifications, etc., that impact the performance of a commodity can also be fed to the system during the manufacturing process. Built-in machine learning capabilities can help the machine learn such user expectations over time thus boosting machine efficiency and plant performance
Major deterrents to Augmented Analytics :
- Worry that machines will take over human jobs- Technicians, experts, data scientists, etc., in the manufacturing sector, fear that technology and machines will take over their jobs which will translate to a reduction in the headcount. This fear may keep them from pursuing a career in the manufacturing sector. Gartner predicts that “through to the end of 2022, data management manual tasks will be reduced by 45 percent through the addition of ML and automated service-level management”. Implementing Augmented Analytics in an organization’s fabric can fully or partially automate manual tasks that are highly repetitive in nature, allowing Data Scientists to concentrate on deeper research that machines cannot support
- Costly and complex buy: The manufacturing industry may think that implementing Augmented Analytics and Artificial Intelligence can be a costly and complex affair. Though the sector as a whole may come to terms with the importance of insights, the idea might take a while to reach the marketing side organizations
Manufacturing companies that can work through the above concerns surrounding the implementation of Augmented Analytics can reap its benefits to create an omnichannel approach to data analytics, with potential solutions and insights storyboarded by the technology.
Conclusion:
The manufacturing sector can stay abreast of the technological developments by implementing augmented analytics in its core business fabric. Augmented Analytics may prove crucial for the sector to stay relevant and competitive. It can help industries in a range of activities right from planning downtimes to predicting unexpected shutdowns.
Factors like increasing the need to scale up productivity, the need to utilize abundant amounts of data, and the growing need to make data accessible for citizen data scientists are driving this technology. Augmented Analytics can help cut down the long, drawn-out process of getting questions answered by a team of data-literate members and automatically populate insights and solutions in a format that is understandable to all business users. It would be good to bear in mind that, like other technologies, Augmented Analytics is not a replacement for critical thinking and does not intend to take over humans and their jobs. In fact, it can prompt organizations to think about data and analytics more critically than ever before. Currently, the scarcity for good data scientists is preventing medium and small-scale businesses from using their data effectively. Augmented Analytics can make the data analysis more affordable, more accessible, easier and better — enabling more businesses of all sizes to benefit from the technology.
For more information or further queries, please email us at sales@clairvoyantsoft.com or call us at (623)282–2385. Learn more at: http://clairvoyantsoft.com.
References -
Dorian Pyle and Cristina San José., (June, 2015): An Executive’s guide to Machine Learning. Retrieved from-
https://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning
Pete Reilly.,: Augmented Analytics 101: A Starter’s Guide. Retrieved from-
https://selecthub.com/business-analytics/augmented-analytics-guide/
(November, 2018): How Augmented Analytics Is Helping Business Analysts Build Prediction Models. Retrieved from-
https://imarticus.org/how-augmented-analytics-is-helping-business-analysts-build-prediction-models/
Kevin Price., (January, 2019): Reducing the Barriers to Entry in Advanced Analytics. Retrieved from-
https://www.manufacturing.net/article/2019/01/reducing-barriers-entry-advanced-analytics
Infographic: Business analytics and financial technology concept, 3d chart from the screen of digital tablet computer. Retrieved from-
Infographic: (December, 2018): A comparison between the current data analysis approach and Augmented Analytics. Retrieved from-
https://www.modefinance.com/en/blog/2018-12-06-augmented-analytics-what-does-it-really-mean-
