Machine Learning in Logistics and Supply Chain 6 Use Cases Included

Sustainability is a growing concern of supply chain managers since most of an organization’s indirect emissions are produced through its supply chain. AI can help improve supply chain operations to make them greener and more sustainable. To improve demand planning in your business, check out our data-driven list of Demand Planning Software. AI-enabled computer vision (CV) systems can help automate quality checks for products.

Therefore, it is difficult to pinpoint volatile customer behavior due to a surplus of orders from online retailers. Here, we are going to cover the topics that describe the best use cases of AI in supply chain management. If you’re not ready for transformation, start by preparing a plan to implement artificial intelligence in supply chain.

Use cases of AI/ML in Supply Chain

Most product parts are assembled across various production plants, so ensuring that the entire supply chain works perfectly at all times is a must. The process involves multiple essential processes and functions, including production, procurement, marketing, sales, and logistics. Moreover, manufacturers also have to take care of product packaging, shipping, and many other details. Forecasting and inspection are both important, but the biggest impact will come when supply chains can be tailored to specific customer needs.

By leveraging the power of Generative AI, supply chain stakeholders can analyze massive volumes of data, generate valuable insights, and facilitate better decision-making processes. In March 2023, Microsoft announced Microsoft Dynamics 365 Copilot, an AI-driven assistant integrated into CRM and ERP systems. Imagine a supply chain workflow moving along like a well-oiled machine (as it should!).

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The solution to the above challenges is based on the platform’s functions and consists of three scenarios. First, planning the production schedule; second, monitoring production; and third, adjusting the production process in real-time. Together, these scenarios cover the process of demand, forecasting, planning and monitoring the flow of production in a typical manufacturing company [37, 38]. Fourth, the architecture also incorporates a data integration and management layer that is responsible for ingesting data from the supply network into the platform. This can be data from a factory floor, manufacturing systems, warehouses or even vehicles, and from diverse and geographically dispersed sources. This module thus ensures the interoperability between different components which is indispensable, especially from the point of view of a supply network.

Autonomous vehicles and autonomous things are used to automate logistic processes such as delivery and inventory management. Machine learning and artificial intelligence solutions are used to optimize performance and cost efficiency. A McKinsey survey showed that 80% of supply chain executives expect to or are already using AI/ML in planning.

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In this pilot program, RoboDispatch automates the dispatch process for the movement of full and empty trailers from parts supplier locations to its manufacturing plants. Generative artificial intelligence can make it easier for companies to keep tabs on their shipments and stay on top of potential delays. And the last few years have seen the proliferation of autonomous delivery vehicles like drones.

Such classification is used for configuring, applying, and implementing a customized strategy for every class. Such analysis makes the implementation more effective because A-class products need completely different treatment as compared to the ‘C’ class. For example, for ‘A’ class products, the organization may not allow any changes to the numbers as predicted by the model.

This is where computer vision technology, one of machine learning in supply chain use cases, comes in handy. As an example, Facebook uses computer vision to find existing users on photos and tag them. Using the identified best practices and insights, the system can generate standards for various aspects of the supply chain. It can create guidelines, protocols, and procedures that define how different operations should be conducted to achieve desired outcomes. These standards can cover areas such as procurement, inventory management, production, logistics, quality control, and sustainability, among others.

From a strategic perspective, a company’s management must understand the benefits of using AI in its business activities. For example, relevant expertise and skills should be in place, even if the goal of software developers and designers is to develop more self-explanatory solutions. Data requirements also need to be understood and confidentiality and compliance requirements defined, both in terms of business needs and legal requirements. In addition, organisations would do well to define a roadmap for the adoption and use of AI solutions and understand how new solutions can be integrated with existing processes and legacy technology. Supply chain companies are now looking at how AI can help them optimize their production planning on the supply side as well.

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