The role of data in lean manufacturing

The role of data in lean manufacturing

Modern technology - Big Data analytics and artificial intelligence have entered various industries over the past few years, including manufacturing. Advances in system processes and work operations will continue in the coming years. How big data sets and artificial intelligence will create streamlined and more efficient processes. Production monitoring is entering a whole new level. Machine learning, artificial intelligence and big data analytics will have a significant impact on various industries in the coming years.


Do big data sets play a role in lean manufacturing?

The potential of machine learning for real-time optimization is just beginning to emerge, and the evolution of the technology will accelerate over the coming years. Industry 4.0 is a concept where production monitoring essentially relies on machine learning based on big data analytics and artificial intelligence.


These technologies can do many things, but perhaps one of the biggest benefits they offer is streamlined and more efficient processes. Big data and analytics can train artificial intelligence, which will then control and power automated systems. Automated systems will then augment - or in some cases completely replace - human labor. See Amazon's robot warehouse employees for a real-world example.

The impact of Big Data on manufacturing

Traditionally, you won't see these patterns or trends anywhere but the end of the production line. By then, the damage is done, especially if the problems or complications are severe. Predictive analytics and large data sets can prevent this.

Reducing wastage in processes

A major concern with lean manufacturing is the generation of waste and the environmental impact you have while working. Big Data analytics, machine learning and simulation systems can help organizations identify more cost-effective and efficient opportunities while reducing the total amount of waste produced.

In addition, these technologies can streamline processes, making them more efficient and reliable - a change that in itself has a way of reducing waste. Overcoming weaknesses is a core principle of lean manufacturing.

The use of data in modern manufacturing

Factories and production facilities of the future will rely on monitoring production through automation and scalability. Schedules will be automated and directly influenced by supply and demand and will produce more or less of a good depending on market changes. Because of this, large data sets, advanced analytics and artificial intelligence will become critical to the success of the industry.

How to react faster and more flexibly to changes

Customer service and support are boorish in manufacturing, strictly considering feedback agility and impact. For example, if a large customer demographic talks about a problem with a product, in traditional manufacturing it takes a long time to implement the necessary improvements, even with incredibly streamlined processes.

In particular, big data, analytics and predictive technologies can help brands and organizations respond much faster and more accurately to customer complaints and concerns. As a result, the above technologies streamline the entire process, which benefits everyone - brands and consumers alike.

Production monitoring and improvement of preventive maintenance process

Preventive maintenance is essential for many reasons, especially when your installation or equipment needs to remain operational. Your deadlines stay in place and your development chain remains stable and reliable. But what if there was a way to really know that something is wrong with your hardware before it gets stuck or causes problems?

By equipping production equipment with sensors and synchronizing these devices with analytics and machine learning platforms, predictive maintenance and production monitoring (e.g., of equipment) can become extremely efficient. Downtime will decrease significantly, if not disappear entirely.

Compliance management - identification of phenomena

Sensors and monitoring tools are essential for machines and equipment that require compliance, transparency and traceability. Performance and training, usage scenarios and the relationship between man and machine can be tracked and analyzed. This provides many things, including improved worker safety and security, increased productivity and reliability, and true insight into overall production quality.

In healthcare, for example, hospitals use systems like this to identify doctors, nurses, and specialists with high rates of medical errors so they can replace them appropriately. The systems we're talking about have also become commonplace in the manufacturing and development industries, especially in those affected by lean practices.

Data in the service of quality

In general, when you're working to improve a process, a system, or a series of mechanics, a little bit of quality is sacrificed in the process. This isn't always true, but we're not here to debate semantics. It's about being aware that this is happening.

However, using software and big data can ensure that quality remains at the highest level no matter what changes, evolves or is replaced. That's because you gain access to a more reliable series of reporting and monitoring systems that provide an accurate picture of quality and performance throughout the supply and development chain.

Big Data and lean manufacturing - summary

However, using software and big data can ensure that quality remains at the highest level no matter what changes, evolves, or is replaced. That's because you gain access to a more reliable series of reporting and production monitoring systems that provide an accurate picture of quality and performance throughout the supply and development chain.

Traditionally, you won't see these patterns or trends anywhere but the end of the production line. By then the damage is done, especially if the problems or complications are severe. 



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