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Machine Learning has Significant Potential for the Manufacturing Sector

Since the start of the renaissance era, technology started to take over the lives of the humans and to gown to greater heights in the coming decade with completely replacing man from few spots in terms of the manual work or heavy work, they most influenced sector was the industrial sector. Since, it’s more oriented to the manual work which has rapidly changed the face of the industrial sector.

But now when the machines already captured most of the manual workspaces from man, now they are moving their steps towards new spaces and being evolved into Smart machines using Machine Learning.

With the passing evolutions of technology, protecting data has become a crucial subject along with completing the task in an appropriate manner, which initiated the development of Artificial Intelligence.

Artificial intelligence with the help of machine learning has revamped the basic mechanics of the machines and turned them into artificial beings with the capability to understand the work without any human intervention, who develop themselves using the experience gained by them; in layman terms ‘Machines with Brains’.

Applying the same process machine learning has shown great potential for benefitting the manufacturing sector with its ability to safeguard the data, also reduce the manual efforts and improve the quality.

But what exactly is machine learning? And what are the key points that make them so significant?

Machine learning is an extended arm of Artificial Intelligence that provides the systems with the ability to automatically improve learn and evolve from the experience without any explicit programming. It majorly focuses on the development of computer programs that can access data and use it to learn for themselves.

Machine learning is not only changing the face of the manufacturing sector but it’s revamping it with these six key factors:

1. Machine Learning has revolutionized Quality Control.

Quality control has always been practiced by the group of specialized workers of the factory to assure the quality of the products produced in the factory.

Even after the introduction of machines, many companies have continued to rely on manual checking than mechanical checking, as their work was called ‘superior’ than the machines and a smart. But since the launch of AI and machine learning, they have completely revolutionized this concept.

These machines are smart and can detect the errors in the product that the workers can’t find out, even with their years of experience and the time taken is very less compared to the working men making it more time efficient.

A study by Forbes shows that machine learning increased defect detection rates by up to 90%. Where the humans just consider factors such as visual, weight or shape, the machines can delve into deeper prospects such as off colors, luster levels, misaligned labels and even cracks which are difficult for humans to quickly distinguish but are an easy task for the machine to carry.

2. Machine learning helps curb Equipment Failure.

Maintaining pieces of equipment is a greater task to manage, as each time a machine fails and has to undergo maintenance it affects the flow of factory work causing an unstructured delay. And the frequent repair work eventually leads to fair losses and cause costly breakdowns.

The global cost for such repair caused losses, add up to $647 Billion Dollars annually, which means an average international cost of around$5,600 per minute.

It is no rocket science to understand that the industry is searching for methods to nip this issue in the bud more efficiently and to that solution to this is ‘Machine learning’. How?

Machine learning with the help of algorithms excellently balances multiple sources of data that can determine but also predict the optimal repair time. This is done simply and the errors and defects are easily recognized and addressed, unlike the human detections which come after a long time. Machine learning algorithms use the available data of the equipment history to identify the pattern of equipment failure, creating a regular maintenance flow.

The data can also be obtained automatically from inside the equipment, helping it eliminate the need for the manual check, which in turn increases the speed and efficiency, which in turn helps to decrease the manpower and save costs.

3. Maintenance Prediction.

As we learned above, how efficiently machine learning helps to provide the data for the required for the repair of the failed equipment, the given data is then boiled down to a shift of reactive and proactive repair work.

When equipment has to undergo a maintenance work the machine has to be offloaded and taking down the equipment takes high cost as they have to be manually checked for the potential problems, which leaves the managers in a dilemma to whether take the equipment down and incur loss now or risk even greater losses down the line.

The machine plays a vital role here by identifying the ideal moment to clear the manager’s dilemma and resolve the costly and stressful guesswork. The machine learning exactly predicts when the breakdown is likely to occur with the bits of data to help provide the company with an insight to finish the servicing prior to that. This helps to cut the errors, less downtime, and lower human-capital costs.

A recent study conducted by Deloitte found that ‘poor maintenance’ can decrease the production rate by 5-20 %. Hence, machine learning becomes more useful in the long run.

4. Supply Chain Optimization

Machine learning is not only involved in the improvement of equipment but it also helps in more essential factors for the manufacturing business such as reliable Supply chains. With the complexity of the global economy, it has become important to optimize these supply chains to maintain the flow.

A slight change in the weather, damaged transport vessels or even change in fuel can reverberate throughout your supply chains that greatly impact your business. This can cost you the above-mentioned charges of \$5,600 per minute due to delay in the shipment of raw materials for the products.

Machine learning considers all these complex factors and optimizes each element in the supply chain in response. Which means, it calculates how much extra time to give to the shipment or deciding where to ship a product from based on the available weather pattern data?

This minimizes the delays and creates cash flow, avoiding any losses with a more reliable environment.

5. Inventory Optimization

Similar to supply chain optimization, machine learning has the exact same impact on optimizing inventory. The cost incurred for holding the goods is massive and usually hovers around 20-30% of the cost of a product. This doesn’t sound like a major problem but can immensely affect the cash flow.

This can traditionally be the job of humans, who carefully consider all factors and provide a balanced structure. But machine learning makes this task more balanced with analysis the millions of bits of data present which is beyond the capability of any human analyst. And it shouldn’t come as a surprise, that the data provided by machine is more sustainable.

6. Using Machine Learning for Electricity Consumption.

Electricity is the most important input for any factory, while some factories operate 24 hours a day, the energy costs are raised pretty high and it’s possible to schedule more energy -intensive activities for different times. This is to ensure that these activities occur when the power is cheapest, depending on the source used during the day (solar energy, depending on the availability.) or during the night (when demand is generally lower.)

There are myriad of other factors that have to be considered but with the help of machine learning capability of processing large amounts of data in no time, it helps you to consider energy prices alongside labor costs, equipment maintenance and minimizing inventory; they provide you with an overview to perform energy-intensive activities for maximum cost saving.

Machine learning has helped manufacturing sectors developed to match the fast pace of the changing technology-based world, it will not be wrong to say, it has put the manufacturing sector a ‘Two-step ahead’ than it was before.

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