{"id":12732,"date":"2024-12-17T12:05:51","date_gmt":"2024-12-17T12:05:51","guid":{"rendered":"https:\/\/www.palpack.co.uk\/?p=12732"},"modified":"2024-12-17T12:17:54","modified_gmt":"2024-12-17T12:17:54","slug":"automation-manufacturing-2025","status":"publish","type":"post","link":"https:\/\/www.palpack.co.uk\/news\/automation-manufacturing-2025\/","title":{"rendered":"What’s in store for automation in manufacturing in 2025?"},"content":{"rendered":"
[vc_row][vc_column][vc_column_text css=\u201d\u201d]As we\u2019re poised to enter a new year, so the world of industrial automation in manufacturing is entering a new chapter.<\/p>\n
New automation trends and technological advancements are reshaping the way businesses operate, with Artificial Intelligence (AI) and automation at the forefront of the transformation. In this blog, we\u2019ll explore what the increasing use of AI means for manufacturers and how it can help them stay agile and efficient in a competitive market, as well as look at which other trends we can expect to see in 2025.<\/p>\n
<\/p>\n
By far the most significant development expected is the continued adoption of AI and machine learning in a manufacturing setting. As we\u2019ve seen in other industries, AI enables systems to analyse, learn and even make decisions autonomously, driving greater efficiency and quality control in a number of ways:<\/p>\n
By analysing data from multiple sources, AI systems can identify inefficiencies and patterns and optimise production processes. Real-time data can be used to make small adjustments to improve efficiency and productivity, while long-term data trends guide strategic planning, enhancing areas such as supply chain management and demand forecasting.<\/p>\n
The data can also be used to monitor equipment condition and detect the signs of wear or failure and the need for maintenance. This means manufacturers can schedule repairs proactively, reducing costly downtime and improving reliability, productivity and equipment life.<\/p>\n
AI-powered machines monitor their surroundings in real-time, adjusting to environmental changes, material variations or unexpected disruptions without compromising quality. This dynamic responsiveness ensures consistent output, even under rapidly changing demands.<\/p>\n
We\u2019ve got used to introducing Robotic Process Automation (RPA) into manufacturing operations, which is the automation of repetitive rule-based tasks, such as sack-filling or palletising. However, it\u2019s unable to manage more complex tasks, and this is where we expect to see a rise in the use of intelligent automation. Intelligent automation integrates AI, RPA and machine learning, allowing businesses to introduce more diverse, complicated processes and automate interconnected tasks. This more holistic approach generates new levels of productivity and flexibility in readiness for future growth.[\/vc_column_text][vc_single_image image=\u201d12734\u2033 img_size=\u201dfull\u201d css=\u201d\u201d][vc_column_text css=\u201d\u201d]<\/p>\n