What is the use case of generative AI in the supply chain

GenAI Use Cases in Supply Chain

Before moving forward with GenAI applications in the supply chain, supply chain leaders must assess which GenAI capabilities align with company goals and the applicable benefits and limitations.


Some use cases with potential for supply chain management include:


1. Demand forecasting


With the onset of the COVID-19 pandemic, rapid shifts in demand have resulted in unpredictable supply chain activity as consumers shift spending from services to goods. These changes demonstrate how important it is for companies to anticipate changes in demand.

Organisations can use GenAI models on historical sales data, market trends, and other factors to simulate potential supply and demand scenarios and improve demand forecasting accuracy. Tracking demand patterns can help organisations minimise disruptions and avoid storage issues.


2. Inventory evaluation


GenAI can also help improve inventory management.

Trained on key data such as inventory levels, warehouse capacity and manufacturing times, GenAI can suggest ways to optimise inventory processes, recommend when to replenish or reduce inventory, and help reduce excess inventory. Because storing excess product costs a company more money, reducing excess inventory saves money.


3. Supplier and customer communications


Frequent communication between a company and its suppliers and between a company and its customers is important for an effective supply chain, but making that communication as effective as possible can be difficult.

GenAI can automatically send messages so your employees don't have to. Big language models and natural language processing can consume data from sources such as market events affecting suppliers and transportation delays associated with specific shipments, and GenAI chatbots can inform suppliers about risks. GenAI chatbots can also handle some customer inquiries, such as processing returns or tracking shipments.


4. Activity


New technologies and fluctuations in demand can lead to operational issues, and GenAI can suggest ways to improve them.

GenAI can evaluate operational aspects, such as supplier performance and manufacturing speed, and then recommend ways to optimise processes. These optimizations can, among other things, save companies money.


5. Logistics


Logistics disruptions can cause a variety of problems. Delivery may be delayed due to a traffic accident or unexpected shortages may occur due to extreme weather, making it difficult to maintain an on-time delivery schedule.

Armed with data such as historical weather patterns, traffic maps, fuel prices and more, GenAI models can identify optimal travel routes and highlight future disruptions as well as alternative routes if necessary. Doing so will prevent your order from being delayed, helping us keep deliveries on schedule and improve customer service.


6. Stability and scalability


Sustainability is currently a key focus for many organisations and GenAI can highlight an area for development.

Training a GenAI model on a company's current material usage and market forecasts for renewable materials can provide insight into how to make processes more sustainable while considering cost-effectiveness and long-term scalability.


7. Analysis


GenAI can run simulations and probabilistic scenarios, assess risk, and compile results into reports.

As with all GenAI supply chain use cases, caution is required when using this technology as GenAI and the models that fuel it continue to evolve. Current concerns include faulty data and incomplete output, also known as AI illusions, which can hinder effective use.


Read Also : Things to know about artificial intelligence for supply chain management


Benefits of AI in Supply Chain Management


AI has numerous advantages for supply chain management and planning.Some of the key benefits include:


  • Improve efficiency: AI can help companies improve the efficiency of their supply networks by optimising inventory levels, routing, and scheduling.

  • Cut costs: AI enables businesses to increase productivity and optimise their operations in order to cut costs.

  • Improved customer service: AI can assist companies in enhancing customer service by giving real-time delivery information and expediting the resolution of consumer complaints.

  • Enhanced agility: AI allows businesses to respond more quickly to changes in supply and demand, which increases their nimbleness.

  • Enhanced resilience: By increasing their ability to withstand disturbances, AI can help firms become more robust.


AI's Potential Impact on Supply Chain Management


Supply chain processes will be progressively redefined by AI technology as it develops. Supply chain operations will be increasingly shaped by automation, machine learning, and predictive analytics.

AI has the potential to completely change how businesses operate their supply chains, increasing their resilience, efficiency, and agility.

These are some expected future implications of artificial intelligence (AI) on supply chain operations.


  • Robots powered by AI: Picking, packing, and shipping are among the operations that robots with AI capabilities automate. This lowers order fulfilment expenses and increases efficiency.

  • Virtual Assistants: Support and customer assistance are provided by virtual assistants. This enhances client happiness.

  • Predictive analytics: AI is used in predictive analytics to forecast demand, optimise inventory levels, and pinpoint hazards. This assists companies in making wise choices and averting hiccups.

  • Autonomous cars: Goods are transported by autonomous vehicles. This boosts productivity and lowers transportation expenses.

  • Blockchain: Blockchain is used to increase transparency and trace the flow of goods. This boosts productivity and reduces fraud.


Read Also : Best IOT apps for iPhone for supply chain tracking

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