In today's volatile and complex business world, it is very difficult to make a reliable demand forecasting model for supply chains. Most 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. These models are not designed to learn continuously from data and make decisions. Therefore, they become obsolete when new data comes in and forecasting is undertaken. The answer to this problem is machine learning, which can help a supply chain to forecast efficiently and manage it properly. (For more on machines and intelligence, see Thinking Machines: The Artificial Intelligence Debate.)
How a Supply Chain Works
A company's supply chain is managed by its supply chain management system. A supply chain works to control the movement of different kinds of goods in a business. It also involves the storage of materials in inventory. So supply chain management is the planning, control and execution of daily supply chain activities, with the aim to improve the business quality and customer satisfaction, while negating wastage of goods, in all the nodes of a business.
What Are Supply Chain Management Pain Points?
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 inaccurate results, causing them to make severe economic mistakes. They cannot properly understand the changing market patterns and market fluctuations, and this hampers its power to properly calculate market trends and provide results accordingly.
Often, because of the demand forecasting’s limitations, the planning team tends 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 is becoming more and more complex. Previously, it could be interpreted very easily. However, with newer data generation technologies coming into play, the data has become very complex and nearly impossible to manage with existing technology.
Formerly, the demands could be easily calculated by using a simple historical demand pattern. But now, demand is known to fluctuate on very short notice and thus, historical data is useless.
How Machine Learning Can Help
These problems 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 traditional algorithms, machine learning learns from the market scenario and can create a dynamic model.
Through machine learning, the computer system can actually refine the model without the help of any human interaction. This means that as more data enters the machine learning system’s reservoir, it will 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 with current planning systems. In simple terms, this means that companies can use data signals from other sites which are generated by 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 can improve sales.
What Areas Need Improvement?
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 discussed below:
Planning Team’s Problems
Often, planning teams use old forecasting techniques, which involve manually evaluating all the data. This process is extremely time consuming, and the results are often not accurate enough. This kind of situation not only decreases 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. The planners can also enhance the model even more through their experiences. (To learn more about using data to plan ahead, see How Contextual Integration Can Empower Predictive Analytics.)
Safety Stock Levels
With traditional planning methods, a company has to keep its safety stock levels high nearly all the time. However, machine learning can help by evaluating many more variables for setting an optimum security stock level.
Sales and Operations Planning
If the forecast from your sales and operations planning (S&OP) team is unsatisfactory and inaccurate, or isn’t flexible enough to adapt according to the market behavior, then maybe 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 gaps 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 much improved.
Practical Use Cases
Due to the many advantages of machine learning in demand forecasting, it is being used in a variety of fields. However, these organizations haven’t completely changed their systems to learning ones – they are using machine learning systems alongside traditional ones. The machine learning systems cover the gaps of the legacy systems and enhance their performance. Some examples of such use cases are given below.
This is an Italian dairy company, which has used machine learning to increase their forecasting accuracy by five percent. Delivery times have also been decreased by about half of the original time, which has resulted in better customer satisfaction as well.
This company is based in France and sells many different types of products. Earlier, predictions for response to promotional offers made by the company turned out to be 70 percent inaccurate, which resulted in great losses. However, with the implementation of machine learning in its planning architecture, it has seen a lot of improvement in both sales and forecasting.
Lennox is a U.S. company which manufactures cooling and heating devices. It has expanded throughout North America. So, in order to provide full customer satisfaction, while coping with the expansion process, Lennox integrated machine learning with its forecasting architecture. With the help of machine learning, they could accurately predict the needs of their customers, which further helped them to understand common customer demands better. Machine learning also largely helped the company to fully automate its planning procedure.
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 accurate models for demand forecasting and can also make the work of the planning department easier. It's not necessary to completely change an entire system now, but in the very near future, every supply chain will certainly use machine learning to improve forecasting capability by the creation of dynamic models that will be updated regularly by the machine learning system. So, this new technology will prove to be an indispensable tool for businesses.