Example of the use of artificial intelligence in industry 4.0: supply chain

 

 

In recent years, we have all witnessed the transformation of the traditional linear supply chain to a digital supply network (DSN). Covid-19 has only accelerated this process, causing companies to reassess their global supply chains in the light of the new reality. With the help of software development services such as IoT, artificial intelligence and machine learning, traditional linear supply chains can be transformed into digital supply networks that are connected, intelligent, scalable and scalable

The benefits of applying machine learning and AI can be seen in the parts that make up the supply chain, including purchasing , manufacturing , inventory management , warehousing , logistics go , and customer service

Let’s explore the benefits of machine learning in the supply chain and look at some of the applications of AI in the supply chain.

Inventory management

Storing and maintaining inventory in good condition is expensive. Therefore, supply chain professionals must address inventory planning very thoroughly as it has a direct impact on a company's cash flow and profit margins.

Machine learning can help solve the problem of stockouts or overstocks . Based on the data that can be obtained from many areas, such as the market environment, seasonal trends, promotions, sales and historical analysis, with machine learning  it is possible to predict the growth of demand and thus prepare to fill the shelves with anticipation, as well as avoiding excess merchandise or important parts for manufacturing.

For forecasting to be accurate, you need to have a wide range of data . When the number of data sets is insufficient for effective analysis, machine learning offers several methods to solve the problem:

Data augmentation techniques used in Deep Learning .Incremental learning, which does not require a large amount of data to train a model.Reinforcement learning, which uses rewards and punishments as cues for positive and negative behavior, typical of robotics and industrial automation.

Another example of machine learning  application is the use of computer vision ( CV ) for inventory management. Computer vision can be used in several tasks related to inventory management, for example:

To count and classify arriving items.CV-based inspection: Detects visual package damage.

It can also detect empty spaces on shelves, look for items that are no longer available, and take appropriate action to fill them as needed.

Computer vision is one of the areas where all types of machine learning techniques (supervised, unsupervised, and reinforcement learning) can be applied

Warehouse management

In warehouses, we can use AI to automate manual work, predict potential problems, and reduce paperwork for warehouse staff.

For example, we can use computer vision to:

Monitor the work of the conveyor belt and predict when it will block.Automatically detect the arrival of packages and change their delivery statuses.Read barcodes and labels on packages and send all necessary information directly to the system.Use NLP ( Natural Language Processing ) for document processing.

Another example would be using machine learning to program autonomous vehicles and robots. With the help of guides built into the system, autonomous vehicles and robots help receive, pack/unpack, transport and load/unload boxes. Computer vision, in this case, is used to find a free place for a box, check if it is placed correctly and avoid collisions between robots and vehicles in warehouses.

Modernized and scalable management will significantly improve warehouse efficiency, reducing operational overhead and warehouse downtime.

Logistics and Transportation

With the help of AI custom enterprise software development we can know where a package is in the entire logistics cycle.

This allows supply chain operators to see where goods are at the time of delivery. In addition, it enables identification of conditions under which packages are transported. With the help of sensors, it is possible to monitor parameters such as humidity, vibration and temperature.

Additionally, using machine learning techniques we can optimize roads in real time: we can monitor weather and road conditions and make recommendations on how to optimize roads to reduce driving time. In this way, trucks can be diverted from their route whenever a more convenient route becomes available.

The applications for machine learning in the supply chain are very diverse. Here is a list of the ones we think offer the most value to supply chain professionals.

If you have to manage suppliers or large warehouses, supply chain management can be a daunting task. But software development solutions like machine learning and artificial intelligence can help you at all stages of the supply chain.

You can use machine learning algorithms to accurately forecast demand, improve logistics, reduce paperwork, and automate manual processes .