Role of AI in Inventory Management
Inventory management and forecasting are essential for businesses around the world, especially in retail and manufacturing industries. They ensure that raw materials are available to manufacturers and processing facilities without going over or under stock, keeping the pace of production as consistent as possible. For many businesses, the strength of their inventory management can have a powerful impact on their bottom line, making products cheaper to produce and sell.
As the need for inventory management grows and businesses acquire more customers, their need to expand and develop their inventory management becomes more important and, to do so, many companies are using Artificial Intelligence (AI) to create programs that can help automate inventory management at an exponential pace.
The Basics of AI in Inventory Management
AI uses a combination of Deep Learning and Machine Learning methods to automate tasks that can assist with inventory management for different businesses like logistics. It begins with data processing which is when an AI model is fed raw data that it can use to spot patterns and create insights for humans to use.
These algorithms can help companies comb through historical data like sales records and stock levels to create a better scope of projected sales called Predictive Analytics. This allows businesses to move inventory around in time for heightened sales seasons like late Autumn when the holidays come around and consumers to become more active.
However, AI’s true power in inventory management can be observed by its advanced automation capabilities that allow humans to step back from tedious management. By finding solutions with AI, employees can spend less time preparing new solutions and more time executing them. A second benefit exists here too because AI models can use that same data for adaptive learning which trains the algorithm to become more effective with consistent use.
Benefits of AI in Inventory Management:
Automated Data Collection: Data can be gathered automatically from various sources including point-of-sales systems, online platforms, and supply chain management tools.
Demand Forecasting: Predictive AI models can forecast consumer habits at scale, helping businesses make seasonal adjustments.
Optimized Stock Levels: Automated inventory management can help maintain stock inventory levels so that consumers can always find the products they are looking for.
Role of AI in Supply Chain Predictions
Supply chain management is another facet of inventory management that is being enhanced by AI. These advanced models are creating more efficient routes and deliveries that are speeding up delivery times and costs for consumers.
By analyzing vast amounts of consumer data, algorithms can provide meaningful insights into the conditions of different supplies from around the world. This can help businesses identify suppliers that are the most accommodating for their business by evaluating their historical performance and combining that with real-time conditions like global events and other major factors.
Another example is how AI is being used to create dynamic routing which can avoid congested road and sea conditions. This can be used to optimize delivery times around the world by finding new routes that can reduce fuel consumption of bypass areas with heavy traffic.
Advanced Applications of AI in Inventory Management
As AI models become more advanced, new features for inventory management are being developed. These new programs not only create more efficient chatbots and customer support, but also create visual recognition for consumers and anomaly detection to mitigate harm. They include:
Order Tracking: Chatbots can use real-time data to help customers access information about their order statuses without having to navigate through multiple menus.
Inventory Queries: Online assistants can help customers determine if certain items are out of stock and if any available replacements will suffice.
Shelf Management: In-store cameras can use computer vision to monitor store shelves and identify which ones need restocking.
Fraud Detection: Unusual order patterns or repetitive bulk orders can set off AI-powered fraud detection and prevent transactions from being completed.
Supply Chain Anomalies: Algorithms can monitor entire supply chains and identify any anomalies or strange patterns that need improvement.
Challenges and Concerns
Like most instances involving AI, there are still many hurdles that need to be overcome before inventory management can truly take off with Deep Learning models. One of the major concerns is data privacy. In order for AI models to be trained, they require a large dataset to learn from. In the case of inventory management, that data normally comes from businesses operations and consumer habits, however customers don’t always want their purchase to be used for any purpose which has led to new requirements for online businesses to give customers an opt-out option when it comes to their data privacy.
High costs are another area of concern because it requires a lot of resources to hire engineers to develop effective AI models. This can be an even larger issue too for businesses that are not related to tech and may not have the in-house means of developing these systems. Fortunately, many IT companies are beginning to release B2B products for businesses to purchase instead of creating their own programs.
Future of AI Inventory Management
As AI continues to develop at an exponential rate, the future of inventory management is beginning to look more simple than ever before. Couple this with the advent of self-driving cars and robotic automation and it becomes clear that our logistics infrastructure will soon look entirely different than what humans have become accustomed to.
While the challenges of data collection and rising costs pose major problems, AI’s ability to reduce workloads means that many products could become cheaper to produce and ship, helping them to balance the rising costs of goods caused by inflation.
