Use Cases of Generative AI in Supply Chain

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

supply chain ai use cases

Ensuring compliance with the stringent regulations governing these documents is paramount in this process. Precise and accurate generation is crucial to avoid miscommunication or violation of these regulations. Generative AI can maintain compliance across all documents it generates, providing an effective, efficient, and reliable means of automating this crucial aspect of supply chain management. According to research reports, it is believed to use the latest AI-integrated GPS for logistics and supply chain delivery; users will save $ 50 million (Approx.) per year.

supply chain ai use cases

The industry can adopt agile and flexible strategies using its integrated supply chains. This allows to rapidly react to changing customer needs while optimising processes and quality control. A learning environment ensures that future deviations in planned processes can be addressed better. Generative AI is a powerful tool for businesses to identify and manage risks within their supply chains. By scrutinizing historical data and external variables such as meteorological conditions, political uncertainties, or disruptions from suppliers, generative AI models can pinpoint potential hazards.

Getting Started with AI/ML to Build Intelligent Supply Chains

This allows organizations to plan for repairs or replacements proactively, consequently reducing downtime, prolonging equipment life, boosting operational efficiency, and curbing maintenance expenses. Hence, it helps improve the overall dependability and performance of the supply chain. Stay competitive and responsive to market demands with our generative AI solutions designed to optimize supply chain processes.

  • With AI-driven autonomous vehicles operating with a 99.9% confidence rate within 0.5m, we are within reach of a future of self-driving cars.
  • It also notifies company agents about events such as a new order placed or a schedule change.
  • Luckily, demand prediction is one of the most popular uses of artificial intelligence in operations and supply chain planning.
  • Also, in many cases, products and parts are also phased-in and phased-out regularly, which can cause proliferation leading to uncertainties and the bullwhip-effects up and down the supply chain.

Apart from saving operating costs, robots can provide you with data-based decision making. Supply Planning or Supply network planning optimizes production using a production capacity at a very broad level. However, further optimization and scheduling are done using an advanced optimizer, which may consider additional constraints such as sequencing or constraints specific to a production process in the industry. If it is not feasible to optimize using MILP or other optimization algorithms, then specialized approaches like genetic programming are used.

Use Case 2: Substitution Solution by Walmart

The artificial intelligence system can quickly detect any deviations and identify their causes and consequences for upstream and downstream activities. This allows for timely interventions to rectify any problems and minimize their impact on the production process. The use of historical data and real-time monitoring also allows the system to offer suggestions for possible decisions. This helps employees to make informed decisions quickly and efficiently, saving time and reducing the likelihood of errors. By allowing employees to intervene and improve the system’s database, the AI system can continuously learn and improve over time, making it even more effective at identifying and addressing production issues.

supply chain ai use cases

Today’s generative AI tools can even suggest several courses of action if things go awry. Risk management may be the most promising area, particularly in preparing for risks that supply chain planners haven’t considered. The world’s leading engineering and technology company uses an AI analytics platform capable of reading terabytes of data in seconds and can achieve zero defects.

Sensing market situations

The AI reads barcodes, text, and other information in the images and automatically compares it with what’s in the warehouse management system (WMS), providing warehouse managers with real-time inventory data via a dashboard. It deploys drones that fly through warehouses to photograph inventory stored in pallet locations. The SO99+ software defined the optimal mix of inventory across the manufacturer’s supply chain to achieve service level targets. For instance, Gaviota, an automated sun protection and shutter maker, deployed the SO99+ solution from ToolsGroup and achieved a 43% reduction in stock levels, slashing inventory from 61 to 35 days.

supply chain ai use cases

AI can be used to analyze customer and market data to generate predictive forecasts and demand patterns. AI solutions can also be used to develop an accurate inventory management plan, which can help to prevent over-ordering or under-ordering of certain items. In order to predict customer behaviours, many spreadsheet-based methods were proposed, but with the rise of big data they turned out to be obsolete. The main reason that spreadsheet models fail at demand forecasting is that they’re not scalable for large-scale data.

Real-Time Visibility To Improve Customer Experience

The following six use cases show how leaders can reach a more efficient and cost-friendly way of storing, handling and moving goods. An agile approach enables  organizations to begin implementing AI in cost-effective ways. By integrating third-party vendors, they can start where they are, learn what works for their businesses, and scale up as needed. This tactic allows for much faster AI integration than building a new platform from the ground up or building on top of legacy solutions.

supply chain ai use cases

Once the platform identifies damaged goods, it will send you a PDF report, complete with defect images. F|AIR will help you to improve the consistency of quality inspection across your supply chain and reduce quality control costs up to 80%. From quality control to warehouse management, AI is applicable at all stages of product distribution. AI and ML in the supply chain have created new performance standards for supply chain effectiveness.

The Benefits of AI in Supply Chain Management

Read more about here.

Will supply chain be replaced by AI?

Ultimately, AI will optimize supply chains to meet specific customer needs for any given situation. The enabling technology exists but the remaining challenge is it requires a level of data sharing that can't be found in supply chains today.

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