AI Machine Learning for the Supply Chain How Do We Use It? Practical and Visionary Use Cases

Monthly supplier reviews often involve considerable time and effort as procurement teams gather and analyze performance data. Supply chain users can collaborate with impacted suppliers to promptly set new delivery timelines and redirect purchase orders if needed. Firms can thus fulfill high-priority customer orders via alternate distribution centers, streamlining operations and saving time. Here are the best answers for how artificial intelligence improves the supply chain process. It’s a fact that AI/ML is a game-changer for most industries, especially supply chain and logistics.

• A better approach will be segmenting SKUs using clustering (e. g. K-Means) and then applying different strategies to each segment.
• The market is based on human emotions on any given day, and it makes the whole market very unpredictable and difficult to comprehend.
• As a result, human workers are freed up to perform more complex jobs that computers can’t handle.
• Now imagine a piece of machinery unpredictably breaking down, and others following suit over the next couple of months.

The use cases presented in the article are at a conceptual level and need further analysis and detailing to implement them. Most SCM solutions implement traditional algorithms and optimization as part of their backend logic and rarely use AI/ML algorithms. The possibilities for human engagement in a supply chain shaped by cognitive technologies have only been touched upon here.

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One thing that can help satisfy them, is recommending the right products at the right time. The supply chain management system is interlinked with different regional distribution centers, and these centers are connected via transportation. This type of pattern recognition system for studying the market can help companies improve their product portfolio, and offer a better customer experience. This inconsistent-order pattern can lead to miscommunication between your team and loss of productivity. AI and ML give us a closer prediction of the inconsistent nature of customer behavior much earlier at optimal level during such situations.

The employees, who are embedded in various work process loops and who are also learning themselves, form a cognitive, learning organisation with artificial intelligence. This means that employees can flexibly adapt their respective work processes, which are embedded in the network in the broadest sense, and also change them at short notice. Generative AI can aid product design and innovation by generating new concepts, optimizing product configurations, and simulating different scenarios. It can assist in creating innovative and customized products that meet specific customer requirements while considering supply chain constraints and cost factors. Integrating generative AI into existing supply chain systems and processes can be challenging. Ensuring seamless integration, scalability, and compatibility with existing infrastructure and tools requires careful planning and consideration.

Watch: E-Commerce Delivery Trends: Riding the Seesaw of Supply and Demand

Generative AI can play a significant role in transportation and routing optimization within supply chain management. By analyzing vast amounts of data from various sources, AI can generate efficient transportation plans, save time, and improve the overall efficiency of supply chain logistics. Generative AI can process market data, customer feedback, and competitor information to generate insights about potential gaps or opportunities in the market. This can guide businesses in the development of new products or services that cater to emerging trends or customer satisfaction criteria. AI systems are able to process huge amounts of data, such as news, images, market trends, and social media posts, and predict when and where potential risk events might happen. Knowing this information, companies can save money and avoid potential charges or penalties.

Until recently, they used traditional methods, so they didn’t have to worry about adopting enterprise-wide software solutions. However, once they find the best solution for their operation, companies have to closely follow the integration process to ensure that it doesn’t exceed the budget and creates real value. However, each of them is designed for a specific use or industry, so the next challenge is to find the ideal software for your operation. LivePerson’s AI-driven conversational platform facilitates customer support by measuring consumer intent and sentiment while determining where a conversation should go next. The platform also juggles every conversation simultaneously, whether it’s being held by a human, bot, third-party tech or a combination of all three.

Introduction AI and Supply Chain

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• With 94% of retailers seeing omnichannel fulfillment as a high priority, proper inventory management is a must-have.
• This KPI reflects both the time it takes to respond to a disruption or unexpected event in the supply chain and a robust supply chain design.
• Having the data collection, storage and infrastructure is essential to begin implementing a ML strategy.
• Wei Shiang Kao worked closely with data science and marketing teams to drive adoption in the DataRobot AI platform.
• In the future, AI/ML may be able to provide a more ‘perfect’ solution to the above problem, which balances the requirements mentioned above.
• Machine learning in supply chain with its models, techniques and forecasting features can also solve the problem of both under or overstocking and completely transform your warehouse management for the better.