Tech advancements are revolutionizing the production of goods worldwide. Automation, especially in machines and robots, has turned the manufacturing industry around. With new technologies, manufacturers can now produce more complex and intricate parts, components, and equipment with high precision, quality, and speed.
Today, almost anything can be manufactured through automation with minimal human intervention. From small parts to large and complex components, automation has eliminated the limitations of traditional manufacturing processes.
Some examples of automated manufacturing systems are:
1. Automotive parts, such as engines, brakes, and steering systems.
2. Electronic devices and components like smartphones, tablets, and laptops.
3. Medical equipment, prosthetics, and surgical tools.
4. Aerospace equipment, such as turbines, engines, and aircraft components.
5. Household appliances like refrigerators, washing machines, and ovens.
6. Industrial machinery and tools like drills, lathes, and presses.
7. Construction materials and equipment like pipes, beams, and concrete blocks.
8. Food and beverage products include canned goods, bottles, and packaging materials.
9. Clothing and textile items like shirts, pants, and fabrics.
According to Mordor Intelligence, the global workflow automation market is expected to reach $34.18 billion by 2029, with a compound annual growth rate of 9.52%.
This shows the growing adoption and demand for automation technology in various industries. Despite these advancements, many companies still rely on manual processes.
Only 31% of companies have adopted robotic process automation (RPA) technology, while AI is at the bottom with only 18% adoption. This suggests that there is still a significant percentage of companies that are lagging in terms of automation.
Let's explore the common manufacturing issues that automation can solve or significantly reduce.
4 Most Common Issues Faced by Manufacturers
Manufacturing businesses can be incredibly complex, involving multiple stages, machines, and people. This complexity often leads to challenges and obstacles hindering production efficiency, quality, and profitability. Here are the 4 most common issues faced by manufacturers.
1. Production delays: when the clock never seems on your side
Production delays are a major concern for manufacturers. Every minute of delay can result in losses and setbacks, especially with tight deadlines and high demand. Manual processes are often the main culprit for production delays. Human errors, machine breakdowns, and inefficient workflows all contribute to delays in production.
Automation in manufacturing industry reduces production time significantly by streamlining processes and minimizing human intervention. Automated machines work 24/7 without breaks, reducing downtime and increasing productivity. For example, in automotive manufacturing, a robotic arm can paint a car's body in minutes, whereas it would take hours for a human to do the same job.
2. Inconsistencies in product quality: striving for consistency in every piece
Manufacturers always strive for consistency in product quality. However, manual processes are prone to human errors, leading to variations in product quality. This is particularly problematic for complex and intricate parts that require high precision. Even minor deviations from the required specifications can cause significant issues.
Automation in manufacturing process ensures consistent quality by following exact specifications and measurements every time. This eliminates variations in product quality, resulting in higher customer satisfaction and lower rejection rates.
3. High labor costs: the wage challenge you can't ignore
Labor costs are a significant concern for manufacturers, especially in countries with high minimum wages. Labor-intensive processes tend to be costly and less efficient as they require more resources, including time and personnel. This can significantly impact a company's profitability and competitiveness.
Automation of manufacturing process reduces labor costs by eliminating the need for manual labor in production processes. Automated machines can perform tasks that would otherwise require multiple workers, resulting in cost savings in the long run. Additionally, automated processes are more efficient and accurate, reducing the need for rework and saving time and resources.
For example, in the textile industry, automated sewing machines can perform tasks requiring multiple seamstresses to complete manually. This reduces labor costs and increases productivity.
4. Inventory management challenges: a delicate dance between surplus and shortfall
Finding the perfect balance between surplus and shortfall in inventory management is a common challenge for manufacturers. Excess inventory ties up resources and capital, leading to storage costs and waste. On the other hand, insufficient inventory can result in missed opportunities and delayed production.
Automated production line streamlines inventory management by providing real-time data on stock levels and demand. This allows manufacturers to make accurate forecasts and plan production accordingly, reducing excess inventory and avoiding stock shortages. Automated systems can also trigger orders when stock levels reach a certain point, ensuring a continuous supply chain.
For example, automated inventory management systems can monitor stock levels of ingredients and packaging materials in food and beverage production. This allows manufacturers to adjust their production schedule accordingly, ensuring they always have the right supplies.
How Automation of Manufacturing Works?
Robotics, artificial intelligence (AI), and control systems are the three main components of manufacturing process automation.
Robotics involves using machines to perform repetitive and labor-intensive tasks, reducing the need for human intervention. These machines are programmed to perform specific tasks and can work continuously without breaks, resulting in higher productivity.
AI plays a vital role in programmable automation by enabling machines to process data, make decisions, and learn from experiences. For example, AI-powered robots can analyze data from sensors and adjust their movements accordingly for precise and efficient performance.
Control systems are the brains behind manufacturing and automation. These systems monitor and control machines, ensuring they function correctly and efficiently. They also collect sensor data, analyze it, and provide feedback to ensure smooth operations.
For example, in the automotive industry, production automation involves using robotics to assemble vehicles, AI to detect defects, and control systems to monitor production processes. These technologies work together seamlessly to optimize and automate production.
Types of Automation in Manufacturing
Automation in manufacturing can be broadly categorized into two types: low-level digital transformation and high-level digital transformation.
Low-level digital transformation in manufacturing
Low-level digital transformation involves automating specific tasks or processes within production systems. This type of automation typically focuses on reducing errors, improving efficiency, and streamlining operations.
Data entry and validation
One example of low-level digital transformation in manufacturing is the use of software tools to extract, validate, and transfer data between systems automatically. This eliminates the need for manual data entry, reducing errors and improving accuracy.
Inventory management software
Another example is implementing inventory management software that automates tracking, reordering, and updating inventory levels based on predefined parameters. This reduces excess inventory and ensures a continuous supply chain.
Email notifications and alerts for machine status
Automated email notifications and alerts can also be set up using software to monitor machine status, maintenance needs, or production milestones. This allows for timely and proactive responses to potential issues.
Reporting software
Reporting software can also be used for low-level digital transformation in manufacturing. It can automatically generate, format, and distribute reports based on predefined schedules or triggers, saving time and improving accuracy.
Document management software
Adopting document management software is another form of low-level digital transformation in manufacturing. This software organizes, categorizes, and retrieves documents automatically, streamlining access and ensuring version control.
Scheduling software
Scheduling software is another tool that can be automated to optimize production processes. It can create, adjust, and communicate production schedules based on real-time data and predefined rules, improving efficiency and reducing lead times.
High level of digital transformation in manufacturing
High-level digital transformation involves fully automating entire production systems or processes. This type of automation typically focuses on optimization, cost reduction, and innovation.
Advanced analytics and predictive maintenance
High-level digital transformation in manufacturing involves using advanced analytics software to analyze real-time data and anticipate maintenance needs. This technology can also be integrated with Robotic Process Automation (RPA) to automate the scheduling and execution of maintenance tasks, resulting in reduced downtime and optimized resource allocation.
Digital twin technology
Digital twin technology is another form of high-level production systems automation. It involves creating virtual replicas of physical processes and combining them with RPA for continuous monitoring. This ensures real-time synchronization between the digital model and the actual production environment, allowing for efficient optimization and innovation.
Supply chain integration
High-level digital transformation also includes automating supply chain management through supply chain integration software. This technology can be integrated with RPA to automate data entry, order processing, and inventory management, resulting in a synchronized and efficient supply chain.
Smart manufacturing and IoT connectivity
Smart manufacturing is another aspect of high-level digital transformation in manufacturing. With Internet of Things (IoT) technology, intelligent manufacturing enables real-time data exchange between machines for improved efficiency and optimization.
Cloud-based manufacturing execution systems (MES)
Adopting cloud-based MES enhances scalability and flexibility in production processes. From real-time data analysis to inventory management, cloud-based MES can be integrated with RPA for comprehensive industrial process automation.
Human-machine collaboration
Collaborative robots (cobots) and advanced Human-Machine Interfaces (HMIs) have taken human-machine collaboration to a new level in manufacturing. With advanced technology and RPA integration, these tools improve interaction, increasing productivity and efficiency.
Robotic Process Automation (RPA) at scale
Deploying software robots across multiple processes allows for comprehensive automation of entire production systems. To deploy RPA at scale, organizations must have a well-defined strategy and a robust infrastructure. This form of high-level digital transformation in manufacturing can result in significant cost savings, improved efficiency, and increased innovation.
How Automation Rescues Manufacturers
With the increasing adoption of automation in manufacturing, organizations are experiencing a significant transformation in their production processes. This transformation results in increased efficiency, improved quality, cost reduction, and a safer working environment.
Increased efficiency - faster, smoother work
Automation helps manufacturers optimize production processes by reducing cycle times and improving workflow. Organizations can achieve faster and smoother work processes by automating repetitive tasks, increasing productivity and efficiency.
Improved quality - consistency, fewer mistakes
Automation ensures consistency in product quality by minimizing human error and reducing defects. Organizations can achieve higher accuracy and consistency in their products by automating quality control processes.
Cost reduction - smarter spending, leaner ways
By implementing automation, manufacturers can decrease labor costs and optimize resource allocation. With real-time data analysis and predictive maintenance, organizations can also reduce operational costs and achieve leaner ways of production.
More safety - stronger shields, better care
Safer working environments are a top priority for any organization. Automation using robots and AI-powered tools can help reduce human involvement in hazardous tasks, ensuring a safer working environment for employees.
Trends for Automation in Manufacturing for 2024
As technology continues to evolve, automation in manufacturing is expected to grow and bring about significant changes. Some of the trends that are likely to shape the future of manufacturing include: (With links)
1. The urgency of reskilling the workforce to fill the gaps in industry diversity. Deloitte Global Human Capital Trends Study found that 75% of industrial organizations believe that reskilling the workforce is crucial for success. Still, only 10% feel confident in their readiness to address this trend.
2. Automation will become necessary, not just a "nice to have." Contrary to popular belief, automation creates more job opportunities and allows human workers to focus on more meaningful work. Future of Jobs report states that 97 million new roles may emerge due to adopting automation, and only less than 10% of jobs can be automated entirely.
3. Speed will emerge as a top priority in the wake of increased demand for automation. Assembly Magazine reports that lead times have increased significantly, impacting customer commitments. Delivery time is now ranked the third most important factor when selecting automation components.
4. Do-it-yourself automation and creating Advanced Manufacturing Teams led by small-sized businesses will rise. Vention predicts a growing prevalence of DIY automation in the manufacturing sector, especially among companies with fewer than 200 employees.
5. Artificial Intelligence (AI) will enable faster and more efficient processes through human-machine collaboration, autonomous robots, and machine learning. Automation World predicts the rise of "Industrial Copilots," which combine human and machine collaboration for improved efficiency. Deloitte also anticipates the growth of autonomous robots, while Forbes predicts the use of machine learning to enhance AI capabilities in manufacturing further.
Automate your manufacturing processes with iRonin.IT
At iRonin.IT, we understand the unique challenges of the manufacturing industry and how automation can help improve daily operations. We provide our clients with custom automation solutions tailored to their needs, ensuring maximum efficiency and cost-effectiveness.
We work closely with manufacturers to identify areas for automation and develop solutions that integrate seamlessly into their existing processes. With our expertise in AI, robotics, and machine learning, we can help businesses stay ahead of the curve and embrace the latest trends in automation.
Contact us today to learn more about how iRonin.IT can help automate your manufacturing processes.
