What is AI in the supply chain?

Recently, this technology gained popularity as further advancements such as generative AI and tools such as chatbots, robots and AI assistants demonstrate the value AI brings to risk mitigation and supply chain resilience. Meanwhile, the COVID-19 pandemic illustrated just how fragile the global supply chain can be, highlighting the need for smarter tools to reduce delivery times and cut costs.

A key component of AI is machine learning (ML), where systems learn from data instead of relying on pre-programmed rules. ML can forecast customer demand, discover patterns, make market predictions, interpret voice and written text, and analyze a multitude of factors that can optimize a supply chain’s workflow.  

While it’s important to embrace AI, implementing AI requires thoughtful preparation. Manufacturers and logistics providers should take the necessary steps to prepare their supply chains for AI systems and understand that an optimization of this magnitude can take time and resources.

AI-driven supply chain systems help companies optimize routes, streamline workflows, improve procurement, minimize shortages and automate processes end-to-end.

Modern supply chains are complex, especially for manufacturers that rely on multiple partners to ship goods on time and in an organized manner that minimizes disruptions. By analyzing large volumes of data from across the supply chain, AI delivers actionable insights that improve efficiency and enhance customer satisfaction.

AI finds practical application in supply chain operations, such as forecasting, route optimization for drivers, cutting down on fuel consumption and lowering operational costs. Route optimization tools use data from Internet of Things (IoT) devices, logistics providers and supplier networks deployed across the supply chain to optimize logistics networks.  

AI can also be used for tracking  inventory levels and market trends. A specific capability is in inventory management. AI can enhance supply chain visibility, automate documentation for physical goods and intelligently enter data whenever items change hands.  

An emerging trend in the AI space is agentic AI, where each AI agent takes a natural language query and analyzes data to deliver relevant responses. AI agents can work across business functions, such as procurement, supply chain management and logistics planning. These AI agents can go far beyond routine tasks and are instead making informed decisions based on the internal and external data sources that are input.  

Tailored inventory management, shipment readiness and sustainability enhancements further contribute to AI’s positive impact on supply chain operations. The outcomes are unmatched time and cost savings, along with real-time data analysis to inform stakeholders and supply chain teams on how to best run their supply chain operation. 

The future of supply chain operations lies with AI technology and an overall reduction of manual intervention.  

AI understands complex behaviors and learns repetitive tasks, such as tracking inventory, and completes them quickly and accurately. AI solutions reduce overall operating costs by identifying inefficiencies and mitigating bottlenecks. In addition, some AI tools are used to analyze supplier performance and conduct price comparisons ensuring every dollar being spent is purposeful. AI also redirects organizations to alternative suppliers and update delivery schedules fast, with little to no human intervention.

AI uses historical and real-time data to decide and analyze market conditions. Furthermore, AI tools prevent potential disruptions or stockouts due to external factors outside of suppliers control like weather forecasts. There is no longer the need for time-consuming manual data entry and instead AI provides end-to-end visibility. These AI tools can analyze demand fluctuations and prevent overstock through predictive maintenance capabilities.

One of the benefits of AI technology is its ability to spot behaviors and patterns. By doing so, manufacturers and warehouse operators can train algorithms to find flaws, such as employee errors and product defects, long before bigger mistakes are made. Furthermore, AI can help streamline an enterprise resource planning (ERP) framework and can be directly embedded. This approach bolsters supply chain risk management efforts and works to prevent errors before they occur.

AI agents optimize inventory operations by monitoring stock levels, reallocating resources and streamlining adjustments across warehouses. They reduce carrying costs, ensure product availability and minimize manual updates—delivering smooth operations at optimum cost.

It also helps manufacturers and supply chain managers gauge a customer’s interest in a product and determine whether a customer’s demand is rising or falling and adjust accordingly. It can aid in a manufacturer’s decision-making process and improve the accuracy of demand forecasting.

AI agents improve order accuracy and speed by checking shipment status, updating customer orders and verifying stock availability. They reduce manual errors, enhance productivity for order support teams, and improve customer satisfaction with timely, open updates. AI agents can streamline the shipment systems and minimize the need for human oversight throughout the process. 

By using the predictive analytics that AI offers, companies are able to make supply chains more sustainable and better for the environment. Manufacturers can use AI and ML models to optimize truckloads, predict the most efficient delivery routes and reduce product waste in the marketplace.

Supply chain managers are constantly looking to better understand their operation. With AI-powered simulations, they’re able to not only gain insight, but also understand and find ways to improve. AI, working alongside digital twins, can visualize potential supply chain disruptions and through 2D visual models, any external processes that might create unnecessary downtime.

AI implementation can be complicated, and businesses should understand the challenges and risks of introducing this new technology.

Anytime a company brings in a new technology, they need to train the individuals who will be interacting with it at any level. Due to this necessity, downtime is likely to occur, so it’s best to prepare and schedule accordingly to limit disruptions. All supply chain professionals should be aware of potential downtime and be open with partners that it might occur.

There are several cost considerations in implementing AI. Along with the cost of the software to run the system, machine learning models are also an expense to consider. If the company prefers that option, some come prebuilt or can be built from scratch. Either way, it’s important to train the model on your own clean, historical data before inputting AI algorithms.

The work doesn’t stop when the AI has been implemented. An AI system at a global scale is complex and requires supply chain planners to constantly stay on top of how the tools are performing and fine-tune as needed.

There are three common risks when integrating AI in supply chains:

AI is built and generated from large amounts of data found from a range of sources. Due to the nature of the origin of the data, inaccuracies and bias might be present, which would result in the spread of misinformation. For that reason, AI requires human review to ensure that the data is fair, unbiased and explainable.

Human interaction should be the superior solution and the key expert in managing and handling supply chain risks. AI is a tool; it cannot build relationships. There is a misconception that AI can replace human intelligence, but in fact, AI should augment it. Furthermore, if the technology fails, humans with expertise must keep the supply chain running.

The increased collection and use of customer data for AI models also increases the risks of surveillance, hacking and cyberattacks. Businesses must prioritize and safeguard consumers’ privacy and data rights, providing explicit assurances about how data is used and protected.

Before a business implements an AI solution, it must prepare its traditional supply chain planning and management system.

See what is and what isn’t working for your business. Take stock of the bottlenecks or areas where constant issues arise to ensure that the AI technology is benefiting you in the best way possible.

What you can do:

• Identify concerns within the supply chain from end-to-end.
• Clean data to determine how structured and unstructured data should be used.

Decide which issues your business wants to address first and which ones are less of a necessity. It’s likely there are going to be multiple issues for a supply chain so prioritization is key.

What you can do:

• Prioritize issues based on what your supply chain needs.
• Take on the more difficult, pressing issues first and then delineate between the medium to lower importance.

There are several types of systems to choose from, and which one a business selects depends on its needs and the roadmap it has developed. In this case, a business might bring in a consultant or industry expert for guidance.

What you can do:

• Go through each system option to see which best fits the company’s supply chain management goals.

• Consider gaining professional insight from an industry expert.

The business needs to begin implementation of the AI technology. The system integrator is likely going to be working with the internal IT team and the AI solution vendor to get things up and running.

What you can do:

• Prepare and educate a team on AI technology.

• Be ready for setbacks or errors to occur in the process.

AI technology can be a major change that requires training, patience and a plan. Employees need to learn how to do their jobs, and open communication is key to successful AI technology implementation.

What you can do:

• Make a plan for communication with all employees before implementation begins.

• Consider the downtime that it takes to train employees and create a schedule.

AI technology is constantly changing, improving and adjusting. The teams who must manage the technology need to test and track what happens when adjustments occur so that periodic refinements can be made.

What you can do:

• Regularly test the AI solution and troubleshoot its capabilities.

• Ensure that there is an organized tracking method for when testing occurs.