With the recent surge in demand for real-time, automatic interaction between systems & devices, among manufacturers and supply chains, it is no surprise that Industry 4.0, Data Analytics and AI have become the catchphrases of the time.
In the first installment of this 2-part blog series, we will explore the business value of Data Analytics for the manufacturing sector, in particular. We will also discuss the key emerging trends in the space and use cases with business potential. In the next part, we will discuss one of our successful Data Analytics implementation cases, with in-depth notes on our client’s requirements, challenges they faced and how our Data Analytics driven solution worked to smart transform their business processes.
Data Analytics & AI in Manufacturing- What’s the Business Value?
- Anytime, anywhere accessibility of asset status, and therefore, faster response time
- Preventative maintenance and reduced downtime
- Real-time data streaming & analysis
- Enhanced diagnostic capabilities
- Remote monitoring capability
With the confluence of AI and Data Analytics, devices have become smarter, and industries are now well equipped to manage their supply chain and enhance their production cycle, increasing returns, while cutting down on expenditure. Today, manufacturers are increasingly adapting to the evolving market segment to stay competitive. What was earlier considered niche technologies, such as Data Analytics and AI, have now gained space in the mainstream.The digital transformation in manufacturing is now prevalent in several different ways- sensor networks collect critical data points, leverage the cloud and actionize on data to smart transform manufacturing operations. In the midst of all this, data continues to be the key currency that companies are harnessing to accelerate innovation, and profits.
In fact, Data Analytics implementations have seen a surge across healthcare, smart cities and manufacturing industries, paving the way for further innovation across the board. According to a recent report by McKinsey, Data Analytics has the potential to generate $4T to $11T in economic value by 2025.
Where are the Data Analytics Use Cases in Manufacturing?
- Facilitate production flow at manufacturing unit
- Manage warehouse operations and monitor inventory/ stock
- Supply chain management
- Improved shop floor and field operations
- Product quality control- identify bottlenecks, prevent machine damage etc
- Industrial safety and security
- Asset management and tracking
Confluence of AI and Data Analytics
While data is collected in real-time from multiple sensors, and transferred to the cloud, it is also analyzed- factors such as equipment usage and maintenance data are collected from various sources- this data is then visualized, and presented on a dashboard/ web/ mobile app for floor workers to access.
The available data is then run through machine learning algorithms, so abnormalities and possible equipment damage can be forecast, ahead of time. This here, is the role that AI plays- predictive models can be formulated from recognizable data patterns. Following testing, these models can be applied, to enable predictive maintenance and anomaly detection.
For our customers, this means a single solution for all their key use cases ranging from Smart Device Management and Connectivity to Edge Computing, Predictive Maintenance, Anomaly Detection and Video Analytics.
We deliver business value across key domains
By maximizing productivity, maintaining uptime, reducing costs and eliminating redundancies, Data Analytics has an active role to play in both small and large enterprises. And the results are clear enough for all to see- faster speed to market, improved demand forecasting & intuitive customer experience.
Stay tuned for our second blog in this series, to learn about our successful Data Analytics implementation with one of our clients in the Cement industry.
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