3 Reasons to Use AI Models in Manufacturing
58% of business and technology professionals are researching AI systems, yet only 12% are actively using them.
Manufacturers have already been compelled to maximize the value of every asset due to high unpredictability and slow growth. Their own data is the next target.
When compared to other industries, manufacturing has seen the most favourable effects from artificial intelligence (AI).
1. Why then are so many firms hesitant to board the AI train despite its shown success? And are those that invested in AI getting the full benefits it has to offer?
According to a McKinsey analysis, using AI to monitor and analyze production equipment can cut downtime by up to 50%. In order to foresee potential service requirements and enable repair before any machinery malfunctions, this is accomplished through the analysis of numerous data points and previous data. In addition to reducing downtime per machine, doing this can also increase a machine's life expectancy by up to 40%. The global financial savings from predictive maintenance are estimated by McKinsey to be between $0.5 and $0.7 trillion.
According to a recent article in the trade publication The Manufacturer, 92% of senior manufacturing executives view artificial intelligence as a vital tool for boosting efficiency. While ciol.com, predicts that by 2035, average profit would improve by 39% as a result of the cost savings from predictive maintenance mixed with the increase in productivity brought on by the reduction in downtime.
2. How much and how fast will AI transform the manufacturing industry?
Manufacturers all around the world are investing quickly in the Internet of Things (IoT) to develop new goods and services while, over time, lowering production costs. This change is altering how businesses approach consumer engagement, employee empowerment, and operational optimization. Delivering IoT is only one step in the process of achieving manufacturing excellence, though. Companies must integrate the data gathered from connected devices with quickly growing Artificial Intelligence to allow "smart machines" in order to fully realise the potential of IoT. In turn, these will mimic intelligent behaviour with minimal to no human involvement. Currently, the AI in smart machines primarily takes care of the more routine, repetitive duties, although this is developing extremely quickly.
A paradigm shift will occur when machines learn enough to be able to provide recommendations that people can rely on, moving from aided intelligence to complete autonomy. In addition to intelligent equipment on the shop floor, the usage of AI and big data will grow significantly over the next five years. Reliable algorithms will be employed in every aspect of an operation, from weather forecasts for raw material shipments to proactive maintenance of the finished product.
3. How do you think that disruption will affect business operations and strategy?
A few essential core technologies must exist before AI can be applied to the manufacturing sector. In order to create an integrated, intelligent operation, a smart factory will need to be networked and collect data from manufacturing lines, design & engineering teams, and quality control. Without the proper smart machines and data gathering points, manufacturers will only have data with little to no insight, and insight is what produces world-class optimised operations. As assets and customers become more central to their operations, industrial enterprises will need to transition to become digital businesses in order to thrive.
The days of a straight path from product procurement to ongoing support are over. Having a complete picture of the client, being able to put the product at the centre of operations, and knowing exactly how a product is used all have a positive impact on everything from new product creation to enhanced go-to-market strategies. Naturally, each of these will increase the data points available for data collecting for cognitive, AI, and machine learning services to better the business and eventually the customer experience.