What is AI Inventory Management? Benefits and Challenges

When human writing was developed around 3200 B.C.E., it often was used to document item counts, like barley or sheep, or track the receipt and payment of goods and services. Essentially, it was a way to manage inventory.

Inventory management techniques began including mechanical automation in the early 1900s to make the process faster and more accurate. Turn-of-the-century tracking methods moved from pencil and paper to punch cards and tabulator machines. By the 1980s, computers with spreadsheet software had brought inventory tracking into the digital sphere.

The workflow has only gotten more sophisticated—and more automated—from there, as enterprise resource planning (ERP) systems integrate business processes further.

Now, artificial intelligence (AI) is taking inventory management automation to the next level, making it intelligent, proactive, seamlessly integrated, and hyper-efficient.

What is AI inventory management?

AI inventory management is the practice of using artificial intelligence to help manage the flow of goods along a business’s supply chain.

AI is a broad category of technologies, but typically, inventory management deals with five subsets of this technology:

• Machine-learning algorithms. These can learn from data, make demand predictions, and improve without explicit programming

• Natural language processing (NLP) algorithms. These can understand, interpret, and generate text or spoken human language.

• Robotics. These unmanned machines, like aerial drones and other robots, perform specific tasks independently or semi-independently of human intervention. 

• Computer vision. This technology allows AI systems to recognize and interpret visuals from warehouse camera footage or images.

• Generative AI. This type of machine-learning model can generate new content, including text, images, and audio, to create purchase orders, optimized warehouse layouts, demand scenarios, and more.

In various combinations, these technologies can mimic human intelligence and perform traditional inventory management tasks like tracking the movement of goods and materials. It can also monitor inventory turnover and optimize replenishment without human intervention. 

AI-enabled inventory management workflows build on the efficiencies created by automated inventory management systems in one crucial way: they are able to continuously learn from new data and feedback humans give to the system. They get smarter over time. This capability allows AI tools to adapt to changing circumstances and make increasingly accurate predictions about sales volumes, optimal stock levels throughout the year, lead times for replenishment, and more. 

AI applications in inventory management

• Inventory and demand forecasting
• Real-time inventory tracking
• Warehouse optimization
• Supplier management

AI technology can be applied to the inventory management workflow in several ways: 

Inventory and demand forecasting

Traditional demand forecasting methods are powered by humans. They conduct market research, compile data from various teams and databases, synthesize those inputs, and look for patterns and insights. It’s a largely manual process.

AI demand forecasting tools can gather and analyze vast amounts of data from both internal and external sources, including current sales and stock levels, competitor pricing, market trends, and even weather patterns. AI-powered analysis can be fast and highly accurate, as it avoids human error. It can also get incredibly granular, forecasting inventory down to the size and color level.

Real-time inventory tracking

Current real-time inventory monitoring systems leverage software and barcodes or radio frequency identification (RFID) tags to track inventory levels. They then update inventory records as items are received, distributed, and sold. Humans have to manually scan barcodes. While scanners can automatically register RFID tags from a distance, materials like aluminum, water, and carbon fiber can interfere with the signal.

AI inventory management can also solve bottlenecks with computer vision and robotic technology. Intelligent warehouse management systems can use visual data from warehouse cameras, aerial drones, and autonomous robots to track units along the supply chain. They can even locate items within a warehouse. 

Warehouse optimization

AI can help businesses reduce storage and shipping costs by optimizing warehouse layouts and workflows. Machine learning algorithms can analyze data gathered from cameras equipped with computer vision to understand product sizes, demand trends, turnover rates, and shelf life for disparate goods. It can then recommend the best storage configurations, packaging procedures, and shipping routes. 

Supplier management

Running an ecommerce business isn’t a self-contained enterprise, and it’s important to ensure that your suppliers are first-rate, reliable, and cost-effective. Machine-learning algorithms within some enterprise management platforms can analyze a supplier’s performance metrics, pricing, reviews, and more to guide supplier selection and negotiations. A general-purpose LLM could also be integrated into the supplier vetting workflow to accomplish these tasks.

Pro tip: The Shopify App Store features several AI inventory management tools, such as Monocole’s AI inventory forecasts and SP AI Inventory Management.

Benefits of AI inventory management

Integrating AI into your inventory management systems can lead to improvements across your company, potentially resulting in lower costs and improved customer satisfaction.

Cost reduction

AI tools can lower costs in multiple ways. Accurate and granular inventory and demand forecasts can help reduce the chance of overstocking and costly markdowns. Automating stock checks with robotics can reduce labor costs, and AI optimization of your warehouse can help minimize carrying and storage costs.

Improved customer satisfaction

The goal of AI inventory management is supply chain optimization, which ends with a satisfied customer. Stockouts can push customers to competitors.

Scalability

Traditional inventory management methods are limited by what your employees can manage. AI technology can help you grow your business without expanding your workforce.

Challenges of implementing AI in inventory management workflows

• Initial costs
• Workforce readiness
• Data quality
• Security

According to an IBM survey of more than 700 high-performing organizations, 95% see AI as central to their innovation success. But many ecommerce businesses may not be ready for large-scale adoption of the technology, due to the following concerns: 

Initial costs

Purchasing and implementing emergent technology can incur significant costs. Not only will you need to buy software, but you’ll have to spend time and resources integrating tools with your current tech stack. There’s also a cost associated with training or hiring highly skilled personnel to use and maintain any AI systems.

Increasingly, cloud-based inventory management solutions like Shopify are addressing this pain point by adding optional AI tools and add-ons into their platforms. This can mitigate the costs of purchasing and integrating a new tool.

Workforce readiness

There’s a fair amount of fear surrounding the growing presence of AI in the workforce. Employees may be resistant to change and fear for their jobs. A smooth transition to an AI inventory management system would need to incorporate clear communication, strong leadership, and training to help your team adjust to the new tools and workflows. 

Data quality

The key to AI’s precision lies in high-quality data—and lots of it. For an AI workflow to deliver value, it needs current, complete, and accurate data. If your data is siloed into different tools or systems, you may face a time-intensive and costly process of unifying and cleaning your data. You’ll also need to invest in better data capture processes going forward. 

Security

Cybersecurity is a significant challenge. While ecommerce companies that use cloud-based platforms are familiar with the importance of safeguarding private customer data, the huge volumes of customer data required by AI amplify the challenge. Sufficient training and skilled personnel who specialize in the AI tools you adopt are important to preventing cybersecurity breaches.

You’ll also need to know how to comply with current US federal privacy laws that govern the use of personal data. Businesses that operate in the EU may need to comply with the EU Artificial Intelligence Act, as well as the General Data Protection Regulation (GDPR). Because AI is so new, the regulatory environment is constantly evolving, so business owners must stay up to date with any changes.