Optimizing Inventory Management with AI
For businesses that derive their revenue from selling Inventory, these are the analytics and workflows that offer a significant competitive advantage when enriched with AI.
Demand Forecasting
Warehouse Automation
Route Optimization
Predictive Shipping
Autonomous Vehicles
Dynamic Pricing and Prromotions
Customer Service and Tracking
Supply Chain Management
Fraud Detection and Prevention
Companies that Rely on AI for Inventory Management
Amazon
Uses AI to predict demand and manage inventory across its vast logistics network.
Walmart
Employs AI and machine learning to forecast demand and manage inventory levels in real-time.
Zara (Inditex)
Uses AI to analyze sales data and manage inventory, ensuring popular items are always in stock.
Alibaba
Utilizes AI to optimize inventory management in its extensive e-commerce operations.
Procter & Gamble
Uses AI-driven analytics for inventory management to improve supply chain efficiency.
Coca-Cola
Implements AI to manage inventory and optimize production schedules.
Unilever
Employs AI to manage inventory and forecast demand across its global supply chain.
Nestlé
Uses AI for demand forecasting and inventory management to ensure product availability.
IBM
Provides AI solutions for inventory management to other businesses through its Watson Supply Chain offerings.
Nike
Uses AI to predict trends and manage inventory, ensuring the right products are available at the right time.
Demand Forecasting Example
In my experience as a Business Consultant, Demand Forecasting is the most common challenge that Inventory Centric businesses face, so I picked this one for the example of how AI can offer a competitive Advantage.
The purpose of the Inventory Optimizer below, is to recommend actions to take to Optimize the amount of inventory held in stock. For most inventory centric businesses, holding too little inventory results in a poor customer experience and holding too much inventory results in higher inventory holding costs. So Optimizing inventory levels is central then, to a great customer experience and to reducing costs.
Below are the major components of Inventory Demand Forecasting, beginning with the Inventory Reorder Formula.
“(On Hand + On Route - (Committed + Sales Forecast)) X Lead Time”
On Hand + On Route
This is the Inventory On Hand plus any Open Purchase Orders. The caution here is that the total number of Purchase Orders is not as accurate as the individual Purchase Orders because each Purchase Order likely has a different arrival date.
Committed
These are all the Sales Orders which represent inventory that has been committed to customers.
Daily Movement & Coverage
This is your Sales Forecast, showing how fast your inventory will move. Depending on your business, it may make sense to see this in days, weeks or months.
State
Your inventory can find itself 1 of 3 states at any point in time - Understocked, Overstocked or Slow Moving.
AI Recommendations
This is where AI makes sense of your Sales Forecasts and Lead Time predictions, to take appropriate actions for optimizing your inventory and likely give you a perpetual competitive advantage.
Benefits of AI in Demand Forecasting
Increased Accuracy: AI can handle complex and large datasets, leading to more precise forecasts.
Cost Savings: Improved demand forecasting reduces inventory costs and minimizes waste.
Enhanced Agility: Real-time updates allow businesses to respond quickly to market changes.
Improved Customer Satisfaction: Better demand forecasting ensures product availability, leading to higher customer satisfaction.
Challenges
Data Quality: Accurate forecasting depends on the quality of the data used.
Implementation Costs: Initial setup and integration of AI systems can be costly.
Skills Gap: Specialized knowledge is required to develop and maintain AI systems.
By leveraging AI, businesses can significantly enhance their demand forecasting capabilities, leading to more efficient operations and better alignment with market needs.
Conclusion
For most Inventory centric businesses, Demand Forecasting remains the the most important aspect of inventory management and also the hardest to perfect.
Perfection is highly correlated to the level of accuracy with which Sales Forecasts and Lead Times - impacted by changes in the Supply Chain - can be predicted.
Getting started with obtaining a competitive Advantage with AI for your business is easier than you may think. It can be as easy as powering your Sales Forecasts and Lead Time Predictions with AI and feeding those results to your Inventory Dashboards,