It has become more crucial than ever to maintain ideal inventory levels in the modern, fast-paced global economy. However, a lot of companies have trouble striking the correct balance between understocking, which exposes them to the danger of reduced revenue and unhappy consumers, and overstocking, which consumes valuable capital.
According to Mckinsey(1), when compared to slower-moving competitors, early adopters have been able to optimize SCM in logistics costs by 15%, inventory levels by 35%, and service levels by 65% through the successful implementation of AI-enabled supply-chain management.
In such a complex setting, how can companies make sure they have the right products at the right place at the right time? Is it possible to predict changes in demand before they happen? Is AI the key to revolutionizing inventory control and converting barriers into opportunities? Let’s examine how firms can optimize inventory levels using AI, not only as an aid but as a game-changer.
Complexity of Modern Supply Chains
Nowadays, supply chains are more intricate than ever, involving global connections that connect several nations, suppliers, and consumers. Inventory management has become more advanced as e-commerce, just-in-time production, and global sourcing techniques have grown in popularity.
Conventional inventory management techniques, which depend on static models and historical data, are frequently unable to handle this complexity.
Demand can be affected by a wide range of factors, including market trends, seasonal variations, promotions, and even unforeseen occurrences like pandemics or natural catastrophes. For example, demand forecasting based solely on historical sales data is unable to take these factors into account. Similar to this, safety stock calculations and static reorder points might not be able to adjust fast enough to lead times, supplier dependability, or delays in transit.
Role of AI in optimizing Inventory levels
- Forecasting Demand in SCM
Recommendations of Generative AI on SCM have enhanced order fulfillment, and inventory control thus improving supply chain operations forecasting overall. Because of its extensive supply chain knowledge, it could offer suggestions for increasing efficiency and reducing costs.
Using historical sales data, social media trends, and even local weather forecasts, a retailer, for instance, could employ AI to estimate which products would be in great demand during the holiday season. This helps the store to prevent overstocking things that are unlikely to sell and to proactively manage inventory levels so they have the appropriate goods in stock when customers need them.
- Dynamic Inventory Replenishment
AI plays varied roles in dynamic inventory replenishment using real-time data and predictive analytics to automate the reorder process. AI systems can dynamically modify reorder levels based on variables like current sales velocity, supplier lead times, and inventory holding costs, rather than depending on predefined reorder points.
AI-powered inventory management in SCM, for example, can recognize an abrupt spike in demand for a specific product and immediately initiate a reorder from the supplier, guaranteeing that the product stays in stock without the need for manual involvement.
- Inventory Optimization Across the Supply Chain
Artificial Intelligence in the Supply chain is capable of optimizing inventory levels on the whole, not just at one particular point. Retail establishments, warehouses, and distribution centers are included in this. Through the examination of data from all points in the supply chain, AI can minimize surplus stock, manage inventory, and boost productivity.
A Fortune 500 telecom company faced difficulties in maintaining optimal inventory levels because of its wide worldwide warehouse network and varied SKU usage patterns. The CAPEX was high as a consequence.
GeakMinds method involved estimating each part’s demand by examining historical consumption patterns. Demand was also affected by any large new order that might be placed ahead of time and taken into consideration.
Network planners may now review daily stock levels and all inventory-related KPIs and AI models that were developed in collaboration with them. To achieve a Just-In-Time Inventory, this decreased overstocking and made it possible to take proactive steps to minimize understocking through efficient monitoring and recommendations.
- Enhancing Supplier Collaboration and Reliability
One of the most significant elements of inventory management is supplier reliability. Businesses can use AI to track parameters like lead times, order correctness, and delivery reliability and monitor supplier performance in real-time.
AI can analyze this data and find trends and patterns that could point to possible concerns with a supplier, including shipments being delayed or quality difficulties that can suit diverse types of SCM models.
With this, companies can deal with suppliers proactively before their problems affect inventory levels. For instance, AI may recommend placing an order sooner or using a different supplier if it notices that a provider’s lead times are getting longer to prevent stockouts.
- Improving Customer Satisfaction
The ultimate objective of inventory optimization is to guarantee that consumers obtain the goods they want at the appropriate time. By ensuring that the correct products are accessible at the right time without the delays and hassles associated with stockouts, AI plays a critical role in improving the satisfaction of consumers.
For instance, based on regional consumer preferences and trends, AI-powered inventory management systems may forecast which products are most likely to be in demand in a certain area. This makes it possible for companies to carry the goods that consumers desire, increasing availability and lowering the possibility of lost sales opportunities.
Conclusion
The demand for advanced, AI-driven inventory management systems will only rise as supply chains’ complexity continues to rise. Using supply chain AI solutions, businesses can optimize inventory levels.
Businesses can convert their supply chains into flexible, responsive, and customer-focused operations by using AI and switching from reactive to proactive inventory management. In a world where supply chain disruptions are a continual concern and customer expectations are higher than ever, AI is more than simply a tool—it’s a strategic necessity for success.
With GeakMinds AI solutions, improve your supply chain management by scheduling a demo right now!
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