If there’s one aspect of logistics that has the most potential to impact the entire supply chain, it’s inventory management. Well-orchestrated inventory has a positive impact on the pricing strategy, transportation, cross-border shipments, last-mile delivery and customer retention, while errors immediately translate to adverse events across the same domains. Small wonder, then, that the adoption of AI in inventory management is becoming a noticeable trend. What’s really interesting, though, is how diverse the applications of AI in inventory management really are. From data management to generative artificial intelligence, businesses are experimenting with different ways to use the technology, pulling different “strings” in the Gordian knot, addressing different aspects of operations. In this article, we look at the most promising practical applications of AI and how they are already helping the trailblazer companies.
AI for inventory management and logistics in general
Logistics in general has been experimenting with AI for a while now. According to the 2022 MHI Annual Industry Report, about 15% of supply chain companies were already using AI in their operations, while the predicted adoption level after 5 years was evaluated at 72%.
The reason is simple: scaling logistics processes requires significant investment, but some of the processes, especially those happening within a warehouse or back office, can be automated, ridding the company of the risk of overblown budgets.
In other words, it’s mostly about cost savings, including those costs that are associated with human error. A study by McKinsey & Company found that AI-powered supply chain management can reduce forecasting mistakes by up to 50% and overall inventory levels by 20-50%. When we combine the effects of automation per se and error minimization, AI applications in logistics are expected to generate $1.3-$2 trillion in economic value annually.
In inventory management specifically, AI is already helping achieve impressive results. IBM reports that AI-driven inventory optimization can lead to a 5-10% reduction in inventory costs while simultaneously improving product availability by 2-5%. In fact, they themselves used cognitive supply chain to reduce supply chain costs by $160 million.
IBM’s positive experience was largely due to predictive analytics, but AI has a number of roles besides that. Let’s now look briefly at its three main capacities before plunging into the more concrete practical applications.
The main capacities of artificial intelligence inventory management can use
There are three main things where AI excels compared to humans: learning from large datasets, making predictions based on multiple variables, and generating information based on other information (again, taking into account multiple variables).
The capability of AI to handle and analyze data allows it to take on the most notoriously error-prone jobs and streamlining them until the process becomes continuous. For example, in inventory management, the traditional ways of inventory tracking involved someone reporting the stock levels over a certain period of time. AI can do that continuously, updating the information every time there’s a change (e.g. someone ordered something, or a number of items were transferred to a different warehouse). Same goes for identifying inefficiencies – what used to be a matter of monthly or quarterly reports can now be done continuously.
Additionally, data pattern recognition can be used for powering warehouse robotics with computer vision capacities for picking and packing, which, again, generates more data to be automatically transmitted into the system.
As for predictive capabilities of AI, these are currently especially valued by companies that have to deal with seasonality or potential supply chain disruptions. Finally, generative AI allows the creation of chatbots and virtual assistants, and (even more intriguingly) warehouse layouts to boost efficiency.
Top 10 ways of using AI in inventory management
The ways these three main AI capacities are actually used in inventory management are very diverse. We’ve already hinted at some of them; now it’s time to look at the top 10 most widely adopted ones.
#1 Real-time Inventory Tracking
Inventory tracking is a well-known challenge, with 62% of organizations reporting incorrect monitoring has financial impacts. The challenge here is that with a switch to multi-warehouse and multichannel operations, manual tracking is becoming exponentially harder. That’s where AI enters the picture, alongside sensors, RFID tags, and computer vision. AI can be used to process the data from multiple sources to identify patterns and anomalies, while reporting continuously, for better visibility across the supply chain.
#2 Demand Prediction
Predicting demand at least to some extent is a highly desirable ability, especially seeing as how it’s not just the value of the product that’s at stake, but also the storage and transportation costs. What AI does here is analyze historical sales data, consumer sentiment, economic metrics, and other patterns to provide a balanced forecast devoid of bias. Importantly, the updates can be made more or less continuous, too, meaning a company can now redistribute inventory across locations to minimize risks.
#3 Optimized Warehousing
Needless to say, warehousing is more difficult than playing casual brick games, if only because the different items in your typical warehouse will require different storage conditions. Dimensions also matter for proper placement: it’s an almost infinite variation of the classical “one 18-inch pizza is more than two 12-inch ones” problem. Machine Learning can simulate and run through the various possible configurations to find the optimal layout, thus ensuring you don’t have to pay for extra warehouse space or risk compromising the storage conditions.
#4 Dynamic Pricing
Pricing changes are typically fraught with some level of risk, since you need to balance the possible benefits with the actual factors at play: market conditions, competition, inventory levels, and so on. AI-driven dynamic pricing tools already exist, though, and can help balance supply and demand by increasing prices for low-stock items or offering discounts on overstocked products. Machine learning algorithms can also predict the impact of price changes on demand, allowing for more strategic pricing decisions that optimize both sales and inventory turnover.
#5 Auto Reordering
One of the more exciting applications of predictive analytics and generative artificial intelligence, AI-powered auto reordering allows not to wait until a stockout on a needed item is imminent, but have a purchase order generated in advance based on lead times, demand forecasts, and supplier routines.
#6 Synchronization of Inventory Across Warehouses
Another thing AI excels in is synchronizing and managing inventory across multiple locations. Algorithms can be trained to analyze real-time data from warehouses miles apart and redistribute inventory in accordance with demand patterns. Another factor that can be taken into account is the state of the transportation network. AI can also optimize cross-docking and transfer operations between warehouses.
#7 Supplier Relationship Management
AI can also help manage a supplier network by taking in the data on supplier performance and analyzing them alongside external and internal conditions. As an output, the system can predict how reliable a supplier will be in the mid- or long term, suggest selecting suppliers for particular operations, etc. When inventory data is plugged in, AI can also suggest optimal order quantities and facilitate negotiations.
#8 Predictive Maintenance
In its predictive capability, AI can forecast likely failure or wearout of warehouse equipment, thus minimizing downtime by scheduling maintenance proactively. This function has already been developed and tested successfully in manufacturing, with warehousing being the next big receptor of change.
#9 Inventory Segmentation and Classification
AI’s ability to operate in large datasets is sometimes used for inventory segmentation based on various criteria. Possible groups can be customized and include fast- or slow-moving goods, etc. This, in turn, facilitates prioritization, since it’s easier to define which inventory groups require more attention.
#10 Return Management and Reverse Logistics
Machine learning models can analyze historical data to forecast return volumes, allowing businesses to allocate appropriate resources. AI can also streamline the return process by automatically categorizing returned items and determining the most cost-effective disposition method. For inventory management, this capability helps maintain accurate stock levels, reduces processing times for returns, and maximizes the recovery value of returned items.
Notable use cases of Ai in inventory management
AI-powered inventory management has already been pioneered by a number of enterprises, including large retailers and several leading brands. Here are some of the most widely discussed cases.
Amazon’s Anticipatory Shipping
Amazon came up with its AI-driven “anticipatory shipping” as early as 2013, spawning memes about how they deliver “even before you think you need the product”. This is partially true, not on an individual level, but rather close to it. Essentially, Amazon makes AI analyze data like past purchases, search queries, wishlists, etc. to predict demand in a certain area, after which they preemptively move inventory to the selected fulfillment centers. The result is the famous eerily fast delivery and high customer satisfaction metrics.
Walmart’s FAST Unloader
In the realm of warehouse operations, Walmart’s case with the “Fast Unloader” is a great example of AI advantages. Fast Unloader uses computer vision to scan items that are unloaded from trucks, identifying the contents of boxes and prioritizing unloading based on inventory levels and sales data. The result is improved inventory accuracy and more rational warehouse space use.
Zara’s Integrated Stock Management System
Being an epitome of fast fashion comes with its costs, and these come in the form of logistics and order fulfillment. Perhaps this is one of the reasons Zara, of all brands, is known for a telltale case in AI-powered inventory management. Their stock management system is fueled by data from sales, customer feedback, and social media sentiment analysis to predict demand. This approach to restocking allows Zara to maintain the minimal inventory levels while still meeting customer demand, while they can be confident there will be as few unsold items as possible.
Benefits of AI in inventory management
The practical benefits of implementing AI in inventory management are multiple, provided that the particular solution in place addresses exactly the right pain points within the organization.
- One of the main advantages is the ability to predict demand and adjust inventory accordingly. This, of course, means the inventory visibility should be sufficient for AI to work properly, but once the data is accurate and continuously updated, the reduction in overall inventory costs can reach up to 50%. Companies who have the technology in place commonly report around 20-30% decrease in stock-outs, making inventory management less of a risky affair.
- A slightly less commonly adopted AI-powered solution is automation within warehouses. This is understandable since enhancing operational efficiency at this level requires more hardware. However, the results are impressive, since automated systems for picking, packing, etc. work faster than humans and reduce human error, thus increasing warehouse productivity by about 25%.
- Another significant benefit is real-time inventory tracking and analysis. AI systems can continuously monitor stock levels, sales rates, and supply chain disruptions, allowing businesses to make informed decisions quickly. Businesses using AI for inventory management have reported up to a 40% improvement in forecast accuracy.
How to make sure your organization is ready?
As of now, around 70% of supply chain companies are planning to invest in AI development. The adoption, however, requires a certain level of strategic preparedness. It’s a world of custom solutions, so most successful projects are also quite unique, and based on a deep understanding of the company’s actual needs. Here’s a short checklist to help “pave the way” for AI in your organization:
- It’s prudent to begin with assessing the current inventory management processes and identifying the narrowest bottlenecks – or, alternatively, the areas where AI could bring enough value to compensate for the more problematic points. These can include predictive analytics, tracking, or auto reordering.
- Then you can document your expectations of AI and formulate them into business objectives. This will help communicate with the developer team and ensure the project planning will be comprehensive.
- AI needs data as much as humans need air. Implementing artificial intelligence, then, requires a significant amount of “mopping up” the data management strategy so that the datasets used for AI will be clean and relevant enough.
- Depending on what function AI will be performing, there may be different needs in terms of infrastructure, including IT systems, cloud, or even hardware in some cases.
- Consulting with the staff (potential users) is also a good practice, allowing to determine what will make adoption of new tools easier.
Based on this, you can start by having a pilot or small-scale project implemented, with clear metrics to measure the success of implementation. In this process, starting at points 2-3, you can count on expert assistance and consultation.
Lionwood’s AI team provides comprehensive guidance throughout AI implementation, drawing on our expertise in the target industry and the recent best practices in AI and ML. Contact us anytime to learn how we can assist with launching a custom solution perfectly tailored to your company’s goals.
