Importance of Manufacturing Analytics in Industry 4.0

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Importance of Manufacturing Analytics in Industry 4.0

Data analytics in manufacturing, called manufacturing analytics, is highly useful for enterprises to use their data in making proactive decisions, eliminating unwanted processes, identifying bottlenecks and overcoming them, increasing production quality, improving employee performance, and amplifying revenue. 

According to the Business Research Company, the global manufacturing analytics market is likely to be $14.26 billion in 2024 and is expected to reach $32.39 billion by 2028 at a CAGR (compound annual growth rate) of 22.8%. Additionally, Emergen Research shows that the global Industry 4.0 market value will touch $279.75 billion by 2028 with a CAGR of 16.3%. 

It’s clear that manufacturing analytics is a huge part of Industry 4.0 and will continue to play a vital role in helping enterprises adopt the data-driven model to make faster, better, and more effective decisions in real time. 

Let’s find out how manufacturing analytics solutions can help an enterprise achieve its objectives by streamlining various aspects of the business. 


What is Manufacturing Analytics?

Manufacturing analytics is the use of data analytical and business intelligence tools to collect, store, clean, process, and analyze large amounts of data to derive actionable insights and share data visualization reports throughout the enterprise. Manufacturing analytics solutions can help implement smart manufacturing methodologies through the adoption of digital tools and technologies. Data is collected from internal and external sources and stored in a central repository like a data warehouse or a data lake. This data can be accessed by employees from different departments to make timely decisions. Data is also collected from factories and machinery using IoT (Internet of Things) devices. 

The insights provided by manufacturing analytics are useful for improving operational efficiency, preventing machine breakdowns, making the factory safer for workers, automating production, and making the business model more agile and customer-centric. Manufacturing analytics companies offer end-to-end services to assist enterprises in strategizing, building, implementing, customizing, and maintaining digital data-driven models and technologies.


Why is Industry 4.0 Important in Manufacturing?

Industry 4.0 aka, the Fourth Industrial Revolution, deals with the digital transformation of the manufacturing industry and the adoption of advanced technologies like AI, ML, big data, etc., in MSMEs and large enterprises around the world. It aims to make manufacturing units agile, flexible, and scalable to handle the changing customer demands without increasing expenses. It is a continuous and long-term process where the entire business infrastructure, procedures, systems, tools, etc., are digitalized to create seamless connectivity across the enterprise. 

Data Democratization 

The manufacturing industry was no different from other sectors and followed the same traditional methods of storing data in individual silos in various departments and levels. This leads to data duplication and prevents the enterprise from fully utilizing the potential of its datasets. Industry 4.0 provides a comprehensive solution for data democratization by collecting it from different silos and building a central repository that can be accessed by anyone in the enterprise. It also allows decision-makers to take advantage of advanced manufacturing analytics and derive real-time insights. 

Reduce Cost 

Increasing expenditure is always a concern in the manufacturing sector, no matter what products they produce. Industry 4.0 promotes greater transparency across the supply chain to help manufacturers decrease downtime, increase throughput, improve the OEE score, and find suppliers who offer quality products at cost-effective prices. 

Optimize Resources 

Wouldn’t it be helpful when an enterprise can save its resources without compromising quality or productivity? Industry 4.0 helps to achieve this by highlighting areas with excess or unwanted resource consumption. Manufacturing analytics provides detailed reports and offers effective solutions to overcome the challenges. When a manufacturing unit can reduce resource consumption and improve overall performance, it can become sustainable in the long run. 

Better Customer Experience

Customers are the key to any business. Manufacturers need to consider customer requirements at every stage, be it procurement, production, or logistics. Providing a better experience to customers will increase retention and acquisition rates. The enterprise can use Industry 4.0 practices to understand what the target audience wants and adjust their processes and products accordingly. 


What are the Industry 4.0 Manufacturing Processes?

Several technologies are a part of the fourth industrial revolution apart from data analytics in the manufacturing industry. 

Artificial Intelligence 

AI has a diverse role in manufacturing. It facilitates automation, analytics, decision-making, transparency, and resilience for a sustainable future. AI and ML models are used to streamline production, supply chain, marketing, transportation, equipment maintenance, customer experience, and much more. 

Industrial Internet of Things (IIoT)

IoT devices are connected to the factory equipment or provided to workers as wearables to collect data in real time and share it with other applications in the network. IIoT devices are attached to robots, machinery, products, vehicles, etc., to help managers stay on track at all times by anticipating and avoiding delays. 

Cloud Computing 

Analytics in manufacturing or any other Industry 4.0 process is effective when it is used on the cloud platform. Enterprises digitally transform their systems to move the IT infrastructure to the cloud (private, public, or hybrid) so that decision-makers can access the data and insights at any time and from any location. It is the first step to adopting Industry 4.0 technologies in an enterprise.

Big Data Analytics 

Big data analytics uses data from multiple sources to discover hidden patterns, trends, and correlations. It provides measurable and actionable insights in real-time for manufacturers to stay one step ahead of competitors and make proactive decisions to grab market opportunities. AI and ML tools are used for advanced analytics such as predictive analytics, production analytics, sales analytics, etc.  

Autonomous Robots 

Autonomous robots are AI-based devices that can perform a set of tasks repeatedly with minimal human intervention. This frees up time and resources for employees to focus on the core aspects of the job. It also increases workplace safety as robots can be used to complete risky jobs on the factory floor. 

Additive Manufacturing 

Though addictive manufacturing started as a prototyping tool, it can now be used for mass customization, distributed manufacturing, 3D printing, etc., to reduce production costs, save time, and offer a wider range of products to customers. 

Horizontal and Vertical Integration 

While horizontal integration refers to the connections between different processes at the field level, vertical integration deals with bringing together the various layers of the enterprise. This allows the manufacturer to consider the establishment, as a single unit with interconnected processes. It also allows employees to easily access data from other departments to make faster and better decisions. 

Digital Twins 

A digital twin is a virtual simulation of a real-world setting, be it a machine, process, or product based on the data collected through IoT sensors. Changes are made to the virtual version to understand whether the results will be favorable. Similarly, the virtual version also helps in identifying malfunctioning spare parts, reducing downtime, etc. 

Cybersecurity

With cloud computing, manufacturing data analytics, big data, AI, ML, etc., enterprises need to make sure their data is safe from unauthorized access. This requires robust cybersecurity measures in multiple layers to ensure outsiders cannot breach the systems to steal confidential data. It should also include threat detection, cyber attack prevention, etc. 


Why is Data Analytics Important to the Manufacturing Industry?

Enterprises partner with manufacturing analytics companies to integrate data analytical tools into their systems and derive real-time insights for the following reasons: 

Product Research and Development 

Manufacturing analytics can help an enterprise develop products based on customer preferences and market demands. Customer feedback and competitor’s products can be analyzed to create a new product that offers better quality, price, and performance. Existing products can be redesigned to eliminate their faults. It also helps provide more variants of the same product to the customers. 

Computer Vision 

Computer vision can be termed as one of the manufacturing analytics tools and technologies used to process visual data like images, videos, etc. It has a role in detecting defective products, identifying risky areas in the factory, and providing better surveillance around the various assets belonging to the manufacturer. 

Preventive Maintenance 

Unexpected machine breakdowns are a concern in many factories. This can lead to severe losses due to delays in the production cycle. By analyzing data collected from IoT sensors, managers can schedule predictive maintenance sessions during non-operational hours to replace defective spare parts and ensure the machinery is without any damage. 

Fault Prediction 

Similar to the previous point, factory managers and supervisors can predict faulty equipment and know when it will cause trouble. They can highlight high-risk machinery to take appropriate action before everything comes to a standstill or leads to accidents. 

Demand Forecasting 

Manufacturing analytics solution has a role in forecasting the future demand for a product based on historical and present data. The sales and marketing teams can use these insights to plan effective promotional strategies, identify the target audience, and create personalized campaigns to take the product to more customers and increase sales. 

Inventory Management 

What do manufacturers do with their inventory? How much is too much? How much is too much? Enterprises can rely on analytical reports to adjust the production rate based on the supply vs. demand ratio and possible future opportunities and risks. It allows the business to maintain the right level of stock without incurring losses. 

Price Optimization 

Data analytics for manufacturing is also useful to determine the right price for the product and gain a competitive edge. Manufacturers have to find a balance between the cost incurred to produce the item and the price for which competitors are selling. By combining analytics and AI-based automation, enterprises can automate price optimization to attract more customers. 

Process Optimization and Automation 

Another reason to take advantage of manufacturing analytics is its role in streamlining various internal processes in the enterprise. For example, it can help identify steps that can be eliminated from the production cycle to shorten the process. This results in lesser production expenditure and increases productivity. 

Warranty Analysis 

Warranty support can be a significant expenditure for many manufacturers. Warranty programs cannot be the same for all products as it leads to greater confusion and losses. Data analytics is useful in analyzing active warranties to identify the weak points. It also helps in improving the products to reduce warranty claims. 

Supply Chain and Logistics Management 

Data analytics and manufacturing go hand in hand when streamlining the supply chain and transportation. Supply chain analytics is highly effective in helping enterprises identify reliable suppliers, manage inventory, and plan the distribution of goods from the warehouse to the end-user or seller. 

Greater Operational Agility 

Manufacturing agility is a must in today’s world. Enterprises should have the necessary setup to adjust production at short notice. Data analytics in manufacturing can improve operational agility by creating a seamless workflow in the enterprise that requires minimum human control. 

Transparency, Flexibility, and Scalability  

One more reason to use real-time manufacturing analytics is to future-proof the enterprise and strengthen it to be more scalable and flexible as the production and sales volumes change over time. Making the supply chain more transparent will allow the enterprise to become sustainable.

Conclusion

The manufacturing industry is going through various disruptions. Digital transformation is in full swing and helping enterprises unlock their true potential through data-driven and cloud technologies. 

Find a reliable partner to make use of their expertise and implement manufacturing analytics services to achieve business goals. Reduce downtime, eliminate bottlenecks, overcome challenges, and gain a competitive edge in the global market. 

Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer

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