Machine learning In Manufacturing

The manufacturing industry is one of the most complex yet essential and evolving industries. To deliver high-quality products, many manufacturing companies have begun incorporating intelligent machines, tools, and technology for efficiency.

One of those technologies is Machine Learning!

Machine learning helps the manufacturing industry identify opportunities to improve industrial operations and Overall Equipment Effectiveness at any stage of the manufacturing process.
Before diving deeper into the use cases of machine learning, let’s have a basic understanding of what Machine Learning is!

What is Machine Learning in Manufacturing Industry?

Machine learning is the process of training computers to think like human beings. This means giving them the inputs—i.e., vast quantities of real-world data—to develop their own autonomous “thought processes” over time.

Machine learning can be used to analyze current manufacturing models, identifying flaws and pain points. So, that Business can quickly address any issues in this way, ensuring that the manufacturing pipeline remains in top shape.

Let’s discuss the key areas of MACHINE LEARNING to better understand the full potential and how you can harness it for your businesses.

Predictive Maintenance

Instead of allocating resources for planned maintenance, manufacturers spend too much time repairing breakdowns. Machine learning algorithms can predict equipment failure, allowing businesses to plan maintenance schedules better and improve asset reliability and quality.

Benefits For Manufacturers:

1. Lowering costs by reducing planned and unplanned downtime.
2. Supplying technicians with specific inspection, repair, and tooling needs
3. Extending the machinery’s remaining useful life (RUL) by avoiding secondary damage during repairs.
4. Reducing the size of the repair team

Quality Control

According to manufacturers, customer satisfaction is determined by product quality. To improve efficiency and product quality, they are always looking to reduce waste and variability in their manufacturing processes.
Machine learning allows businesses to get to the root of losses in quality, yield, energy efficiency, and other areas, protecting their bottom line and allowing them to stay competitive. It got conducted through process-tailored ML algorithms and machine learning-enabled Root Cause Analysis (RCA).

Benefits For Manufactures

ML RCA, in particular, is a powerful tool in tackling process-based wastage and is far more effective than manual RCA for the following reasons:

1. With automated RCA, machine learning algorithms use historical data models to identify patterns in new data and predict where losses might occur—preventing problems before they happen.
2. This method is entirely data-driven and unbiased, unlike manual RCA.
3. It is also free of daily admin and other manual tasks performed by process experts, focusing solely on process optimization.

Product Development

One of the most common applications of machine learning is in product development. New product design and improvement require extensive data analysis to achieve the best results.

Machine learning solutions can aid in collecting and analyzing large amounts of product data to understand consumer demand better, uncover flaws and new business opportunities. This can also help in the improvement of existing product designs as well as the development of new products that will generate new revenue streams for the company.

Benefits For Manufactures

1. Create new designs for products that have been specified.
2. Distinguish between manufactured and genuine goods.
3. Train deep learning algorithms to recognize and define every possible design solution, resulting in an optimized design for a specific task.
4. Turn the computer into a “design partner.”

Supply Chain

Supply chains are the lifeblood of any manufacturing business. Whether it is the replacement parts for the factory equipment or its products, having a reliable supply chain is crucial for any manufacturing company!

Machine learning algorithms can assist manufacturers in assessing the state of their supply chains and driving efficiencies through inventory optimization, demand planning, supply planning, operations planning, and logistics, among other things.

Benefits For Manufactures

1. Warehouse control- Stock control aided by deep learning-based computer vision systems allows quick supply replenishment.
2. Demand forecasting- Customer behavior and preferences are analyzed using time series analysis, feature engineering, and natural language processing techniques.
3. Logistics route optimization- Using machine learning algorithms, manufacturers can review and allocate the most optimal routes for shipping goods.
4. Transportation optimization – Using machine and deep learning algorithms to assess impacts on shipments and deliverables to optimize transportation solutions.

Conclusion

A manufacturing line is a set of sequential operations set up in a factory where materials go through a refining process to produce a finished product that can be used. Monitoring manufacturing lines is necessary to ensure that the production process and products meet the desired quality standards.
Machine Learning has the potential to make ongoing manufacturing line analysis & management more efficient & accessible to all. People can do their jobs better, faster, and more efficiently due to technological advances. Machine Learning assists people in continuously improving the bottom line to solving some of the biggest challenges facing manufacturers today.