Published: 20 May 2024
Contributors: Amanda Downie, Teaganne Finn
Using artificial intelligence (AI) in supply chains can revolutionize the planning, production, management and optimization of supply chain activities. By processing vast amounts of data, predicting trends and performing complex tasks in real time, AI can improve supply chain decision-making and operational efficiency.
Recently, this technology gained popularity as further advancements such as generative AI and tools such as chatbots have taken off and shown how beneficial the systems can be for supply chain management. Meanwhile, the COVID-19 pandemic illustrated just how fragile the global supply chain can be and why better management tools are necessary.
A subset of AI is machine learning (ML), which is the process of a system taking in data sets and learning processes from them, as opposed to being programmed with built-in instructions. ML can go far beyond what a traditional software can do. It 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. More use cases are emerging than ever before.
While it’s important to embrace AI, it’s also imperative to understand all the benefits and challenges that can come with it before introducing a new system into a supply chain. 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.
Read about the current challenges facing supply chains and how to improve overall resiliency.
Supply chain systems powered by AI are helping companies optimize routes, streamline workflows, improve procurement, minimize shortages and automate tasks end-to-end.
A supply chain can become complicated, especially for manufacturers of goods who oftentimes rely on their partners to ship their goods in a timely and organized fashion. AI can keep all parts of a supply chain in balance with its ability to find patterns and relationships unlike a traditional non-AI system. These patterns can help optimize logistics networks all the way from the warehouse to cargo freighters to distribution centers.
Modern supply chains are expansive and require thorough oversight to avoid unnecessary disruptions. AI systems can offer assistance in forecasting, such as demand planning or being able to predict production and warehouse capacity based on customer demand. Some are using AI to gain insights from a broader data set collected from Internet of Things (IoT) devices deployed across the supply chain.
AI can also be used in supply chain operations for tracking inventory levels and market trends. In inventory management, AI can enhance supply chain visibility, automate documentation for physical goods and intelligently enter data whenever items change hands.
It can help with transparency for the manufacturer and provide valuable data for all stakeholders in the supply chain. AI's enhancement of supply chain transparency offers unmatched time and cost savings. It also helps companies meet ethical and sustainability standards, which have historically been time-consuming and expensive.
An AI-powered supply chain has many potential benefits for building supply chain resilience and a stronger base for manufacturers.
Lower operating costs
AI can learn and understand complex behaviors and can learn repetitive tasks, such as tracking inventory, and complete them quickly and accurately. AI solutions can reduce overall operating costs by identifying inefficiencies and mitigating bottlenecks.
Advanced real-time decisions
AI uses historical and real-time data to make real-time decisions, oftentimes with conversational answers. AI processes the data and can analyze the root of the problem and suggest a solution, in that moment.
Cut down on errors and waste
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 ERP framework and can be directly embedded.
More tailored inventory management
As previously discussed, AI can help forecast demand with its extensive use of inventory information. It can help 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.
Improved warehouse efficiency
AI, specifically ML models, helps lay out warehouses more efficiently by being able to evaluate the quantity of materials coming in and improve service levels. The AI system can also plan the optimal routes for machinery and for workers and be an overall warehouse management powerhouse.
Better supply chain sustainability
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.
Optimized operations through simulation
Supply chain managers are always 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 visualize through 2D visual models 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 transparent 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. Some come prebuilt or can be built from scratch, if the company prefers that option. Either way, it’s important to train the model on your own clean, historical data before inputting AI algorithms.
The work doesn’t stop as soon as 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:
Inaccuracy of data
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.
Overreliance on AI
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.
Security and privacy vulnerabilities
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 legacy 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 pain points 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 will depend on its needs and the roadmap it has developed. At this point, 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 at this point. 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 the 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 to all employees before implementation begins.
Consider the downtime that it takes to train employees and create a schedule.
AI technology is always 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.
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