AI News, How Machine Learning Can Improve Supply Chain Efficiency

How Machine Learning Can Improve Supply Chain Efficiency

In today's volatile and complex business world, it is very difficult to make a reliable demand forecasting model for supply chains.

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.

They cannot properly understand the changing market patterns and market fluctuations, and this hampers its ability to properly calculate market trends and provide results accordingly.

However, with newer data generation technologies coming into play, the data has become very complex and nearly impossible to manage with existing technology.

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.

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.

However, with machine learning, the system can take many variables according to their priorities based on the data, and make a highly accurate model.

(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.

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.

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, Lennox could accurately predict the needs of its customers, which further helped the company to understand common customer demands better.

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.

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