How to Use AI in Supply Chain Management? [Case Study]

Updated Jun 13, 2024 • 7 min read

A well-organized supply chain has always been a powerful source of competitive advantage.

It's even more so in today's interconnected economy. With the development of Artificial Intelligence (AI) solutions, logistics departments can solve complex optimization problems, cutting forecasting errors by half and reducing losses in sales caused by product unavailability by up to 65%, according to a recent paper by McKinsey Global Institute.

While the US corporations get all the global publicity, German industry has traditionally stayed in the vanguard of innovation future. German companies have already quietly embraced Industry 4.0, a concept brought in by the German government. The third industrial revolution was propelled by computerization and automation, while Industry 4.0 is driven by cyber-physical systems, internet of things (IoT), and cognitive computing. These elements make a perfect environment for machine learning (ML) projects.

AI technology already performs natural language processing or visual object recognition with a precision that enables large-scale implementation in many industries, state McKinsey Global Institute analysts in a white paper: "Smartening up with Artificial Intelligence (AI) - What’s in it for Germany and its Industrial Sector?". They predict that at least 30% of activities in 62% of German occupations can be automated.

The McKinsey team emphasizes that AI can be especially beneficial in supply chain management, solving many problems modern logistics departments face and help improve companies’ sales.

The challenges for logistics departments

Supply chain management aims at delivering the right quantity of the right product, in the right place and the right time. These means many variables that need to be optimized. A supply chain working at a maximum performance all the time is almost impossible without implementing AI.

Here are some of the most significant challenges logistics managers are facing today.

Customers’ demands are extremely high and have increased recently. They expect full transparency and information about where their delivery is at all times. Meanwhile, only about one-third of consumers are willing to pay extra an under two-day shipping.
Operating value control is a priority in the globalized economy. Energy and fuel are increasingly costly, the price of labor is rising, and changing fractured regulations make it difficult to scale operations up.
Risk management in the dynamic business environment means adjustments have to be made on the fly. New products are launched faster, supply networks change often, credit availability is fluid, intellectual property is more difficult to control in the globalized system, and political risk is no lower.
Environmental issues force regulators to introduce limits on greenhouse gas emissions that bring more uncertainty and raise costs.
Matching supply and demand in just-in-time production settings. Companies cannot afford to have any redundancies in the inventory. Extremely high supply chain efficiency is achieved at the expense of flexibility.
Growing internal and external complexity caused by multiple factors: new products, distribution networks expansion, short-term promotions, long-tail product range, extreme product seasonality.
Data overload: as Industry 4.0 delivers additional information (interconnectivity, IoT) that can be used in logistics optimization, traditional systems for forecasting and replenishment are not able to process it.

AI-based supply chain management solutions

ML methods prove to be very useful in supply chain optimization. McKinsey analysts point out supervised learning approaches based on Bayesian networks not only use historical sales data and the setup of the supply chains but can also analyze real-time data from advertisement campaigns, pricing as well as local weather forecasts. As a result, ML can help the logistics departments increase forecasting accuracy and optimize replenishment process. AI-powered supply chain optimization allows businesses to:

Combine the highly accurate demand forecasting with optimized replenishment strategies.
Enable flexible adaptation to the changes in the product mix or the distribution network.
Address the entire value chain – from the supplier of raw materials to the end customer.
Increase data granularity (the level of detail that may be analyzed) by focusing on, for example, individual stock points.
Accurately predict demand spikes and automatically adjust the routes and volumes of material flows.

Benefits of AI supply chain optimization

The final goal of introducing AI solutions in logistics aims at creating a fully automated, self-adjusting decision-making system for supply chain management. While it's still some way to go, machine learning enhanced supply chain management greatly improves forecasting accuracy.

McKinsey Global Institute estimates companies can increase their key performance indicators related to sales and logistics thanks to the use of ML methods.

AI-based forecasting can reduce errors by 30 to 50 percent.
The number of sales lost due to the products not being available may be reduced by up to 65%.
At the same time, the companies may reduce the inventories by 20 to 50 percent.
Transport, warehousing and supply chain administration costs are expected to decrease by 5 to 10 percent and 25 to 40 percent, respectively.

According to the McKinsey paper, AI-enhanced supply chain management has an impact across all industries, with the automotive suppliers and industrial equipment sectors benefiting the most.

In many industries, superior logistics alone can be enough for any company to beat the competition by matching supply and demand in just-in-time production settings. Contemporary supply chain optimization is a game only the most powerful machines can play.

There are different attitudes to the challenge of the necessary and inevitable AI-assisted logistics management. Some companies work on their own internal AI predictive forecasting and replenishment solutions, others try to implement third-party solutions. No matter which way they choose, supply chain managers should start experimenting with machine learning methods as soon as possible, as the process of model training takes time. While early adopters will fight for the competitive advantage, latecomers will adopt AI solutions in order to survive.