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Machine Learning in Manufacturing Sector

What happens when you tap the power of machine learning in the manufacturing sector?

When the industrial revolution happened the manufacturing sector changed forever. Though there have been technologies that have improved the way manufacturing happens, this is one domain that is highly labour intensive. That is one main reason why the employment rates are pretty high in the regions where the major manufacturing units are located. We are now progressing towards Industry 4.0.

  • Will the fourth industrial revolution be as impactful as the first one?
  • Will it lead to the increase or decrease in the jobs?
  • What would this mean for the major organisations, the manufacturing companies in the world?
  • How will this affect the prices of the manufactured commodities and how will this impact the economy in general?

With all these questions running the manufacturing sector is now going on a roller coaster ride with the friction that some areas experience. This friction is mainly because technologies like machine learning have challenged the ideals of the conventional systems. The friction is also because if the machines start dominating the manufacturing units then the employment rates would take a hit. But what we should remember is that this transition period when tackled with confidence would result in a state of better and more profitable employment. The less the resistance offered, the smoother would the transition be and the quicker would the rebound in employment rates be. In fact after this brief period there would be an impressive surge in the number of jobs available.

Machine Learning in Manufacturing Sector

Where to begin

When we say that manufacturing units would benefit from using machine learning you would probably picture a scenario where there are bots in the assembly line in place of human laborers. The bots perform the assembly and the entire zone is unmanned!

The above scenario, as magical as it might sound, might take some time to turn into reality. There are indeed some small manufacturing units that have fully automated assembly lines. But we are only talking about the majority. So machine learning in manufacturing can do a lot of other activities as well. For starters, establishing automation in inventory management, asset management and supply chain management is a great first step to take. Irrespective of the industry to which a business belongs, these are the areas where a major portion of the expenses go to. So transforming these processes would help cut down costs and improve the quality of the processes.

Preventive maintenance is the need of the hour

Technology has revolutionised not just the way products are manufactured but also the machines that actually manufacture them. Machines are now getting more and more sophisticated in terms of the features, precision and usability. High end manufacturing plants might cost a lot to be set up initially. They might also incur high costs in maintenance. But this can be tackled if machine learning is incorporated in order to periodically review the health of the machines. So maintenance and repairs can happen even before the actual problem becomes evident. Machine breakdowns lead to repair costs. The downtimes lead to delays in deliveries which in turn would turn out to be expensive for the business. Sensor technology is being used almost everywhere. We talk about how wearables can monitor the body parameters regularly and provide better and timely healthcare assistance. The same way smart sensors can help monitor the health of the machines. The pattern recognition capacities of the machine learning systems would identify the anomalous patterns and send an early warning about an impending failure. Early diagnosis would result in lower repair costs and lower chances of delays.

Better testing environment

Machine learning allows the system to simulate real world scenarios and thus result in more accurate testing strategies. Automation makes it easy to increase the number of repetitions. Higher the number of repetitions higher the chances of identifying flaws and possible quality issues. Thus businesses would be able to work on fine tuning their quality control processes and would be able to deliver better quality products.

Timely inventory management

One other major hurdle for a manufacturing unit is poor inventory management. When there is a huge order coming up analysing the inventory would be the first thing that a business does. Machine learning would help automate this process of studying the inventory. The system would periodically look for the quantities of raw materials as well as finished products keeping the future needs in mind. Automatic placement of orders to refill the inventory can also be achieved. The computations on the quantity being ordered would be done based on the standard requirement, current and future orders and other factors.

Machine learning in supply chain management

There has been an increase in the number of businesses that offer supply chain management services. Businesses have started outsourcing this area because it takes a lot of time and expenditure. Leaving it to the experts would mean the use of the most reliable tools to avoid bottlenecks. Having a healthy supply chain goes a long way in retaining the credibility and reputation of a business among the customers and the clients.

New product development can be planned based on the study of the product life cycle. Forecasting the demand would be essential for this. This would also have to be backed up with the supply forecast. After all mass manufacture of a new product cannot be planned without the support of the most trusted suppliers. Using machine learning in supply chain management is not a straightforward process. But it is sure to be a cost effective one. Though there are many ways and many faces of machine learning that could be used to enhance supply chain management here are a few examples:

  • Warehouse management and inventory management could be handled more efficiently. This in turn would lead to better supply chain planning.
  • Logistics could be revolutionised if the shipping could be handled by unmanned vehicles or even the manned ones could be tracked real time.
  • Procurement interactions could all be handled by the chatbots. This would reduce the time taken to place orders as well as ensure a smooth process flow from the placement of the order to the delivery.

In short machine learning could play a major role in improving the relationships between suppliers and manufacturing units.

Machine learning could thus touch nearly every aspect of the manufacturing industry. This would result shortening the time taken for manufacturing. It can help reduce the overall manufacturing costs while also improving the output quality. In the long run it could also lead to the growth in the economy as the value delivered for the price paid would be higher.

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