Top 10 use cases for Machine Learning & AI in Supply Chain

But the inventory management process involves multiple inventory related variables (order processing, picking and packing) that can make the process both, time consuming and highly prone to errors. Maybe, but there are many caveats and many reasons to doubt that such a system would ever be fully trusted. Receiving less attention is the potential for Generative AI to transform the business through the augmentation of countless mundane tasks.

Additionally, by analyzing large amounts of data and providing predictive insights, AI can help optimize the supply chain and provide the necessary competitive edge. As AI continues to evolve, it has the potential to dramatically shape the logistics and supply chain industry in the years to come. Generative AI models can analyze demand patterns, lead times, and other factors to determine the optimal inventory levels at various points in the supply chain. By generating suggestions for reorder points and safety stock levels, AI can help businesses warehouse management by minimizing stockouts, reducing excess inventory, and lowering carrying costs. In the intricate world of supply chain management, maintaining optimal stock levels is critical to prevent out-of-stock crises or surplus inventory.

How can businesses implement machine learning into logistics and supply chain processes?

It can also be used to identify any defects or faults in order to ensure the highest quality of goods. Autonomous vehicles are being developed to transport goods, saving time, money, and resources. AI can be used to power these vehicles and make them smarter, safer, and more efficient. Autonomous vehicles can reduce the need for manual labor and make the supply chain more efficient and cost-effective. UPS claims to deliver thousands upon thousands of deliveries each day, and in each business day the UPS driver makes an average of about 100 deliveries.

Currently, various strategies and optimizers are being used to generate a delivery schedule and truckload. In the future, AI/ML may be able to provide a more ‘perfect’ solution to the above problem, which balances the requirements mentioned above. Generally, while implementing an SCM solution, ABC analysis of SKUs (classifying products based on their importance i.e. on sales value or volume (quantity) or the margin, etc.) is done.

Key Advantages of Using Machine Learning in Supply Chain Operations

By automating these processes, robots can eliminate mistakes and improve accuracy, saving time and money. AI can match freight with the best available carriers and negotiate better transport costs. It can also be used to optimize and streamline the booking and tracking process, improving customer satisfaction and reducing turnaround times.

 

 

Having a robust supply chain forecasting system means the business is equipped with resources and intelligence to respond to emerging issues and threats. And, the effectiveness of the response increases proportionally to how fast the business can respond to problems. A steep scarcity of supply chain professionals is yet another challenge faced by logistics firms that can make the supplier relationship management cumbersome and ineffective. Inventory management in supply chain is largely about striking a balance between timing the purchase orders to keep the operations going smoothly while not overstocking the items they won’t need or use. ML typically uses data or observations to train a computer model wherein different patterns in the data (combined with actual and predicted outcomes) are analysed and used to improve how the technology functions. Machine Learning techniques process large volumes of real-time data to bring automation into the process and improve decision making – across various industries.

Three use cases where AI is implemented in Supply Chain management

This is a popular use case for machine learning to help intelligently and automatically cluster various type of data such as products, customers, and attributes to accelerate and enhance decision making. Companies can leverage clustering and segmentation together with machine learning algorithms to establish relationships and find contextual information that can be used to create strategies. An example is the clustering of like products to understand halo or cannibalization impact. For instance, during the pandemic, an oil & gas distributor adjusted forecasts as residential propane use increased as more people worked from home and commercial use fell as less people worked from offices.

At its core, SNP involves generating & solving a large mathematical optimization problem using Mixed Integer Linear Programming (MILP) technique from the Operational Research (OR) tools repository. MILP is a very effective optimization technique, where variables defined can be either continuous or integer (taking binary values). Integer variables are typically used to define step functions and similar constraints. The optimization problem is generated by the SCM solution based on various configurations, master data (e.g., transportation lanes, capacity, etc.), constraints such as production capacity, and of course demand numbers. The output of the SNP module i.e., optimal supply plan is released to the next Production Planning module. Real-time adjustments to the production process involves constantly monitoring and analyzing the production process.

AI in Supply Chain: A Future Full of Promises

Generative AI analyses extensive data from various sources, aiding in route optimization and transportation, resulting in time and cost savings and overall improvements in logistical efficiency. Its capabilities span route enhancement, vehicle and fleet optimization, and dynamic routing, contributing to a more robust and resilient supply chain. Equipment malfunctions and unexpected downtime can significantly disrupt supply chain operations, often leading to considerable financial loss. Generative AI becomes an invaluable asset for implementing predictive maintenance strategies in this context. It achieves this by analyzing information such as sensor readings, historical maintenance documentation, and equipment performance indicators.

Bringing in the perfect balance here is mastering the art of inventory and warehouse management. Beyond negotiations, generative AI presents an opportunity to improve supplier relationships and management, with recommendations on what to do next. These tools are useful to quickly extract information from large contracts and help you better prepare for renewal discussions, for example. Web scraping, social media (SM) listening, and translation can help to track data from internal (shipment, supply, partner) and external (news, SM platforms, compliance) sources.

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For your clients and other stakeholders, intelligent software makes tracking different moving parts along the supply chain much easier as well. Using AI when shipping goods along your supply chain can revolutionize the process, both in terms of quality and speed. You’ll spot patterns – chronic problems to fix and areas of success to exploit further – you didn’t previously know existed. Manually produced reports just don’t provide you with the data and analytics you need by comparison. The masses of data and statistics that certain AI tools can compile about how your company’s logistics are functioning dwarf the possibilities without them. If your logistics or supply chain teams aren’t making full use of automation yet, here are eight use cases to show you how that can change.

Allison Champion leads marketing communication at Flowspace, where she works to develop content that addresses the unique challenges facing modern brands in omnichannel eCommerce. The real-world applications of AI span diverse industries, each exemplifying the tangible benefits of AI integration. Machine learning methodologies used in the supply chain vary according to both the type of problems to be solved and the data available to solve them with. Before an algorithm can be developed and trained, an organization’s data—often staggering quantities of it—must be prepared for modeling. AI increasing customer satisfaction may seem counterintuitive at first, but instantaneous messaging and well-planned funnels can save time for clients and allow them to complete their queries seamlessly. While several industries are still struggling to overcome the post-pandemic effects, there are a few industries, like supply chain, that took the opportunity to adopt these modern technologies at a large scale.

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• This specific technology is predicated on rather large learning models that have acquired significant levels of knowledge.
• As we speak, the future of the logistics and supply chain industry is already being revolutionized by AI in 2023 in ways that we’ve never seen before.
• Moreover, Generative AI can dynamically adapt plans in real-time, considering unforeseen circumstances or disruptions, thus improving overall supply chain resilience.
• Having a robust demand forecast enables merchants to make smarter decisions around procurement, all the way down to the SKU level.

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