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
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