In the centre of the industrial rebirth of the twenty-first century there is an ongoing subtle revolution. Once valued for its mechanical expertise, the manufacturing industry is quickly changing to become one driven by data and analytics. The “Factory of the Future” concept is an idea that goes beyond conventional production paradigms and into a world where data is just as important as any tangible raw material.
Industry 4.0 and Smart Factories
In industrial manufacturing, the fourth industrial revolution is the current trend that began in the late 2000s and is continuing to evolve. Currently, companies are talking about the next generation of manufacturing, which is Industry 5.0. However, Industry 5.0 in comparison to Industry 4.0 is a concept that is still in the development stage. The former is designed to connect humans with machines whereas the purpose of the fourth generation of manufacturing is to automate tasks, connect machines to the network and collect data in a centralised location.
In Industry 4.0 the interconnectedness of devices not only enhances efficiency and productivity but also redefines the manufacturing process. Today, smart factories are not just production facilities; they are sophisticated hubs of technology and data, integrated seamlessly through the Internet of Things (IoT), machine learning, AI and the cloud.
The Role of Big Data and Analytics in Manufacturing
Big data, understood as the vast amount of information generated from various sources within an industrial environment, is the cornerstone of manufacturing excellence. Data-driven organisations are more effective and efficient at predicting and preventing problems. Through real-time analysis of data, manufacturing operations can identify and respond to issues before they become problems. From predictive maintenance and quality control to enhanced supply chain and inventory management, data-driven strategies are helping manufacturers optimise their operations.
“In manufacturing, operations managers can use advanced analytics to take a deep dive into historical process data, identify patterns and relationships among discrete process steps and inputs, and then optimise the factors that prove to have the greatest effect on yield. Many global manufacturers in a range of industries and geographies now have an abundance of real-time shop-floor data and the capability to conduct such sophisticated statistical assessments.”
– Auschitzky Eric, Hammer Markus, and Rajagopaul Agesan, McKinsey & Company, How big data can improve manufacturing
Unfortunately, as per World Economic Forum (2021 study), only 39% of executives in the manufacturing industry say they have effectively expanded data-driven use cases outside a particular product’s production process. Businesses should follow the example of big companies like Nestle, Roche, and ABB. They are using data and analytics to enhance decision-making and improve efficiency and quality. This trend reflects a broader shift towards data-driven approaches in manufacturing and business operations. If you do not want to be left behind, read on to find out the benefits of big data and analytics in manufacturing and ways to leverage them to optimise your manufacturing operations.
Sources of Big Data in Manufacturing
Currently, big data is used in manufacturing in a variety of ways. From tracking product quality to monitoring the performance of production lines, big data helps manufacturers improve their operations and make better decisions.
The following are some of the most common uses of big data in manufacturing:
Sensors and IoT Devices
Many devices of advanced industrial equipment have sensors installed to gather data on temperature, pressure, vibration, and throughput, among other characteristics. This capability is further enhanced by IoT devices, like the interface gateways from Moxa or the industrial cellular routers from Teltonika, which connect different systems and equipment. The real-time data collected from sensors and IoT devices help manufacturers identify issues with machines, optimise production processes and implement predictive maintenance.
Machines and Equipment
In addition to sensors, modern manufacturing machines themselves are abundant data sources. They are often equipped with computers and digital interfaces allowing them to collect information about productivity, maintenance logs, and operational data. It helps with analysing machine efficiency, predicting downtime, and improving maintenance schedules.
Production Lines
Data collected from the production line can be used to optimise the entire production process, from the start to stop times, production rates to quality control checks, thereby maximising productivity and minimising waste.
Quality Control Systems
Automated quality control systems gather data on the quality of products and its features, including shape, dimensions, weight, visual defects and other parameters. This is crucial in determining the quality of products, minimising defects and meeting the quality standards.
Supply Chain and Logistics
Data from the supply chain includes information about stock levels, order processing times, shipment dates, delivery schedules, and supplier performance. Analysing such data helps in better planning of inventory levels, distribution and material sourcing by reducing costs, and improving delivery times. If you still need help on how to cope with supply chain issues, read our article here and minimise your supply chain costs.
Human-Interface Systems
Operations challenges and efficiencies can be gained from the inputs of technicians and operators, which are frequently obtained through human-machine interfaces (HMIs). HMIs are often utilities in industrial settings and manufacturing systems by giving information about progress and mechanical performance, assisting operators in controlling machinery and maximising efficiency. Find out more on HMIs in this article and select the ideal human-machine interface for your project.
Benefits of Data-Driven Manufacturing
In the era of Industry 4.0, big data and analytics have become fundamental in driving efficiency, innovation, and productivity. Data-driven manufacturing offers a wide range of advantages that help organisations stay competitive in today’s fast-paced and dynamic market. Here are some of the most significant benefits:
- Increased efficiency – optimised production processes for higher output and better resource use.
- Quality control – early detection of defects allows for timely intervention and minimises waste.
- Predictive maintenance – anticipating equipment failures can help with reducing downtime and maintenance costs.
- Cost savings – enables businesses to identify inefficiencies and opportunities to reduce costs in energy, materials and labour.
- Supply chain optimisation – helps with inventory management and logistics for a more efficient supply chain.
- Customisation and market responsiveness – enables businesses to quickly adapt to market changes, customer demand and new product development.
- Informed decision-making – through real-time analysis and reporting, businesses can make more informed and accurate decisions.
- Enhanced safety – safety risks can be identified and addressed in advance to reduce the number of accidents and minimise the impact on operations.
- Sustainability – through optimised resource usage, data-driven manufacturing supports eco-friendly practices and helps businesses with their sustainability goals.
- Competitive advantage – manufacturers can stay ahead of their competition by leveraging data-driven insights and new technologies.
- Customer satisfaction – customers will appreciate having their needs met quicker, higher product quality and the ability to customise their purchases.
- Strategic business insights – through long-term data collection and analysis, businesses can better understand their customers, and plan more effective and efficient strategies.
5 Steps to Optimise Your Manufacturing Process Using Data
To change your plant into a smart factory and optimise manufacturing processes, it requires using data. A systematic approach to data can improve efficiency, reduce costs and enhance product quality. Have a look at these exemplary steps that can help you successfully gather data and optimise your manufacturing processes:
- Data collection and Integration
Objective: Gather comprehensive data from all relevant sources.
Actions:
- To collect real-time operational data, install sensors and IoT devices on equipment in your facility.
- To integrate business and customer data, utilities systems, such as CRM (Customer Relationship Management) and ERP (Enterprise Resource Planning) can be used.
- Ensure data from supply chain, inventory, and logistics is accessible and easily connected.
Outcome: A holistic view on the manufacturing processes with an integrated dataset.
- Data Analysis and Insights Generation
Objective: Convert raw data into actionable insights.
Actions:
- Use advanced data analytics tools to process and analyse the data.
- Apply statistical methods and machine learning algorithms to identify patterns, trends, and anomalies.
- Collaborate with domain experts to contextualise data insights.
Outcome: Identification of areas for improvement, like quality issues, inefficiencies, and production bottlenecks.
- Process Optimisation and Automation
Objective: Implement changes to enhance manufacturing efficiency and quality.
Actions:
- By modifying machine settings, optimising workflows, or reallocating resources, you can use insights to improve production operations.
- Automate repetitive tasks and processes where possible.
- Key performance indicators (KPIs) should be regularly checked in order to assess the effects of changes.
Outcome: Increased product quality, reduced waste, and increased operational efficiency.
- Predictive Maintenance and Proactive Management
Objective: Increase equipment lifespan and avoid downtime.
Actions:
- Use predictive maintenance algorithms to predict and stop equipment breakdowns.
- Instead of setting fixed intervals for maintenance tasks, base your schedule on data-driven predictions.
- Train staff to react proactively to alerts and indications based on data.
Outcome: Decreased maintenance costs, increased equipment lifespan, and decreased unplanned downtime.
- Continuous Improvement and Adaptation
Objective: Create an environment that is always optimised and adjusted.
Actions:
- Encourage a feedback loop where operators and managers can contribute insights and suggestions based on data.
- Regularly review and update processes in response to new data and changing market conditions.
- Invest in training and development to enhance data literacy and analytical skills among staff.
Outcome: A manufacturing process that is robust and flexible, capable of evolving continuously to satisfy shifting needs and opportunities.
Enhancing Efficiency in the Era of Data-Driven Smart Manufacturing
Adopting sophisticated data strategies is essential for manufacturers looking to enhance operations and obtain a competitive edge in the era of data-driven smart manufacturing. By incorporating these strategies into every aspect of corporate operations, from supply chain management to predictive maintenance, companies may increase productivity and innovate while also positioning themselves for success in a rapidly changing global marketplace. This change, which is essential to Industry 4.0 and will also advance in Industry 5.0, involves rethinking production processes for a time when strategic data use is critical for competitive advantage. It goes beyond simple technology advancements.
