Can AI Eliminate Stockouts? Pros and Cons of Intelligent Inventory Management

By Emily Newton is a tech journalist with over five years of experience covering how technology is changing industry and business. Keep up with Emily by connecting with her on LinkedIn

Stockouts are a disruptive but all too common problem in modern supply chains. Better inventory practices can help, but even the best manual approaches fail to prevent all shortages. Using artificial intelligence (AI) for inventory management instead could pose a solution.

As many as 40% of business owners today either use AI for inventory management or plan to do so soon. That makes the process the fifth most common application for AI, but popularity alone doesn’t justify investment. Before implementing this technology, you must understand how it can help you eliminate stockouts.

Pros of AI Inventory Management

AI’s skyrocketing adoption is more than a fad. The technology offers several impressive benefits in inventory management, including accuracy, efficiency, workload reduction and ongoing improvements.

Increased Accuracy

The most impactful advantage of AI in inventory workflows is its ability to avoid mistakes. On average, companies maintain inventory accuracies of around 91%, but some are as low as 67%. Much of the room for improvement is due to human errors like misreading details or misentering data.

Because AI applies the same data-backed processes to each instance of work, it upholds the same accuracy standard at all times. Machines are also generally better at repetitive, data-intensive tasks, making AI a more reliable way to track goods. As a result, AI-managed inventory records feature fewer mistakes, leading to more informed ordering decisions.

AI inventory management solutions may not be the perfect answer to all supply chain problems, but they do offer significant advantages over conventional alternatives.

Real-Time Reactivity

AI tools can also react in real-time. Your organization may have already implemented real-time tracking solutions like Internet of Things (IoT) sensors — many businesses have. As helpful as such systems are, they’re only as efficient as your capacity to respond in time, which AI improves.

Intelligent management platforms can connect to IoT networks to automatically adjust inventory records according to new data. Doing so means you always have the most up-to-date registers, making it easier to order new items when necessary to prevent stockouts.

Reduced Workloads

Applying AI in inventory management is also an optimal way to boost overall efficiency. In addition to updating records, intelligent systems can automate order fulfillment and replenishment in response to real-time data. Automating these tasks gives you additional time to focus on other, more complex work.

When you and your employees have fewer monotonous tasks to handle, you can complete the rest of your work with greater efficiency and accuracy. As a result, overall inventory practices improve.

Ongoing Optimization

Finally, AI solutions have the advantage of pulling easy-to-miss insights from operational data over time. As you use these systems, they collect enough information about inventory trends to identify patterns or highlight inefficiencies. They can then suggest ways to improve you may otherwise overlook.

Stockouts come from various sources, so there are often multiple ways to reduce them. Predictive analytics can identify such opportunities over time, providing a path to continual improvement and fewer supply constraints.

Cons of AI Inventory Management

Of course, no technology is perfect. While AI inventory management platforms have many benefits, they also introduce a few challenges you should consider.

Costs and Complexity

One of AI’s biggest drawbacks in any context is its upfront costs. Developing or training an AI model is far from easy, and it often requires extensive complementary systems to reach its full potential. Such complexity can easily lead to significant investment, making it less accessible to some businesses.

However, AI tends to reduce costs over time. Organizations have saved as much as $40 million in a single year by optimizing their supply chains through this technology. It’s important to note that the initial expenses may slow smaller companies from recognizing that kind of return.

Data Concerns

It’s also worth considering how using AI for inventory management will significantly raise your data demands. Intelligent systems work best with extensive amounts of information, which you may not have on hand at first. Quantity isn’t the only issue, either, as quality can heavily impact AI’s reliability.

Businesses lose revenue each year from models trained on inaccurate or misleading data. Acting on insights drawn from unreliable information could have the opposite effect of what companies want — leading to even more stockouts and similar disruptions.

Overdependence on AI

Similarly, it’s easy to over-rely on AI inventory systems. This technology does provide meaningful improvements over manual alternatives, which makes it tempting to take its insights at face value. However, even the most accurate machine learning models occasionally make mistakes.

AI hallucinations or inaccurate predictions are not a massive problem if human experts confirm the outputs before acting on them. The issue arises when organizations become complacent and rely on AI without ever verifying its accuracy.

Is AI a Viable Solution to Eliminate Stockouts?

Given these advantages and disadvantages, AI for inventory management remains a promising but imperfect solution to stockouts. It likely will not eliminate supply shortages entirely, but it can substantially reduce their likelihood.

Some retailers have already seen impressive results from their AI solutions. Zara reduced stockouts by 20% while reducing excess inventory by 15% after deploying an AI management platform. Walmart, Kroger, Macy’s and Unilever have all successfully lowered their stockout frequencies and improved inventory accuracy through AI, too.

AI Inventory Management Best Practices

Achieving results like this is only possible when you deploy AI inventory management while considering common challenges. Each business is unique, but a few best practices make AI increasingly reliable across all instances.

First, you must align your AI solution with your specific inventory goals. Failure to apply AI to real, relevant problems or where it’s effective accounts for three of the five most common reasons AI projects fail. Find where your inventory practices can see the most improvement, compare these to AI’s capabilities, and plan how you’ll use the technology to address these areas.

Next, it’s important to gather enough data — both historical and current — to ensure your AI platform’s reliability. Collect as many past inventory and supplier records as possible, and implement real-time tracking solutions if you haven’t already. Pass all information through an automated data cleansing process before feeding it to AI models to avoid duplicates or incomplete entries.

Finally, realize that it’s often best to take a restrained approach to AI. Use it in one inventory practice in one facility first, and wait until you see a positive return before expanding it to other applications. That way, you spread out the costs and avoid the largest effects of any early implementation errors.

AI Can Revolutionize Inventory Management

AI inventory management solutions may not be the perfect answer to all supply chain problems, but they do offer significant advantages over conventional alternatives. Recognizing this potential — as well as the technology’s downsides — is key to using it effectively. As more organizations recognize this opportunity, AI could become the new industry standard.