This is even more true in today’s interconnected economy. With the development of Artificial Intelligence (AI) Solutionslogistics services can solve complex optimization problems, cutting forecasting errors in half and reducing lost sales caused by product unavailability by up to 65%, according to a recent article from the McKinsey Global Institute.
While American companies benefit from all the global publicity, German industry has traditionally remained at the forefront of future innovation. German companies have already quietly adopted Industry 4.0, a concept introduced by the German government. The third industrial revolution was powered by computerization and automation, while Industry 4.0 is driven by cyber-physical systems, the Internet of Things (IoT) and cognitive computing. These elements provide a perfect environment for machine learning (ML) projects.
AI The technology already performs natural language processing or visual object recognition with a precision that allows for large-scale implementation across many industries, McKinsey Global Institute analysts say in a white paper: “Getting smarter thanks to artificial intelligence (AI): what are the benefits for Germany and its industrial sector?“. They predict that at least 30% of activities in 62% of German professions can be automated.
The McKinsey team highlights that AI can be particularly beneficial in supply chain managementsolving many problems that modern logistics services face and helping to improve business sales.
The challenges of logistics services
Supply chain management aims to deliver the right quantity of the right product, to the right place at the right time. This means many variables that need to be optimized. A supply chain constantly operating at peak performance is almost impossible without the implementation of AI.
Here are some of the biggest challenges logistics managers face today.
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Customer requests are extremely high and have increased recently. They expect full transparency and information about the delivery location at all times. Meanwhile, only about a third of consumers are willing to pay extra for shipping in less than two days.
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Operating value check is a priority in the globalized economy. Energy and fuel are becoming more expensive, labor prices are rising, and evolving fragmented regulations make it difficult to expand operations.
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Risk management In a dynamic business environment, adjustments must be made on the fly. New products are launched more quickly, supply networks change often, credit availability is fluid, intellectual property is more difficult to control in a globalized system, and political risk is no less.
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Environmental issues forcing regulators to introduce limits on greenhouse gas emissions that cause more uncertainty and increase costs.
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Matching supply and demand in just-in-time production parameters. Businesses cannot afford to have layoffs in their inventory. Extremely high supply chain efficiency is achieved at the expense of flexibility.
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Growth internal and external complexity caused by multiple factors: new products, expansion of distribution networks, short-term promotions, long-tail product range, extreme product seasonality.
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Data Overload: Industry 4.0 provides additional information (interconnectivity, IoT) that can be used in logistics optimization, traditional forecasting and replenishment systems are not able to process it.
AI-powered supply chain management solutions
ML methods prove very useful in supply chain optimization. McKinsey analysts point out that supervised learning approaches based on Bayesian networks not only use historical sales data and the configuration of supply chains, but can also analyze real-time data from advertising campaigns, prices as well as local weather forecasts. As a result, ML can help logistics departments increase forecast accuracy and optimize the replenishment process. AI-powered supply chain optimization enables businesses to:
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Combine the very precise Demand forecasting with optimized replenishment strategies.
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Enable flexibility adapting to changes in the product range or in the distribution network.
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Address the entire value chain – from the supplier of raw materials to the end customer.
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Increase data granularity (the level of detail that can be analyzed) focusing, for example, on individual storage points.
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Accurately predict peaks in demand and automatically adjust material flow routes and volumes.
Benefits of AI Supply Chain Optimization
The final goal of the introduction AI solutions in logistics aim to create a fully automated and self-adjusting decision-making system for supply chain management. Although there is still a way to go, improved supply chain management through machine learning significantly improves forecasting accuracy.
The McKinsey Global Institute believes that companies can increase their sales and logistics-related KPIs through the use of ML methods.
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AI-based forecasts can reduce errors by 30 to 50 percent.
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THE number of sales lost due to product unavailability can be reduced by up to 65%.
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At the same time, businesses can rreduce inventory by 20 to 50 percent.
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Transportation, warehousing and supply chain administration costs expected to decrease by 5 to 10 percent and 25 to 40 percentrespectively.
According to the McKinsey article, AI-enhanced supply chain management is impacting all industries, with automotive OEMs 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 that only the most powerful machines can play.
There are different attitudes towards the challenge of the necessary and inevitable AI-assisted logistics management. Some companies are working on their own in-house AI predictive forecasting and replenishment solutions, others are trying to implement third-party solutions. Regardless of which path they choose, supply chain managers should start experimenting with machine learning methods as soon as possible because the process of training models takes time. While early adopters will fight for competitive advantage, latecomers will adopt AI solutions to survive.