Control your inventory using Machine Learning

Digital transformation is becoming increasingly important in the corporate world. This trend has become even more evident with the changes brought about by the new Coronavirus (Covid-19) pandemic. An example of this involves industry 4.0 which adopts Machine Learning to improve inventory management. The greater the chances of reducing costs and increasing profitability as a company has more efficient control over demand. We will show several actions related in this article to the application of machine learning in companies.

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Understand what it is

Artificial intelligence is a very widespread concept today that encompasses the use of systems to perform activities based on collected information. An example of this is the Netflix platform which indicates films according to the subscriber’s history.

Machine Learning has a similar function but involves the use of equipment (machines) that operate in an automated way based on data previously provided. Thus a device can have its activity increased or decreased according to a real-time demand assessment.

Working with information in a practical way is essential for inventory control to be done correctly. The higher the automation’s level of supply management the lower the risks of having warehouses with items that will not be useful at the moment or in the near future.

See how Machine Learning can be applied in the industry

As much as an innovation has the potential to generate considerable advantages and resistance to adopt it is common. This stance on the other hand can no longer be accepted because the digital transformation must be monitored efficiently.

Otherwise, a company is unable to remain competitive. Therefore, the use of machine learning in industry 4.0 is an action that is no longer a luxury and has become an obligation.

Through Machine Learning, it’s possible to check equipment behavior patterns. Based on up-to-date and accurate information a company can assess whether or not it is time to carry out preventive or corrective maintenance.

The interruption of activities during a certain period can be fatal for an efficient management of stocks. If an industry isn’t producing the items according to expectations it won’t be able to serve a supermarket chain in retail efficiently.

Having real-time information on the performance of machines used in production is a valuable item to keep the production chain functioning properly. This more than justifies the adoption of Machine Learning.

Reduction of misunderstandings

Another reason why this technology is so important in industry 4.0 is related to the automation of several tasks which contributes in a decisive way to the reduction of errors.

With production reaching an adequate level and not suffering downtime inventories are better able to meet internal and external demands in an exemplary manner.

With the application of Machine Learning it’s much easier to predict equipment failures and prevent production from being significantly affected. Through artificial intelligence it is possible to have an understanding of anomalies in an engine.

Alerts can be issued depending on the equipment that indicates the need for maintenance to prevent the problem from making the device inactive for a long period.

See the relationship between Machine Learning and inventory management

This technology is also being decisive for the optimization of stock control. From the moment it is possible to estimate when the items should be replaced with mastery. It is easier to get the ideal amount of materials to be purchased.

This attitude makes it possible to significantly reduce waste. For example in the food sector many products may be unavailable for consumption due to expiration.

It is a problem that can be minimized as long as inventory management is done with strategy and common sense. It is possible for a company to take a huge step towards eliminating unnecessary spending on purchases with Machine Learning.

Why is investing in this technology valid in the current situation? A good answer lies in the fact that Machine Learning is becoming more and more agile as equipment and systems have a higher level of efficiency compared to the last five years.

Technological advances create a more favorable environment for Machine Learning to be applied in different business modalities. This is because the results become more impactful at the same time. There is a cost reduction in the investments necessary to adopt this technology.


Having an accurate notion of demand is a very complex task in a scenario of economic instability. On the other hand the use of Machine Learning makes it very easy to have a dimension of how a product is moving in inventories.

Based on correct and updated data on the demand for an item a company can adopt perfect logistics so that the goods reach the consumer on the scheduled date and in the ideal quantity.

For example if a virtual store doesn’t correctly estimate the demand for smartphones from a famous brand it will be in serious difficulties to maintain and captivate the target audience.

If customers find that the product they are looking for is out of stock. They will seek out the competition as soon as possible. Therefore, a company must have, at the same time, an intelligent pricing policy combined with a good inventory management strategy.

This factor shows how Machine Learning can make a marked difference in a company’s turnover and consequently in profits. It is very important that this technology collaborates in inventory management and logistics for this to be achieved.

Stay tuned to the implementation process

Betting on a resource that has positive results is undoubtedly something that encourages entrepreneurs. But it is necessary to have planning for the performance to reach the expected level in the short term.

It is not enough to apply technology in an agile way. It is also necessary that employees know how to use it strategically in their daily lives. This aspect can’t be ignored under any circumstances as it influences the proper use of such an advanced resource such as Machine Learning.

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