Modernizing global logistics with AI Inventory Forecasting

Supply chains, logistics, and inventory management come with a handful of real-time challenges. The introduction of Artificial Intelligence can bring about a much-needed transformation through inventory forecasting. This blog provides a glimpse of the future of AI inventory management and logistics with the impact of Artificial Intelligence.

Challenges with traditional inventory management

34% of businesses unintentionally end up selling an out-of-stock product that is unavailable in inventories, leading to higher delivery time and poor customer experience. Likewise, a vast amount of businesses suffer an opportunity loss when their highest-selling product runs out of inventories.

But overstocking is the worst challenge. It can lead to unused products on shelves, which blocks capital and might lead to a loss. Therefore, inventory management and planning have remained a tricky mystery for businesses. Not being able to predict the demands and logistics could trouble businesses.

That’s where an AI solution can help companies. Companies can gauge demands and prepare for global logistics challenges through AI inventory forecasting.

How can AI transform inventory forecasting?

Artificial Intelligence and Large Learning Models (LLMs) have what it takes to make inventory forecasting more productive and efficient. AI can analyze insights and identify patterns within numerous market parameters with the help of previous data, which can help with inventory forecasting. 

Here’s how AI can be involved in inventory forecasting:

• Harnessing data:

Machine Learning models like LLM have the ability to process vast sets of data available within the organization. With Knowledge Graphs, these data can derive insights and identify insightful patterns. Companies can leverage historical sales data, demand patterns, seasonal trends, and any such X factor affecting inventory forecasting.

• Understanding feature attributes:

AI  can also account for different feature attributes of products or service offerings. From a vast amount of data, companies can understand the product demands and situational influence for future predictions. AI can account for attributes such as product pricing, lead time, target customers, sales trends, and other attributes for feeding and training AI models. 

• Keeping up with market dynamics

Inventory management has a lot to do with current market dynamics. With the volatile global logistics and changing consumer demands, an AI inventory forecasting model must keep up with the real-time dynamics.

Deep learning and convolutional neural networks can entail several parameters, such as trade policy changes, shipping route complications, and varying costs. This ensures that AI forecasting can be much more accurate compared to traditional methods.

• Inventory forecasting POC

Proof of concepts (POCs), particularly for logistics, helps verify factors such as trade dynamics, consumer demand patterns, and supply chain lead times against the predictive analysis. AI can start small as a proof of concept to identify risks or challenges associated with inventory predictions. It can help companies to zoom in, make adjustments, and attain the desired F score of the model.

• AI & inventory Key performance indicators (KPIs)

The right metrics to evaluate the performance of inventory forecasting models play a pivotal role. Inventory turnover, order fulfillment rate, on-time delivery, and customer feedback are some of the ways to ensure that AI’s involvement in inventory forecasting is actually helpful.

Insights to action: AI for inventories

AI insights and ML models can utilize the available data & statistics to uncover insights. These AI inventory management insights can lead to effective decision-making about logistics operations end-to-end. 

Dynamic inventory allocation:

Proportionate product distribution among different warehouses is a key aspect of inventory management. Dynamic inventory allocation refers to minimizing warehouse holding and shipping costs. AI can help identify the most suitable warehouses to store the product in so that it caters to the majority of customers in the nearest proximity. 

Dynamic inventory allocation for the product saves turnaround time while also reducing delivery costs and operational expenses. Such warehouse holdings can prepare companies for any forecasted demands, providing a better overall customer experience.

AI Route Optimization: 

AI and ML can also help improve logistics by providing better en-route assistance through the supply chain. Through ML in logistics, brands can come up with the most efficient routes to supply products in time through the best medium of transportation.

With machine Learning to assist, companies can account for traffic patterns, major roadblocks, and transportation costs to devise the best supply chain strategy for their products.

Stock Management:

A big challenge with inventory management is running out of stock. However, businesses often overlook overstocking and end up with stock on inventory shelves. Overstocking means that businesses spend more than what consumers demand. Unless sold on time, these products often end up at a loss.

Imagine an overstocked item growing past its shelf life or going out of consumer trend. These occurrences could be a direct loss to businesses. However, AI forecasting can help avoid and overcome such threats through efficient insights and recommendations.

AI Demand Forecasting:

AI inventory forecasting focuses primarily on the accurate analysis of ongoing market trends, seasonal conditions, consumer patterns, and competitive presence. Demand forecasting plays a crucial role in promoting the right product in suitable demographics. It can also be a key to devising an adaptive product marketing strategy. 

By generating these tailored insights, the model enables decision-makers to make informed choices that optimize the supply chain, improve efficiency, and ultimately meet customer demand effectively across different regions.

Moreover,

That’s not all of it. AI and ML models have a lot more to offer to AI inventory management every step of the way. There are plenty of Machine Learning use cases that can focus on areas such as inventory inspection, strategic restocking, administrative automation, and inventory management recommendations.

Efficient inventory management can help organizations improve their profits and reduce losses. This, in turn, allows organizations to offer lower prices and better customer experience competitive advantage.