How AI is Disrupting Supply Chain Management

Artificial intelligence and machine learning are disruptive digital technologies that are beginning to rattle the supply chain management fraternity, and to change the way products are made, distributed, and delivered.

However, recent studies reveal that many companies have been slow to embrace AI supply chain technologies—and that those which do invest are not harnessing their full transformative power. 

In this article, we will examine how AI is currently being applied by intuitive logistics managers and suggest ways that predictive and adaptive technologies could significantly reshape global supply chains. 

Before we Proceed, Some Definitions

To give precision to the supply chain management AI related terms we will be using in this article, it will be helpful to define them:

Artificial Intelligence (AI): The deployment of computer systems that can perform tasks normally associated with human intelligence, such as visual perception, speech recognition, and decision-making.

Machine Learning (ML): Seen as part of AI, ML involves the application of software programs that can accurately predict outcomes without being programmed to do so.

The Internet of Things (IoT): A network of physical objects with embedded sensors, software, and other technologies enabling them to connect and exchange data with other devices and systems over the Internet.

The Application of AI in Supply Chain Operations

Although fully AI-powered supply chains are still few and far between, many companies are using this sophisticated technology successfully to support and deliver the following operational improvements:

• Smarter procurement using intelligent logistics data


• More accurate demand, supply, and sales forecasting


• Warehouse inventory optimization


• Faster and more streamlined shipping

• Speedier deliveries enabled by route optimization 


• Customer service enhancements.


How AI in Supply Chain Could be Better Applied

Technical experts assert that artificial intelligence in supply chain management can facilitate improved decision-making by providing visibility of all operations and rapidly analyzing vast amounts of data.

However, three recent surveys highlight the general under-application by companies of AI technologies.

PricewaterhouseCoopers (PwC)

A 2022 PwC Supply Chain Survey found that only one in five companies has so far seen positive results from investing in supply chain technology.

The survey revealed the following:

• Cloud is the leading technology investment by supply chain managers (25 percent)


 - The second biggest investment is in IoT development (20 percent)


 - Third on the list is scan and intelligent data capture (18 percent).


 PwC concludes that despite heavy investments, many companies have not realized the vision of AI-managed supply chains. This vision involves transforming supply chains into self-regulating entities that require minimal human intervention. 

Boston Consulting Group

A BCG study reaches a similar conclusion. Most companies, it says, still focus on using AI in the supply chain for analytics and predicting production needs. They have not seen the bigger picture: using AI to make autonomous decisions by recognizing patterns in big data. 

The survey found that supply chain technological investment focused on demand forecasting, tracking, planning, and automation—in that order.

McKinsey & Company

A 2021 McKinsey study showed the enormous benefits of AI-based supply chain management. It lists the following average improvements reported by companies that have invested in disruptive technology:



 - Logistics costs: 15 percent lower


• Inventory levels: 35 percent lower


• Service levels: 65 percent higher


The report noted that 61 percent of manufacturing executives reported decreased costs and 53 percent reported increased revenues through introducing AI in the supply chain. 

The report adds, however, that most supply chain chiefs still manage the supply chain by reacting to events after they have occurred—leaving them prone to risk of the so-called bullwhip effect.

Those who have embraced AI-enabled supply chain management, however, can see into the future and act to bolster supply to avoid shortages and reduce inventory before demand drops.

The Benefits of the AI-driven Supply Chain

AI in supply chains has been shown to help achieve the optimization capabilities needed for improved capacity planning, better productivity, higher quality, lower costs, and greater output. 

Let’s look at some of these in greater detail:

1. More Efficient Procurement

It starts right at the beginning of the supply chain. Artificial Intelligence supply chain management at the procurement stage permits data integration with suppliers and predictive analytics that allow for improved supplier selection and lower prices.

2. Improved Productivity

Arguably, the greatest value of automated intelligent operatives is that they don’t quit working at 5 pm, arrive late for work, demand holidays, or take sick leave. They can work error-free for unlimited periods without the risk of workplace incidents. In short, they are dream employees.

3.  Improved Capacity Planning

AI-based management can ensure the right flow of items in and out of the warehouse through intelligent order processing, picking and packing. It can also control inventory by helping prevent inadequate stock, overstocking, and unforeseen stock-outs.

AI-driven tools can analyze vast amounts of data rapidly to help achieve more accurate supply and demand forecasting. They can also predict consumer habits and future demand.

4. Greater Output, Faster Delivery

AI systems can accelerate warehouse procedures while reducing the errors and bottlenecks associated with manual efforts. These factors make the warehouse processes smarter and faster, facilitating speedier delivery and greater customer satisfaction.



 ### 5. Total Visibility ###

Supply networks are becoming increasingly complex, to the point where manufacturers need complete visibility of the entire value chain. A cognitive automated platform offers data analytics that quickly highlight bottlenecks, causes and effects, and opportunities for improvement.

Think about it: AI-driven processes can sift through massive amounts of scattered information to detect patterns. Try getting the human brain or conventional supply chain systems to match the prowess of ML and you will fall hopelessly short.

6. Improved Fleet Management

If air traffic controllers have one of the most stressful jobs in the world, spare a thought for fleet managers, whose job is not that much different except that they must contend with ever-changing conditions on the ground.

The good news for fleet managers is that AI in supply chain and logistics deploys tracking devices that provide real-time insights into optimum routes, bottlenecks, unexpected road closures, traffic accidents, and other road transport obstacles.

Some Examples of AI at Work in the Supply Chain

Now that we have looked at what AI systems can achieve, let’s explore some practical applications of these advanced technologies.

1. In the Warehouse

The following is a typical scenario in an automated warehouse or distribution center (DC): As goods enter the DC, they are monitored and analyzed by AI-powered software. 


 The system compares this information with historical inventory data.


 Using algorithms, the system determines the optimal storage method and location.


 The system then assigns picking and packing tasks to robots and allocates time slots to each task.


 It also assigns tasks to warehouse staff, ensuring close human/machine cooperation.


 ### 2. In Fresh Food Supply Management ###

Fresh food supply managers are all too aware that the products they handle have short shelf lives and are perishable. Food waste typically occurs during distribution and consumption. For these managers, getting the balance right between too little and too much product is a crucial factor in determining a company’s bottom line.

Fortunately for them, demand prediction is one of the most popular uses of AI in supply chain planning.  Many available platforms use machine learning algorithms to identify demand patterns.  In time, as more and more food supply chain managers turn to AI platforms to predict demand, the vast amount of food waste that occurs globally will be reduced—and company profits will be increased.

3. In the Decision-making Process

While many companies are using AI platforms to predict demand and manage inventory, very few are using available technologies to make company decisions. 

They have a right to be wary, of course, as the brave new technological world of autonomous transport, delivery drones, and mobile robotics is only just beginning to dawn.

However, companies with vision are programming AI platforms to do the following:

Analyze how past decisions have affected objectives like cost to serve and service levels.


 Use these historical patterns to recommend responses to new but similar situations. 


 Give feedback to data scientists on ways to improve the platform.


 Analyze real-time data to make recurring decisions for optimal performance—without any human engagement.


 Over time, the platform evolves iteratively towards increased automated decision-making with less and less human intervention.

The Drawbacks of AI Management in the Supply Chain

AI management, as we have shown, greatly enhances efficiency and reduces costs when applied to the supply chain.

So, why aren’t supply chain managers rushing to harness these wonder machines?



There are four good reasons—all finance-related.

1. They are Expensive to Install

The specialized hardware needed to access AI capabilities typically involves substantial company investment. Supply chain managers of smaller companies often find it difficult to justify such an outlay to C-suite executives.

2. They Gobble up Bandwidth

AI systems are usually cloud-based and require expansive—and expensive—bandwidth to achieve optimum performance.

3. Managers Require Specialized Skills

Managing an AI-driven supply chain is impossible without a team that has undergone specialized training. This involves a considerable investment in time and personnel that many smaller companies cannot afford.

4. Maintenance Costs are High

AI-operated machines comprise a complex network of individual processors, all of which need maintenance and perhaps replacement from time to time. The associated costs are significant and directly impact these systems’ overall cost of ownership.

The Future of AI in Supply Chain Management

Few supply chain experts doubt that within a few years, AI-powered supply chains will become the global norm.

Managers aiming for best-in-class operations will increasingly have to shift to predictive and adaptive technologies that tap into Machine Learning, the Internet of Things, and Artificial Intelligence.

These disruptive technologies are set to bring about the biggest changes in domestic, regional, and global supply chain management since Microsoft created its Excel spreadsheet application in 1985. 

Smart companies know this and are embracing the changes. Companies that ignore AI-driven supply chain management will increasingly suffer operational difficulties until they either become enlightened to their folly and adapt, or fall by the wayside.