AI in Supply Chain: Use Cases, Examples and How AI Used in Supply Chain Management?

A supply chain is the most crucial element of any business. It represents a network interconnecting all the business activities. In this article, we will explore the role of AI in supply chain, look at the benefits, challenges, examples and future of artificial intelligence in supply chain management.

Importance of AI in Optimising Supply Chain Processes

Let’s explore the importance of AI in supply chain management first. Artificial intelligence improves the visibility of the supply chain by analysing large data sets. This helps uncover insights for addressing bottlenecks and enhancing capacity. Moreover, AI-powered tools provide businesses with real-time tracking of supplies to ensure transparent operations and ethical sourcing practices. This helps save time and cost and enables companies to meet consumer and regulatory expectations for sustainability and quality.

Benefits of Using AI in Supply Chain Management

Now that you know about the significance, here are the main benefits of AI in supply chain:

• Improved warehouse efficiency

AI in supply chain management helps optimise warehouse efficiency by designing better floor layouts and rack arrangements to reduce the travel time needed to access the inventory. Machine learning models are used to streamline the routes for robots and workers. In addition, AI-enabled systems analyse demand signals from the production line, marketing, and point-of-sale systems. This helps businesses balance inventory against carrying costs.

• Reduced operating costs

AI can understand complex behaviours and operate under irregular conditions and recurring tasks such as calculating, tracing and recording inventory. This helps avoid the bottlenecks with less labour and greater accuracy, thus reducing the overall operating cost. AI in supply chain also assists businesses in minimising the downtime of essential equipment. It identifies early-stage malfunctions to limit any disruption and the associated monetary loss.

• Fewer errors and less waste

AI significantly identifies workflow irregularities, staff mistakes, and defective products. This assists manufacturers and logistics companies recognise and rectify weaknesses, decreasing recalls, return rates, or repairs. AI also carries out root cause analysis to address failures adequately and minimise the number of errors made to reduce waste.

• Optimised operations through simulations

Supply chain managers can use AI to perform simulations, leveraging data from digital copies of the company’s logistical network. This way, they can try different options without any interference from real-life situations.

• Improved worker and material safety

Within an AI environment, workplaces are monitored to identify dangers and ensure conformity with safety standards. In addition, a wearable that uses AI technology ensures that employees are secure by monitoring how they move and alerting them to risks. It also makes further predictions regarding machine failures, avoiding dangerous events, and even taking care of risky work.

How is AI Used in Supply Chain Management?

Apart from the benefits, let’s now explore how AI is used in supply chain management:

• Back-office automation

AI automates back-office tasks by combining conversational AI with robotic process automation (RPA). This includes document processing, data entry, and other routine administrative tasks.

• Logistics automation

AI automates logistics through technologies like autonomous trucks. Companies such as Amazon, TuSimple, and Nuro deploy AI to optimise delivery routes and transport processes within the supply chain.

• Warehouse automation

AI enables the collaborative robots (cobots) for automated warehouse management. These cobots help with sorting, packing, and moving goods, improving overall warehouse efficiency.

• Automated quality checks

AI-driven computer vision systems automate quality checks on production lines. These systems continuously monitor and inspect products, ensuring they meet quality standards without human intervention.

• Automated inventory management

AI and computer vision bots automate real-time inventory scanning and tracking. These bots operate in warehouses and retail stores, maintaining accurate inventory records and ensuring timely stock replenishment.

• Predictive analytics/forecasting

AI uses machine learning algorithms to analyse data and predict future demand and market trends. Supply chain managers use these predictions to plan inventory levels, production schedules, and procurement strategies.

• Inventory optimisation

AI tools analyse historical data to determine optimal inventory levels. This involves using algorithms to assess past demand and supply patterns, ensuring efficient inventory maintenance.

• Region-specific forecasts

AI provides region-specific demand forecasts by analysing local data such as events, holidays, and trends. Businesses use these forecasts to tailor their supply chain processes to meet regional demands more accurately.

What is GenAI in the Supply Chain?

GenAI can generate new content, such as numerical data, pictures, text information, sounds, or videos, using the learnt dataset. Although the technology has been around for some time, it is now easier to use and more accessible.

How can Generative AI be Used in the Supply Chain?

Generative AI can be used in the supply chain for performing the following tasks:  

• Inventory Management

This technology creates optimised replenishment plans based on real-time demand, supplier lead times, and inventory levels. This keeps stock levels balanced and supports accurate fulfilment calculations.

• Supplier Relationship Management

Analysing supplier performance and market conditions identifies risks and opportunities, suggests alternative suppliers, and helps negotiate better terms.

• Risk Management

It simulates risk scenarios like supplier disruptions, natural disasters, and geopolitical events, helping companies identify vulnerabilities and create contingency plans.

• Route Optimisation

Transportation routes are optimised based on traffic, weather, and delivery deadlines, ensuring the most efficient paths are used.

• Demand Forecasting

It analyses historical data and market trends to produce accurate demand forecasts. This technology quickly processes large-scale data for real-time scenario analysis, helping companies adjust to changes in demand.

• Sustainability

It uses scenario analysis and optimisation algorithms to help reduce carbon emissions, minimise waste, and promote ethical sourcing. Integrating with blockchain keeps data about product origins clear and unchangeable.

AI in Supply Chain Examples

Examples of AI in the supply chain include enhancing decision-making, demand sensing, inventory management, and logistics. For instance, Flipkart uses AI to optimise inventory management, ensuring products are in stock and reducing waste. Reliance Retail leverages AI for demand forecasting, helping predict customer needs and manage inventory levels more efficiently. Tata Steel utilises AI to monitor supplier performance and improve logistics, ensuring timely availability of raw materials and reducing downtime. Mahindra & Mahindra employs AI for real-time inventory tracking and automated replenishment, leading to more efficient operations and cost savings.

Challenges of Using AI in the Supply Chain

Although AI in the supply chain comes with several benefits, there are challenges, too, such as:

• System Complexities

AI systems are typically cloud-based and require expansive bandwidth. Accessing AI capabilities often necessitates specialised hardware, which can pose a significant initial investment for supply chain partners.

• The Scalability Factor

While AI and cloud-based systems are scalable, achieving impactful and effective deployment may require a higher initial start-up investment in terms of users and systems. Each AI system is unique, necessitating thorough discussions with service providers.

• The Cost of Training

Adopting AI effectively involves significant investment in personnel training. Supply chain partners must collaborate closely with AI providers to develop impactful and affordable training solutions during integration.

• Operational Costs

AI-operated systems require maintenance and occasional replacement of individual processors, which can incur high operational costs. Energy expenses and utility bills may also increase, impacting overall overhead expenses.

Future of AI in the Supply Chain

AI in supply chains has already transformed how businesses operate, and the future looks even more promising. AI's predictive analytics will improve inventory management and operational efficiency. Autonomous vehicles and drones will speed up deliveries and increase safety. AI-powered robots in warehouses will boost productivity and cut costs. Embracing these advances will help businesses streamline operations, enhance customer satisfaction, and stay competitive globally.

In conclusion, AI in supply chains offers businesses worldwide the potential to enhance efficiency and competitiveness. Companies can navigate challenges effectively and thrive in a rapidly evolving market landscape by optimising inventory management, improving logistics with autonomous technology, and leveraging predictive analytics and AI-driven robotics.