Influence of machine learning on supply chain management

Machine learning & supply chain management

Machine learning & supply chain management

Overview:

In today’s volatile and complex business world, it is very difficult to make a reliable demand forecasting model in supply chain. Most of the forecasting techniques produce disappointing results. The root causes behind these errors are often found to be lying in the techniques that are used in the old models. The old models are not designed to learn continuously from data and take decisions. So, it becomes obsolete when new data comes in and forecasting is undertaken. The answer to this problem is ‘machine learning’, which can help supply chain to forecast efficiently and manage it properly.








How supply chain works?

The supply chain of a company is managed by its supply chain management system. Actually, supply chain works to control the movement of different kinds of goods in a business. It also involves the storage of materials in the inventory. So supply chain management is the planning, control and execution of daily supply chain activities which aims to improve the business quality and customer satisfaction, while negating wastage of goods, in all the nodes of a business.

What are the pain points in supply chain management?

The forecasting of demands is one of the most difficult parts of supply chain management. The current technology for forecasting often presents the user with wrong results, causing them to make grave economical mistakes. It cannot even properly understand about the changing market patterns and market fluctuations. This hampers its power to properly calculate market trends and provide a result accordingly.

Often, because of the demand forecasting’s limitations; the planning team tend to get discouraged. They blame the leaders for their lack of interest in improving the planning process. This challenge arises because of the fact that the data collected from customer demands are becoming more and more complex. Earlier, it could be interpreted very easily. However, with newer data generation technologies coming in, they have become very complex and are nearly impossible to manage with the existing technology.

Earlier, the demands could be easily calculated by using a simple historical demand pattern. But now, demand is known to fluctuate at very short notices and thus, historical data is useless.

How machine learning can help?

These problems, mentioned above, cannot be solved by traditional algorithms due to their fluctuations. However, with the help of machine learning, companies can easily solve them. Machine learning is a special type of technology through which the computer system can learn many useful things from the given data. With the help of machine learning, companies can model a powerful algorithm which will go with the flow of the market. Unlike algorithms, machine learning learns from the market scenario and can create a dynamic model.

Also, through machine learning, the computer system can actually refine the model without the help of any human beings. This means that as more data will enter the machine learning system’s reservoir, it’ll become more intelligent and the data will become more manageable and easier to interpret.








Machine learning can also integrate with Big Data sources like social media, digital markets and other Internet based sites. This is so far not possible by current planning systems. In simple terms, this means that companies can use data signals from other sites which are generated by the consumers. This data includes data from social networking sites and online marketplaces. This data helps the company to know how newer techniques like advertising and the use of media to improve the sales and also help to understand their advantages and disadvantages.

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What are the improvement areas?

There are many places where machine learning can be used for improvement. However, there are three main places where traditional planning procedures create problems. These problems and the improvement of these aspects through machine learning are given below:

Planning team’s problems

Often, planning teams use old forecasting techniques, which involve physically evaluating all the data. This process is extremely time consuming, and the result is often not accurate enough. This kind of situation not only decreases the employee morale, but also hampers the growth of the company. However, with machine learning, the system can take many variables according to their priorities based on the data, and make a highly accurate model. These models can be used by the planners for much more effective planning and they don’t take a lot of time either. Also, the planners can enhance the model even more through their experiences.

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Safety stock levels

With traditional planning methods, a company has to keep its safety-stock levels high nearly every time. However, machine learning can help you by much more variables for getting an optimum security stock level.

Bad planning process for sales and operation of the company

If the forecast from your S&OP (sales and operations planning) team is unsatisfactory and inaccurate, or isn’t flexible enough to adapt according to the market behaviour, then maybe you should know that it is time to upgrade the system. Machine learning finds a perfect use here, as it can improve the quality of forecasting by learning the current market trends through different kinds of data. Thus, machine learning can make the work of S&OP much easier.

All these areas have a scope for improvement and these loopholes can be filled by the technique of machine learning. Machine learning can completely overhaul the architecture of the supply chain management of a company. Many companies have already started using it, and they find that their planning division is a much more improved one than before.








Some practical use cases

Due to the many advantages of machine learning in demands forecasting, it is being used in a variety of fields. However, they haven’t completely changed their system to learning ones. They are using machine learning systems alongside traditional ones. The machine learning systems cover the loopholes of the legacy systems and enhance their performance. Some examples of such use cases are given below.

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Granarolo

This is an Italian dairy company which did many promotions producing large promotion combinations. The demand grew about 30 times the baseline sales. With machine learning, their forecasting accuracy has grown about 5 percent more. Also, the delivery times have been decreased by about half of the original time. This has resulted in better customer satisfaction too.

Groupe Danone

This company is based in France and sells many different types of products. Earlier, promotional offers happened to be 70% inaccurate for the company, which was a great loss. However, with the implementation of machine learning in its planning architecture, it has seen a lot of improvement in both sales and forecasting.

Lennox International

Lennox is a US company which manufactures cooling and heating devices. It has expanded throughout the North America. So, in order to give full customer satisfaction, while coping with this, Lennox then integrated machine learning with its forecasting architecture. This helped the company to fully automate its planning procedure.

Must Read – AI and the Classroom – Machine Learning in Education 

Conclusion

Machine learning, if implemented at the right place and at the right time, can prove to be very beneficial for the supply chain of a company. It can help make perfect models for demand forecasting and can also make the work of the planning department easier. You don’t have to change the system completely now, but it is sure that in the very near future, every supply chain will use machine learning to improve their forecasting capability by the creation of dynamic models that will be updated regularly by the machine learning system. Also, larger amounts of data could be scanned thorough for information. So, this new technology will prove to be an indispensable tool for businesses.

 

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