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Machine Learning for Augmented Supply Chain Networks

We are being dependent on AI or Artificial Intelligence in many ways, it has taken over almost every sector we work in. Even our homes are becoming smart with the implementation of AI and reducing the human effort to a great extent by making the machines able to work like humans but in a better way. There isn’t a single industry left in the world that hasn’t adopted AI technology in its processes. Like that, the logistics are also not far from utilizing AI in their process. Human errors that cannot be controlled by any kind of poka-yoke or kaizen can be completely replaced by machine learning algorithms. Optimized processes can also be well achieved by analyzing the huge data that is impossible to compute at a manual level thus eliminating human error and saving time.

How Using Machine Learning in Supply Chain Can Be Beneficial?

The advantage of machine learning is a long list. So why not have a little walk through the wonders of machine learning in the supply chain network. Following are some of the common benefits of using Machine Learning in the supply chain,

Machine Learning in Coding the SCP

We are well aware of how much Supply Chain Planning accounts in logistics. There are huge training sessions of 30 – 40 hours for beginners, and gaining beneficial experience keeping in mind the large number of strategies included in the SCP module, takes almost 3 to 4 years. And when that experienced individual retires or switches their jobs, it takes another 3 to 4 years for another employee to learn the work in that organization or to get trained by another potential employee. So much of the time and revenue of the company goes into vain only for making an employee experienced for the work, which can be reduced by hiring a machine instead of a person. So by incorporating machine learning, organizations can create an algorithm for every kind of situation over the years, which can be used even after the employee leaves the company. It is such a beneficial investment for organizations that face repeated issues with their supply chain network.

Automated Forecasting and Demand Chart through Machine Learning

The demand chart and forecasting of a company are being made either on a weekly or monthly basis. It requires to matriculate work for 3 hours straight to create a demand chart for an experienced professional, simultaneously the works for the Forecasting also take a considerable amount of time. This excess consumption of time issues can be solved when machine learning comes into play. It creates automated forecasting and demand charts, by saving time from the time-consuming work and making humans free from prioritizing other important works where a human touch is a must. Thus the company can make progress in their journey and make itself a step closer to achieve the goal.

Distance Optimization for Logistics

The most important thing to consider in logistics is to always ship through the shortest route. But with so many products, it is impossible to optimize the route. But it can be easily calculated using machine learning. Easy calculation of the shortest route and automatically shipping through it will reduce a big load of manual work. Not so many employees will be required to run the company as the workload will be sustainably reduced. This will help the company to focus more on the existing employees’ growth and betterment as their count will be lesser.

Predict Unwanted Issues for the Future

Probability has always been an excellent tool for humankind. But with a large number of variables in the supply chain, it is unfeasible for humans to compute it. Many times people miss even the most obvious issues. But, with the introduction of machine learning, this can be eradicated. Advanced machine learning can monitor and indicate the various upcoming issues like work traffic, peak hours, shipping time, etc. Moreover, it can also suggest alternative solutions to these issues with previously stored information.

Right Supplier Selection

Another work of the supply chain employees is to keep a track record of suppliers and their relationships. It basically is a record where the entire supplier history is kept. Sometimes organizations want to extend their demand and look for good company registered suppliers, these records then come in handy. It can be easily found out which supplier is better, but browsing among bunches of data and keeping a record of them is a recurring job. Plus, it consumes up a lot of company time and is quite tedious, which can be easier if machine learning is being used to record and keep a track of these suppliers. Also, it can easily produce a relationship report of the company with the supplier in very few time.

Thus we can undeniably accept the fact that machine learning can change the entire protocol of the Supply Chain for a better deal. Dave waters were absolutely correct when he quoted “Machine learning will automate jobs that most people thought could only be done by people”. We have come across so many jobs that can be easily replaced by machine learning. Also, it saves the company a lot of revenue by cutting off employee count.

Thank you for reading the article. In case you want to use machine learning in your business, kindly contact us. Team WinSquares will be happy to help you. It will be our immense pleasure if you give your valuable feedback in the comment section. Please share this article with your loved ones if you like it.


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