How to use AI for optimal stock levels

In today’s fast-paced business world, the need for efficient supply chain management has never been greater. Optimizing stock levels is a crucial aspect of this process, since overstocking can tie up valuable capital and lead to increased storage costs, while under stocking can result in lost sales and dissatisfied customers. Therefore, optimizing stock levels directly impacts a company’s profitability, operational efficiency, minimizing costs and meeting customer demands.

The global supply chain industry is worth a whopping $15.85 billion. (National Law Review, 2023)

In this blog post, we’ll explore how you can leverage AI to achieve optimal stock levels in your supply chain, improve inventory management, and ultimately drive business growth.

The Importance of Optimal Stock Levels

In logistics, optimal stock designates the exact amount of inventory a business requires to fulfill routine demand without the occurrence of a stockout. Maintaining optimal stock is not just about inventory management; it’s a strategic imperative that impacts customer satisfaction, streamlines order fulfillment processes, reduces lead times, and improves order cycle times, thus enhancing overall efficiency of the entire supply chain.

Fortunately, advancements in artificial intelligence (AI) have made it easier than ever to accurately predict and manage stock levels.

Strategies for determining optimal stock levels

Strategies for determining optimal stock levels often involve a combination of various methods and concepts, including the Economic Order Quantity (EOQ), JIT, Safety Stock, and ABC & XYZ Analysis.

While the EOQ method helps companies determine how much to order at a given time, ensuring that they neither overstock nor under stock, Just-in-Time principle on the other hand is to meet the immediate production or customer demand. Safety stock is a buffer inventory that acts as a safety net to prevent stockouts during unforeseen spikes in demand or delays in the supply chain.

ABC & XYZ Analysis categorizes inventory into three groups based on the items’ value (ABC) and the variability of their demand (XYZ). With the combination of these two methods, the items are being grouped into 9 categories, from AX to CZ, allowing you to analyze the most important KPIs for each class and each item, such as share and coefficient of variation. This strategy helps in prioritizing items and allocating resources effectively allowing businesses to focus on critical items and reduce costs associated with less essential inventory.

Integrating these strategies into inventory management practices creates a well-rounded approach to achieving the optimal stock levels necessary for success in today’s competitive business landscape.

Approaches to stock level management

Traditional approaches to stock level management often relied on manual forecasting based on past sales data and rule-based systems predetermined rules to decide when and how much to order. Both methods had their limitations: manual forecasting was labour-intensive and lacked the precision of data-driven solutions, while rule-based systems often struggled to adapt to changing market conditions and customer demand patterns.

43% of small businesses use a manual method for tracking their inventory or they don’t track it at all. (Waspbarcode, 2022)

As businesses increasingly adopt advanced technologies like Artificial Intelligence (AI) and data analytics, these traditional approaches are giving way to more sophisticated and automated methods that offer greater accuracy, agility, and efficiency in stock level management.

How to use AI to optimize stock levels

Within the larger context of supply chain optimization, AI proves to be a versatile tool in the quest for optimal stock levels. It begins with precise demand forecasting, extends to intelligent inventory optimization, and offers real-time demand sensing. Moreover, AI excels in preventing stockouts and overstocking, promotes supplier collaboration, and optimizes warehouse management. In sum, AI is a cornerstone of effective stock level optimization in supply chain management.

The inventory management software market will reach at an estimated value of USD 3.82 billion and grow at a CAGR of 6.12% in the forecast period of 2021 to 2028. (Databridge, 2021)

Demand Forecasting with AI

The foundation of maintaining optimal stock levels is accurate demand forecasting. AI-driven algorithms can analyze historical data, market trends, weather patterns, economic indicators, and various other factors to provide real-time insights into shifts in demand and predict future demand with a high degree of precision. This enables you to adapt to changes in customer behavior and market conditions swiftly and to proactively adjust your stock levels based on projected demand, reducing the risk of overstocking or under stocking.

34% of Businesses Struggle with Unavailable Products and Delayed Shipments. (MyOS, 2023)

Inventory Optimization

AI algorithms can take demand forecasting a step further by helping you optimize your inventory. They can factor in variables like lead times, supplier reliability, carrying costs, and more to recommend the right quantities to order and when to reorder. This mitigates the supply chain challenge of balancing stockouts and overstocking. AI can continuously monitor your inventory levels and automatically trigger replenishment orders or alerts when certain thresholds are met. Additionally, AI streamlines warehouse management by optimizing layout, picking routes, and storage policies. This helps in minimizing carrying costs while ensuring you have the right products in stock when you need them.

By addressing the issues of overstocking and understocking, businesses can achieve a significant 10% reduction in inventory costs. (Zebra, 2017)

Supplier Collaboration

AI can also be used to enhance collaboration with your suppliers. By sharing AI-generated forecasts and data with your suppliers, you can improve supply chain visibility, reduce lead times, and work together to ensure optimal stock levels throughout the supply chain. This collaborative approach can result in cost savings and improved service levels.

Conclusion

By harnessing AI, businesses optimize stock levels, improve inventory management, and drive growth. Through machine learning, predictive analytics, and real-time monitoring, they achieve precise demand forecasts, proactive decisions, and timely replenishment.

However, AI implementation requires attention to data quality, scalability, and change management. In today’s competitive landscape, AI becomes essential for efficient supply chains, reducing costs, and ensuring customer satisfaction through reliable and timely deliveries.

Embracing AI for supply chain optimization not only benefits businesses’ bottom line but also enhances customer satisfaction through reliable and timely deliveries. Leveraging AI revolutionizes stock level management, keeping businesses competitive in dynamic markets and ensuring your company’s supply chain operates at peak efficiency with optimal stock levels.