Use of AI and Machine Learning in Manufacturing Today
In manufacturing, artificial intelligence-based systems are evolving rapidly. AI leverage in the industry is a natural stage in the evolution of process automation. As a result, today many automated production lines are already using AI and machine learning to determine effective workflows.
Meticulous Market Research predicts this sector will grow at a CAGR of 41.2% from 2020 to 2027 to reach $18.8 billion during the forecast period. Thus, it is no surprise that examples of the introduction of AI and machine learning systems confirm the effectiveness of the technology. It helps to get the most out of existing production facilities and build new, most efficient ones.
4 Ways to Benefit from AI and Machine Learning in Manufacturing
Manufacturing companies can take advantage of AI and ML leverage in plentiful ways. Embedding these systems will advance the entire process and ramp up production output. Let’s have a look at what ways implementing AI and ML systems can enhance the manufacturing process.
Higher Operational Efficiency and Lower Costs
Implementation of AI systems is important for supply chain optimization. It helps to achieve and maintain the highest levels of quantity and quality. Operational optimization consists in identifying patterns that the human eye couldn’t determine. It finds less wasteful and more efficient ways to structure manufacturing processes.
The use of artificial intelligence and machine learning helps to reduce the number of personnel errors, simplify the production process. It enables us to reduce downtime when changing technological processes.
Regarding this point, companies often optimize processes by creating a digital twin of some machine or the entire production process. Such digital models empower employees to inspect machines on the digital level. In this way, AI-based algorithms can detect bottlenecks or other inefficiencies without stopping the manufacturing network.
Also, machine learning analytics in AI eliminates the root causes of unexpected losses and other costs that may incur. Timely anomalies and quality detection allows the company to increase production speed, advance performance, and avoid losing money from long downtime periods or faulty products.
Defects Monitoring
ML-based processes adapt automatically to every change in the manufacturing process for constant accuracy. Automated quality control and increased accuracy will contribute to high product quality, lower spending, and cutting down on waste.
The use of AI-enabled software helps the company achieve improved quality inspection. The level of such monitoring will be beyond the capabilities of human inspectors. Machine vision, for example, empowers employees to control the quality of manufactured items at speeds and costs that human inspectors can’t match.
Machine learning techniques monitor defects and look for patterns or trends that may help to identify the hidden cause. As a result, minimizing scrap and rework reduces costs and increases margins.
Predictive Analytics
Using analytical tools, predictive analytics can spot small anomalies in production output, quality, and item availability. Systems equipped with machine learning algorithms can automatically examine different types of data – shortages and excesses of stock, shipments, production processes, weather forecasts, transportation and logistics routes.
The analysis of the data types above will result in optimal solutions and more accurate data-driven decisions. This facilitates and improves the decision-making process because companies can see the possible results of some trend, process, or decision.
Machine learning analytics in AI will help manufacturers predict both repeating and non-historical problems in early stages. This matters a lot since problems rarely repeat themselves in a process plant. Having identified the problem, it analyses the data and delivers recommendations on how to prevent the losses by the time they incur.
Predictive analytics also forecasts fluctuations in demand and supply. It calculates staff, inventory, and the product supply. The factors taken into account may include weather, consumer behavior, political and economic situation. In this way, the company can understand and react promptly to changes in the market demand. The satisfaction and the number of customers will grow, securing the competitiveness of the company in the market.
Improved Employee Safety
Manufacturing remains in the TOP-3 of most dangerous occupations in terms of the number of workplace injuries. Artificial intelligence and machine learning algorithms can help predict possible faults in equipment, safety measurements, and thus, reduce accidents.
A common cause of accidents in the industry is non-compliance. The common cases include people not wearing the uniform, working in unsafe areas or at heights, etc. In this case, AI-based systems can analyze the presence of personal protective equipment kits on employees, monitor hazardous areas. So, non-compliance is something that can be monitored through AI systems.
For example, image recognition techniques can analyze the movements of employees and mobile equipment, the condition of equipment, and increase the safety level in the plant. This kind of activity reduces the level of work-related injuries by 50%.
One more advantage to be mentioned is that AI-based devices benefit from low cost and wireless connectivity. The team can place them in as many places as possible, including items of equipment and the most hard-to-reach locations. This removes the need for employees to work in dangerous or hard-to-reach places.
Wrapping Up
The implementation of AI and machine learning algorithms in manufacturing will deliver many benefits. Their implementation will result in tangible effects for the company’s costs, revenue, production quality, safety at the workplace, and overall labor productivity. Data-driven accurate decisions will contribute to increased profitability, customer growth, and higher competitiveness.
The largest companies worldwide are already utilizing AI and machine learning in manufacturing. That’s why it is the solution that enables companies to adapt to unexpected changes and improve production capacity in the fast-changing competitive market.
Source: InData Labs
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