AI Analytics, a subset of Business Intelligence (BI), uses Machine Learning (ML) algorithms to monitor and analyze large chunks of data. These mimic human reasoning and learning in the data analysis process but only with increased speed and accuracy that humans fall short of. In fact, the sheer amount of data that AI analytics can process is simply staggering.
In other words, it takes over the traditionally time-consuming and people-intensive task of comparing data using AI and ML. In the process, it can analyze structured and unstructured data sources using AI analytics tools such as Natural Language Processing (NLP), computer vision, and speech and text transcriptions.
The collection of technologies helps extract patterns and insights to predict factors that drive the outcomes.
That said, AI analytics helps:
- Improve text mining accuracy
- Replicate content to the cloud to aid hybrid analytics
- Exploit insights on mobile
- Streamline dashboard creation
Incorporating AI in Analytics facilitates deriving more value from existing data while unifying it with those from external sources. Legacy analytics tools incapable of handling unstructured data are giving way to AI analytics that integrates data results in the form of texts, transcriptions, and video and image analytics.
Traditional Data Analytics vs. AI Analytics
AI analytics is not meant to replace data analytics. Rather, its goal is to make BI and reporting more efficient. In fact, several researchers worldwide have used AI-based analytics for Covid-19 prediction in the past two years, which acts as a testament to its viability.
On that note, here’s a rundown of the significant differences between traditional analytics and AI analytics.
| Traditional Analytics | AI Analytics |
| Humans are required to test the hypotheses | Possibility of machine-led analysis that tests out an infinite number of hypotheses combinations |
| Commonalities in data trends are reported | Identifies causes of commonalities in the data |
| Results are utilized by data analysts/scientists | Finds insights for analysts as well as business owners |
| A time-consuming process that requires days/weeks | A quick analysis with low time latency |
AI Analytics combines the best of traditional methods with fine AI computational technologies to bring out stellar results for businesses in less time. This serves to explain why a broad spectrum of industries, including manufacturing and automotive sectors, consistently leverage AI analytics.
Use Cases of AI Analytics in the Manufacturing Industry
Since the technology captures, cleanses, and analyzes machine-generated data to predict future uses, forecast maintenance, prevent failures, and identify areas for improvement, it finds itself at the helm of several domains within the manufacturing sector.
Supply Chain Management
AI Analytics is helping establish and maintain a strong communication channel across manufacturing logistics departments. Demand forecasting relies on historical data from supply chains, purchase trends, and consumer trends. Manufacturing analytics can help
- Identify products in high demand across definitive periods
- Calculate and define the scale of production
- Forecast sales figures
- Identify items needed for manufacturing, monitor and replenish stock for uninterrupted production cycles
Maintenance Optimization
Manufacturing machines, tools, and devices, along with aligned devices, give out data collected and analyzed using AI Analytics. These help manufacturing units
- Detect possible machine failure
- Predict maintenance cycles
- Identify causes resulting in such failure to mitigate future issues
Product Development
Historical data sets from production units of similar products using analogous materials, tools, and machines are analyzed for future product improvement and development. Analytics delivers estimations about
- Best product launch time
- Production process
- Possible pitfalls
- Future roadmaps
Digital Twins
Digital twins let manufacturers create virtual copies of the plant process paired with AI. The analytics results let operators find the value propositions and identify lax areas leveraging them to drive improvements. This optimizes the entire operation by:
- Reducing human resources needed to monitor processes, thereby cutting labor costs
- Mobilizing total data for drawing new insights and optimizing operational objectives
- Improving uptime through better predictive maintenance and asset health;
Use Cases of AI Analytics in the Automotive Sector
From the design development of vehicles to their final sales and marketing, the automotive industry is among the most prominent sectors using AI Analytics. There is active use of AI, the IoT, and ML throughout the sector. Let’s explore the most prominent AI Analytics uses:
Design and Development
The use of Augmented Reality and Virtual Reality using sharp AI analytics results is helping automobile manufacturers bring out thoughtful design concepts. The technology eliminates shortcomings in existing designs, making upcoming models more attractive and price-worthy.
Intelligent systems use the data to suggest thousands of designs for future parts and the overall model. Volkswagen is using this Generative Design approach and addressing specific issues. The technology in use is helping realize compact car designs without compromising space or quality.
Cause Analysis
Predictive maintenance is as much a part of the automotive industry as any other manufacturing industry vertical. The system analyzes equipment and compares its specifications with industry safety standards.
These and other relevant historical data are then compared with those of the operative unit to forecast possible performance flaws. It helps eliminate unforeseen machinery or parts crashes while finding the root cause. The entire process is based on AI and ML analysis using a huge stream of current and historical data.
Wrapping Up
AI analytics is already playing a critical role across industries in various capacities. From maintaining streamlined inventories to checking out digital twin performance, giving out predictive maintenance alerts to optimizing post-productions sales strategies, the technology covers the full ambit.
It is helping manufacturing units, including those in the automotive sector, to operate optimally in a highly competitive environment. Their success can be largely attributed to the colossal data found on the floors of modern factories that are primed for analysis and yield lucrative results.
Manufacturers who want to mine this data, analyze it thoroughly, and use insights obtained to devise improved methods of operation are already benefitting from new generation analytics. It is faster, efficient, and leaves no room for human error or oversight.
Get in touch with the AI experts at Ascentt to create a blueprint of your analytics requirements.