Data has always been a part of the manufacturing industry. Still, it was only with the rise of Big Data that everything changed.
Manufacturers can now collect and analyze large amounts of data from their machines, processes, and supply chain. All these insights help them optimize their operations, improve product quality, and reduce costs.
Besides that, Big Data is also driving innovation within companies. Manufacturers can use advanced analytics to identify new business opportunities, design products based on customer needs, and develop new revenue streams.
Overall, Big Data has become the unlocking key to unfolding the full potential of Industry 4.0.
Big Data refers to massive data sets utilized for strategic decision-making. In the manufacturing industry, Big Data involves collecting data at every production stage, including information from machines, devices, and operators.
In this article, we’ll explore the importance of big data for the manufacturing industry and how it is transforming the sector. We will also explore some of the benefits of big data, its use cases, analytics tools, challenges, risks, and the future of big data in manufacturing.
- Why Big Data Analytics is Crucial for Manufacturers?
- Benefits of Big Data in Manufacturing
- Production efficiency
- Quality control
- Predictive Maintenance and Risk Assesment
- Supply chain optimization
- Improved decision-making processes
- Competitive Intelligence
- Overcoming Challenges and the Future of Big Data Analytics in Manufacturing
Why Big Data Analytics is Crucial for Manufacturers?
Industrial manufacturers must deal with complex production processes, intricate cross-company relationships within the supply chain, and continuous pressure to avoid errors (For example, a mistake on the shop floor can be costly and time-consuming).
That is why manufacturers need to expand their data sources to reduce costs, improve product quality, and increase efficiency. Machines, software systems, social media monitoring…Companies can access vast data volumes and turn them into valuable insights.
Overall, companies that use Big Data analytics are:
- Improving key processes
- Eliminating bottlenecks
- Predicting demand
- Anticipating possible failures and delays.
Want to learn more about Big Data Analytics? Read Our Complete Guide
The era of “Smart manufacturing”
Manufacturers can combine digital technologies with advanced Big Data analytics to make their facilities and operations more efficient, productive, and of higher quality.
Data solutions are reducing a company’s time to market and improving the overall service value. “Smart manufacturing” refers to the ability to extract and analyze data from sensors, software, and robotics.
By 2030, the “Smart Manufacturing” growth is expected to increase to 754.1 billion with the help of technologies like Artificial Intelligence, the Internet of Things (IoT), cloud data, Big Data, and Machine Learning.
What are the benefits of “Smart manufacturing“? Better decision-making, wider visibility, and better production and operational efficiency (Both within a company and across its partners).
Manufacturers can unlock more value and speed up innovation by sharing data across different companies. The best practices show that the potential value of data sharing can reach over $100 billion.
Benefits of Big Data in Manufacturing
Within the industry, there is a constant challenge of decreasing costs while increasing outputs and maintaining high-quality standards.
With Big Data solutions, manufacturers can gain real-time insights into production processes. These insights help identify inefficiencies, reduce waste, and increase throughput.
According to McKinsey, a 10% increase in data accessibility can result in a 7% increase in efficiency.
Quality control is everything for manufacturing companies. It goes beyond delivering trustworthy products but also involves safety, compliance, risk reduction, waste reduction, process standards, and more.
This is why Big Data is so relevant in defect detection. By analyzing data from sensors and other sources, manufacturers can detect defects early in production.
How Bosch uses Big Data for quality control
Bosch, an automotive parts manufacturer, uses big data analytics to improve quality control in its production processes. By analyzing data from sensors on production equipment and tracking products throughout production, Bosch can detect potential defects and make corrections in real time, reducing waste and improving product quality.
Bosch has built a robust data analytics capability to support its Big Data initiatives, including advanced analytics tools and platforms. The company uses various technologies to quickly and accurately analyze large volumes of data, including Machine Learning, natural language processing, and deep learning. Bosch also uses cloud computing to store and process its data, which allows it to scale its analytics capabilities as needed.
This is how the company can detect potential defects and make corrections in real-time by analyzing data from sensors on production equipment and tracking products throughout production, reducing waste and improving product quality.
It is important to emphasize that Big Data analytics has become a critical tool for the company to stay competitive, from optimizing manufacturing processes to developing new products and services.
Predictive Maintenance and Risk Assesment
Big Data plays a vital role in predictive maintenance, as it provides the necessary information to identify patterns and trends in equipment performance.
Learn more about Big Data for Risk Assessment: The role of Big Data in risk assessment
By analyzing data from sensor readings, machine logs, and other relevant sources, predictive maintenance systems can identify potential issues before they cause equipment downtime, supply chain disruptions, or production delays.
Learn more about Predictive Analytics: Read Our Full Guide
How John Deere takes predictive analytics to another level using data?
John Deere, a manufacturer of agricultural machinery, has implemented a predictive maintenance program that has resulted in a 20% reduction in downtime.
When a piece of equipment breaks down, it costs the dealer money to repair or replace it. The issue results in customer downtime, leading to lost productivity and revenue. By identifying potential problems before they occur, John Deere can schedule maintenance during the off-season (or at a convenient time for the customer), reducing any downtime impact.
To implement predictive maintenance, John Deere collects data from sensors on its equipment, such as engine temperature, oil pressure, and hydraulic pressure. Then, the company’s data analytics platform uses this information to identify potential issues.
As a final process, the platform uses machine learning algorithms to detect patterns indicating when a piece of equipment is likely to fail.
In addition to predictive maintenance, John Deere also uses data analytics to improve its equipment design. By analyzing data from its equipment sensors, the company can identify improvement areas (Such as reducing fuel consumption or improving performance).
Supply chain optimization
By analyzing data from suppliers, transportation companies, and other sources, manufacturers can optimize their supply chains, reducing costs and improving delivery times.
You can also read: Types of Supply Chain Analytics & why is important?
Do you know? Using Big Data tools you can research and understand the supply chain of any company to identify, measure and understand their supplier & customer relations. Learn more about Supply Chain Mapping Tools.
How UPS uses Big Data to optimize its supply chain?
UPS, the global package delivery and logistics company, has been using Big Data analytics to improve its supply chain operations for several years.
Using Big Data solutions, UPS gains insights into customer behavior, optimizes delivery routes, and streamlines its operations. The result? Significant improvements in its performance and efficiency.
One of the ways UPS uses data analytics is by collecting and analyzing information about its deliveries. By tracking package data like location, weight, and destination, UPS can optimize its routes and reduce delivery times.
For example, the company uses advanced analytics and machine learning algorithms to identify the most efficient delivery routes, avoid traffic congestion, and optimize the use of its vehicles.
Another way that UPS uses Big Data is by analyzing customer data. By collecting behavioral and preferences data, UPS can better understand its customers’ needs and preferences. That is how the company came up with new ideas to offer faster delivery options and provide real-time updates.
Also, the company can maintain high service levels and minimize downtime by collecting and analyzing data from its vehicles, warehouses, and distribution centers. This competitive advantage is crucial in the high-demanding world of logistics.
Improved decision-making processes
As we have already mentioned, manufacturers can gain competitive intelligence regarding customer preferences, production processes, and other key areas by collecting and analyzing data from various sources.
How is GE Aviation gaining more competitive intelligence using Big Data?
GE Aviation, one of the world’s leading aviation technology references, has been using big data to make better decisions and improve its operations.
The company collects and analyzes large amounts of data from various sources, including aircraft sensors, maintenance records, and weather reports, to gain insights and optimize performance.
For example, it is leveraging Big Data to improve fuel efficiency. The company has developed a tool called FlightPulse, which uses data from aircraft sensors to provide pilots with real-time feedback on optimizing fuel consumption. This information helps airlines save money on fuel costs and reduce their carbon footprint.
In addition, GE Aviation is also using Big Data to improve safety. By analyzing data from flight data recorders and other sources, the company can identify safety risks and take proactive measures to prevent accidents.
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We assist our clients in mapping out and monitoring markets and supply chains, and revealing and presenting covert connections among different companies.
Overcoming Challenges and the Future of Big Data Analytics in Manufacturing
Although big data analytics can offer significant benefits to the industry, some challenges need to be addressed. For example, data quality, security, and interoperability are some elements that manufacturers need to consider.
Nonetheless, the potential benefits of big data analytics in manufacturing outweigh the challenges.
What to expect from Big Data in the future? Certainly, it will continue to transform the industry. Companies will adopt more advanced analytics to improve their processes and gain a competitive advantage.
Big Data Analytics solutions have become necessary, so manufacturers will need to continue exploring new applications, tools, and techniques.
In conclusion, Big Data (as a whole) offers numerous benefits to manufacturing companies, including increased efficiency, better inventory management, improved decision-making processes, and predictive maintenance.