Artificial Intelligence (AI) in Supply Chains

Applying artificial intelligence (AI) is one way supply chain professionals are solving key issues and improving global operations. 

AI-enhanced tools are being used throughout supply chains to increase efficiency, reduce the impact of a worldwide worker shortage, and discover better, safer ways to move goods from one point to another. 

Why Should Your Business Use Artificial Intelligence?

AI applications can be found throughout supply chains, from the manufacturing floor to front-door delivery. Shipping companies are using Internet of Things (IoT) devices to gather and analyze data about goods in shipment and track the mechanical health and constant location of expensive vehicles and related transportation tools. 

Customer-facing retailers are using AI to gain a better understanding of their key demographics to make better predictions about future behavior. The list goes on — anywhere some goods need to make it from point A to point B, there’s a good chance AI is being used to enhance, refine, and analyze supply chain operations.

Some of the benefits derived from AI in supply chains are less tangible than others. For example, determining the impact of predictive analytics based on supply chain data can eventually yield benefits, but some companies are reporting a direct link between revenue shifts and the addition of AI in supply chains. 

For more: Top Performing Artificial Intelligence Companies

Common Supply Chain Tasks That Can Be Automated

Automation with AI for supply chain tasks can reduce time and money spent on traditionally manual tasks. Supply chain tasks that can be automated for businesses include:

• Warehouse robotics: A company can use automated systems and specialized software to move materials and perform other tasks.
• IoT: Automation can also offer IoT which are physical tools with sensors, processing ability, and software that connects and sends or receives data with other devices or other communications networks.
• AI/ML: Artificial intelligence (AI) and machine learning (ML) can help automated supply chains to learn and expect user activity.
• Predictive analytics: Predictive analytics helps automate supply chains using data mining, predictive modeling, and machine learning to analyze past and current facts to make predictions about what may happen in the future.
• Digital process automation (DPA): DPA automates multiple tasks for the supply chain across applications.
• Optical Character Recognition (OCR): OCR is a form of text recognition that helps supply chains.
• Data entry automation: Data entry can be time-consuming, but with automation, a supply chain company can get the information they need without any manual tasks.

AI automation is a game-changer and a necessity for any supply chain to keep up with the fast-moving industries.

For more tools for supply chains: 15 Best Data Warehouse Software & Tools

4 Benefits of Using AI in Supply Chains

Artificial intelligence developments are increasing among businesses, assisting with a company’s development and planning. AI is used to find and identify risks in a company’s infrastructure.

Listed are more benefits of using AI in supply chains:

• Increases productivity: AI techniques, such as automation, saves a company time so their employees can focus on higher-level tasks instead of tasks that can be done through automation.
• Constant visibility: If a company needs it, the AI tools can operate without any breaks or downtime.
• Used by experts and beginners: AI increases the capabilities of employees who are not experts in their business’s technology tools.
• Decision-making easier: AI makes the decision-making process easier, increasing decision speeds and making smarter decisions.

4 Challenges of Using AI in Supply Chains

While artificial intelligence has an abundance of benefits, no technology is perfect. AI is growing and changing every day meaning the technology will become outdated or not meet a company’s needs.

Listed are the challenges supply chains may face with AI:

• Difficult Scalability: AI requires a large amount of data to work effectively, so AI/ML can create algorithms, prediction models, and analysis of insights.
• Lack of trust in AI: With recent developments in AI, companies can be hesitant to consider them for their supply chains. Computers also do not have the same capabilities as a human would, making it difficult to make the switch. 
• AI technology constraints: While AI is a positive tool, it is a new tool and not fully developed. There may be tasks a company wants to automate that cannot be or will take more of the company’s time rather than the deducting time.
• High costs: While AI technology can save time and money, the initial cost can be expensive for many supply chains. Integration and operating processes can also cost more than a company wants to spend.

AI machines can be complicated especially if they need replacement or updates. However, with the correct AI solution, supply chains can benefit from AI tools.

5 Examples of Supply Chain AI in Use 

1. Demand Forecasting Is Improving Warehouse Supply And Demand Management

Machine learning is being used to identify patterns and influential factors in supply chain data with algorithms and constraint-based modeling, a mathematical approach where the outcome of each decision is constrained by a minimum and maximum range of limits. This data-rich modeling empowers warehouse managers to make much more educated decisions about inventory stocking. 

This type of big data predictive analysis is transforming the way warehouse managers handle inventory by providing deep levels of insight, which would be impossible to unravel with manual, human-driven processes and endless, self-improving forecasting loops.

C3 AI uses AI to power its Inventory Optimization platform, which gives warehouse managers data on inventory levels in real-time, including information about parts, components, and finished goods. As the machine learning ages, the platform produces stocking recommendations based on data from production orders, purchase orders, and supplier deliveries. 

2. AI Is Optimizing Routing Efficiency And Delivery Logistics

In a world where just about anything can be ordered online and delivered within data, companies that don’t have a firm handle on delivery logistics are at risk of falling behind. Customers today expect quick, accurate shipping, and they’re all too happy to turn somewhere else when a company is unable to deliver on that expectation.

McKinsey & Company reports that around 40% of customers who tried grocery delivery for the first time intend to keep using these services indefinitely. Customers in major markets like New York and Chicago have dozens of choices. 

AI-driven route optimization platforms and GPS tools powered by AI like ORION, a company used by logistics leader UPS, create the most efficient routes from all the possibilities, a task untenable with conventional approaches, which have been inadequate for fully analyzing the myriad route possibilities. 

3. Machine Learning AI Is Improving The Health And Longevity Of Transportation Vehicles

IoT device data and other information taken from in-transit supply chain vehicles can provide invaluable insights into the health and longevity of the expensive equipment required to keep goods moving through supply chains. Machine learning makes maintenance recommendations and failure predictions based on past and real-time data. This allows companies to take vehicles out of the chain before performance issues create a cascading backlog of delays. 

Chicago-based Uptake uses AI and machine learning to analyze data to predict mechanical failures for a wide range of vehicles and cargo containers, including trucks, cars, railcars, combines, and planes. The company uses data from IoT devices, GPS information, and data pulled directly from vehicle performance records to arrive at its predictions, which can greatly reduce downtime. 

4. AI Insights Are Adding Efficiency And Profitability To Loading Processes

Supply chain management includes a great deal of detail-oriented analysis, including how goods are loaded and unloaded from shipping containers. Both art and science are needed to determine the fastest, most efficient ways to get goods on and off trucks, ships, and planes. 

Companies like Zebra Technologies use a combination of hardware, software, and data analytics to deliver real-time visibility into loading processes. These insights can be used to optimize space inside trailers, reducing the amount of “air” being shipped. Zebra can also help companies design quicker, less risky, and more efficient processing protocols to manage parcels.

5. Supply Chain Managers Are Uncovering Cost-Saving And Revenue-Increasing Methods With AI

Moving goods around the world are expensive, and only becoming more expensive. Bloomberg reports that the cost of moving goods by ship, for example, increased by 12% in 2020, the highest level in the five years before. 

Companies like Echo Global Logistics use AI to negotiate better shipping and procurement rates, manage carrier contracts, and pinpoint where changes in supply chains could deliver better profits. Users access a centralized database that takes virtually every aspect of supply chains into account to deliver financial decision-making advice. 

AI in supply chain innovations are paving the way for a future where we can eventually expect to see AI-powered, autonomous vehicles used throughout supply chains. The data these platforms are mining and analyzing today will continue improving the cost and efficiency of an increasingly complicated global supply chain.

For more information of AI task management: Anticipating the Birth of AI Employee Clones

How to Implement AI in Supply Chains

AI in supply chains creates stronger efficiency, visibility, and optimization. Implementing AI can benefit and help their business practices. AI can be a large part of evolving a supply chain company and help with adapting to supply chain problems.

Try an AI simulation

One of the benefits of AI is its ability to predict action outcomes. Supply chains can try this capability to make their operation more efficient with AI simulations.

Using a simulation, supply chain businesses have more flexibility to optimize operations using real-world scenarios in the process. AI simulation tools can be effective for many parts of the supply chain.

Through AI simulation, supply chain managers can make an exact digital copy of the entire warehouse they work in. Then the AI logistics can use a simulation on the digital copy to try different optimization strategies. 

Decide what should be automated

If a supply chain is running inefficiently, it could cause serious problems throughout the supply chain. AI can help automate different parts of their warehouses through inventory management, which can save both time and money if used correctly.

IoT tags are also a tool that can help keep track of the status of different items. The IoT tags communicate to an AI hub that manages all of this inventory data updates on data changes. The AI can then alert the supply chain company with any problems.

See the benefits of AI in cybersecurity

Cybersecurity is a necessary part of handling data and is now vital for any supply chain company. Cyber attacks are common, with cybercriminals using different tactics to steal data and sensitive information. Using AI can help protect a supply chain company’s infrastructure.

AI is a highly effective tool to help stay ahead of changes or risks as AI on supply chains can recognize what patterns are most common and when they may change.

A supply chain company can use AI to monitor login activity, traffic, and any irregular processes on its servers. AI can alert the company about the change.

AI analysis for supply and demand

Supply chains can use AI data analysis to see what supply and demand might look like in upcoming quarters. AI algorithms can analyze data to predict how much and what product will be in demand.

Demand forecasting can allow different links in the supply chain to reduce supply strain. If the supply chain business knows how much of a product they will need, they can use it as a better way to decide on the amounts they need.

Less risk of company error

Due to the capabilities of ML, systems can learn to allow different processes such as infrastructure vision to learn how to automate with the supply chain company’s needs.

Along with ML and AI, IoT devices can collect data on how many materials are being used. AI data analysis algorithms can identify where the materials are being used and what materials are being wasted.

Bottom Line: AI in Supply Chains

AI in supply chains will be a part of innovating a better supply chain process to create more efficient supply chains in the future. Every part of the supply chain can implement AI to automate tasks, improve operations, and strengthen cybersecurity practices. 

With AI tools, supply chain businesses can evolve and grow to create a positive change in their business and meet new supply chain challenges.

For more information, also see: AI and Deep Learning