16 Examples of AI in Supply Chain and Logistics

Often, classical methods like linear optimization (aka linear programming) are used. Many-to-many relationships between distribution centers and local supply locations force complex allocation decisions. Poor regional replenishment decisions can lead to local stock-out conditions and lost sales. In Bonsai, the platform let the AI agent t train itself for a broader set of (simulated) situations. At least if those conditions were within the scope of the simulator when the AI agent was trained. Most importantly, the AI agent does not rely on human operators or data scientists to plan, ahead of time, all the possible inputs or environmental conditions variations.

• 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.
• Intel has a quality threshold against which chips are measured to determine whether they should be kept or thrown out.
• Consumers are more demanding than ever when it comes to the quality of the products they purchase.
• It examines weather, and traffic, and predicts the future on the basis of feedback from customers.
• But a company doesn’t need a pandemic-sized disruption to knock a normally operating supply chain off kilter if the company lacks access to vital information.

It could produce a big safety hazard to accept substandard parts not meeting the quality or safety standards. These powerful functionalities make it an ideal solution to address some of the main challenges of the supply chain industry. 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. To understand under what circumstances machine learning use cases in your supply chain would be advantageous to your business, you need to conduct a Discovery Phase and calculate ROI. You need to estimate TCO and the profitability you will gain in the short term and in the long run.

Use cases of AI/ML in Supply Chain

For example, a major auto manufacturer is piloting nuVizz’s RoboDispatch Solution in its inbound logistics operations. 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. AI can help logistics service providers (LSPs) optimize assets for the movement of shippers’ materials or components from suppliers or vendors to their facilities.

“Gen AI” is a term for applying artificial intelligence (AI) in supply chain management. This involves using AI techniques such as machine learning, natural language processing, computer vision, and optimization to improve supply chains’ efficiency, agility, and resilience. The use of Gen AI can assist supply chain managers in automating tasks, analyzing data, predicting demand, optimizing inventory, reducing costs, improving customer service, and mitigating risks. Accurate demand forecasting is invaluable for businesses, enabling them to optimize production planning, manage resources effectively, and meet customer demands efficiently. AI utilizes predictive analytics to analyze historical sales data, market trends, seasonality, and external factors to generate accurate demand forecasts.

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Multiple layout scenarios generated by AI can enable identifying efficient configurations, resulting in lower costs, faster order fulfillment, and enhanced space use. AI-powered warehouse management systems also adapt to demand shifts, maintaining continuous optimization. The adoption of AI into the supply chain is the main priority for 55% of supply chain stakeholders. Such technology helps to increase product quality, improve transparency, and make your business more predictable.

Variables such as evolving regulations, traffic congestion, and capacity limitations add an additional layer of complexity to managing transportation and logistics. Let us understand the common challenges that enterprises encounter in supply chain optimization. Its ability to process data and reduce human error can translate into huge opportunities for efficiency improvements in the last-mile space.

Last-mile delivery is a complex choreography of all the participants involved in a supply chain – a fleet operator, a courier, a freelancer who owns a car, or a scooter. The last mile delivery constitutes a good part of the final price due to its high costs, mainly affected by fuel expenses, driver wages, and vehicle wear. C3 AI is a machine-learning software company specializing in tools that use predictive analysis for inventory management. AI can streamline the supplier evaluation and selection process by analyzing supplier performance data, assessing their capabilities, and identifying potential risks.

For example, it checks for seasonal fluctuations, and whether two or more types of change occur together, or if they’re mutually exclusive. While data sharing remains a challenge, many organizations already benefit from two key things that AI does now for supply chain management. AI can be used to analyze past performance and customer feedback to select the best carrier for the job. AI-driven algorithms can also be used for competitive bidding, making it easier to select the most cost-effective carrier for the job.

Product design

It involves creating a simulation model replicating the real-world dynamics of inventory management, including demand patterns, lead times, order quantities, and replenishment policies. By running simulations, decision-makers can gain insights into inventory performance, evaluate different strategies, and make informed decisions to improve supply chain operations. For example, AI algorithms can analyze past data on supplier performance, transportation routes, and inventory levels to identify patterns and trends that may indicate potential risks. By continuously monitoring these indicators, AI systems can detect anomalies or deviations from normal patterns, alerting supply chain managers to potential disruptions. This early warning system enables companies to take preventive measures, such as finding alternative suppliers or adjusting production schedules, to minimize the impact of potential disruptions.

By monitoring social media conversations and weather forecasts, AI algorithms can detect an upcoming heatwave in a particular region. This information can then be used to forecast an increase in demand for products such as sunscreen, hats, and portable fans. Armed with this insight, the company can proactively increase production of these items, ensuring that they are readily available when customers start looking for them. The current generation of AI can optimize supply chains—and even tailor them to deliver the right product to the right customer at the right price. However, doing so would require a level of data sharing that very few companies are ready for.

Video Analytics

The demand forecasting capabilities of AI come in handy for optimizing inventory turns and reducing stockouts, enabling retailers and manufacturers to understand the seasonality of stock-keeping units. AI can help automate routine supplier communications like invoice sharing and payment reminders. Automating these procedures has the advantage of preventing silly hiccups caused, for example, by failing to pay a vendor on time and having a negative knock-on effect on shipment and production. Manufacturers can improve both storage and retrieval operations by building an AI agent optimize and balance throughput and efficiency within the warehouse to maximize financial return. To improve production planning and solve these limitations, one can build an AI agent using DRL to optimize production by determining amounts of which product SKUs to manufacture and how to best schedule their production. Using Project Bonsai, one can build a brain (AI agent) to dynamically optimize regional inventory replenishment to create more stable and predictable stocking levels, thereby avoiding lost sales.

Read more about Top 3 AI Use Cases for Supply Chain Optimization here.